From d45b65f870af1de1c074a264de4051167135f6ac Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Wed, 24 Oct 2018 16:56:43 +0100 Subject: [PATCH 01/59] adding template pratical stegen based o intro --- content/post/practical-stegen.Rmd | 218 ++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/post/practical-stegen.Rmd diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd new file mode 100644 index 0000000..d45f1db --- /dev/null +++ b/content/post/practical-stegen.Rmd @@ -0,0 +1,218 @@ +--- +title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" +author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" +authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +categories: +tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] +date: 2018-10-24 +slug: stegen +licenses: CC-BY +image: img/highres/graduation-1965.jpg +showonlyimage: true +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# Introduction + +On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants were expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students and some graduates from a nearby school (school B). + +A self-service party buffet was supplied by a commercial caterer in Freiburg. Food was prepared the day of the party and transported in a refrigerated van to the school. + +Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late during the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. + +## The alert + +On 2nd July 1998, the Freiburg local health office reported to the Robert Koch Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis following the graduation party described above. More than 100 cases were suspected among participants and some of them were admitted to nearby hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting lasting for several days. Most believed that the tiramisu consumed at dinner was responsible for their illness. *Salmonella enteritidis* was isolated from 19 stool samples. + +The Freiburg health office sent a team to investigate the kitchen of the caterer. Food preparation procedures were reviewed. Food samples, except tiramisu (none was left over), were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the following: brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and 10 raw eggs. + +The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk factors for transmission in order to better control the outbreak + +## The study + +Cases were defined as any person who had attended the party at St Sebastian High School who suffered from diarrhoea (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998; or who suffered from at least three of the following symptoms: vomiting, fever over 38.5° C, nausea, abdominal pain, headache. + +Students from both schools attending the party were asked through phone interviews to provide names of persons who +attended the party. + +Overall, 291 responded to enquiries and 103 cases were identified. + +## An introduction to the *R* companion + +This text was adapted from the introduction used at the 2016 TSA module. + +R packages are bundles of functions which extend the capability of R. Thousands of add-on packages are available in the main online repository (known as CRAN) and many more packages in development can be found on GitHub. They may be installed and updated over the Internet. + +We will mainly use packages which come ready installed with R (base code), but where it makes things easier we will use add-on packages. In addition, we have included a few extra functions to simplify the code required. All the R packages you need for the exercises can be installed over the Internet. + +> If you get stuck with any of the tasks in this practical, additional information for each task can be found in the [**Help** section](#help) + +## Setting up + +### a) Load the required packages and functions for this practical + +> **n.b.** you should only need to do this once. + +The required packages are: + +* epiR +* Hmisc +* epitools +* here + +The two required functions are **big_table** and **attack_rate** and can be found below. + +```{r stegen-3} +# Function to make tables with counts, proportions and cumulative sum +big_table <- function(data, useNA = "no") { + count <- table(data, useNA = useNA) + prop <- round(prop.table(count)*100, digits = 2) + cumulative <- cumsum(prop) + rbind(count, + prop, + cumulative) +} + + # Function to provide counts, denominator and proportions (equivalent of attack rate) +attack_rate <- function(table) { + prop <- round(prop.table(table, 1), digits = 2) + denominator <- rowSums(table) + output <- cbind(Ill = table[, 2], N = denominator, Proportions = prop[, 2]) + return(output) +} +``` + +The **big_table** function uses data directly and allows combining of counts, proportions and cumulative sums, thus reducing the number of lines of code required for descriptive analyses. The **attack_rate** function makes tables that combine counts, proportions and row sums. + +# The dataset + +In this practical, we will be using the **tirav12.csv** file located in the data folder. + +### b) Read in the dataset for this practical + +# Recoding missing data + +### c) Browse your dataset +What variables does your dataset contain? + +### d) Recode data +Identify variables with missing values (variables that have a records with a value of 9). Recode these to NA. + +### e) Save your new dataset +As a csv file named **stegen_clean**. + +# Help + +
+ a) Install and load the required packages and functions for this practical + +Below you can find the commands for installing and loading the required packages. + +```{r stegen-1, eval = FALSE, message = FALSE, warning = FALSE} +# Installing required packages for the practical +required_packages <- c("epiR", "Hmisc", "epitools", "here", "incidence") +install.packages(required_packages) +``` + +```{r stegen-2, results = 'hide', message = FALSE, warning = FALSE} +# Loading required packages for the practical +library("epiR") +library("Hmisc") +library("epitools") +library("here") +library("incidence") +``` + +
+ +
+ b) Read in the dataset for this practical + +```{r stegen-4, eval = FALSE} +stegen_data <- read.csv(here::here("data", "stegen_raw.csv"), stringsAsFactors = FALSE) +``` + +```{r stegen-4.1, results = 'hide', echo = FALSE} +stegen_data <- read.csv(here::here("static", "data", "stegen_raw.csv"), stringsAsFactors = FALSE) +``` + +
+ +
+ c) Browse your dataset + +*RStudio* has the nice feature that everything is in one browser window, so you can browse your dataset and your code without having to switch between browser windows. + +```{r stegen-5, eval = FALSE} +# to browse your data, use the View command +View(stegen_data) +``` + +Alternatively, you can also view your dataset by clicking on **stegen_data** in the top right "global environment" panel of your *RStudio* browser. Your global environment is where you can see all the datasets, functions and other things you have loaded in the current session. + +
+ +
+ d) Recode data + +Use the "describe" command to assess your data and identify variables with missing values. The describe command showed that the variables salmon, pork and horseradish have a few records with a value of 9. These need to be recoded to NA. + +* Using the square brackets "`[...]`" after a variable allows you to subset for certain observations. To recode values of 9 to NA for the pork variable, select observations where pork **(`stegen_data$pork`)** is equal to 9 **`[stegen_data$pork == 9]`** and set these observations equal to NA + +* Always use the double equals "`==`" within square brackets; this a logical (Boolean) operator + +* Use "`!=`" when you want to write "not equal to" + +```{r stegen-8, eval = TRUE} +# The first line below is read as follows: assign a value of NA to stegen_data$pork WHERE stegen_data$pork is equal to 9 +stegen_data$pork[stegen_data$pork == 9] <- NA + +stegen_data$salmon[stegen_data$salmon == 9] <- NA + +stegen_data$horseradish[stegen_data$horseradish == 9] <- NA +``` + +
+ +
+ e) Save your new dataset + +```{r stegen-10, eval = FALSE} +write.csv(stegen_data, file = here::here("data", "stegen_clean.csv")) +``` + +
+ +## Copyright and license +**Source:** +This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. + +**Authors:** +Alain Moren and Gilles Desve + +**Reviewers:** +Marta Valenciano, Alain Moren + +**Adaptations for previous modules:** +Alicia Barrasa, Ioannis Karagiannis + +**You are free:** + ++ to Share - to copy, distribute and transmit the work ++ to Remix - to adapt the work +Under the following conditions: ++ Attribution - You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). The best way to do this is to keep as it is the list of contributors: sources, authors and reviewers. ++ Share Alike - If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. Your changes must be documented. Under that condition, you are allowed to add your name to the list of contributors. ++ You cannot sell this work alone but you can use it as part of a teaching. +With the understanding that: ++ Waiver - Any of the above conditions can be waived if you get permission from the copyright holder. ++ Public Domain - Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license. ++ Other Rights - In no way are any of the following rights affected by the license: ++ Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; ++ The author's moral rights; ++ Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. ++ Notice - For any reuse or distribution, you must make clear to others the license terms of this work by keeping together this work and the current license. +This licence is based on http://creativecommons.org/licenses/by-sa/3.0/ From c9840ee5c05b6e1f824d3a284a42d7f436fc672b Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 14:01:31 +0100 Subject: [PATCH 02/59] removed a lot of stuff, kept the intro --- content/post/practical-stegen.Rmd | 211 +++++++++++++----------------- 1 file changed, 88 insertions(+), 123 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index d45f1db..85e625b 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -15,121 +15,75 @@ showonlyimage: true knitr::opts_chunk$set(echo = TRUE) ``` -# Introduction +# Context -On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants were expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students and some graduates from a nearby school (school B). +On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, +celebrated a graduation party, where 250 to 350 participants were +expected. Attendants included graduates from that school, their families and +friends, teachers, 12th grade students and some graduates from a nearby school +(school B). -A self-service party buffet was supplied by a commercial caterer in Freiburg. Food was prepared the day of the party and transported in a refrigerated van to the school. +A self-service party buffet was supplied by a commercial caterer in +Freiburg. Food was prepared the day of the party and transported in a +refrigerated van to the school. -Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late during the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. +Festivities started with a dinner buffet open from 8.30 pm onwards and were +followed by a dessert buffet offered from 10 pm. The party and the buffet +extended late during the night and alcoholic beverages were quite popular. All +agreed it was a party to be remembered. -## The alert - -On 2nd July 1998, the Freiburg local health office reported to the Robert Koch Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis following the graduation party described above. More than 100 cases were suspected among participants and some of them were admitted to nearby hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting lasting for several days. Most believed that the tiramisu consumed at dinner was responsible for their illness. *Salmonella enteritidis* was isolated from 19 stool samples. - -The Freiburg health office sent a team to investigate the kitchen of the caterer. Food preparation procedures were reviewed. Food samples, except tiramisu (none was left over), were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the following: brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and 10 raw eggs. - -The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk factors for transmission in order to better control the outbreak - -## The study - -Cases were defined as any person who had attended the party at St Sebastian High School who suffered from diarrhoea (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998; or who suffered from at least three of the following symptoms: vomiting, fever over 38.5° C, nausea, abdominal pain, headache. - -Students from both schools attending the party were asked through phone interviews to provide names of persons who -attended the party. - -Overall, 291 responded to enquiries and 103 cases were identified. - -## An introduction to the *R* companion -This text was adapted from the introduction used at the 2016 TSA module. -R packages are bundles of functions which extend the capability of R. Thousands of add-on packages are available in the main online repository (known as CRAN) and many more packages in development can be found on GitHub. They may be installed and updated over the Internet. - -We will mainly use packages which come ready installed with R (base code), but where it makes things easier we will use add-on packages. In addition, we have included a few extra functions to simplify the code required. All the R packages you need for the exercises can be installed over the Internet. - -> If you get stuck with any of the tasks in this practical, additional information for each task can be found in the [**Help** section](#help) - -## Setting up - -### a) Load the required packages and functions for this practical +## The alert -> **n.b.** you should only need to do this once. +On 2nd July 1998, the Freiburg local health office reported to the Robert Koch +Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis +following the graduation party described above. More than 100 cases were +suspected among participants and some of them were admitted to nearby +hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting +lasting for several days. Most believed that the tiramisu consumed at dinner was +responsible for their illness. *Salmonella enteritidis* was isolated from 19 +stool samples. -The required packages are: +The Freiburg health office sent a team to investigate the kitchen of the +caterer. Food preparation procedures were reviewed. Food samples, except +tiramisu (none was left over), were sent to the laboratory of Freiburg +University. Microbiological analyses were performed on samples of the following: +brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and +10 raw eggs. -* epiR -* Hmisc -* epitools -* here +The Freiburg health office requested help from the RKI in the investigation to +assess the magnitude of the outbreak and identify potential vehicle(s) and risk +factors for transmission in order to better control the outbreak -The two required functions are **big_table** and **attack_rate** and can be found below. -```{r stegen-3} -# Function to make tables with counts, proportions and cumulative sum -big_table <- function(data, useNA = "no") { - count <- table(data, useNA = useNA) - prop <- round(prop.table(count)*100, digits = 2) - cumulative <- cumsum(prop) - rbind(count, - prop, - cumulative) -} - # Function to provide counts, denominator and proportions (equivalent of attack rate) -attack_rate <- function(table) { - prop <- round(prop.table(table, 1), digits = 2) - denominator <- rowSums(table) - output <- cbind(Ill = table[, 2], N = denominator, Proportions = prop[, 2]) - return(output) -} -``` +## The study -The **big_table** function uses data directly and allows combining of counts, proportions and cumulative sums, thus reducing the number of lines of code required for descriptive analyses. The **attack_rate** function makes tables that combine counts, proportions and row sums. +Cases were defined as any person who had attended the party at St Sebastian High +School who suffered from diarrhoea (min. 3 loose stool for 24 hours) between 27 +June and 29 June 1998; or who suffered from at least three of the following +symptoms: vomiting, fever over 38.5° C, nausea, abdominal pain, headache. -# The dataset +Students from both schools attending the party were asked through phone +interviews to provide names of persons who attended the party. -In this practical, we will be using the **tirav12.csv** file located in the data folder. +Overall, 291 responded to enquiries and 103 cases were identified. -### b) Read in the dataset for this practical -# Recoding missing data -### c) Browse your dataset -What variables does your dataset contain? -### d) Recode data -Identify variables with missing values (variables that have a records with a value of 9). Recode these to NA. +# First look at the data -### e) Save your new dataset -As a csv file named **stegen_clean**. +## Importing data from Excel -# Help +## Overview and summaries -
- a) Install and load the required packages and functions for this practical +## Graphics -Below you can find the commands for installing and loading the required packages. +## Data cleaning -```{r stegen-1, eval = FALSE, message = FALSE, warning = FALSE} -# Installing required packages for the practical -required_packages <- c("epiR", "Hmisc", "epitools", "here", "incidence") -install.packages(required_packages) -``` -```{r stegen-2, results = 'hide', message = FALSE, warning = FALSE} -# Loading required packages for the practical -library("epiR") -library("Hmisc") -library("epitools") -library("here") -library("incidence") -``` - -
- -
- b) Read in the dataset for this practical ```{r stegen-4, eval = FALSE} stegen_data <- read.csv(here::here("data", "stegen_raw.csv"), stringsAsFactors = FALSE) @@ -144,21 +98,29 @@ stegen_data <- read.csv(here::here("static", "data", "stegen_raw.csv"), stringsA
c) Browse your dataset -*RStudio* has the nice feature that everything is in one browser window, so you can browse your dataset and your code without having to switch between browser windows. +*RStudio* has the nice feature that everything is in one browser window, so you +can browse your dataset and your code without having to switch between browser +windows. ```{r stegen-5, eval = FALSE} # to browse your data, use the View command View(stegen_data) ``` -Alternatively, you can also view your dataset by clicking on **stegen_data** in the top right "global environment" panel of your *RStudio* browser. Your global environment is where you can see all the datasets, functions and other things you have loaded in the current session. +Alternatively, you can also view your dataset by clicking on **stegen_data** in +the top right "global environment" panel of your *RStudio* browser. Your global +environment is where you can see all the datasets, functions and other things +you have loaded in the current session.
d) Recode data -Use the "describe" command to assess your data and identify variables with missing values. The describe command showed that the variables salmon, pork and horseradish have a few records with a value of 9. These need to be recoded to NA. +Use the "describe" command to assess your data and identify variables with +missing values. The describe command showed that the variables salmon, pork and +horseradish have a few records with a value of 9. These need to be recoded to +NA. * Using the square brackets "`[...]`" after a variable allows you to subset for certain observations. To recode values of 9 to NA for the pork variable, select observations where pork **(`stegen_data$pork`)** is equal to 9 **`[stegen_data$pork == 9]`** and set these observations equal to NA @@ -186,33 +148,36 @@ write.csv(stegen_data, file = here::here("data", "stegen_clean.csv"))
-## Copyright and license -**Source:** -This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. - -**Authors:** -Alain Moren and Gilles Desve - -**Reviewers:** -Marta Valenciano, Alain Moren - -**Adaptations for previous modules:** -Alicia Barrasa, Ioannis Karagiannis - -**You are free:** - -+ to Share - to copy, distribute and transmit the work -+ to Remix - to adapt the work -Under the following conditions: -+ Attribution - You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). The best way to do this is to keep as it is the list of contributors: sources, authors and reviewers. -+ Share Alike - If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. Your changes must be documented. Under that condition, you are allowed to add your name to the list of contributors. -+ You cannot sell this work alone but you can use it as part of a teaching. -With the understanding that: -+ Waiver - Any of the above conditions can be waived if you get permission from the copyright holder. -+ Public Domain - Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license. -+ Other Rights - In no way are any of the following rights affected by the license: -+ Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; -+ The author's moral rights; -+ Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. -+ Notice - For any reuse or distribution, you must make clear to others the license terms of this work by keeping together this work and the current license. -This licence is based on http://creativecommons.org/licenses/by-sa/3.0/ + + + +
+ +# About this document + +## Source + +This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is +based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. + +## Contributors + +- original version: Alain Moren, Gilles Desve + +- reviewers, previous versions: Marta Valenciano, Alain Moren + +- adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis + +- translation to R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart + + +Contributions are welcome via [pull +requests](https://github.com/reconhub/learn/pulls). The source file can be found +[here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). + + + +## Legal stuff + +**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) +**Copyright**: 2018 From 0675d2f2a7f45c6d6bae0427aa992115c20021fc Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 15:43:46 +0100 Subject: [PATCH 03/59] working on stegen, up to data cleaning --- content/post/practical-stegen.Rmd | 213 ++++++++++++++++++++++-------- static/data/stegen_raw.xlsx | Bin 0 -> 24345 bytes 2 files changed, 159 insertions(+), 54 deletions(-) create mode 100644 static/data/stegen_raw.xlsx diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 85e625b..b49caac 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -2,7 +2,7 @@ title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] -categories: +categories: "case study" tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] date: 2018-10-24 slug: stegen @@ -20,11 +20,11 @@ knitr::opts_chunk$set(echo = TRUE) On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants were expected. Attendants included graduates from that school, their families and -friends, teachers, 12th grade students and some graduates from a nearby school +friends, teachers, 12th grade students, and some graduates from a nearby school (school B). A self-service party buffet was supplied by a commercial caterer in -Freiburg. Food was prepared the day of the party and transported in a +Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. Festivities started with a dinner buffet open from 8.30 pm onwards and were @@ -54,101 +54,206 @@ brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk -factors for transmission in order to better control the outbreak +factors for transmission in order to better control the outbreak. -## The study +## The epidemiological study -Cases were defined as any person who had attended the party at St Sebastian High -School who suffered from diarrhoea (min. 3 loose stool for 24 hours) between 27 -June and 29 June 1998; or who suffered from at least three of the following -symptoms: vomiting, fever over 38.5° C, nausea, abdominal pain, headache. +**Case definition**: cases were defined as any person who had attended the party +at St Sebastian High School and who suffered from *diarrhoea* (min. 3 loose +stool for 24 hours) between 27 June and 29 June 1998, or from at least three of +the following symptoms: *vomiting*, *fever over 38.5° C*, *nausea*, +*abdominal pain*, *headache*. Students from both schools attending the party were asked through phone interviews to provide names of persons who attended the party. -Overall, 291 responded to enquiries and 103 cases were identified. +Overall, 291 responded to enquiries and 103 cases were identified. The linelist +analysed in this case study was built from these 291 responses. +## This case study +In this case study, we will take you through the analysis of this epidemic. This +will be the occasion to illustrate more generally useful practices for data +analysis using **R**, including: -# First look at the data +- how to read data from Excel +- how to explore data using tables and summaries +- how to clean data +- how to make graphics to describe the data +- how to test if specific food items are linked to the disease -## Importing data from Excel -## Overview and summaries -## Graphics -## Data cleaning +# First look at the data +## Loading required packages -```{r stegen-4, eval = FALSE} -stegen_data <- read.csv(here::here("data", "stegen_raw.csv"), stringsAsFactors = FALSE) +The following packages will be used in the case study: + +- `here`: to find the path to data or script files +- `readxl`: to read Excel spreadsheets into **R** +- `incidence`: to build epicurves + +To avoid having to specify the name of the package whenever we use a function +using the syntax `package_name::function_name()`, we load these packages using +`library`: + +```{r stegen_libraries} +library(here) # find data/script files +library(readxl) # read xlsx files +library(incidence) # make epicurves +library(epitrix) # clean labels and variables +library(dplyr) # general data handling ``` -```{r stegen-4.1, results = 'hide', echo = FALSE} -stegen_data <- read.csv(here::here("static", "data", "stegen_raw.csv"), stringsAsFactors = FALSE) +Note that you will get an error if the packages have not been installed on your +system. To install them (you only need to do this once!), type: + +```{r stegen_install_libraries, eval = FALSE} +install.packages("here") +install.packages("readxl") +install.packages("incidence") ``` -
-
- c) Browse your dataset +Loading these packages makes all functions implemented by the packages +available, so that `function_name()` can be used directly (without the +`package_name::` prefix). This is not a problem here as these packages do now +implement functions with identical names. + -*RStudio* has the nice feature that everything is in one browser window, so you -can browse your dataset and your code without having to switch between browser -windows. -```{r stegen-5, eval = FALSE} -# to browse your data, use the View command -View(stegen_data) +## Importing data from Excel + +Linelist data can be read from various formats, including flat text files +(e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or Excel +spreadsheets. We illustrate the latter, which is probably the most common +format. We assume that the data file `stegen_raw.xlsx` has been saved in a +`data/` folder of your project, and that your current R session is at the root +of the project. + + +Here we decompose the steps to read data in: finding the path to the data +(`path_to_data`), using the function `read_xlsx` to read data in, and saving the +output in a new object `stegen`: + +```{r eval = FALSE} +path_to_data <- here("data", "stegen_raw.xlsx") +stegen <- read_xlsx(path_to_data) ``` -Alternatively, you can also view your dataset by clicking on **stegen_data** in -the top right "global environment" panel of your *RStudio* browser. Your global -environment is where you can see all the datasets, functions and other things -you have loaded in the current session. +```{r stegen_read_data, results = 'hide', echo = FALSE} +stegen <- readxl::read_xlsx(here::here("static", "data", "stegen_raw.xlsx")) +``` + +To print the content of the dataset, we can use either of these commands: +```{r stegen_print_data, eval = 1} +stegen +View(stegen) +``` -
-
- d) Recode data +
+

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+ +
-Use the "describe" command to assess your data and identify variables with -missing values. The describe command showed that the variables salmon, pork and -horseradish have a few records with a value of 9. These need to be recoded to -NA. +If the above fails, you should check: -* Using the square brackets "`[...]`" after a variable allows you to subset for certain observations. To recode values of 9 to NA for the pork variable, select observations where pork **(`stegen_data$pork`)** is equal to 9 **`[stegen_data$pork == 9]`** and set these observations equal to NA - -* Always use the double equals "`==`" within square brackets; this a logical (Boolean) operator +- that your data file has been saved in the right folder, with the right name + (lower case and upper case do matter) +- that your R session was started from the right project - if unsure, close R +and re-open Rstudio by double-clicking on the `.Rproj` file +- that all packages are installed and loaded (see installation guidelines above) -* Use "`!=`" when you want to write "not equal to" +
+
-```{r stegen-8, eval = TRUE} -# The first line below is read as follows: assign a value of NA to stegen_data$pork WHERE stegen_data$pork is equal to 9 -stegen_data$pork[stegen_data$pork == 9] <- NA -stegen_data$salmon[stegen_data$salmon == 9] <- NA -stegen_data$horseradish[stegen_data$horseradish == 9] <- NA + +## Overview and summaries + +We first have a quick look at the content of the data set. The information we are looking for is: + +- the numbers of cases (rows) and variables (columns) in the data +- the *name* of the variables: do they use consistent capitalisation and separators? +- the *type* of the variables: are dates, numeric or categorical variables used when they should? +- the *coding* of the variables: are explicit labels (e.g. `"male"`/`"female"`) used where relevant? + +We first check the dimensions of the `stegen` object, and the name of the variables: +```{r stegen_dim} +dim(stegen) # rows x columns +names(stegen) # column labels +``` + +We can now try and summarise the dataset using: +```{r stegen_summary} +summary(stegen) +``` +Good news: the dataset has the expected dimensions, and all the relevant +variables seem to be present. There are, however, a few issues: + +1. variable names are a bit messy, and include different separators, spaces, and + irregular capitalisation +2. variable types are partly wrong: + - *unique keys* should be `character` (i.e. character strings) + - *dates of onset* should be `Date` (**R** is good at handling actual dates) + - *sex* should be a `factor` (i.e. a categorical variable) +3. labels used in some variables are ambiguous: + - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, + female) + + +## Data cleaning + +While it is tempting to go back to the Excel spreadsheet to fix issues with +data, it is almost always quicker and more reliable to clean data in **R** +directly. Here, we make a copy of the old data set, and clean `stegen` before +further analysis. + +```{r stegen_old} +stegen_old <- stegen # save 'dirty data' ``` -
+We use `epitrix`'s function `clean_labels` to standardise the variable names: +```{r stegen_clean_labels} +new_labels <- clean_labels(names(stegen)) # generate standardised labels +new_labels # check the result +names(stegen) <- new_labels +``` -
- e) Save your new dataset +We set convert the unique identifiers to character strings (`character`), and +sex to a categorical variable (`factor`): +```{r stegen_unique_ids} +stegen$unique_key <- as.character(stegen$unique_key) +stegen$sex <- factor(stegen$sex) +``` -```{r stegen-10, eval = FALSE} -write.csv(stegen_data, file = here::here("data", "stegen_clean.csv")) +And we use the function `recode` from the `dplyr' package to recode `sex` more +explicitely: +```{r stegen_recode} +stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") ``` -
+## Graphics + + +```{r stegen-8, eval = FALSE} +# The first line below is read as follows: assign a value of NA to stegen$pork WHERE stegen$pork is equal to 9 +stegen$pork[stegen$pork == 9] <- NA + +stegen$salmon[stegen$salmon == 9] <- NA + +stegen$horseradish[stegen$horseradish == 9] <- NA +```
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To install them (you only need to do this once!), type: install.packages("here") install.packages("readxl") install.packages("incidence") +install.packages("epitrix") +install.packages("dplyr") +install.packages("ggplot2") ``` @@ -196,6 +200,19 @@ We can now try and summarise the dataset using: ```{r stegen_summary} summary(stegen) ``` + +Note that binary variables, when treated as numeric values (0/1), are summarised +at such, which may not always be useful. As an alternative, `table` can be used +to list all possible values of a variable, and count how many time each value +appears in the data. For instance, we can compare the `summary` and `table` for +consumption of `tiramisu`: +```{r stegen_summary_table} +stegen$tiramisu # all values +summary(stegen$tiramisu) # summary +table(stegen$tiramisu) # table +``` + + Good news: the dataset has the expected dimensions, and all the relevant variables seem to be present. There are, however, a few issues: @@ -228,11 +245,12 @@ new_labels # check the result names(stegen) <- new_labels ``` -We set convert the unique identifiers to character strings (`character`), and -sex to a categorical variable (`factor`): +We set convert the unique identifiers to character strings (`character`), dates +of onset to actual `Date` objects, and sex to a categorical variable (`factor`): ```{r stegen_unique_ids} stegen$unique_key <- as.character(stegen$unique_key) stegen$sex <- factor(stegen$sex) +stegen$date_onset <- as.Date(stegen$date_onset) ``` And we use the function `recode` from the `dplyr' package to recode `sex` more @@ -242,8 +260,114 @@ stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") ``` +We now have a cleaner dataset: + +```{r stegen_cleaner_data} +stegen +``` + +We can save it for further records, as a `csv` file: +```{r stegen_save_data, eval = FALSE} +out_path <- here("data", "stegen_clean.csv") +write.csv(stegen, file = out_path) +``` + + + +# Data exploration + +## Summaries of ge and sex distribution + +We can have a brief look at age and sex distributions using some basic +summaries; for instance: +```{r stegen_age_sex} +summary(stegen$age) # age stats +summary(stegen$sex) # gender distribution +tapply(stegen$age, stegen$sex, summary) # age stats by gender +``` + +

      +

      **Going further** click on the button below to learn about `tapply`:

      + +
      + +`tapply` is a very handy function to stratify any kind of analyses. You will +find more details by reading the documentation of the functiton `?tapply`, but +briefly, the syntax to be used is `tapply(input_data, stratification, +function_to_use, optional_arguments)`. In the command used above: +```{r eval = FALSE} +tapply(stegen$age, stegen$sex, summary) +``` + +this literally means: select the age variable in the dataset `stegen` +(`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each strata +(`summary`). So for instance, to get the average age by sex (function `mean`), one could use: +```{r stegen_age_per_sex} +tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) +``` +Here we illustrate that further arguments to the function can be passed as extra +arguments; here, `na.rm = TRUE` means "ignore missing data". + +
      +
      + + + +## Graphical exploration + +The summaries above may be useful for reporting purposes, but graphics are +usually better for getting a feel for the data. Here, we illustrate how the +package `ggplot2` can be used to derive informative graphics of age/sex +distribution. It implements an alternative graphics system to the basic **R** +plots, in which the plot is built by adding (literally using `+`) different +layers of information, using: + +- `ggplot()` to specify the dataset to use +- `geom_...()` functions which define a type of graphics to use (e.g. barplot, histogram) +- `aes()` to map elements of the data into aesthetic properties (e.g. x/y axes, color, shape) + +For instance, to get an histogram of age: + +```{r stegen_histogram} +ggplot(stegen) + geom_histogram(aes(x = age)) +``` + +Color can be added using: +```{r stegen_histogram_sex} +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) +``` + + + +
      +

      **Going further** click on the button below to learn about customising `ggplot2` graphics:

      + +
      + +`ggplot2` graphics are highly customisable, and a lot of help and examples can +be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. + +For instance, here we: + +- add white borders to the plot (`color = "white"`) +- specify colors manually for male / female, using specified color codes (see + [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to + define your own colors) (`scale_fill_manual(...)`) +- adding labels for title, *x* and *y* axes (`labs()`) +- specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) +- move the legend inside the plot (`theme(...)`) + +```{r stegen_ggplot2_custom} +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + + labs(title = "Age distribution by gender", x = "Age (years)", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + + theme(legend.position = c(0.8, 0.8)) +``` + +
      +
      -## Graphics ```{r stegen-8, eval = FALSE} @@ -286,3 +410,6 @@ requests](https://github.com/reconhub/learn/pulls). The source file can be found **License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) **Copyright**: 2018 + +
      +
      From 40aec5eea39639c53d6727c2db5dae97ec2c3ce9 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 18:15:43 +0100 Subject: [PATCH 06/59] better optional content --- content/post/practical-stegen.Rmd | 106 ++++++++++++++++++++++-------- 1 file changed, 79 insertions(+), 27 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 48e7475..032630f 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -164,7 +164,8 @@ View(stegen)

      **Problems?** In case of trouble, click on the button below to toggle additional help:

      - +

      +

      If the above fails, you should check: @@ -175,6 +176,7 @@ If the above fails, you should check: and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above) +

      @@ -213,8 +215,8 @@ table(stegen$tiramisu) # table ``` -Good news: the dataset has the expected dimensions, and all the relevant -variables seem to be present. There are, however, a few issues: +**Good news**: the dataset has the expected dimensions, and all the relevant +variables seem to be present. There are, however, **a few issues**: 1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation @@ -225,6 +227,7 @@ variables seem to be present. There are, however, a few issues: 3. labels used in some variables are ambiguous: - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, female) +4. some binary variables have maximum values of 9 (see `summary(stegen)`) ## Data cleaning @@ -253,25 +256,77 @@ stegen$sex <- factor(stegen$sex) stegen$date_onset <- as.Date(stegen$date_onset) ``` -And we use the function `recode` from the `dplyr' package to recode `sex` more +We use the function `recode` from the `dplyr' package to recode `sex` more explicitely: ```{r stegen_recode} stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") ``` -We now have a cleaner dataset: +Finally we look in more depth into these variables having maximum values of 9, +where we expect 0/1; `table` is useful to list all values taken by a variable, +and listing their frequencies: + +```{r stegen_nines} + +table(stegen$pork) +table(stegen$salmon) +table(stegen$horseradish) -```{r stegen_cleaner_data} -stegen ``` -We can save it for further records, as a `csv` file: -```{r stegen_save_data, eval = FALSE} -out_path <- here("data", "stegen_clean.csv") -write.csv(stegen, file = out_path) +The only rogue values are `9`; they are likely either data entry issues, or +missing data, which in **R** should be coded as `NA` ("not available"). We can +replace these values using: + +```{r stegen_replace_9} +stegen$pork[stegen$pork == 9] <- NA +stegen$salmon[stegen$salmon == 9] <- NA +stegen$horseradish[stegen$horseradish == 9] <- NA +``` + + +
      +

      **Going further** click on the button below for more explanation:

      + + +
      + +

      +There are several things going on in a command like: +```{r eval = FALSE} +stegen$pork[stegen$pork == 9] <- NA +``` + +let us break them down: + +1. `stegen$pork` means "get the variable called `pork` in the dataset `stegen`" +2. `[...]` subset the vector according to `...`; if `...` is a series of + `TRUE/FALSE` values, only entries corresponding to `TRUE` are retained +3. `... == 9` for each value of `...` test if it is equal to `9`, and return `TRUE` if so (`FALSE` otherwise) +4. `... <- NA` replace `...` with `NA` (missing value) + +in other words: "isolate the entries of `stegen$pork` which equal `9`, and +replace them with `NA`; here is another toy example to illustrate the procedure: + +```{r stegen_subset} +## make toy input vector +toy_vector <- 1:5 +toy_vector + +## make toy logical vector for subsetting +toy_logical <- c(FALSE, TRUE, TRUE, FALSE, TRUE) +toy_logical +toy_vector[toy_logical] # subset values +toy_vector[toy_logical] <- 0 # replace subset values +toy_vector # check outcome + ``` +

      +
      +
      + # Data exploration @@ -288,11 +343,13 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender

      **Going further** click on the button below to learn about `tapply`:

      - -
      + +
      + +

      `tapply` is a very handy function to stratify any kind of analyses. You will -find more details by reading the documentation of the functiton `?tapply`, but +find more details by reading the documentation of the function `?tapply`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: ```{r eval = FALSE} @@ -308,6 +365,8 @@ tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means "ignore missing data". +

      +
      @@ -341,8 +400,10 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex))

      **Going further** click on the button below to learn about customising `ggplot2` graphics:

      - -
      + +
      + +

      `ggplot2` graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. @@ -365,21 +426,12 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + theme(legend.position = c(0.8, 0.8)) ``` +

      +
      - -```{r stegen-8, eval = FALSE} -# The first line below is read as follows: assign a value of NA to stegen$pork WHERE stegen$pork is equal to 9 -stegen$pork[stegen$pork == 9] <- NA - -stegen$salmon[stegen$salmon == 9] <- NA - -stegen$horseradish[stegen$horseradish == 9] <- NA -``` - -
      # About this document From 5f67b3a90cc38d6c0cbf057e41c3def86b6743d7 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 18:47:56 +0100 Subject: [PATCH 07/59] added epicurves --- content/post/practical-stegen.Rmd | 87 +++++++++++++++++++++++++++++-- 1 file changed, 83 insertions(+), 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 032630f..054aecd 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -12,7 +12,7 @@ showonlyimage: true --- ```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) +knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, warning = FALSE) ``` # Context @@ -223,10 +223,11 @@ variables seem to be present. There are, however, **a few issues**: 2. variable types are partly wrong: - *unique keys* should be `character` (i.e. character strings) - *dates of onset* should be `Date` (**R** is good at handling actual dates) - - *sex* should be a `factor` (i.e. a categorical variable) + - *illness* and *sex* should be `factor` (i.e. a categorical variables) 3. labels used in some variables are ambiguous: - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, female) + - *illness* should be coded explicitely, not as 0 (here, non-case) and 1 (case) 4. some binary variables have maximum values of 9 (see `summary(stegen)`) @@ -249,10 +250,11 @@ names(stegen) <- new_labels ``` We set convert the unique identifiers to character strings (`character`), dates -of onset to actual `Date` objects, and sex to a categorical variable (`factor`): +of onset to actual `Date` objects, and sex and illness are set to categorical variables (`factor`): ```{r stegen_unique_ids} stegen$unique_key <- as.character(stegen$unique_key) stegen$sex <- factor(stegen$sex) +stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` @@ -260,6 +262,7 @@ We use the function `recode` from the `dplyr' package to recode `sex` more explicitely: ```{r stegen_recode} stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") +stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") ``` @@ -331,7 +334,8 @@ toy_vector # check outcome # Data exploration -## Summaries of ge and sex distribution + +## Summaries of age and sex distribution We can have a brief look at age and sex distributions using some basic summaries; for instance: @@ -396,6 +400,7 @@ Color can be added using: ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) ``` +Here, the age distribution is pretty much identical between male and female.
      @@ -432,6 +437,80 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") +
      +## Epidemic curve + +Incidence curves can be built using the package `incidence`, which will compute +the number of new cases given a vector of dates (here, onset) and a time +interval (1 day by default). We use the function `incidence` to achieve this, and +then visualise the results: + +```{r stegen_incidence} +i <- incidence(stegen$date_onset) +i +plot(i) +``` + +Details of the case counts can be obtained using: +```{r stegen_case_counts} +as.data.frame(i) +``` + +Note that the plots produced by `incidence` are `ggplot2` objects, so that the +options seen before can be used for further customisation. + + + +
      +

      **Going further** click on the button below to learn about `incidence`:

      + +
      + +

      + +More information on the `incidence` package can be found from the +[dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. gender: + +```{r incidence_stratified} +i_sex <- incidence(stegen$date_onset, group = stegen$sex) +i_sex +as.data.frame(i_sex) +plot(i_sex) +``` + +We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): + +```{r incidence_stratified_custom} +plot(i_sex, border = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + + labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + + theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1)) +``` + +

      + +
      +
      + + + +## Age and gender distribution of the cases + +We use the same principles as before to visualise the distribution of illness by +age and gender. To split the plot into different panels according to `sex`, we +use `facet_grid()` (see previous extra info on `ggplot2` customisation for +further details otions): + +```{r stegen_case_age_sex} +ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + + facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() +``` + +Illness does not seem to be depending on age or gender. This can be tested: this +is the topic of the next and final section. + + +
      # About this document From 343177d85be73d457a5f80390e41577cbe32de45 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 18:58:24 +0100 Subject: [PATCH 08/59] added epicurve stratification --- content/post/practical-stegen.Rmd | 31 ++++++++++++++++++++----------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 054aecd..a5eaf84 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -455,10 +455,26 @@ Details of the case counts can be obtained using: as.data.frame(i) ``` -Note that the plots produced by `incidence` are `ggplot2` objects, so that the -options seen before can be used for further customisation. +How long is this outbreak? It looks like most cases occurred over the course of +3 days, but that cases kept showing up 10 days after the peak. Is this true? Not +really. Stratifying the epidemic curve by case definition will clarify the +situation: + +```{r incidence_stratified} +i_ill <- incidence(stegen$date_onset, group = stegen$ill) +i_ill +as.data.frame(i_ill) +plot(i_ill, color = c("non case" = "#66cc99", "case" = "#990033")) +``` + +The outbreak really only happened over 3 days: onsets reported after did not +match the epi case definition. This is compatible with a food-borne outbreak +with limited or no person-to-person transmission. +Note that the plots produced by `incidence` are `ggplot2` objects, so that the +options seen before can be used for further customisation (see below). +

      **Going further** click on the button below to learn about `incidence`:

      @@ -468,19 +484,12 @@ options seen before can be used for further customisation.

      More information on the `incidence` package can be found from the -[dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. gender: - -```{r incidence_stratified} -i_sex <- incidence(stegen$date_onset, group = stegen$sex) -i_sex -as.data.frame(i_sex) -plot(i_sex) -``` +[dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): ```{r incidence_stratified_custom} -plot(i_sex, border = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + +plot(i_ill, border = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1)) From 334b18756a042a7f747e87e5b1844a538387f1ab Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 19:32:18 +0100 Subject: [PATCH 09/59] adding t test and bartlett --- content/post/practical-stegen.Rmd | 51 +++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index a5eaf84..523d59b 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -520,6 +520,57 @@ is the topic of the next and final section. +# Looking for the culprits + +## Univariate tests + +Methods for testing the association between two variables can be broken down in 3 types, +depending on which types these variables are: + +1. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar + methods +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with + 2 groups: Student's$t$-test +3. **2 categorical variables**: Chi-squared test on the 2x2 table + (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) + + +We can use these approaches to test if the disease is linked to any of the other +recorded variables. As illness itself is a categorical variable, only approaches +of type 2 and 3 will be illustrated here. + + +### Is illness linked to age? + +We can use the function `t.test` to test if the average age is different across +illness status. As this test assumes that the two categories exhibit similar +variation, we first ensure that the variances are comparable using Bartlett's +test. The syntax`variable ~ group` is used to indicate the variable of interest (left hand-side), and the group + (right hand-side): + +```{r stegen_bartlett_test} +bartlett.test(stegen$age ~ stegen$ill) +``` + +The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)["p.value"], 3)`) suggests the variances are indeed comparable. Wecan thus proceed to a $t$-test: + + +```{r stegen_bartlett_test} +t.test(stegen$age ~ stegen$ill, var.equal = TRUE) +``` + +The p-value of `r round(t.test(stegen$age ~ stegen$ill, var.equal = TRUE)["p.value"], 3)` confirms the previous graphics: the illness does not seem to be linked to age. + + + + +## Making multiple tests + + + +# Conclusion + +
      # About this document From 2ced11c3a4392dd07c0b3ebe46af0d460326a51b Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 19:40:20 +0100 Subject: [PATCH 10/59] adding chi2 test --- content/post/practical-stegen.Rmd | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 523d59b..fc55a2f 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -552,17 +552,43 @@ test. The syntax`variable ~ group` is used to indicate the variable of interest bartlett.test(stegen$age ~ stegen$ill) ``` -The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)["p.value"], 3)`) suggests the variances are indeed comparable. Wecan thus proceed to a $t$-test: +The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)[["p.value"]], 3)`) suggests the variances are indeed comparable. Wecan thus proceed to a $t$-test: ```{r stegen_bartlett_test} t.test(stegen$age ~ stegen$ill, var.equal = TRUE) ``` -The p-value of `r round(t.test(stegen$age ~ stegen$ill, var.equal = TRUE)["p.value"], 3)` confirms the previous graphics: the illness does not seem to be linked to age. +The p-value of `r round(t.test(stegen$age ~ stegen$ill, var.equal = TRUE)[["p.value"]], 3)` confirms the previous graphics: the illness does not seem to be linked to age. +### Is illness linked to gender? + +To test the association between gender and illness (2 categorical variables), we +first build a 2-by-2 (contingency) table, using: + +```{r stegen_2by2} +tab_ill_sex <- table(stegen$ill, stegen$sex) +tab_ill_sex +``` + +Note that proportions can be obtained using `prop.table`: +```{r stegen_prop_table} +## basic proportions +prop.table(tab_ill_sex) + +## expressed in %, rounded: +round(100 * prop.table(tab_ill_sex)) +``` + +Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: +```{r stegen_chisq} +chisq.test(tab_ill_sex) +``` + +Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests illness is not related to sex either. + ## Making multiple tests From 1d352690d5c8e5c96e95a55f5f6596d7ef3e280f Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 21:25:57 +0100 Subject: [PATCH 11/59] first complete version of stegen --- content/post/practical-stegen.Rmd | 195 +++++++++++++++++++++++++++++- 1 file changed, 191 insertions(+), 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index fc55a2f..f4d49a0 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -489,10 +489,11 @@ More information on the `incidence` package can be found from the We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): ```{r incidence_stratified_custom} -plot(i_ill, border = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + +plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + - theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1)) + geom_hline(yintercept = 1:55, color = "white") + + theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) ```

      @@ -555,7 +556,7 @@ bartlett.test(stegen$age ~ stegen$ill) The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)[["p.value"]], 3)`) suggests the variances are indeed comparable. Wecan thus proceed to a $t$-test: -```{r stegen_bartlett_test} +```{r stegen_t_test} t.test(stegen$age ~ stegen$ill, var.equal = TRUE) ``` @@ -592,13 +593,199 @@ Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests ## Making multiple tests +The natural next step is to run multiple tests for all potential risk factors +recorded. There are many ways to go about this in **R**, but we will illustrate +one of the most common workflows: + +1. isolate a subset of variables to test (the ones indicative of food consumption) (using `[]`) +2. for each: + i) build contingency table with illness status (using `table`) + ii) run a Chi-square test on this table (using `chisq.test`) + +All of these have been seen so far; the only missing piece is step 2), which we +will cover using a very handy function called `lapply`. + + +### Isolating the variables to test + +We use the natural structure of the dataset, as variables representing food +consumption are all the last columns: + +```{r stegen_isolate_food} +names(stegen) +``` + +In this case, we need to retain columns 6 to 21, excluding `tportion` and +`mportion`, which are not binary; this can be done using the subsetting operator +`[...]`, where `...` are numbers indicating the columns to retain: + +```{r stegen_food} +to_keep <- c(6, 8, 9, 10, 12:21) +to_keep +food <- stegen[to_keep] +food +``` + + +### Building several contingency tables + +There are many variables to test, and having to enter separate command lines for +each would be cumbersome and prone to errors. As a workaround, the function +`lapply` can be used. This function allows to repeat an operation using a vector +of inputs, using each element in turn. Its general syntax is +`lapply(vector_of_inputs, function_to_use, other_arguments)`, where +`other_arguments` can be any secondary arguments taken by +`function_to_use`. Here, we use it to build a separate contingency table of each +variable in `food` crossed with illness status (`stegen$ill`); note that we show +only the first few tables: + +```{r stegen_food_tables, echo = c(1,3), eval = -2} +food_tables <- lapply(food, table, stegen$ill) +food_tables +head(food_tables) +``` + + +### Realising multiple Chi-squared tests + +The same principle used to compute several contingency tables can be used to +compute Chi-square tests for each table, using `lapply`; note that we show +only the first few tests: + +```{r stegen_food_tests, echo = c(1,3), eval = -2} +food_tests <- lapply(food_tables, chisq.test) +food_tests +head(food_tests) +``` + +Looking at these results, it seems a large number of food items are +significantly correlated to the illness. Which food item is the biggest suspect? +We will try and address this question using risk ratios. + + + +## Risk ratios + +### For one variable + +The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the +ratio between the proportion of illness in one group (typically 'exposed') vs +another ('non-exposed'). For instance, let us consider the contingency table of +pork consumption and illness: +```{r stegen_pork_table} +food_tables$pork +``` + +This can be turned into proportions using `prop.table`, specifying that we want +proportions by rows (`margin = 1`): +```{r stegen_pork_prop} +pork_prop <- prop.table(food_tables$pork, margin = 1) +pork_prop +``` + +the corresponding risk ratio is: +```{r stegen_pork_rr} +## ill & exposed: row 2, column 2 +pork_prop[2, 2] # row 2, column 2 + +## ill & non-exposed: row 1, column 2 +pork_prop[1, 2] + +## compute the risk ratio +pork_rr <- pork_prop[2, 2] / pork_prop[1, 2] +pork_rr +``` + +Note the use of the syntax `[rows, columns]` to subset specific rows and columns +of 2-dimensional objects. + + +### Multiple variables + +In the example above, we have computed the risk ratio using a simple recipe: + +1. select a contingency table +2. convert raw numbers to proportions (using `prop.table`) +3. isolating the risks in exposed and non-exposed groups, and compute their + ratio + +Repeating these tasks manually for all variables in `food` would be +cumbersome. As an alternative, we can define the steps above as `function`, +i.e. a generic recipe which we can then apply to any contingency table. This new +function will be called `risk_ratio`, and is defined as follows: +```{r stegen_risk_ratio_function} +risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table + ## compute table of proportions + tab_prop <- prop.table(tab, margin = 1) + + ## compute the risk ratio + result <- tab_prop[2, 2] / tab_prop[1, 2] + + ## return the output + return(result) # this will be the output of the function +} +``` + +We can now try this function on any of the tables in `food_tables`, for instance: +```{r rr_demo} +food_tables$pork +risk_ratio(food_tables$pork) +``` + +And we can now apply this function sequentially to all variables in `food` as +before, using `lapply` (we show only the first few values): + +```{r stegen_all_rr, echo = 1:2, eval = -2} +all_rr <- lapply(food_tables, risk_ratio) +all_rr +head(all_rr) +``` + +Finally, we re-shape these results into a numeric `vector` (using `unlist)` for +further plotting; note that for this graph, the basic system is easier to use +than `ggplot2`: +```{r stegen_rr_plot} +## convert results from list to numeric vector +rr_data <- unlist(all_rr) +rr_data + +## sort results by decreasing order +rr_data <- sort(rr_data, decreasing = TRUE) + +## plot using the basic system +par(mar = c(5, 7, 4, 1)) # change margins +barplot(rr_data, horiz = TRUE, las = 1, + xlab = "Risk ratio", + col = heat.colors(15), # add colors + main = "Risk ratio by food exposure") +abline(v = 1, lty = 2) # add vertical line x = 1 +``` + +The results are a lot clearer now: the tiramisu is by far the largest risk +factor in this outbreak. + + # Conclusion +This case study illustrated how **R** can be used to import data, clean them, +and derive basic summaries for a first glance at the data. It also showed how to +generate graphics using `ggplot2`, and how to detect associations between +several potential risk factors and the occurrence of illness. One major caveat +here is that we are not accounting for potential confounding factors. These will +be treated in a separate case study, which will focus on the use of logistic +regression in epidemic studies. + + +
      +

      + + + # About this document ## Source @@ -614,7 +801,7 @@ based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. - adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis -- translation to R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart +- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart Contributions are welcome via [pull From 6b3046cc7da06e3b3118f1e2d2f5bdfdc8803a12 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Thu, 25 Oct 2018 21:28:47 +0100 Subject: [PATCH 12/59] adding stegen practical post - md and files --- content/post/practical-stegen.md | 1010 +++++++++++++++++ .../incidence_stratified-1.png | Bin 0 -> 12258 bytes .../incidence_stratified_custom-1.png | Bin 0 -> 35932 bytes .../stegen_case_age_sex-1.png | Bin 0 -> 18550 bytes .../stegen_ggplot2_custom-1.png | Bin 0 -> 22431 bytes .../stegen_histogram-1.png | Bin 0 -> 5429 bytes .../stegen_histogram_sex-1.png | Bin 0 -> 6121 bytes .../stegen_incidence-1.png | Bin 0 -> 5994 bytes .../stegen_rr_plot-1.png | Bin 0 -> 24292 bytes 9 files changed, 1010 insertions(+) create mode 100644 content/post/practical-stegen.md create mode 100644 static/post/practical-stegen_files/figure-markdown_github/incidence_stratified-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/incidence_stratified_custom-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_case_age_sex-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_ggplot2_custom-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_histogram-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_histogram_sex-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_incidence-1.png create mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_rr_plot-1.png diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md new file mode 100644 index 0000000..4d60e3c --- /dev/null +++ b/content/post/practical-stegen.md @@ -0,0 +1,1010 @@ +--- +title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" +author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" +authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +categories: "case study" +tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] +date: 2018-10-24 +slug: stegen +licenses: CC-BY +image: img/highres/graduation-1965.jpg +showonlyimage: true +--- + +Context +======= + +On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants were expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students, and some graduates from a nearby school (school B). + +A self-service party buffet was supplied by a commercial caterer in Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. + +Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late during the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. + +The alert +--------- + +On 2nd July 1998, the Freiburg local health office reported to the Robert Koch Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis following the graduation party described above. More than 100 cases were suspected among participants and some of them were admitted to nearby hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting lasting for several days. Most believed that the tiramisu consumed at dinner was responsible for their illness. *Salmonella enteritidis* was isolated from 19 stool samples. + +The Freiburg health office sent a team to investigate the kitchen of the caterer. Food preparation procedures were reviewed. Food samples, except tiramisu (none was left over), were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the following: brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and 10 raw eggs. + +The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk factors for transmission in order to better control the outbreak. + +The epidemiological study +------------------------- + +**Case definition**: cases were defined as any person who had attended the party at St Sebastian High School and who suffered from *diarrhoea* (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998, or from at least three of the following symptoms: *vomiting*, *fever over 38.5° C*, *nausea*, *abdominal pain*, *headache*. + +Students from both schools attending the party were asked through phone interviews to provide names of persons who attended the party. + +Overall, 291 responded to enquiries and 103 cases were identified. The linelist analysed in this case study was built from these 291 responses. + +This case study +--------------- + +In this case study, we will take you through the analysis of this epidemic. This will be the occasion to illustrate more generally useful practices for data analysis using **R**, including: + +- how to read data from Excel +- how to explore data using tables and summaries +- how to clean data +- how to make graphics to describe the data +- how to test if specific food items are linked to the disease + +Initial data processing +======================= + +Loading required packages +------------------------- + +The following packages will be used in the case study: + +- `here`: to find the path to data or script files +- `readxl`: to read Excel spreadsheets into **R** +- `incidence`: to build epicurves + +To avoid having to specify the name of the package whenever we use a function using the syntax `package_name::function_name()`, we load these packages using `library`: + +``` r +library(here) # find data/script files +## here() starts at /home/thibaut/dev/recon/learn +library(readxl) # read xlsx files +library(incidence) # make epicurves +library(epitrix) # clean labels and variables +library(dplyr) # general data handling +## +## Attaching package: 'dplyr' +## The following objects are masked from 'package:stats': +## +## filter, lag +## The following objects are masked from 'package:base': +## +## intersect, setdiff, setequal, union +library(ggplot2) # advanced graphics +``` + +Note that you will get an error if the packages have not been installed on your system. To install them (you only need to do this once!), type: + +``` r +install.packages("here") +install.packages("readxl") +install.packages("incidence") +install.packages("epitrix") +install.packages("dplyr") +install.packages("ggplot2") +``` + +Loading these packages makes all functions implemented by the packages available, so that `function_name()` can be used directly (without the `package_name::` prefix). This is not a problem here as these packages do now implement functions with identical names. + +Importing data from Excel +------------------------- + +Linelist data can be read from various formats, including flat text files (e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or Excel spreadsheets. We illustrate the latter, which is probably the most common format. We assume that the data file `stegen_raw.xlsx` has been saved in a `data/` folder of your project, and that your current R session is at the root of the project. + +Here we decompose the steps to read data in: finding the path to the data (`path_to_data`), using the function `read_xlsx` to read data in, and saving the output in a new object `stegen`: + +``` r +path_to_data <- here("data", "stegen_raw.xlsx") +stegen <- read_xlsx(path_to_data) +``` + +To print the content of the dataset, we can use either of these commands: + +``` r +stegen +## # A tibble: 291 x 21 +## `Unique key` ill `Date-onset` SEX Age tiramisu tportion wmousse +## +## 1 210 1 1998-06-27 1 18 1 3 0 +## 2 12 1 1998-06-27 0 57 1 1 0 +## 3 288 1 1998-06-27 1 56 0 0 0 +## 4 186 1 1998-06-27 0 17 1 1 1 +## 5 20 1 1998-06-27 1 19 1 2 0 +## 6 148 1 1998-06-27 0 16 1 2 1 +## 7 201 1 1998-06-27 0 19 1 3 0 +## 8 106 1 1998-06-27 0 19 1 2 1 +## 9 272 1 1998-06-27 1 40 1 2 1 +## 10 50 1 1998-06-27 0 53 1 1 1 +## # ... with 281 more rows, and 13 more variables: dmousse , +## # mousse , mportion , Beer , redjelly , `Fruit +## # salad` , tomato , mince , salmon , +## # horseradish , chickenwin , roastbeef , PORK +## View(stegen) +``` + +
      +

      +**Problems?** In case of trouble, click on the button below to toggle additional help: +

      +

      + +If the above fails, you should check: + +- that your data file has been saved in the right folder, with the right name (lower case and upper case do matter) +- that your R session was started from the right project - if unsure, close R and re-open Rstudio by double-clicking on the `.Rproj` file +- that all packages are installed and loaded (see installation guidelines above) + +

      + +
      +Overview and summaries +---------------------- + +We first have a quick look at the content of the data set. The information we are looking for is: + +- the numbers of cases (rows) and variables (columns) in the data +- the *name* of the variables: do they use consistent capitalisation and separators? +- the *type* of the variables: are dates, numeric or categorical variables used when they should? +- the *coding* of the variables: are explicit labels (e.g. `"male"`/`"female"`) used where relevant? + +We first check the dimensions of the `stegen` object, and the name of the variables: + +``` r +dim(stegen) # rows x columns +## [1] 291 21 +names(stegen) # column labels +## [1] "Unique key" "ill" "Date-onset" "SEX" "Age" +## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" +## [11] "mportion" "Beer" "redjelly" "Fruit salad" "tomato" +## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" +## [21] "PORK" +``` + +We can now try and summarise the dataset using: + +``` r +summary(stegen) +## Unique key ill Date-onset SEX +## Min. : 1.0 Min. :0.000 Length:291 Min. :0.0000 +## 1st Qu.: 73.5 1st Qu.:0.000 Class :character 1st Qu.:0.0000 +## Median :146.0 Median :0.000 Mode :character Median :1.0000 +## Mean :146.0 Mean :0.354 Mean :0.5223 +## 3rd Qu.:218.5 3rd Qu.:1.000 3rd Qu.:1.0000 +## Max. :291.0 Max. :1.000 Max. :1.0000 +## +## Age tiramisu tportion wmousse +## Min. :12.00 Min. :0.0000 Min. :0.0000 Min. :0.0000 +## 1st Qu.:18.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 +## Median :20.00 Median :0.0000 Median :0.0000 Median :0.0000 +## Mean :26.66 Mean :0.4231 Mean :0.6678 Mean :0.2599 +## 3rd Qu.:27.00 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 +## Max. :80.00 Max. :1.0000 Max. :3.0000 Max. :1.0000 +## NA's :8 NA's :5 NA's :5 NA's :14 +## dmousse mousse mportion Beer +## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 +## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 +## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 +## Mean :0.3937 Mean :0.4256 Mean :0.6523 Mean :0.3911 +## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 +## Max. :1.0000 Max. :1.0000 Max. :3.0000 Max. :1.0000 +## NA's :4 NA's :2 NA's :12 NA's :20 +## redjelly Fruit salad tomato mince +## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000 +## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 +## Median :0.0000 Median :0.000 Median :0.0000 Median :0.000 +## Mean :0.2715 Mean :0.244 Mean :0.2852 Mean :0.299 +## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:1.000 +## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.000 +## +## salmon horseradish chickenwin roastbeef +## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000 +## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 +## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000 +## Mean :0.4811 Mean :0.3093 Mean :0.2887 Mean :0.09966 +## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.00000 +## Max. :9.0000 Max. :9.0000 Max. :1.0000 Max. :1.00000 +## +## PORK +## Min. :0.0000 +## 1st Qu.:0.0000 +## Median :0.0000 +## Mean :0.4742 +## 3rd Qu.:1.0000 +## Max. :9.0000 +## +``` + +Note that binary variables, when treated as numeric values (0/1), are summarised at such, which may not always be useful. As an alternative, `table` can be used to list all possible values of a variable, and count how many time each value appears in the data. For instance, we can compare the `summary` and `table` for consumption of `tiramisu`: + +``` r +stegen$tiramisu # all values +## [1] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +## [24] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +## [47] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 NA 1 +## [70] 0 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 +## [93] 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 +## [116] 0 0 0 NA 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 +## [139] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 +## [162] 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 +## [185] 0 0 0 NA 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 +## [208] 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 +## [231] 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 +## [254] 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 NA 0 1 0 0 0 +## [277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 +summary(stegen$tiramisu) # summary +## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's +## 0.0000 0.0000 0.0000 0.4231 1.0000 1.0000 5 +table(stegen$tiramisu) # table +## +## 0 1 +## 165 121 +``` + +**Good news**: the dataset has the expected dimensions, and all the relevant variables seem to be present. There are, however, **a few issues**: + +1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation +2. variable types are partly wrong: + - *unique keys* should be `character` (i.e. character strings) + - *dates of onset* should be `Date` (**R** is good at handling actual dates) + - *illness* and *sex* should be `factor` (i.e. a categorical variables) + +3. labels used in some variables are ambiguous: + - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, female) + - *illness* should be coded explicitely, not as 0 (here, non-case) and 1 (case) + +4. some binary variables have maximum values of 9 (see `summary(stegen)`) + +Data cleaning +------------- + +While it is tempting to go back to the Excel spreadsheet to fix issues with data, it is almost always quicker and more reliable to clean data in **R** directly. Here, we make a copy of the old data set, and clean `stegen` before further analysis. + +``` r +stegen_old <- stegen # save 'dirty data' +``` + +We use `epitrix`'s function `clean_labels` to standardise the variable names: + +``` r +new_labels <- clean_labels(names(stegen)) # generate standardised labels +new_labels # check the result +## [1] "unique_key" "ill" "date_onset" "sex" "age" +## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" +## [11] "mportion" "beer" "redjelly" "fruit_salad" "tomato" +## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" +## [21] "pork" +names(stegen) <- new_labels +``` + +We set convert the unique identifiers to character strings (`character`), dates of onset to actual `Date` objects, and sex and illness are set to categorical variables (`factor`): + +``` r +stegen$unique_key <- as.character(stegen$unique_key) +stegen$sex <- factor(stegen$sex) +stegen$ill <- factor(stegen$ill) +stegen$date_onset <- as.Date(stegen$date_onset) +``` + +We use the function `recode` from the `dplyr' package to recode`sex\` more explicitely: + +``` r +stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") +stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") +``` + +Finally we look in more depth into these variables having maximum values of 9, where we expect 0/1; `table` is useful to list all values taken by a variable, and listing their frequencies: + +``` r + +table(stegen$pork) +## +## 0 1 9 +## 169 120 2 +table(stegen$salmon) +## +## 0 1 9 +## 183 104 4 +table(stegen$horseradish) +## +## 0 1 9 +## 217 72 2 +``` + +The only rogue values are `9`; they are likely either data entry issues, or missing data, which in **R** should be coded as `NA` ("not available"). We can replace these values using: + +``` r +stegen$pork[stegen$pork == 9] <- NA +stegen$salmon[stegen$salmon == 9] <- NA +stegen$horseradish[stegen$horseradish == 9] <- NA +``` + +

      +**Going further** click on the button below for more explanation: +

      + +

      +There are several things going on in a command like: + +``` r +stegen$pork[stegen$pork == 9] <- NA +``` + +let us break them down: + +1. `stegen$pork` means "get the variable called `pork` in the dataset `stegen`" +2. `[...]` subset the vector according to `...`; if `...` is a series of `TRUE/FALSE` values, only entries corresponding to `TRUE` are retained +3. `... == 9` for each value of `...` test if it is equal to `9`, and return `TRUE` if so (`FALSE` otherwise) +4. `... <- NA` replace `...` with `NA` (missing value) + +in other words: "isolate the entries of `stegen$pork` which equal `9`, and replace them with `NA`; here is another toy example to illustrate the procedure: + +``` r +## make toy input vector +toy_vector <- 1:5 +toy_vector +## [1] 1 2 3 4 5 + +## make toy logical vector for subsetting +toy_logical <- c(FALSE, TRUE, TRUE, FALSE, TRUE) +toy_logical +## [1] FALSE TRUE TRUE FALSE TRUE +toy_vector[toy_logical] # subset values +## [1] 2 3 5 +toy_vector[toy_logical] <- 0 # replace subset values +toy_vector # check outcome +## [1] 1 0 0 4 0 +``` + +

      + +Data exploration +================ + +Summaries of age and sex distribution +------------------------------------- + +We can have a brief look at age and sex distributions using some basic summaries; for instance: + +``` r +summary(stegen$age) # age stats +## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's +## 12.00 18.00 20.00 26.66 27.00 80.00 8 +summary(stegen$sex) # gender distribution +## male female +## 139 152 +tapply(stegen$age, stegen$sex, summary) # age stats by gender +## $male +## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's +## 12.00 17.50 19.00 26.38 28.00 80.00 4 +## +## $female +## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's +## 13.00 18.00 20.00 26.93 24.50 65.00 4 +``` + +

      +**Going further** click on the button below to learn about `tapply`: +

      + +

      +`tapply` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: + +``` r +tapply(stegen$age, stegen$sex, summary) +``` + +this literally means: select the age variable in the dataset `stegen` (`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each strata (`summary`). So for instance, to get the average age by sex (function `mean`), one could use: + +``` r +tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) +## male female +## 26.37778 26.92568 +``` + +Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means "ignore missing data". + +

      + +Graphical exploration +--------------------- + +The summaries above may be useful for reporting purposes, but graphics are usually better for getting a feel for the data. Here, we illustrate how the package `ggplot2` can be used to derive informative graphics of age/sex distribution. It implements an alternative graphics system to the basic **R** plots, in which the plot is built by adding (literally using `+`) different layers of information, using: + +- `ggplot()` to specify the dataset to use +- `geom_...()` functions which define a type of graphics to use (e.g. barplot, histogram) +- `aes()` to map elements of the data into aesthetic properties (e.g. x/y axes, color, shape) + +For instance, to get an histogram of age: + +``` r +ggplot(stegen) + geom_histogram(aes(x = age)) +## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +``` + +![](practical-stegen_files/figure-markdown_github/stegen_histogram-1.png) + +Color can be added using: + +``` r +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) +## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +``` + +![](practical-stegen_files/figure-markdown_github/stegen_histogram_sex-1.png) + +Here, the age distribution is pretty much identical between male and female. + +

      +**Going further** click on the button below to learn about customising `ggplot2` graphics: +

      + +

      +`ggplot2` graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. + +For instance, here we: + +- add white borders to the plot (`color = "white"`) +- specify colors manually for male / female, using specified color codes (see [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to define your own colors) (`scale_fill_manual(...)`) +- adding labels for title, *x* and *y* axes (`labs()`) +- specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) +- move the legend inside the plot (`theme(...)`) + +``` r +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + + labs(title = "Age distribution by gender", x = "Age (years)", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + + theme(legend.position = c(0.8, 0.8)) +## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +``` + +![](practical-stegen_files/figure-markdown_github/stegen_ggplot2_custom-1.png) + +

      + +Epidemic curve +-------------- + +Incidence curves can be built using the package `incidence`, which will compute the number of new cases given a vector of dates (here, onset) and a time interval (1 day by default). We use the function `incidence` to achieve this, and then visualise the results: + +``` r +i <- incidence(stegen$date_onset) +i +## +## [131 cases from days 1998-06-26 to 1998-07-09] +## +## $counts: matrix with 14 rows and 1 columns +## $n: 131 cases in total +## $dates: 14 dates marking the left-side of bins +## $interval: 1 day +## $timespan: 14 days +## $cumulative: FALSE +plot(i) +``` + +![](practical-stegen_files/figure-markdown_github/stegen_incidence-1.png) + +Details of the case counts can be obtained using: + +``` r +as.data.frame(i) +## dates counts +## 1 1998-06-26 1 +## 2 1998-06-27 56 +## 3 1998-06-28 51 +## 4 1998-06-29 10 +## 5 1998-06-30 3 +## 6 1998-07-01 3 +## 7 1998-07-02 3 +## 8 1998-07-03 0 +## 9 1998-07-04 1 +## 10 1998-07-05 1 +## 11 1998-07-06 1 +## 12 1998-07-07 0 +## 13 1998-07-08 0 +## 14 1998-07-09 1 +``` + +How long is this outbreak? It looks like most cases occurred over the course of 3 days, but that cases kept showing up 10 days after the peak. Is this true? Not really. Stratifying the epidemic curve by case definition will clarify the situation: + +``` r +i_ill <- incidence(stegen$date_onset, group = stegen$ill) +i_ill +## +## [131 cases from days 1998-06-26 to 1998-07-09] +## [2 groups: case, non case] +## +## $counts: matrix with 14 rows and 2 columns +## $n: 131 cases in total +## $dates: 14 dates marking the left-side of bins +## $interval: 1 day +## $timespan: 14 days +## $cumulative: FALSE +as.data.frame(i_ill) +## dates case non case +## 1 1998-06-26 0 1 +## 2 1998-06-27 48 8 +## 3 1998-06-28 46 5 +## 4 1998-06-29 8 2 +## 5 1998-06-30 0 3 +## 6 1998-07-01 0 3 +## 7 1998-07-02 0 3 +## 8 1998-07-03 0 0 +## 9 1998-07-04 0 1 +## 10 1998-07-05 0 1 +## 11 1998-07-06 0 1 +## 12 1998-07-07 0 0 +## 13 1998-07-08 0 0 +## 14 1998-07-09 0 1 +plot(i_ill, color = c("non case" = "#66cc99", "case" = "#990033")) +``` + +![](practical-stegen_files/figure-markdown_github/incidence_stratified-1.png) + +The outbreak really only happened over 3 days: onsets reported after did not match the epi case definition. This is compatible with a food-borne outbreak with limited or no person-to-person transmission. + +Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). + +

      +**Going further** click on the button below to learn about `incidence`: +

      + +

      +More information on the `incidence` package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: + +We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): + +``` r +plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + + labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + + geom_hline(yintercept = 1:55, color = "white") + + theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) +``` + +![](practical-stegen_files/figure-markdown_github/incidence_stratified_custom-1.png) + +

      + +Age and gender distribution of the cases +---------------------------------------- + +We use the same principles as before to visualise the distribution of illness by age and gender. To split the plot into different panels according to `sex`, we use `facet_grid()` (see previous extra info on `ggplot2` customisation for further details otions): + +``` r +ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + + facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() +## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +``` + +![](practical-stegen_files/figure-markdown_github/stegen_case_age_sex-1.png) + +Illness does not seem to be depending on age or gender. This can be tested: this is the topic of the next and final section. + +Looking for the culprits +======================== + +Univariate tests +---------------- + +Methods for testing the association between two variables can be broken down in 3 types, depending on which types these variables are: + +1. **2 quantitative variables**: Pearson's correlation coefficient (\(r\)) and similar methods +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with 2 groups: Student's\(t\)-test +3. **2 categorical variables**: Chi-squared test on the 2x2 table (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) + +We can use these approaches to test if the disease is linked to any of the other recorded variables. As illness itself is a categorical variable, only approaches of type 2 and 3 will be illustrated here. + +### Is illness linked to age? + +We can use the function `t.test` to test if the average age is different across illness status. As this test assumes that the two categories exhibit similar variation, we first ensure that the variances are comparable using Bartlett's test. The syntax`variable ~ group` is used to indicate the variable of interest (left hand-side), and the group (right hand-side): + +``` r +bartlett.test(stegen$age ~ stegen$ill) +## +## Bartlett test of homogeneity of variances +## +## data: stegen$age by stegen$ill +## Bartlett's K-squared = 0.82311, df = 1, p-value = 0.3643 +``` + +The resulting p-value (0.364) suggests the variances are indeed comparable. Wecan thus proceed to a \(t\)-test: + +``` r +t.test(stegen$age ~ stegen$ill, var.equal = TRUE) +## +## Two Sample t-test +## +## data: stegen$age by stegen$ill +## t = -0.55979, df = 281, p-value = 0.5761 +## alternative hypothesis: true difference in means is not equal to 0 +## 95 percent confidence interval: +## -4.509729 2.512679 +## sample estimates: +## mean in group non case mean in group case +## 26.31148 27.31000 +``` + +The p-value of 0.576 confirms the previous graphics: the illness does not seem to be linked to age. + +### Is illness linked to gender? + +To test the association between gender and illness (2 categorical variables), we first build a 2-by-2 (contingency) table, using: + +``` r +tab_ill_sex <- table(stegen$ill, stegen$sex) +tab_ill_sex +## +## male female +## non case 86 102 +## case 53 50 +``` + +Note that proportions can be obtained using `prop.table`: + +``` r +## basic proportions +prop.table(tab_ill_sex) +## +## male female +## non case 0.2955326 0.3505155 +## case 0.1821306 0.1718213 + +## expressed in %, rounded: +round(100 * prop.table(tab_ill_sex)) +## +## male female +## non case 30 35 +## case 18 17 +``` + +Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: + +``` r +chisq.test(tab_ill_sex) +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: tab_ill_sex +## X-squared = 0.6562, df = 1, p-value = 0.4179 +``` + +Here, the p-value of 0.418 suggests illness is not related to sex either. + +Making multiple tests +--------------------- + +The natural next step is to run multiple tests for all potential risk factors recorded. There are many ways to go about this in **R**, but we will illustrate one of the most common workflows: + +1. isolate a subset of variables to test (the ones indicative of food consumption) (using `[]`) +2. for each: + 1. build contingency table with illness status (using `table`) + 2. run a Chi-square test on this table (using `chisq.test`) + +All of these have been seen so far; the only missing piece is step 2), which we will cover using a very handy function called `lapply`. + +### Isolating the variables to test + +We use the natural structure of the dataset, as variables representing food consumption are all the last columns: + +``` r +names(stegen) +## [1] "unique_key" "ill" "date_onset" "sex" "age" +## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" +## [11] "mportion" "beer" "redjelly" "fruit_salad" "tomato" +## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" +## [21] "pork" +``` + +In this case, we need to retain columns 6 to 21, excluding `tportion` and `mportion`, which are not binary; this can be done using the subsetting operator `[...]`, where `...` are numbers indicating the columns to retain: + +``` r +to_keep <- c(6, 8, 9, 10, 12:21) +to_keep +## [1] 6 8 9 10 12 13 14 15 16 17 18 19 20 21 +food <- stegen[to_keep] +food +## # A tibble: 291 x 14 +## tiramisu wmousse dmousse mousse beer redjelly fruit_salad tomato mince +## +## 1 1 0 1 1 0 0 0 0 0 +## 2 1 0 1 1 0 0 1 0 1 +## 3 0 0 0 0 0 0 0 1 1 +## 4 1 1 0 1 0 1 0 0 0 +## 5 1 0 0 0 1 0 0 0 0 +## 6 1 1 1 1 0 0 1 0 1 +## 7 1 0 1 1 0 0 1 0 0 +## 8 1 1 1 1 0 1 1 0 0 +## 9 1 1 1 1 1 0 0 1 0 +## 10 1 1 1 1 0 1 0 0 0 +## # ... with 281 more rows, and 5 more variables: salmon , +## # horseradish , chickenwin , roastbeef , pork +``` + +### Building several contingency tables + +There are many variables to test, and having to enter separate command lines for each would be cumbersome and prone to errors. As a workaround, the function `lapply` can be used. This function allows to repeat an operation using a vector of inputs, using each element in turn. Its general syntax is `lapply(vector_of_inputs, function_to_use, other_arguments)`, where `other_arguments` can be any secondary arguments taken by `function_to_use`. Here, we use it to build a separate contingency table of each variable in `food` crossed with illness status (`stegen$ill`); note that we show only the first few tables: + +``` r +food_tables <- lapply(food, table, stegen$ill) +head(food_tables) +## $tiramisu +## +## non case case +## 0 158 7 +## 1 27 94 +## +## $wmousse +## +## non case case +## 0 156 49 +## 1 23 49 +## +## $dmousse +## +## non case case +## 0 148 26 +## 1 37 76 +## +## $mousse +## +## non case case +## 0 144 22 +## 1 42 81 +## +## $beer +## +## non case case +## 0 96 69 +## 1 76 30 +## +## $redjelly +## +## non case case +## 0 154 58 +## 1 34 45 +``` + +### Realising multiple Chi-squared tests + +The same principle used to compute several contingency tables can be used to compute Chi-square tests for each table, using `lapply`; note that we show only the first few tests: + +``` r +food_tests <- lapply(food_tables, chisq.test) +head(food_tests) +## $tiramisu +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 161.64, df = 1, p-value < 2.2e-16 +## +## +## $wmousse +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 43.526, df = 1, p-value = 4.183e-11 +## +## +## $dmousse +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 79.574, df = 1, p-value < 2.2e-16 +## +## +## $mousse +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 82.943, df = 1, p-value < 2.2e-16 +## +## +## $beer +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 4.519, df = 1, p-value = 0.03352 +## +## +## $redjelly +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: X[[i]] +## X-squared = 20.781, df = 1, p-value = 5.148e-06 +``` + +Looking at these results, it seems a large number of food items are significantly correlated to the illness. Which food item is the biggest suspect? We will try and address this question using risk ratios. + +Risk ratios +----------- + +### For one variable + +The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the ratio between the proportion of illness in one group (typically 'exposed') vs another ('non-exposed'). For instance, let us consider the contingency table of pork consumption and illness: + +``` r +food_tables$pork +## +## non case case +## 0 115 54 +## 1 72 48 +``` + +This can be turned into proportions using `prop.table`, specifying that we want proportions by rows (`margin = 1`): + +``` r +pork_prop <- prop.table(food_tables$pork, margin = 1) +pork_prop +## +## non case case +## 0 0.6804734 0.3195266 +## 1 0.6000000 0.4000000 +``` + +the corresponding risk ratio is: + +``` r +## ill & exposed: row 2, column 2 +pork_prop[2, 2] # row 2, column 2 +## [1] 0.4 + +## ill & non-exposed: row 1, column 2 +pork_prop[1, 2] +## [1] 0.3195266 + +## compute the risk ratio +pork_rr <- pork_prop[2, 2] / pork_prop[1, 2] +pork_rr +## [1] 1.251852 +``` + +Note the use of the syntax `[rows, columns]` to subset specific rows and columns of 2-dimensional objects. + +### Multiple variables + +In the example above, we have computed the risk ratio using a simple recipe: + +1. select a contingency table +2. convert raw numbers to proportions (using `prop.table`) +3. isolating the risks in exposed and non-exposed groups, and compute their ratio + +Repeating these tasks manually for all variables in `food` would be cumbersome. As an alternative, we can define the steps above as `function`, i.e. a generic recipe which we can then apply to any contingency table. This new function will be called `risk_ratio`, and is defined as follows: + +``` r +risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table + ## compute table of proportions + tab_prop <- prop.table(tab, margin = 1) + + ## compute the risk ratio + result <- tab_prop[2, 2] / tab_prop[1, 2] + + ## return the output + return(result) # this will be the output of the function +} +``` + +We can now try this function on any of the tables in `food_tables`, for instance: + +``` r +food_tables$pork +## +## non case case +## 0 115 54 +## 1 72 48 +risk_ratio(food_tables$pork) +## [1] 1.251852 +``` + +And we can now apply this function sequentially to all variables in `food` as before, using `lapply` (we show only the first few values): + +``` r +all_rr <- lapply(food_tables, risk_ratio) +## all_rr +## $tiramisu +## [1] 18.31169 +## +## $wmousse +## [1] 2.847222 +## +## $dmousse +## [1] 4.501021 +## +## $mousse +## [1] 4.968958 +## +## $beer +## [1] 0.6767842 +## +## $redjelly +## [1] 2.08206 +``` + +Finally, we re-shape these results into a numeric `vector` (using `unlist)` for further plotting; note that for this graph, the basic system is easier to use than `ggplot2`: + +``` r +## convert results from list to numeric vector +rr_data <- unlist(all_rr) +rr_data +## tiramisu wmousse dmousse mousse beer redjelly +## 18.3116883 2.8472222 4.5010211 4.9689579 0.6767842 2.0820602 +## fruit_salad tomato mince salmon horseradish chickenwin +## 2.5006177 1.2898653 1.0568237 1.0334249 1.2557870 1.1617347 +## roastbeef pork +## 0.7607985 1.2518519 + +## sort results by decreasing order +rr_data <- sort(rr_data, decreasing = TRUE) + +## plot using the basic system +par(mar = c(5, 7, 4, 1)) # change margins +barplot(rr_data, horiz = TRUE, las = 1, + xlab = "Risk ratio", + col = heat.colors(15), # add colors + main = "Risk ratio by food exposure") +abline(v = 1, lty = 2) # add vertical line x = 1 +``` + +![](practical-stegen_files/figure-markdown_github/stegen_rr_plot-1.png) + +The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. + +Conclusion +========== + +This case study illustrated how **R** can be used to import data, clean them, and derive basic summaries for a first glance at the data. It also showed how to generate graphics using `ggplot2`, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic regression in epidemic studies. + +


      + +About this document +=================== + +Source +------ + +This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. + +Contributors +------------ + +- original version: Alain Moren, Gilles Desve + +- reviewers, previous versions: Marta Valenciano, Alain Moren + +- adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis + +- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart + +Contributions are welcome via [pull requests](https://github.com/reconhub/learn/pulls). The source file can be found [here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). + +Legal stuff +----------- + +**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) **Copyright**: 2018 + +

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vertical space to button html --- content/post/practical-stegen.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 4d60e3c..c8a8a84 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -329,12 +329,17 @@ stegen$horseradish[stegen$horseradish == 9] <- NA ```

      + **Going further** click on the button below for more explanation: +

      + There are several things going on in a command like: ``` r From a4d884085c49d7c6a012c1a236dae97e62ab7967 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 08:36:41 +0100 Subject: [PATCH 14/59] change div to details --- content/post/practical-stegen.Rmd | 27 +- content/post/practical-stegen.md | 457 +++++++++++++++++++++--------- 2 files changed, 329 insertions(+), 155 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index f4d49a0..dfa9116 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -103,13 +103,13 @@ To avoid having to specify the name of the package whenever we use a function using the syntax `package_name::function_name()`, we load these packages using `library`: -```{r stegen_libraries} -library(here) # find data/script files -library(readxl) # read xlsx files +```{r stegen_libraries, warning=FALSE, message=FALSE} +library(here) # find data/script files +library(readxl) # read xlsx files library(incidence) # make epicurves -library(epitrix) # clean labels and variables -library(dplyr) # general data handling -library(ggplot2) # advanced graphics +library(epitrix) # clean labels and variables +library(dplyr) # general data handling +library(ggplot2) # advanced graphics ``` Note that you will get an error if the packages have not been installed on your @@ -162,11 +162,11 @@ View(stegen) ``` -

      -

      **Problems?** In case of trouble, click on the button below to toggle additional help:

      -

      - -

      +
      +

      Problems?

      In case of trouble, click here to toggle additional help:
      + + + If the above fails, you should check: @@ -176,10 +176,7 @@ If the above fails, you should check: and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above) -

      -
      -
      - + diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index c8a8a84..cc6a7ee 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -14,34 +14,66 @@ showonlyimage: true Context ======= -On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants were expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students, and some graduates from a nearby school (school B). +On 26 June 1998, the St Sebastian High School in Stegen (school A), +Germany, celebrated a graduation party, where 250 to 350 participants +were expected. Attendants included graduates from that school, their +families and friends, teachers, 12th grade students, and some graduates +from a nearby school (school B). -A self-service party buffet was supplied by a commercial caterer in Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. +A self-service party buffet was supplied by a commercial caterer in +Freiburg. Food was prepared on the day of the party and transported in a +refrigerated van to the school. -Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late during the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. +Festivities started with a dinner buffet open from 8.30 pm onwards and +were followed by a dessert buffet offered from 10 pm. The party and the +buffet extended late during the night and alcoholic beverages were quite +popular. All agreed it was a party to be remembered. The alert --------- -On 2nd July 1998, the Freiburg local health office reported to the Robert Koch Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis following the graduation party described above. More than 100 cases were suspected among participants and some of them were admitted to nearby hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting lasting for several days. Most believed that the tiramisu consumed at dinner was responsible for their illness. *Salmonella enteritidis* was isolated from 19 stool samples. - -The Freiburg health office sent a team to investigate the kitchen of the caterer. Food preparation procedures were reviewed. Food samples, except tiramisu (none was left over), were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the following: brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and 10 raw eggs. - -The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk factors for transmission in order to better control the outbreak. +On 2nd July 1998, the Freiburg local health office reported to the +Robert Koch Institute (RKI) in Berlin the occurrence of many cases of +gastroenteritis following the graduation party described above. More +than 100 cases were suspected among participants and some of them were +admitted to nearby hospitals. Sick people suffered from fever, nausea, +diarrhoea and vomiting lasting for several days. Most believed that the +tiramisu consumed at dinner was responsible for their illness. +*Salmonella enteritidis* was isolated from 19 stool samples. + +The Freiburg health office sent a team to investigate the kitchen of the +caterer. Food preparation procedures were reviewed. Food samples, except +tiramisu (none was left over), were sent to the laboratory of Freiburg +University. Microbiological analyses were performed on samples of the +following: brown chocolate mousse, caramel cream, remoulade sauce, +yoghurt dill sauce, and 10 raw eggs. + +The Freiburg health office requested help from the RKI in the +investigation to assess the magnitude of the outbreak and identify +potential vehicle(s) and risk factors for transmission in order to +better control the outbreak. The epidemiological study ------------------------- -**Case definition**: cases were defined as any person who had attended the party at St Sebastian High School and who suffered from *diarrhoea* (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998, or from at least three of the following symptoms: *vomiting*, *fever over 38.5° C*, *nausea*, *abdominal pain*, *headache*. +**Case definition**: cases were defined as any person who had attended +the party at St Sebastian High School and who suffered from *diarrhoea* +(min. 3 loose stool for 24 hours) between 27 June and 29 June 1998, or +from at least three of the following symptoms: *vomiting*, *fever over +38.5° C*, *nausea*, *abdominal pain*, *headache*. -Students from both schools attending the party were asked through phone interviews to provide names of persons who attended the party. +Students from both schools attending the party were asked through phone +interviews to provide names of persons who attended the party. -Overall, 291 responded to enquiries and 103 cases were identified. The linelist analysed in this case study was built from these 291 responses. +Overall, 291 responded to enquiries and 103 cases were identified. The +linelist analysed in this case study was built from these 291 responses. This case study --------------- -In this case study, we will take you through the analysis of this epidemic. This will be the occasion to illustrate more generally useful practices for data analysis using **R**, including: +In this case study, we will take you through the analysis of this +epidemic. This will be the occasion to illustrate more generally useful +practices for data analysis using **R**, including: - how to read data from Excel - how to explore data using tables and summaries @@ -61,27 +93,21 @@ The following packages will be used in the case study: - `readxl`: to read Excel spreadsheets into **R** - `incidence`: to build epicurves -To avoid having to specify the name of the package whenever we use a function using the syntax `package_name::function_name()`, we load these packages using `library`: +To avoid having to specify the name of the package whenever we use a +function using the syntax `package_name::function_name()`, we load these +packages using `library`: ``` r -library(here) # find data/script files -## here() starts at /home/thibaut/dev/recon/learn -library(readxl) # read xlsx files +library(here) # find data/script files +library(readxl) # read xlsx files library(incidence) # make epicurves -library(epitrix) # clean labels and variables -library(dplyr) # general data handling -## -## Attaching package: 'dplyr' -## The following objects are masked from 'package:stats': -## -## filter, lag -## The following objects are masked from 'package:base': -## -## intersect, setdiff, setequal, union -library(ggplot2) # advanced graphics +library(epitrix) # clean labels and variables +library(dplyr) # general data handling +library(ggplot2) # advanced graphics ``` -Note that you will get an error if the packages have not been installed on your system. To install them (you only need to do this once!), type: +Note that you will get an error if the packages have not been installed +on your system. To install them (you only need to do this once!), type: ``` r install.packages("here") @@ -92,21 +118,32 @@ install.packages("dplyr") install.packages("ggplot2") ``` -Loading these packages makes all functions implemented by the packages available, so that `function_name()` can be used directly (without the `package_name::` prefix). This is not a problem here as these packages do now implement functions with identical names. +Loading these packages makes all functions implemented by the packages +available, so that `function_name()` can be used directly (without the +`package_name::` prefix). This is not a problem here as these packages +do now implement functions with identical names. Importing data from Excel ------------------------- -Linelist data can be read from various formats, including flat text files (e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or Excel spreadsheets. We illustrate the latter, which is probably the most common format. We assume that the data file `stegen_raw.xlsx` has been saved in a `data/` folder of your project, and that your current R session is at the root of the project. +Linelist data can be read from various formats, including flat text +files (e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or +Excel spreadsheets. We illustrate the latter, which is probably the most +common format. We assume that the data file `stegen_raw.xlsx` has been +saved in a `data/` folder of your project, and that your current R +session is at the root of the project. -Here we decompose the steps to read data in: finding the path to the data (`path_to_data`), using the function `read_xlsx` to read data in, and saving the output in a new object `stegen`: +Here we decompose the steps to read data in: finding the path to the +data (`path_to_data`), using the function `read_xlsx` to read data in, +and saving the output in a new object `stegen`: ``` r path_to_data <- here("data", "stegen_raw.xlsx") stegen <- read_xlsx(path_to_data) ``` -To print the content of the dataset, we can use either of these commands: +To print the content of the dataset, we can use either of these +commands: ``` r stegen @@ -130,34 +167,41 @@ stegen ## View(stegen) ``` -
      -

      -**Problems?** In case of trouble, click on the button below to toggle additional help: -

      -

      - -If the above fails, you should check: +

      + +

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      +In case of trouble, click here to toggle additional help:
      -- that your data file has been saved in the right folder, with the right name (lower case and upper case do matter) -- that your R session was started from the right project - if unsure, close R and re-open Rstudio by double-clicking on the `.Rproj` file -- that all packages are installed and loaded (see installation guidelines above) + + +If the above fails, you should check: -

      +- that your data file has been saved in the right folder, with the + right name (lower case and upper case do matter) +- that your R session was started from the right project - if unsure, + close R and re-open Rstudio by double-clicking on the `.Rproj` file +- that all packages are installed and loaded (see installation + guidelines above) -
      + Overview and summaries ---------------------- -We first have a quick look at the content of the data set. The information we are looking for is: +We first have a quick look at the content of the data set. The +information we are looking for is: - the numbers of cases (rows) and variables (columns) in the data -- the *name* of the variables: do they use consistent capitalisation and separators? -- the *type* of the variables: are dates, numeric or categorical variables used when they should? -- the *coding* of the variables: are explicit labels (e.g. `"male"`/`"female"`) used where relevant? +- the *name* of the variables: do they use consistent capitalisation + and separators? +- the *type* of the variables: are dates, numeric or categorical + variables used when they should? +- the *coding* of the variables: are explicit labels (e.g. + `"male"`/`"female"`) used where relevant? -We first check the dimensions of the `stegen` object, and the name of the variables: +We first check the dimensions of the `stegen` object, and the name of +the variables: ``` r dim(stegen) # rows x columns @@ -224,7 +268,11 @@ summary(stegen) ## ``` -Note that binary variables, when treated as numeric values (0/1), are summarised at such, which may not always be useful. As an alternative, `table` can be used to list all possible values of a variable, and count how many time each value appears in the data. For instance, we can compare the `summary` and `table` for consumption of `tiramisu`: +Note that binary variables, when treated as numeric values (0/1), are +summarised at such, which may not always be useful. As an alternative, +`table` can be used to list all possible values of a variable, and count +how many time each value appears in the data. For instance, we can +compare the `summary` and `table` for consumption of `tiramisu`: ``` r stegen$tiramisu # all values @@ -250,30 +298,40 @@ table(stegen$tiramisu) # table ## 165 121 ``` -**Good news**: the dataset has the expected dimensions, and all the relevant variables seem to be present. There are, however, **a few issues**: +**Good news**: the dataset has the expected dimensions, and all the +relevant variables seem to be present. There are, however, **a few +issues**: -1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation +1. variable names are a bit messy, and include different separators, + spaces, and irregular capitalisation 2. variable types are partly wrong: - - *unique keys* should be `character` (i.e. character strings) - - *dates of onset* should be `Date` (**R** is good at handling actual dates) - - *illness* and *sex* should be `factor` (i.e. a categorical variables) - + - *unique keys* should be `character` (i.e. character strings) + - *dates of onset* should be `Date` (**R** is good at handling + actual dates) + - *illness* and *sex* should be `factor` (i.e. a categorical + variables) 3. labels used in some variables are ambiguous: - - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, female) - - *illness* should be coded explicitely, not as 0 (here, non-case) and 1 (case) - -4. some binary variables have maximum values of 9 (see `summary(stegen)`) + - *sex* should be coded explicitely, not as 0 (here, male) and 1 + (here, female) + - *illness* should be coded explicitely, not as 0 (here, non-case) + and 1 (case) +4. some binary variables have maximum values of 9 (see + `summary(stegen)`) Data cleaning ------------- -While it is tempting to go back to the Excel spreadsheet to fix issues with data, it is almost always quicker and more reliable to clean data in **R** directly. Here, we make a copy of the old data set, and clean `stegen` before further analysis. +While it is tempting to go back to the Excel spreadsheet to fix issues +with data, it is almost always quicker and more reliable to clean data +in **R** directly. Here, we make a copy of the old data set, and clean +`stegen` before further analysis. ``` r stegen_old <- stegen # save 'dirty data' ``` -We use `epitrix`'s function `clean_labels` to standardise the variable names: +We use `epitrix`’s function `clean_labels` to standardise the variable +names: ``` r new_labels <- clean_labels(names(stegen)) # generate standardised labels @@ -286,7 +344,9 @@ new_labels # check the result names(stegen) <- new_labels ``` -We set convert the unique identifiers to character strings (`character`), dates of onset to actual `Date` objects, and sex and illness are set to categorical variables (`factor`): +We set convert the unique identifiers to character strings +(`character`), dates of onset to actual `Date` objects, and sex and +illness are set to categorical variables (`factor`): ``` r stegen$unique_key <- as.character(stegen$unique_key) @@ -295,14 +355,17 @@ stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` -We use the function `recode` from the `dplyr' package to recode`sex\` more explicitely: +We use the function `recode` from the `dplyr' package to recode`sex\` +more explicitely: ``` r stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") ``` -Finally we look in more depth into these variables having maximum values of 9, where we expect 0/1; `table` is useful to list all values taken by a variable, and listing their frequencies: +Finally we look in more depth into these variables having maximum values +of 9, where we expect 0/1; `table` is useful to list all values taken by +a variable, and listing their frequencies: ``` r @@ -320,7 +383,9 @@ table(stegen$horseradish) ## 217 72 2 ``` -The only rogue values are `9`; they are likely either data entry issues, or missing data, which in **R** should be coded as `NA` ("not available"). We can replace these values using: +The only rogue values are `9`; they are likely either data entry issues, +or missing data, which in **R** should be coded as `NA` (“not +available”). We can replace these values using: ``` r stegen$pork[stegen$pork == 9] <- NA @@ -329,17 +394,12 @@ stegen$horseradish[stegen$horseradish == 9] <- NA ```

      - **Going further** click on the button below for more explanation: -

      - There are several things going on in a command like: ``` r @@ -348,12 +408,18 @@ stegen$pork[stegen$pork == 9] <- NA let us break them down: -1. `stegen$pork` means "get the variable called `pork` in the dataset `stegen`" -2. `[...]` subset the vector according to `...`; if `...` is a series of `TRUE/FALSE` values, only entries corresponding to `TRUE` are retained -3. `... == 9` for each value of `...` test if it is equal to `9`, and return `TRUE` if so (`FALSE` otherwise) +1. `stegen$pork` means “get the variable called `pork` in the dataset + `stegen`” +2. `[...]` subset the vector according to `...`; if `...` is a series + of `TRUE/FALSE` values, only entries corresponding to `TRUE` are + retained +3. `... == 9` for each value of `...` test if it is equal to `9`, and + return `TRUE` if so (`FALSE` otherwise) 4. `... <- NA` replace `...` with `NA` (missing value) -in other words: "isolate the entries of `stegen$pork` which equal `9`, and replace them with `NA`; here is another toy example to illustrate the procedure: +in other words: "isolate the entries of `stegen$pork` which equal `9`, +and replace them with `NA`; here is another toy example to illustrate +the procedure: ``` r ## make toy input vector @@ -380,7 +446,8 @@ Data exploration Summaries of age and sex distribution ------------------------------------- -We can have a brief look at age and sex distributions using some basic summaries; for instance: +We can have a brief look at age and sex distributions using some basic +summaries; for instance: ``` r summary(stegen$age) # age stats @@ -402,17 +469,24 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender

      **Going further** click on the button below to learn about `tapply`:

      - + +

      -`tapply` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: +`tapply` is a very handy function to stratify any kind of analyses. You +will find more details by reading the documentation of the function +`?tapply`, but briefly, the syntax to be used is +`tapply(input_data, stratification, function_to_use, optional_arguments)`. +In the command used above: ``` r tapply(stegen$age, stegen$sex, summary) ``` -this literally means: select the age variable in the dataset `stegen` (`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each strata (`summary`). So for instance, to get the average age by sex (function `mean`), one could use: +this literally means: select the age variable in the dataset `stegen` +(`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each +strata (`summary`). So for instance, to get the average age by sex +(function `mean`), one could use: ``` r tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) @@ -420,18 +494,26 @@ tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) ## 26.37778 26.92568 ``` -Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means "ignore missing data". +Here we illustrate that further arguments to the function can be passed +as extra arguments; here, `na.rm = TRUE` means “ignore missing data”.

      Graphical exploration --------------------- -The summaries above may be useful for reporting purposes, but graphics are usually better for getting a feel for the data. Here, we illustrate how the package `ggplot2` can be used to derive informative graphics of age/sex distribution. It implements an alternative graphics system to the basic **R** plots, in which the plot is built by adding (literally using `+`) different layers of information, using: +The summaries above may be useful for reporting purposes, but graphics +are usually better for getting a feel for the data. Here, we illustrate +how the package `ggplot2` can be used to derive informative graphics of +age/sex distribution. It implements an alternative graphics system to +the basic **R** plots, in which the plot is built by adding (literally +using `+`) different layers of information, using: - `ggplot()` to specify the dataset to use -- `geom_...()` functions which define a type of graphics to use (e.g. barplot, histogram) -- `aes()` to map elements of the data into aesthetic properties (e.g. x/y axes, color, shape) +- `geom_...()` functions which define a type of graphics to use + (e.g. barplot, histogram) +- `aes()` to map elements of the data into aesthetic properties + (e.g. x/y axes, color, shape) For instance, to get an histogram of age: @@ -451,23 +533,31 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) ![](practical-stegen_files/figure-markdown_github/stegen_histogram_sex-1.png) -Here, the age distribution is pretty much identical between male and female. +Here, the age distribution is pretty much identical between male and +female.

      -**Going further** click on the button below to learn about customising `ggplot2` graphics: +**Going further** click on the button below to learn about customising +`ggplot2` graphics:

      - + +

      -`ggplot2` graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. +`ggplot2` graphics are highly customisable, and a lot of help and +examples can be found online. The [official +website](https://ggplot2.tidyverse.org/) is a good place to start. For instance, here we: - add white borders to the plot (`color = "white"`) -- specify colors manually for male / female, using specified color codes (see [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to define your own colors) (`scale_fill_manual(...)`) +- specify colors manually for male / female, using specified color + codes (see [html color + picker](https://www.w3schools.com/colors/colors_picker.asp) to + define your own colors) (`scale_fill_manual(...)`) - adding labels for title, *x* and *y* axes (`labs()`) -- specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) +- specify a lighter general color theme, using Times font of a larger + size by default (`theme_light(...)`) - move the legend inside the plot (`theme(...)`) ``` r @@ -486,7 +576,10 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + Epidemic curve -------------- -Incidence curves can be built using the package `incidence`, which will compute the number of new cases given a vector of dates (here, onset) and a time interval (1 day by default). We use the function `incidence` to achieve this, and then visualise the results: +Incidence curves can be built using the package `incidence`, which will +compute the number of new cases given a vector of dates (here, onset) +and a time interval (1 day by default). We use the function `incidence` +to achieve this, and then visualise the results: ``` r i <- incidence(stegen$date_onset) @@ -526,7 +619,10 @@ as.data.frame(i) ## 14 1998-07-09 1 ``` -How long is this outbreak? It looks like most cases occurred over the course of 3 days, but that cases kept showing up 10 days after the peak. Is this true? Not really. Stratifying the epidemic curve by case definition will clarify the situation: +How long is this outbreak? It looks like most cases occurred over the +course of 3 days, but that cases kept showing up 10 days after the peak. +Is this true? Not really. Stratifying the epidemic curve by case +definition will clarify the situation: ``` r i_ill <- incidence(stegen$date_onset, group = stegen$ill) @@ -562,20 +658,29 @@ plot(i_ill, color = c("non case" = "#66cc99", "case" = "#990033")) ![](practical-stegen_files/figure-markdown_github/incidence_stratified-1.png) -The outbreak really only happened over 3 days: onsets reported after did not match the epi case definition. This is compatible with a food-borne outbreak with limited or no person-to-person transmission. +The outbreak really only happened over 3 days: onsets reported after did +not match the epi case definition. This is compatible with a food-borne +outbreak with limited or no person-to-person transmission. -Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). +Note that the plots produced by `incidence` are `ggplot2` objects, so +that the options seen before can be used for further customisation (see +below).

      **Going further** click on the button below to learn about `incidence`:

      - + +

      -More information on the `incidence` package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: +More information on the `incidence` package can be found from the +[dedicated website](https://www.repidemicsconsortium.org/incidence/). +Here, we illustrate how incidence can be stratified e.g. by case +definition: -We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): +We can also customise this graphic like other `ggplot2` plots (see [this +tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) +for more): ``` r plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + @@ -592,7 +697,10 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 Age and gender distribution of the cases ---------------------------------------- -We use the same principles as before to visualise the distribution of illness by age and gender. To split the plot into different panels according to `sex`, we use `facet_grid()` (see previous extra info on `ggplot2` customisation for further details otions): +We use the same principles as before to visualise the distribution of +illness by age and gender. To split the plot into different panels +according to `sex`, we use `facet_grid()` (see previous extra info on +`ggplot2` customisation for further details otions): ``` r ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + @@ -603,7 +711,8 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + ![](practical-stegen_files/figure-markdown_github/stegen_case_age_sex-1.png) -Illness does not seem to be depending on age or gender. This can be tested: this is the topic of the next and final section. +Illness does not seem to be depending on age or gender. This can be +tested: this is the topic of the next and final section. Looking for the culprits ======================== @@ -611,17 +720,29 @@ Looking for the culprits Univariate tests ---------------- -Methods for testing the association between two variables can be broken down in 3 types, depending on which types these variables are: +Methods for testing the association between two variables can be broken +down in 3 types, depending on which types these variables are: -1. **2 quantitative variables**: Pearson's correlation coefficient (\(r\)) and similar methods -2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with 2 groups: Student's\(t\)-test -3. **2 categorical variables**: Chi-squared test on the 2x2 table (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) +1. **2 quantitative variables**: Pearson’s correlation coefficient + (*r*) and similar methods +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; + particular case with 2 groups: Student’s*t*-test +3. **2 categorical variables**: Chi-squared test on the 2x2 table + (a.k.a. *contingency* table) and similar methods (e.g. Fisher’s + exact test) -We can use these approaches to test if the disease is linked to any of the other recorded variables. As illness itself is a categorical variable, only approaches of type 2 and 3 will be illustrated here. +We can use these approaches to test if the disease is linked to any of +the other recorded variables. As illness itself is a categorical +variable, only approaches of type 2 and 3 will be illustrated here. ### Is illness linked to age? -We can use the function `t.test` to test if the average age is different across illness status. As this test assumes that the two categories exhibit similar variation, we first ensure that the variances are comparable using Bartlett's test. The syntax`variable ~ group` is used to indicate the variable of interest (left hand-side), and the group (right hand-side): +We can use the function `t.test` to test if the average age is different +across illness status. As this test assumes that the two categories +exhibit similar variation, we first ensure that the variances are +comparable using Bartlett’s test. The syntax`variable ~ group` is used +to indicate the variable of interest (left hand-side), and the group +(right hand-side): ``` r bartlett.test(stegen$age ~ stegen$ill) @@ -632,7 +753,8 @@ bartlett.test(stegen$age ~ stegen$ill) ## Bartlett's K-squared = 0.82311, df = 1, p-value = 0.3643 ``` -The resulting p-value (0.364) suggests the variances are indeed comparable. Wecan thus proceed to a \(t\)-test: +The resulting p-value (0.364) suggests the variances are indeed +comparable. Wecan thus proceed to a *t*-test: ``` r t.test(stegen$age ~ stegen$ill, var.equal = TRUE) @@ -649,11 +771,13 @@ t.test(stegen$age ~ stegen$ill, var.equal = TRUE) ## 26.31148 27.31000 ``` -The p-value of 0.576 confirms the previous graphics: the illness does not seem to be linked to age. +The p-value of 0.576 confirms the previous graphics: the illness does +not seem to be linked to age. ### Is illness linked to gender? -To test the association between gender and illness (2 categorical variables), we first build a 2-by-2 (contingency) table, using: +To test the association between gender and illness (2 categorical +variables), we first build a 2-by-2 (contingency) table, using: ``` r tab_ill_sex <- table(stegen$ill, stegen$sex) @@ -682,7 +806,8 @@ round(100 * prop.table(tab_ill_sex)) ## case 18 17 ``` -Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: +Once a contingency table has been built, the Chi-square test can be run +using `chisq.test`: ``` r chisq.test(tab_ill_sex) @@ -693,23 +818,29 @@ chisq.test(tab_ill_sex) ## X-squared = 0.6562, df = 1, p-value = 0.4179 ``` -Here, the p-value of 0.418 suggests illness is not related to sex either. +Here, the p-value of 0.418 suggests illness is not related to sex +either. Making multiple tests --------------------- -The natural next step is to run multiple tests for all potential risk factors recorded. There are many ways to go about this in **R**, but we will illustrate one of the most common workflows: +The natural next step is to run multiple tests for all potential risk +factors recorded. There are many ways to go about this in **R**, but we +will illustrate one of the most common workflows: -1. isolate a subset of variables to test (the ones indicative of food consumption) (using `[]`) +1. isolate a subset of variables to test (the ones indicative of food + consumption) (using `[]`) 2. for each: 1. build contingency table with illness status (using `table`) 2. run a Chi-square test on this table (using `chisq.test`) -All of these have been seen so far; the only missing piece is step 2), which we will cover using a very handy function called `lapply`. +All of these have been seen so far; the only missing piece is step 2), +which we will cover using a very handy function called `lapply`. ### Isolating the variables to test -We use the natural structure of the dataset, as variables representing food consumption are all the last columns: +We use the natural structure of the dataset, as variables representing +food consumption are all the last columns: ``` r names(stegen) @@ -720,7 +851,10 @@ names(stegen) ## [21] "pork" ``` -In this case, we need to retain columns 6 to 21, excluding `tportion` and `mportion`, which are not binary; this can be done using the subsetting operator `[...]`, where `...` are numbers indicating the columns to retain: +In this case, we need to retain columns 6 to 21, excluding `tportion` +and `mportion`, which are not binary; this can be done using the +subsetting operator `[...]`, where `...` are numbers indicating the +columns to retain: ``` r to_keep <- c(6, 8, 9, 10, 12:21) @@ -747,7 +881,16 @@ food ### Building several contingency tables -There are many variables to test, and having to enter separate command lines for each would be cumbersome and prone to errors. As a workaround, the function `lapply` can be used. This function allows to repeat an operation using a vector of inputs, using each element in turn. Its general syntax is `lapply(vector_of_inputs, function_to_use, other_arguments)`, where `other_arguments` can be any secondary arguments taken by `function_to_use`. Here, we use it to build a separate contingency table of each variable in `food` crossed with illness status (`stegen$ill`); note that we show only the first few tables: +There are many variables to test, and having to enter separate command +lines for each would be cumbersome and prone to errors. As a workaround, +the function `lapply` can be used. This function allows to repeat an +operation using a vector of inputs, using each element in turn. Its +general syntax is +`lapply(vector_of_inputs, function_to_use, other_arguments)`, where +`other_arguments` can be any secondary arguments taken by +`function_to_use`. Here, we use it to build a separate contingency table +of each variable in `food` crossed with illness status (`stegen$ill`); +note that we show only the first few tables: ``` r food_tables <- lapply(food, table, stegen$ill) @@ -791,7 +934,9 @@ head(food_tables) ### Realising multiple Chi-squared tests -The same principle used to compute several contingency tables can be used to compute Chi-square tests for each table, using `lapply`; note that we show only the first few tests: +The same principle used to compute several contingency tables can be +used to compute Chi-square tests for each table, using `lapply`; note +that we show only the first few tests: ``` r food_tests <- lapply(food_tables, chisq.test) @@ -844,14 +989,19 @@ head(food_tests) ## X-squared = 20.781, df = 1, p-value = 5.148e-06 ``` -Looking at these results, it seems a large number of food items are significantly correlated to the illness. Which food item is the biggest suspect? We will try and address this question using risk ratios. +Looking at these results, it seems a large number of food items are +significantly correlated to the illness. Which food item is the biggest +suspect? We will try and address this question using risk ratios. Risk ratios ----------- ### For one variable -The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the ratio between the proportion of illness in one group (typically 'exposed') vs another ('non-exposed'). For instance, let us consider the contingency table of pork consumption and illness: +The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as +the ratio between the proportion of illness in one group (typically +‘exposed’) vs another (‘non-exposed’). For instance, let us consider the +contingency table of pork consumption and illness: ``` r food_tables$pork @@ -861,7 +1011,8 @@ food_tables$pork ## 1 72 48 ``` -This can be turned into proportions using `prop.table`, specifying that we want proportions by rows (`margin = 1`): +This can be turned into proportions using `prop.table`, specifying that +we want proportions by rows (`margin = 1`): ``` r pork_prop <- prop.table(food_tables$pork, margin = 1) @@ -889,17 +1040,24 @@ pork_rr ## [1] 1.251852 ``` -Note the use of the syntax `[rows, columns]` to subset specific rows and columns of 2-dimensional objects. +Note the use of the syntax `[rows, columns]` to subset specific rows and +columns of 2-dimensional objects. ### Multiple variables -In the example above, we have computed the risk ratio using a simple recipe: +In the example above, we have computed the risk ratio using a simple +recipe: 1. select a contingency table 2. convert raw numbers to proportions (using `prop.table`) -3. isolating the risks in exposed and non-exposed groups, and compute their ratio +3. isolating the risks in exposed and non-exposed groups, and compute + their ratio -Repeating these tasks manually for all variables in `food` would be cumbersome. As an alternative, we can define the steps above as `function`, i.e. a generic recipe which we can then apply to any contingency table. This new function will be called `risk_ratio`, and is defined as follows: +Repeating these tasks manually for all variables in `food` would be +cumbersome. As an alternative, we can define the steps above as +`function`, i.e. a generic recipe which we can then apply to any +contingency table. This new function will be called `risk_ratio`, and is +defined as follows: ``` r risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table @@ -914,7 +1072,8 @@ risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table } ``` -We can now try this function on any of the tables in `food_tables`, for instance: +We can now try this function on any of the tables in `food_tables`, for +instance: ``` r food_tables$pork @@ -926,7 +1085,8 @@ risk_ratio(food_tables$pork) ## [1] 1.251852 ``` -And we can now apply this function sequentially to all variables in `food` as before, using `lapply` (we show only the first few values): +And we can now apply this function sequentially to all variables in +`food` as before, using `lapply` (we show only the first few values): ``` r all_rr <- lapply(food_tables, risk_ratio) @@ -950,7 +1110,9 @@ all_rr <- lapply(food_tables, risk_ratio) ## [1] 2.08206 ``` -Finally, we re-shape these results into a numeric `vector` (using `unlist)` for further plotting; note that for this graph, the basic system is easier to use than `ggplot2`: +Finally, we re-shape these results into a numeric `vector` (using +`unlist)` for further plotting; note that for this graph, the basic +system is easier to use than `ggplot2`: ``` r ## convert results from list to numeric vector @@ -977,12 +1139,20 @@ abline(v = 1, lty = 2) # add vertical line x = 1 ![](practical-stegen_files/figure-markdown_github/stegen_rr_plot-1.png) -The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. +The results are a lot clearer now: the tiramisu is by far the largest +risk factor in this outbreak. Conclusion ========== -This case study illustrated how **R** can be used to import data, clean them, and derive basic summaries for a first glance at the data. It also showed how to generate graphics using `ggplot2`, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic regression in epidemic studies. +This case study illustrated how **R** can be used to import data, clean +them, and derive basic summaries for a first glance at the data. It also +showed how to generate graphics using `ggplot2`, and how to detect +associations between several potential risk factors and the occurrence +of illness. One major caveat here is that we are not accounting for +potential confounding factors. These will be treated in a separate case +study, which will focus on the use of logistic regression in epidemic +studies.


      @@ -992,7 +1162,9 @@ About this document Source ------ -This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. +This case study was first designed by Alain Moren and Gilles Desve, +EPIET. It is based on an investigation conducted by Anja Hauri, RKI, +Berlin, 1998. Contributors ------------ @@ -1003,13 +1175,18 @@ Contributors - adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis -- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart +- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, + Zhian N. Kamvar, Thibaut Jombart -Contributions are welcome via [pull requests](https://github.com/reconhub/learn/pulls). The source file can be found [here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). +Contributions are welcome via [pull +requests](https://github.com/reconhub/learn/pulls). The source file can +be found +[here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). Legal stuff ----------- -**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) **Copyright**: 2018 +**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) +**Copyright**: 2018

      From cb2e32e86a0032ee10cafae946c1471c94b601b7 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 08:40:34 +0100 Subject: [PATCH 15/59] change h4 to bold --- content/post/practical-stegen.Rmd | 2 +- content/post/practical-stegen.md | 7 ++----- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index dfa9116..cdbf420 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -163,7 +163,7 @@ View(stegen)

      -

      Problems?

      In case of trouble, click here to toggle additional help:
      +Problems? In case of trouble, click here to toggle additional help: diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index cc6a7ee..a7d5639 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -168,11 +168,8 @@ stegen ```
      - -

      -Problems? -

      -In case of trouble, click here to toggle additional help:
      +Problems? In case of trouble, click here to toggle +additional help: From e57ab0c1847a926cd029bc48e9d5434f5f533ff9 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 09:13:40 +0100 Subject: [PATCH 16/59] add more details tags --- content/post/practical-stegen.Rmd | 18 +++++++++--------- content/post/practical-stegen.md | 15 +++++++-------- 2 files changed, 16 insertions(+), 17 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index cdbf420..6368f40 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -286,14 +286,17 @@ stegen$horseradish[stegen$horseradish == 9] <- NA ``` -
      -

      **Going further** click on the button below for more explanation:

      +**Going further** click on the button below for more explanation: + +
      + + Why does this work? + + - -
      -

      There are several things going on in a command like: + ```{r eval = FALSE} stegen$pork[stegen$pork == 9] <- NA ``` @@ -323,10 +326,7 @@ toy_vector # check outcome ``` -

      -
      -
      - +
      # Data exploration diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index a7d5639..8934272 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -390,13 +390,13 @@ stegen$salmon[stegen$salmon == 9] <- NA stegen$horseradish[stegen$horseradish == 9] <- NA ``` -

      **Going further** click on the button below for more explanation: -

      - -

      + +

      + Why does this work? + + + There are several things going on in a command like: ``` r @@ -435,8 +435,7 @@ toy_vector # check outcome ## [1] 1 0 0 4 0 ``` -

      - +
      Data exploration ================ From 8ec06b0207cb756c582b2ecc2961078c2f0b45c2 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 09:30:45 +0100 Subject: [PATCH 17/59] add more describe tags I also added vertical space between code chunks --- content/post/practical-stegen.Rmd | 60 +++++++++++++++++-------------- content/post/practical-stegen.md | 39 ++++++++++---------- 2 files changed, 51 insertions(+), 48 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 6368f40..5d15cca 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -336,23 +336,26 @@ toy_vector # check outcome We can have a brief look at age and sex distributions using some basic summaries; for instance: + ```{r stegen_age_sex} summary(stegen$age) # age stats summary(stegen$sex) # gender distribution tapply(stegen$age, stegen$sex, summary) # age stats by gender ``` -
      -

      **Going further** click on the button below to learn about `tapply`:

      - -
      +**Going further** click on the button below to learn about `tapply`: + + + -

      +

      + More about tapply `tapply` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: + ```{r eval = FALSE} tapply(stegen$age, stegen$sex, summary) ``` @@ -360,16 +363,14 @@ tapply(stegen$age, stegen$sex, summary) this literally means: select the age variable in the dataset `stegen` (`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each strata (`summary`). So for instance, to get the average age by sex (function `mean`), one could use: + ```{r stegen_age_per_sex} tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) ``` Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means "ignore missing data". -

      - -
      -
      +
      @@ -400,12 +401,13 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) Here, the age distribution is pretty much identical between male and female. -
      -

      **Going further** click on the button below to learn about customising `ggplot2` graphics:

      - -
      +**Going further** click on the button below to learn about customising `ggplot2` graphics: + + + -

      +

      + More on ggplot2 `ggplot2` graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. @@ -428,10 +430,7 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + theme(legend.position = c(0.8, 0.8)) ``` -

      - -
      -
      + ## Epidemic curve @@ -448,6 +447,7 @@ plot(i) ``` Details of the case counts can be obtained using: + ```{r stegen_case_counts} as.data.frame(i) ``` @@ -473,12 +473,14 @@ Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). -
      -

      **Going further** click on the button below to learn about `incidence`:

      - -
      +**Going further** click on the button below to learn about `incidence`: -

      + + + + +

      + More on incidence More information on the `incidence` package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: @@ -493,10 +495,7 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) ``` -

      - -
      -
      + @@ -581,6 +580,7 @@ round(100 * prop.table(tab_ill_sex)) ``` Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: + ```{r stegen_chisq} chisq.test(tab_ill_sex) ``` @@ -669,18 +669,21 @@ The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the ratio between the proportion of illness in one group (typically 'exposed') vs another ('non-exposed'). For instance, let us consider the contingency table of pork consumption and illness: + ```{r stegen_pork_table} food_tables$pork ``` This can be turned into proportions using `prop.table`, specifying that we want proportions by rows (`margin = 1`): + ```{r stegen_pork_prop} pork_prop <- prop.table(food_tables$pork, margin = 1) pork_prop ``` the corresponding risk ratio is: + ```{r stegen_pork_rr} ## ill & exposed: row 2, column 2 pork_prop[2, 2] # row 2, column 2 @@ -710,6 +713,7 @@ Repeating these tasks manually for all variables in `food` would be cumbersome. As an alternative, we can define the steps above as `function`, i.e. a generic recipe which we can then apply to any contingency table. This new function will be called `risk_ratio`, and is defined as follows: + ```{r stegen_risk_ratio_function} risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table ## compute table of proportions @@ -724,6 +728,7 @@ risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table ``` We can now try this function on any of the tables in `food_tables`, for instance: + ```{r rr_demo} food_tables$pork risk_ratio(food_tables$pork) @@ -741,6 +746,7 @@ head(all_rr) Finally, we re-shape these results into a numeric `vector` (using `unlist)` for further plotting; note that for this graph, the basic system is easier to use than `ggplot2`: + ```{r stegen_rr_plot} ## convert results from list to numeric vector rr_data <- unlist(all_rr) diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 8934272..0fedf77 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -462,13 +462,13 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender ## 13.00 18.00 20.00 26.93 24.50 65.00 4 ``` -

      **Going further** click on the button below to learn about `tapply`: -

      - -

      + + +

      + More about tapply + `tapply` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply`, but briefly, the syntax to be used is @@ -493,8 +493,7 @@ tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means “ignore missing data”. -

      - +
      Graphical exploration --------------------- @@ -532,14 +531,14 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) Here, the age distribution is pretty much identical between male and female. -

      **Going further** click on the button below to learn about customising `ggplot2` graphics: -

      - -

      + + +

      + More on ggplot2 + `ggplot2` graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. @@ -567,8 +566,7 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + ![](practical-stegen_files/figure-markdown_github/stegen_ggplot2_custom-1.png) -

      - +
      Epidemic curve -------------- @@ -662,13 +660,13 @@ Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). -

      **Going further** click on the button below to learn about `incidence`: -

      - -

      + + +

      + More on incidence + More information on the `incidence` package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case @@ -688,8 +686,7 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 ![](practical-stegen_files/figure-markdown_github/incidence_stratified_custom-1.png) -

      - +
      Age and gender distribution of the cases ---------------------------------------- From 9314ac14f0c5a78806ef0bf652275de610b6620e Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Fri, 26 Oct 2018 12:48:27 +0100 Subject: [PATCH 18/59] adding support for collapse button to head --- layouts/partials/head.html | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/layouts/partials/head.html b/layouts/partials/head.html index b79e7a3..800e81e 100644 --- a/layouts/partials/head.html +++ b/layouts/partials/head.html @@ -29,5 +29,12 @@ {{ if .Site.Params.RSSLink}} {{else}}{{ if eq .URL "/" }} {{ end }} {{ end }} -{{ partial "mathjax-support.html" . }} + {{ partial "mathjax-support.html" . }} + + + + + + + From 5f05b32f409a5e8041baf3622b5582f1467d1fd3 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Fri, 26 Oct 2018 13:32:03 +0100 Subject: [PATCH 19/59] adding test files for buttons --- content/post/test-buttons.Rmd | 29 ++++++++++++++++++++++++++ content/post/test-buttons.md | 39 +++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+) create mode 100644 content/post/test-buttons.Rmd create mode 100644 content/post/test-buttons.md diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd new file mode 100644 index 0000000..7d5e7c3 --- /dev/null +++ b/content/post/test-buttons.Rmd @@ -0,0 +1,29 @@ +--- +title: "Test collapse button" +author: "Thibaut Jombart" +authors: "Thibaut Jombart" +date: 2018-10-24 +licenses: CC-BY +categories: "case study" +slug: button-test +image: img/highres/graduation-1965.jpg +showonlyimage: true +--- + + +Here is an attempt at collapse button, taken from: +https://www.w3schools.com/bootstrap/tryit.asp?filename=trybs_collapsible&stacked=h + + +
      +

      Simple Collapsible

      +

      Click on the button to toggle between showing and hiding content.

      + +
      + Lorem ipsum dolor sit amet, consectetur adipisicing elit, + sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, + quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +
      +
      + + diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md new file mode 100644 index 0000000..4ed7f04 --- /dev/null +++ b/content/post/test-buttons.md @@ -0,0 +1,39 @@ +--- +title: "Test collapse button" +author: "Thibaut Jombart" +authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +date: 2018-10-24 +licenses: CC-BY +image: img/highres/graduation-1965.jpg +showonlyimage: true +--- + +Here is an attempt at collapse button, taken from: + + + + + + + + + + + + + + + + +

      +Simple Collapsible +

      +

      +Click on the button to toggle between showing and hiding content. +

      + + Lorem ipsum dolor sit amet, consectetur adipisicing elit, + sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, + quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. From dd09c4a500e835308326d96e1fbc75cd47eff188 Mon Sep 17 00:00:00 2001 From: Thibaut Jombart Date: Fri, 26 Oct 2018 13:33:33 +0100 Subject: [PATCH 20/59] adding comments --- content/post/test-buttons.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md index 4ed7f04..2ee4476 100644 --- a/content/post/test-buttons.md +++ b/content/post/test-buttons.md @@ -12,6 +12,8 @@ Here is an attempt at collapse button, taken from: + + From bc0219643aefc98bbafae86dcfdbe9cae162ec17 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 11:58:56 +0100 Subject: [PATCH 21/59] place bootstrap above mathjax --- layouts/partials/head.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/layouts/partials/head.html b/layouts/partials/head.html index 800e81e..88d1f7b 100644 --- a/layouts/partials/head.html +++ b/layouts/partials/head.html @@ -29,12 +29,12 @@ {{ if .Site.Params.RSSLink}} {{else}}{{ if eq .URL "/" }} {{ end }} {{ end }} - {{ partial "mathjax-support.html" . }} - + {{ partial "mathjax-support.html" . }} + From dc1d64e43bb8aca6fcabb3fcf1120cafd5d77e70 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 12:28:25 +0100 Subject: [PATCH 22/59] use gfm instead of markdown_github This will address #45 --- R/render_new_rmds_to_md.R | 2 +- content/post/test-buttons.Rmd | 6 +++--- content/post/test-buttons.md | 33 ++++++++++----------------------- 3 files changed, 14 insertions(+), 27 deletions(-) diff --git a/R/render_new_rmds_to_md.R b/R/render_new_rmds_to_md.R index 29cd485..ea546f1 100644 --- a/R/render_new_rmds_to_md.R +++ b/R/render_new_rmds_to_md.R @@ -51,7 +51,7 @@ render_new_rmds_to_md <- function(dir = "content/post", for (b in to_build) { rmd <- b rmarkdown::render(rmd, - rmarkdown::md_document(variant = "markdown_github", + rmarkdown::md_document(variant = "gfm", preserve_yaml = TRUE )) } }else{ diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd index 7d5e7c3..ab61407 100644 --- a/content/post/test-buttons.Rmd +++ b/content/post/test-buttons.Rmd @@ -20,9 +20,9 @@ https://www.w3schools.com/bootstrap/tryit.asp?filename=trybs_collapsible&stacked

      Click on the button to toggle between showing and hiding content.

      - Lorem ipsum dolor sit amet, consectetur adipisicing elit, - sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, - quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +Lorem ipsum dolor sit amet, consectetur adipisicing elit, +sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, +quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
      diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md index 2ee4476..d308db2 100644 --- a/content/post/test-buttons.md +++ b/content/post/test-buttons.md @@ -1,41 +1,28 @@ --- title: "Test collapse button" author: "Thibaut Jombart" -authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +authors: "Thibaut Jombart" date: 2018-10-24 licenses: CC-BY +categories: "case study" +slug: button-test image: img/highres/graduation-1965.jpg showonlyimage: true --- Here is an attempt at collapse button, taken from: - - - - - - - - - - - - - - - -

      Simple Collapsible

      Click on the button to toggle between showing and hiding content.

      - - Lorem ipsum dolor sit amet, consectetur adipisicing elit, - sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, - quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. + + +Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod +tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim +veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea +commodo consequat. From b1fb96b52ad5aed3a2d63ced7fc831073bdfcbc4 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 12:29:14 +0100 Subject: [PATCH 23/59] render test file to see if #45 works --- content/post/test-buttons.Rmd | 5 +++++ content/post/test-buttons.md | 17 ++++++++++++++++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd index ab61407..289e716 100644 --- a/content/post/test-buttons.Rmd +++ b/content/post/test-buttons.Rmd @@ -20,10 +20,15 @@ https://www.w3schools.com/bootstrap/tryit.asp?filename=trybs_collapsible&stacked

      Click on the button to toggle between showing and hiding content.

      + Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +
      +
      + + diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md index d308db2..f2cb47d 100644 --- a/content/post/test-buttons.md +++ b/content/post/test-buttons.md @@ -13,16 +13,31 @@ showonlyimage: true Here is an attempt at collapse button, taken from: +
      +

      + Simple Collapsible +

      +

      -Click on the button to toggle between showing and hiding content. + +Click on the button to toggle between showing and hiding +content. +

      + +
      + Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. + +
      + +
      From 935d2e7cc67eed566f0def22dc42ce922334b360 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 12:31:40 +0100 Subject: [PATCH 24/59] Attempt to fix deploy issue --- content/post/test-buttons.Rmd | 2 +- content/post/test-buttons.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd index 289e716..581047a 100644 --- a/content/post/test-buttons.Rmd +++ b/content/post/test-buttons.Rmd @@ -1,7 +1,7 @@ --- title: "Test collapse button" author: "Thibaut Jombart" -authors: "Thibaut Jombart" +authors: ["Thibaut Jombart"] date: 2018-10-24 licenses: CC-BY categories: "case study" diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md index f2cb47d..49c80d1 100644 --- a/content/post/test-buttons.md +++ b/content/post/test-buttons.md @@ -1,7 +1,7 @@ --- title: "Test collapse button" author: "Thibaut Jombart" -authors: "Thibaut Jombart" +authors: ["Thibaut Jombart"] date: 2018-10-24 licenses: CC-BY categories: "case study" From 95ab91fa9f67b2e03d00fc1e0d91226135ffa383 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 12:40:04 +0100 Subject: [PATCH 25/59] test re-adding buttons --- content/post/practical-stegen.Rmd | 13 +- content/post/practical-stegen.md | 237 ++++++++++++++++-------------- 2 files changed, 130 insertions(+), 120 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 5d15cca..8bcd3dd 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -162,11 +162,12 @@ View(stegen) ``` -
      -Problems? In case of trouble, click here to toggle additional help: +
      + +> **Problems?** In case of trouble, click here to toggle additional help: - - + +
      If the above fails, you should check: @@ -176,8 +177,8 @@ If the above fails, you should check: and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above) -
      - +
      + ## Overview and summaries diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 0fedf77..9cbc0e4 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -11,8 +11,7 @@ image: img/highres/graduation-1965.jpg showonlyimage: true --- -Context -======= +# Context On 26 June 1998, the St Sebastian High School in Stegen (school A), Germany, celebrated a graduation party, where 250 to 350 participants @@ -29,8 +28,7 @@ were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late during the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. -The alert ---------- +## The alert On 2nd July 1998, the Freiburg local health office reported to the Robert Koch Institute (RKI) in Berlin the occurrence of many cases of @@ -53,8 +51,7 @@ investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk factors for transmission in order to better control the outbreak. -The epidemiological study -------------------------- +## The epidemiological study **Case definition**: cases were defined as any person who had attended the party at St Sebastian High School and who suffered from *diarrhoea* @@ -68,30 +65,27 @@ interviews to provide names of persons who attended the party. Overall, 291 responded to enquiries and 103 cases were identified. The linelist analysed in this case study was built from these 291 responses. -This case study ---------------- +## This case study In this case study, we will take you through the analysis of this epidemic. This will be the occasion to illustrate more generally useful practices for data analysis using **R**, including: -- how to read data from Excel -- how to explore data using tables and summaries -- how to clean data -- how to make graphics to describe the data -- how to test if specific food items are linked to the disease + - how to read data from Excel + - how to explore data using tables and summaries + - how to clean data + - how to make graphics to describe the data + - how to test if specific food items are linked to the disease -Initial data processing -======================= +# Initial data processing -Loading required packages -------------------------- +## Loading required packages The following packages will be used in the case study: -- `here`: to find the path to data or script files -- `readxl`: to read Excel spreadsheets into **R** -- `incidence`: to build epicurves + - `here`: to find the path to data or script files + - `readxl`: to read Excel spreadsheets into **R** + - `incidence`: to build epicurves To avoid having to specify the name of the package whenever we use a function using the syntax `package_name::function_name()`, we load these @@ -107,7 +101,7 @@ library(ggplot2) # advanced graphics ``` Note that you will get an error if the packages have not been installed -on your system. To install them (you only need to do this once!), type: +on your system. To install them (you only need to do this once\!), type: ``` r install.packages("here") @@ -123,8 +117,7 @@ available, so that `function_name()` can be used directly (without the `package_name::` prefix). This is not a problem here as these packages do now implement functions with identical names. -Importing data from Excel -------------------------- +## Importing data from Excel Linelist data can be read from various formats, including flat text files (e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or @@ -167,34 +160,43 @@ stegen ## View(stegen) ``` -
      -Problems? In case of trouble, click here to toggle -additional help: +
      + +> **Problems?** In case of trouble, click here to toggle additional +> help: + + + +
      - - If the above fails, you should check: -- that your data file has been saved in the right folder, with the + - that your data file has been saved in the right folder, with the right name (lower case and upper case do matter) -- that your R session was started from the right project - if unsure, + - that your R session was started from the right project - if unsure, close R and re-open Rstudio by double-clicking on the `.Rproj` file -- that all packages are installed and loaded (see installation + - that all packages are installed and loaded (see installation guidelines above) -
      -Overview and summaries ----------------------- + + + + +## Overview and summaries We first have a quick look at the content of the data set. The information we are looking for is: -- the numbers of cases (rows) and variables (columns) in the data -- the *name* of the variables: do they use consistent capitalisation + - the numbers of cases (rows) and variables (columns) in the data + - the *name* of the variables: do they use consistent capitalisation and separators? -- the *type* of the variables: are dates, numeric or categorical + - the *type* of the variables: are dates, numeric or categorical variables used when they should? -- the *coding* of the variables: are explicit labels (e.g. + - the *coding* of the variables: are explicit labels (e.g. `"male"`/`"female"`) used where relevant? We first check the dimensions of the `stegen` object, and the name of @@ -302,21 +304,20 @@ issues**: 1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation 2. variable types are partly wrong: - - *unique keys* should be `character` (i.e. character strings) - - *dates of onset* should be `Date` (**R** is good at handling + - *unique keys* should be `character` (i.e. character strings) + - *dates of onset* should be `Date` (**R** is good at handling actual dates) - - *illness* and *sex* should be `factor` (i.e. a categorical + - *illness* and *sex* should be `factor` (i.e. a categorical variables) 3. labels used in some variables are ambiguous: - - *sex* should be coded explicitely, not as 0 (here, male) and 1 + - *sex* should be coded explicitely, not as 0 (here, male) and 1 (here, female) - - *illness* should be coded explicitely, not as 0 (here, non-case) + - *illness* should be coded explicitely, not as 0 (here, non-case) and 1 (case) 4. some binary variables have maximum values of 9 (see `summary(stegen)`) -Data cleaning -------------- +## Data cleaning While it is tempting to go back to the Excel spreadsheet to fix issues with data, it is almost always quicker and more reliable to clean data @@ -393,6 +394,7 @@ stegen$horseradish[stegen$horseradish == 9] <- NA **Going further** click on the button below for more explanation:
      + Why does this work? @@ -436,11 +438,10 @@ toy_vector # check outcome ```
      -Data exploration -================ -Summaries of age and sex distribution -------------------------------------- +# Data exploration + +## Summaries of age and sex distribution We can have a brief look at age and sex distributions using some basic summaries; for instance: @@ -462,18 +463,22 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender ## 13.00 18.00 20.00 26.93 24.50 65.00 4 ``` -**Going further** click on the button below to learn about `tapply`: +**Going further** click on the button below to learn about +`tapply`: + +
      + More about tapply `tapply` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function -`?tapply`, but briefly, the syntax to be used is -`tapply(input_data, stratification, function_to_use, optional_arguments)`. -In the command used above: +`?tapply`, but briefly, the syntax to be used is `tapply(input_data, +stratification, function_to_use, optional_arguments)`. In the command +used above: ``` r tapply(stegen$age, stegen$sex, summary) @@ -494,8 +499,8 @@ Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means “ignore missing data”.
      -Graphical exploration ---------------------- + +## Graphical exploration The summaries above may be useful for reporting purposes, but graphics are usually better for getting a feel for the data. Here, we illustrate @@ -504,10 +509,10 @@ age/sex distribution. It implements an alternative graphics system to the basic **R** plots, in which the plot is built by adding (literally using `+`) different layers of information, using: -- `ggplot()` to specify the dataset to use -- `geom_...()` functions which define a type of graphics to use + - `ggplot()` to specify the dataset to use + - `geom_...()` functions which define a type of graphics to use (e.g. barplot, histogram) -- `aes()` to map elements of the data into aesthetic properties + - `aes()` to map elements of the data into aesthetic properties (e.g. x/y axes, color, shape) For instance, to get an histogram of age: @@ -517,7 +522,7 @@ ggplot(stegen) + geom_histogram(aes(x = age)) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` -![](practical-stegen_files/figure-markdown_github/stegen_histogram-1.png) +![](practical-stegen_files/figure-gfm/stegen_histogram-1.png) Color can be added using: @@ -526,17 +531,21 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` -![](practical-stegen_files/figure-markdown_github/stegen_histogram_sex-1.png) +![](practical-stegen_files/figure-gfm/stegen_histogram_sex-1.png) Here, the age distribution is pretty much identical between male and female. **Going further** click on the button below to learn about customising -`ggplot2` graphics: +`ggplot2` +graphics: + +
      + More on ggplot2 `ggplot2` graphics are highly customisable, and a lot of help and @@ -545,15 +554,18 @@ website](https://ggplot2.tidyverse.org/) is a good place to start. For instance, here we: -- add white borders to the plot (`color = "white"`) -- specify colors manually for male / female, using specified color + - add white borders to the plot (`color = "white"`) + - specify colors manually for male / female, using specified color codes (see [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to define your own colors) (`scale_fill_manual(...)`) -- adding labels for title, *x* and *y* axes (`labs()`) -- specify a lighter general color theme, using Times font of a larger + - adding labels for title, *x* and *y* axes (`labs()`) + - specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) -- move the legend inside the plot (`theme(...)`) + - move the legend inside the plot +(`theme(...)`) + + ``` r ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + @@ -564,11 +576,11 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` -![](practical-stegen_files/figure-markdown_github/stegen_ggplot2_custom-1.png) +![](practical-stegen_files/figure-gfm/stegen_ggplot2_custom-1.png)
      -Epidemic curve --------------- + +## Epidemic curve Incidence curves can be built using the package `incidence`, which will compute the number of new cases given a vector of dates (here, onset) @@ -590,7 +602,7 @@ i plot(i) ``` -![](practical-stegen_files/figure-markdown_github/stegen_incidence-1.png) +![](practical-stegen_files/figure-gfm/stegen_incidence-1.png) Details of the case counts can be obtained using: @@ -650,7 +662,7 @@ as.data.frame(i_ill) plot(i_ill, color = c("non case" = "#66cc99", "case" = "#990033")) ``` -![](practical-stegen_files/figure-markdown_github/incidence_stratified-1.png) +![](practical-stegen_files/figure-gfm/incidence_stratified-1.png) The outbreak really only happened over 3 days: onsets reported after did not match the epi case definition. This is compatible with a food-borne @@ -660,11 +672,15 @@ Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). -**Going further** click on the button below to learn about `incidence`: +**Going further** click on the button below to learn about +`incidence`: + +
      + More on incidence More information on the `incidence` package can be found from the @@ -674,7 +690,8 @@ definition: We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) -for more): +for +more): ``` r plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + @@ -684,11 +701,11 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) ``` -![](practical-stegen_files/figure-markdown_github/incidence_stratified_custom-1.png) +![](practical-stegen_files/figure-gfm/incidence_stratified_custom-1.png)
      -Age and gender distribution of the cases ----------------------------------------- + +## Age and gender distribution of the cases We use the same principles as before to visualise the distribution of illness by age and gender. To split the plot into different panels @@ -702,24 +719,22 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` -![](practical-stegen_files/figure-markdown_github/stegen_case_age_sex-1.png) +![](practical-stegen_files/figure-gfm/stegen_case_age_sex-1.png) Illness does not seem to be depending on age or gender. This can be tested: this is the topic of the next and final section. -Looking for the culprits -======================== +# Looking for the culprits -Univariate tests ----------------- +## Univariate tests Methods for testing the association between two variables can be broken down in 3 types, depending on which types these variables are: 1. **2 quantitative variables**: Pearson’s correlation coefficient - (*r*) and similar methods + (\(r\)) and similar methods 2. **1 quantitative, 1 categorical**: ANOVA types of approaches; - particular case with 2 groups: Student’s*t*-test + particular case with 2 groups: Student’s\(t\)-test 3. **2 categorical variables**: Chi-squared test on the 2x2 table (a.k.a. *contingency* table) and similar methods (e.g. Fisher’s exact test) @@ -747,7 +762,7 @@ bartlett.test(stegen$age ~ stegen$ill) ``` The resulting p-value (0.364) suggests the variances are indeed -comparable. Wecan thus proceed to a *t*-test: +comparable. Wecan thus proceed to a \(t\)-test: ``` r t.test(stegen$age ~ stegen$ill, var.equal = TRUE) @@ -814,8 +829,7 @@ chisq.test(tab_ill_sex) Here, the p-value of 0.418 suggests illness is not related to sex either. -Making multiple tests ---------------------- +## Making multiple tests The natural next step is to run multiple tests for all potential risk factors recorded. There are many ways to go about this in **R**, but we @@ -824,8 +838,8 @@ will illustrate one of the most common workflows: 1. isolate a subset of variables to test (the ones indicative of food consumption) (using `[]`) 2. for each: - 1. build contingency table with illness status (using `table`) - 2. run a Chi-square test on this table (using `chisq.test`) + 1) build contingency table with illness status (using `table`) + 2) run a Chi-square test on this table (using `chisq.test`) All of these have been seen so far; the only missing piece is step 2), which we will cover using a very handy function called `lapply`. @@ -878,12 +892,12 @@ There are many variables to test, and having to enter separate command lines for each would be cumbersome and prone to errors. As a workaround, the function `lapply` can be used. This function allows to repeat an operation using a vector of inputs, using each element in turn. Its -general syntax is -`lapply(vector_of_inputs, function_to_use, other_arguments)`, where -`other_arguments` can be any secondary arguments taken by -`function_to_use`. Here, we use it to build a separate contingency table -of each variable in `food` crossed with illness status (`stegen$ill`); -note that we show only the first few tables: +general syntax is `lapply(vector_of_inputs, function_to_use, +other_arguments)`, where `other_arguments` can be any secondary +arguments taken by `function_to_use`. Here, we use it to build a +separate contingency table of each variable in `food` crossed with +illness status (`stegen$ill`); note that we show only the first few +tables: ``` r food_tables <- lapply(food, table, stegen$ill) @@ -986,15 +1000,14 @@ Looking at these results, it seems a large number of food items are significantly correlated to the illness. Which food item is the biggest suspect? We will try and address this question using risk ratios. -Risk ratios ------------ +## Risk ratios ### For one variable The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the ratio between the proportion of illness in one group (typically -‘exposed’) vs another (‘non-exposed’). For instance, let us consider the -contingency table of pork consumption and illness: +‘exposed’) vs another (‘non-exposed’). For instance, let us consider +the contingency table of pork consumption and illness: ``` r food_tables$pork @@ -1050,7 +1063,8 @@ Repeating these tasks manually for all variables in `food` would be cumbersome. As an alternative, we can define the steps above as `function`, i.e. a generic recipe which we can then apply to any contingency table. This new function will be called `risk_ratio`, and is -defined as follows: +defined as +follows: ``` r risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table @@ -1130,13 +1144,12 @@ barplot(rr_data, horiz = TRUE, las = 1, abline(v = 1, lty = 2) # add vertical line x = 1 ``` -![](practical-stegen_files/figure-markdown_github/stegen_rr_plot-1.png) +![](practical-stegen_files/figure-gfm/stegen_rr_plot-1.png) The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. -Conclusion -========== +# Conclusion This case study illustrated how **R** can be used to import data, clean them, and derive basic summaries for a first glance at the data. It also @@ -1149,26 +1162,23 @@ studies.


      -About this document -=================== +# About this document -Source ------- +## Source This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. -Contributors ------------- +## Contributors -- original version: Alain Moren, Gilles Desve + - original version: Alain Moren, Gilles Desve -- reviewers, previous versions: Marta Valenciano, Alain Moren + - reviewers, previous versions: Marta Valenciano, Alain Moren -- adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis + - adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis -- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, + - rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart Contributions are welcome via [pull @@ -1176,8 +1186,7 @@ requests](https://github.com/reconhub/learn/pulls). The source file can be found [here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). -Legal stuff ------------ +## Legal stuff **License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) **Copyright**: 2018 From f43780ff5dea9d80fe665820f6b3f291106c8d93 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 26 Oct 2018 12:43:20 +0100 Subject: [PATCH 26/59] attempt to resolve markdown not being recognised --- content/post/practical-stegen.Rmd | 5 +++-- content/post/practical-stegen.md | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 8bcd3dd..9fa851a 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -164,10 +164,11 @@ View(stegen)
      -> **Problems?** In case of trouble, click here to toggle additional help: +**Problems?** In case of trouble, click here to toggle additional help: -
      + +
      If the above fails, you should check: diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 9cbc0e4..f58b09c 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -162,8 +162,8 @@ stegen
      -> **Problems?** In case of trouble, click here to toggle additional -> help: +**Problems?** In case of trouble, click here to toggle additional +help: - +

      If the above fails, you should check: @@ -178,6 +178,7 @@ If the above fails, you should check: and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above) +

      diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index f58b09c..ad5c23d 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -171,6 +171,8 @@ Show additional help +

      +

      If the above fails, you should check: @@ -182,6 +184,8 @@ If the above fails, you should check: - that all packages are installed and loaded (see installation guidelines above) +

      +
      From d266fc1dc68308d6e41bd9d1b6e6e03ae13c63a0 Mon Sep 17 00:00:00 2001 From: "Zhian N. 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zeE?}t?eV4_ho3I)4K!*+07^qpip2fuMPUvp0|}m4N#S@1YvRU9p-RarN}_yW)MMUP z87Z=JrHwXAm4}tJ1>lw`zH8aLums!Gj4Li}muEHb8T<1zjlsoWPw?r3j_$QDJjcAo z>%Ea?gzqT?rM(zZ34`zFfVcWwvXL_2BWQ2;dg#=Mh7)$qi}2}u2)RSA?R zd~GPW#Di2?an>YF#0gQxs;2bN(28-NRbe^%;_3IASEW9%;PxhOuY}R`XBj3J$Bxo2 zmnebk2Ix3s<78_o}xUg z<``ISV4@pKogGhdSk6&&`B2ZbsIDHu1WasOKn~n|N&?J>KMjr2f;YH=`@s<+!JT>n zUuBhPd-v~;uK)yMW7>R#C5W0&!B>sQe~bV7<89zkOnwT1ZS2pVg;2v+y?NCtaM;mf zVt<7OzA23!w(38ZM8^jh#O_9f-4Xo3eFx2Wko1k?hu;Q`vVvRm{R!Lj&BG?2$RAKB mRn(J{hu`M^8D70B!;Tp6pVY0pH}l2yeEuIFIqRDM literal 0 HcmV?d00001 From 4a079ee2d83d5c20d8d1de90085d24805a59c7c0 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 29 Oct 2018 17:41:48 +0000 Subject: [PATCH 29/59] add case studies as a menu item --- config.toml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/config.toml b/config.toml index c18164e..30e436f 100644 --- a/config.toml +++ b/config.toml @@ -54,6 +54,12 @@ pygmentsCodeFences = true weight = 99 pre = "" +[[menu.main]] + name = "Case Studies" + url = "/categories/case-studies.html" + weight = 99 + pre = "" + [[menu.main]] name = "Lectures" url = "/categories/lectures.html" From 60c90c10a9ae6ae04d236195f24b5f5c7fe8033c Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 29 Oct 2018 17:43:26 +0000 Subject: [PATCH 30/59] remove bootstrap headers (sorry, Teebz :disappointed:) --- layouts/partials/head.html | 4 ---- 1 file changed, 4 deletions(-) diff --git a/layouts/partials/head.html b/layouts/partials/head.html index 88d1f7b..a46d450 100644 --- a/layouts/partials/head.html +++ b/layouts/partials/head.html @@ -31,10 +31,6 @@ - - - - {{ partial "mathjax-support.html" . }} From 4f1f20f9e82d132949748f2790938cebe230255c Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 29 Oct 2018 17:44:00 +0000 Subject: [PATCH 31/59] add color and background to details tags --- static/css/custom.css | 2 ++ 1 file changed, 2 insertions(+) diff --git a/static/css/custom.css b/static/css/custom.css index 3aa0038..09e6664 100644 --- a/static/css/custom.css +++ b/static/css/custom.css @@ -152,6 +152,8 @@ body, input, select, button, p { details { width: 100%; + color: #3d3d5c; + background: #ffffe0; } .highlight { From fb129591f664082b5294e70b69bcf9d80dfef9e5 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 29 Oct 2018 17:44:16 +0000 Subject: [PATCH 32/59] update practical to use details tags and move imgs to right dir --- content/post/practical-stegen.Rmd | 36 +++++------- content/post/practical-stegen.md | 53 ++++-------------- .../figure-gfm/incidence_stratified-1.png | Bin 0 -> 12633 bytes .../incidence_stratified_custom-1.png | Bin 0 -> 35347 bytes .../figure-gfm/stegen_case_age_sex-1.png | Bin 0 -> 8506 bytes .../figure-gfm/stegen_ggplot2_custom-1.png | Bin 0 -> 22153 bytes .../figure-gfm/stegen_histogram-1.png | Bin 0 -> 5636 bytes .../figure-gfm/stegen_histogram_sex-1.png | Bin 0 -> 6289 bytes .../figure-gfm/stegen_incidence-1.png | Bin 0 -> 6171 bytes .../figure-gfm/stegen_rr_plot-1.png | Bin 0 -> 24157 bytes .../incidence_stratified-1.png | Bin 12258 -> 0 bytes .../incidence_stratified_custom-1.png | Bin 35932 -> 0 bytes .../stegen_case_age_sex-1.png | Bin 18550 -> 0 bytes .../stegen_ggplot2_custom-1.png | Bin 22431 -> 0 bytes .../stegen_histogram-1.png | Bin 5429 -> 0 bytes .../stegen_histogram_sex-1.png | Bin 6121 -> 0 bytes .../stegen_incidence-1.png | Bin 5994 -> 0 bytes .../stegen_rr_plot-1.png | Bin 24292 -> 0 bytes 18 files changed, 25 insertions(+), 64 deletions(-) create mode 100644 static/post/practical-stegen_files/figure-gfm/incidence_stratified-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/incidence_stratified_custom-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_case_age_sex-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_ggplot2_custom-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_histogram-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_histogram_sex-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_incidence-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_rr_plot-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/incidence_stratified-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/incidence_stratified_custom-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_case_age_sex-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_ggplot2_custom-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_histogram-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_histogram_sex-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_incidence-1.png delete mode 100644 static/post/practical-stegen_files/figure-markdown_github/stegen_rr_plot-1.png diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 742da20..2b79f55 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -2,7 +2,7 @@ title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] -categories: "case study" +categories: "case studies" tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] date: 2018-10-24 slug: stegen @@ -162,13 +162,10 @@ View(stegen) ``` -
      +
      -**Problems?** In case of trouble, click here to toggle additional help: +Problems? In case of trouble, click here to toggle additional help: - -

      -

      If the above fails, you should check: @@ -178,9 +175,7 @@ If the above fails, you should check: and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above) -

      -
      -
      + ## Overview and summaries @@ -193,12 +188,14 @@ We first have a quick look at the content of the data set. The information we ar - the *coding* of the variables: are explicit labels (e.g. `"male"`/`"female"`) used where relevant? We first check the dimensions of the `stegen` object, and the name of the variables: + ```{r stegen_dim} dim(stegen) # rows x columns names(stegen) # column labels ``` We can now try and summarise the dataset using: + ```{r stegen_summary} summary(stegen) ``` @@ -208,6 +205,7 @@ at such, which may not always be useful. As an alternative, `table` can be used to list all possible values of a variable, and count how many time each value appears in the data. For instance, we can compare the `summary` and `table` for consumption of `tiramisu`: + ```{r stegen_summary_table} stegen$tiramisu # all values summary(stegen$tiramisu) # summary @@ -243,6 +241,7 @@ stegen_old <- stegen # save 'dirty data' ``` We use `epitrix`'s function `clean_labels` to standardise the variable names: + ```{r stegen_clean_labels} new_labels <- clean_labels(names(stegen)) # generate standardised labels new_labels # check the result @@ -251,6 +250,7 @@ names(stegen) <- new_labels We set convert the unique identifiers to character strings (`character`), dates of onset to actual `Date` objects, and sex and illness are set to categorical variables (`factor`): + ```{r stegen_unique_ids} stegen$unique_key <- as.character(stegen$unique_key) stegen$sex <- factor(stegen$sex) @@ -258,8 +258,9 @@ stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` -We use the function `recode` from the `dplyr' package to recode `sex` more +We use the function `recode` from the `dplyr` package to recode `sex` more explicitely: + ```{r stegen_recode} stegen$sex <- recode_factor(stegen$sex, "0" = "male", "1" = "female") stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") @@ -294,8 +295,6 @@ stegen$horseradish[stegen$horseradish == 9] <- NA
      Why does this work? - - There are several things going on in a command like: @@ -348,9 +347,6 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender **Going further** click on the button below to learn about `tapply`: - - -
      More about tapply @@ -406,9 +402,6 @@ Here, the age distribution is pretty much identical between male and female. **Going further** click on the button below to learn about customising `ggplot2` graphics: - - -
      More on ggplot2 @@ -478,10 +471,6 @@ options seen before can be used for further customisation (see below). **Going further** click on the button below to learn about `incidence`: - - - -
      More on incidence @@ -530,7 +519,7 @@ depending on which types these variables are: 1. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar methods 2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with - 2 groups: Student's$t$-test + 2 groups: Student's $t$-test 3. **2 categorical variables**: Chi-squared test on the 2x2 table (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) @@ -574,6 +563,7 @@ tab_ill_sex ``` Note that proportions can be obtained using `prop.table`: + ```{r stegen_prop_table} ## basic proportions prop.table(tab_ill_sex) diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index ad5c23d..a40c42b 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -2,7 +2,7 @@ title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] -categories: "case study" +categories: "case studies" tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] date: 2018-10-24 slug: stegen @@ -160,20 +160,10 @@ stegen ## View(stegen) ``` -
      - -**Problems?** In case of trouble, click here to toggle additional -help: - - - -

      +

      -
      +Problems? In case of trouble, click here to toggle +additional help: If the above fails, you should check: @@ -184,11 +174,7 @@ If the above fails, you should check: - that all packages are installed and loaded (see installation guidelines above) -

      - -
      - -
      +
      ## Overview and summaries @@ -357,7 +343,7 @@ stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` -We use the function `recode` from the `dplyr' package to recode`sex\` +We use the function `recode` from the `dplyr` package to recode `sex` more explicitely: ``` r @@ -400,8 +386,6 @@ stegen$horseradish[stegen$horseradish == 9] <- NA
      Why does this work? - - There are several things going on in a command like: @@ -467,12 +451,7 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender ## 13.00 18.00 20.00 26.93 24.50 65.00 4 ``` -**Going further** click on the button below to learn about -`tapply`: - - - - +**Going further** click on the button below to learn about `tapply`:
      @@ -541,12 +520,7 @@ Here, the age distribution is pretty much identical between male and female. **Going further** click on the button below to learn about customising -`ggplot2` -graphics: - - - - +`ggplot2` graphics:
      @@ -593,6 +567,7 @@ to achieve this, and then visualise the results: ``` r i <- incidence(stegen$date_onset) +## 160 missing observations were removed. i ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -636,6 +611,7 @@ definition will clarify the situation: ``` r i_ill <- incidence(stegen$date_onset, group = stegen$ill) +## 160 missing observations were removed. i_ill ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -676,12 +652,7 @@ Note that the plots produced by `incidence` are `ggplot2` objects, so that the options seen before can be used for further customisation (see below). -**Going further** click on the button below to learn about -`incidence`: - - - - +**Going further** click on the button below to learn about `incidence`:
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changes to appearance --- content/post/practical-stegen.Rmd | 122 ++++++++++++++++-------------- 1 file changed, 65 insertions(+), 57 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 2b79f55..25e2c0d 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -95,23 +95,28 @@ analysis using **R**, including: The following packages will be used in the case study: -- `here`: to find the path to data or script files -- `readxl`: to read Excel spreadsheets into **R** -- `incidence`: to build epicurves +- *here*: to find the path to data or script files +- *readxl*: to read Excel spreadsheets into **R** +- *incidence*: to build epicurves +- *epitrix*: to clean labels from our spreadsheet +- dplyr: to help with factors +- ggplot2: to create custom visualisations -To avoid having to specify the name of the package whenever we use a function -using the syntax `package_name::function_name()`, we load these packages using -`library`: +If we have these packages installed, we can tell R to load these packages from +our R library: ```{r stegen_libraries, warning=FALSE, message=FALSE} -library(here) # find data/script files -library(readxl) # read xlsx files -library(incidence) # make epicurves -library(epitrix) # clean labels and variables -library(dplyr) # general data handling -library(ggplot2) # advanced graphics +library("here") # find data/script files +library("readxl") # read xlsx files +library("incidence") # make epicurves +library("epitrix") # clean labels and variables +library("dplyr") # general data handling +library("ggplot2") # advanced graphics ``` +

      + Package not installed? Did you get an error? + Note that you will get an error if the packages have not been installed on your system. To install them (you only need to do this once!), type: @@ -124,10 +129,11 @@ install.packages("dplyr") install.packages("ggplot2") ``` +
      Loading these packages makes all functions implemented by the packages available, so that `function_name()` can be used directly (without the -`package_name::` prefix). This is not a problem here as these packages do now +`package_name::` prefix). This is not a problem here as these packages do not implement functions with identical names. @@ -143,7 +149,7 @@ of the project. Here we decompose the steps to read data in: finding the path to the data -(`path_to_data`), using the function `read_xlsx` to read data in, and saving the +(`path_to_data`), using the function `read_xlsx()` from the *readxl* package to read data in, and saving the output in a new object `stegen`: ```{r eval = FALSE} @@ -201,9 +207,9 @@ summary(stegen) ``` Note that binary variables, when treated as numeric values (0/1), are summarised -at such, which may not always be useful. As an alternative, `table` can be used +at such, which may not always be useful. As an alternative, `table()` can be used to list all possible values of a variable, and count how many time each value -appears in the data. For instance, we can compare the `summary` and `table` for +appears in the data. For instance, we can compare the `summary()` and `table()` for consumption of `tiramisu`: ```{r stegen_summary_table} @@ -240,7 +246,7 @@ further analysis. stegen_old <- stegen # save 'dirty data' ``` -We use `epitrix`'s function `clean_labels` to standardise the variable names: +We use *epitrix*'s function `clean_labels()` to standardise the variable names: ```{r stegen_clean_labels} new_labels <- clean_labels(names(stegen)) # generate standardised labels @@ -258,7 +264,7 @@ stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` -We use the function `recode` from the `dplyr` package to recode `sex` more +We use the function `recode()` from the *dplyr* package to recode `sex` more explicitely: ```{r stegen_recode} @@ -268,7 +274,7 @@ stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") Finally we look in more depth into these variables having maximum values of 9, -where we expect 0/1; `table` is useful to list all values taken by a variable, +where we expect 0/1; `table()` is useful to list all values taken by a variable, and listing their frequencies: ```{r stegen_nines} @@ -345,13 +351,13 @@ summary(stegen$sex) # gender distribution tapply(stegen$age, stegen$sex, summary) # age stats by gender ``` -**Going further** click on the button below to learn about `tapply`: +
      - More about tapply + Going further click on the button below to learn about tapply(): -`tapply` is a very handy function to stratify any kind of analyses. You will -find more details by reading the documentation of the function `?tapply`, but +`tapply()` is a very handy function to stratify any kind of analyses. You will +find more details by reading the documentation of the function `?tapply()`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: @@ -361,7 +367,7 @@ tapply(stegen$age, stegen$sex, summary) this literally means: select the age variable in the dataset `stegen` (`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each strata -(`summary`). So for instance, to get the average age by sex (function `mean`), one could use: +(`summary()`). So for instance, to get the average age by sex (function `mean()`), one could use: ```{r stegen_age_per_sex} tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) @@ -377,7 +383,7 @@ arguments; here, `na.rm = TRUE` means "ignore missing data". The summaries above may be useful for reporting purposes, but graphics are usually better for getting a feel for the data. Here, we illustrate how the -package `ggplot2` can be used to derive informative graphics of age/sex +package *ggplot2* can be used to derive informative graphics of age/sex distribution. It implements an alternative graphics system to the basic **R** plots, in which the plot is built by adding (literally using `+`) different layers of information, using: @@ -399,13 +405,10 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) Here, the age distribution is pretty much identical between male and female. - -**Going further** click on the button below to learn about customising `ggplot2` graphics: -
      - More on ggplot2 + Going further: click here to learn about customising ggplot2 graphics: -`ggplot2` graphics are highly customisable, and a lot of help and examples can +*ggplot2* graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. For instance, here we: @@ -418,8 +421,10 @@ For instance, here we: - specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) - move the legend inside the plot (`theme(...)`) + ```{r stegen_ggplot2_custom} -ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + +ggplot(stegen) + + geom_histogram(aes(x = age, fill = sex), color = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + labs(title = "Age distribution by gender", x = "Age (years)", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + @@ -431,9 +436,9 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + ## Epidemic curve -Incidence curves can be built using the package `incidence`, which will compute +Incidence curves can be built using the package *incidence*, which will compute the number of new cases given a vector of dates (here, onset) and a time -interval (1 day by default). We use the function `incidence` to achieve this, and +interval (1 day by default). We use the function `incidence()` to achieve this, and then visualise the results: ```{r stegen_incidence} @@ -465,26 +470,26 @@ match the epi case definition. This is compatible with a food-borne outbreak with limited or no person-to-person transmission. -Note that the plots produced by `incidence` are `ggplot2` objects, so that the +Note that the plots produced by *incidence* are *ggplot2* objects, so that the options seen before can be used for further customisation (see below). -**Going further** click on the button below to learn about `incidence`: -
      - More on incidence + Going further click here to learn about the incidence package: + -More information on the `incidence` package can be found from the +More information on the *incidence* package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: -We can also customise this graphic like other `ggplot2` plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): +We can also customise this graphic like other *ggplot2* plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): ```{r incidence_stratified_custom} plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + geom_hline(yintercept = 1:55, color = "white") + - theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) + theme(legend.position = c(0.8, 0.8)) + + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) ```
      @@ -495,13 +500,16 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 We use the same principles as before to visualise the distribution of illness by age and gender. To split the plot into different panels according to `sex`, we -use `facet_grid()` (see previous extra info on `ggplot2` customisation for +use `facet_grid()` (see previous extra info on *ggplot2* customisation for further details otions): ```{r stegen_case_age_sex} -ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + +ggplot(stegen) + + geom_histogram(aes(x = age, fill = ill)) + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + - facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() + facet_grid(sex ~ .) + + labs(title = "Cases by age and gender") + + theme_light() ``` Illness does not seem to be depending on age or gender. This can be tested: this @@ -531,10 +539,10 @@ of type 2 and 3 will be illustrated here. ### Is illness linked to age? -We can use the function `t.test` to test if the average age is different across +We can use the function `t.test()` to test if the average age is different across illness status. As this test assumes that the two categories exhibit similar variation, we first ensure that the variances are comparable using Bartlett's -test. The syntax`variable ~ group` is used to indicate the variable of interest (left hand-side), and the group +test. The syntax `variable ~ group` is used to indicate the variable of interest (left hand-side), and the group (right hand-side): ```{r stegen_bartlett_test} @@ -593,7 +601,7 @@ one of the most common workflows: ii) run a Chi-square test on this table (using `chisq.test`) All of these have been seen so far; the only missing piece is step 2), which we -will cover using a very handy function called `lapply`. +will cover using a very handy function called `lapply()`. ### Isolating the variables to test @@ -621,11 +629,11 @@ food There are many variables to test, and having to enter separate command lines for each would be cumbersome and prone to errors. As a workaround, the function -`lapply` can be used. This function allows to repeat an operation using a vector +`lapply()` can be used. This function allows to repeat an operation using a vector of inputs, using each element in turn. Its general syntax is `lapply(vector_of_inputs, function_to_use, other_arguments)`, where `other_arguments` can be any secondary arguments taken by -`function_to_use`. Here, we use it to build a separate contingency table of each +`function_to_use()`. Here, we use it to build a separate contingency table of each variable in `food` crossed with illness status (`stegen$ill`); note that we show only the first few tables: @@ -639,7 +647,7 @@ head(food_tables) ### Realising multiple Chi-squared tests The same principle used to compute several contingency tables can be used to -compute Chi-square tests for each table, using `lapply`; note that we show +compute Chi-square tests for each table, using `lapply()`; note that we show only the first few tests: ```{r stegen_food_tests, echo = c(1,3), eval = -2} @@ -667,7 +675,7 @@ pork consumption and illness: food_tables$pork ``` -This can be turned into proportions using `prop.table`, specifying that we want +This can be turned into proportions using `prop.table()`, specifying that we want proportions by rows (`margin = 1`): ```{r stegen_pork_prop} @@ -698,14 +706,14 @@ of 2-dimensional objects. In the example above, we have computed the risk ratio using a simple recipe: 1. select a contingency table -2. convert raw numbers to proportions (using `prop.table`) +2. convert raw numbers to proportions (using `prop.table()`) 3. isolating the risks in exposed and non-exposed groups, and compute their ratio Repeating these tasks manually for all variables in `food` would be -cumbersome. As an alternative, we can define the steps above as `function`, +cumbersome. As an alternative, we can define the steps above as a function, i.e. a generic recipe which we can then apply to any contingency table. This new -function will be called `risk_ratio`, and is defined as follows: +function will be called `risk_ratio()`, and is defined as follows: ```{r stegen_risk_ratio_function} risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table @@ -728,7 +736,7 @@ risk_ratio(food_tables$pork) ``` And we can now apply this function sequentially to all variables in `food` as -before, using `lapply` (we show only the first few values): +before, using `lapply()` (we show only the first few values): ```{r stegen_all_rr, echo = 1:2, eval = -2} all_rr <- lapply(food_tables, risk_ratio) @@ -736,9 +744,9 @@ all_rr head(all_rr) ``` -Finally, we re-shape these results into a numeric `vector` (using `unlist)` for +Finally, we re-shape these results into a numeric `vector` (using `unlist()`) for further plotting; note that for this graph, the basic system is easier to use -than `ggplot2`: +than *ggplot2*: ```{r stegen_rr_plot} ## convert results from list to numeric vector @@ -767,7 +775,7 @@ factor in this outbreak. This case study illustrated how **R** can be used to import data, clean them, and derive basic summaries for a first glance at the data. It also showed how to -generate graphics using `ggplot2`, and how to detect associations between +generate graphics using *ggplot2*, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic From d3c4e4bf6aea1a7b9e4e158ac6098e6df9bfedd1 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Tue, 30 Oct 2018 15:09:59 +0000 Subject: [PATCH 34/59] fix plots in practical --- content/post/practical-stegen.Rmd | 73 +++--- content/post/practical-stegen.md | 237 +++++++++++------- .../incidence_stratified_custom-1.png | Bin 35347 -> 34504 bytes .../figure-gfm/stegen_ggplot2_custom-1.png | Bin 22153 -> 24139 bytes .../figure-gfm/stegen_rr_plot-1.png | Bin 24157 -> 13663 bytes 5 files changed, 187 insertions(+), 123 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 25e2c0d..045c647 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -129,7 +129,7 @@ install.packages("dplyr") install.packages("ggplot2") ``` -
      +
      Loading these packages makes all functions implemented by the packages available, so that `function_name()` can be used directly (without the @@ -162,6 +162,7 @@ stegen <- readxl::read_xlsx(here::here("static", "data", "stegen_raw.xlsx")) ``` To print the content of the dataset, we can use either of these commands: + ```{r stegen_print_data, eval = 1} stegen View(stegen) @@ -296,11 +297,9 @@ stegen$horseradish[stegen$horseradish == 9] <- NA ``` -**Going further** click on the button below for more explanation: -
      - Why does this work? + Going furhter Why does this work? Click here for more details. There are several things going on in a command like: @@ -348,13 +347,13 @@ summaries; for instance: ```{r stegen_age_sex} summary(stegen$age) # age stats summary(stegen$sex) # gender distribution -tapply(stegen$age, stegen$sex, summary) # age stats by gender +tapply(stegen$age, INDEX = stegen$sex, FUN = summary) # age stats by gender ```
      - Going further click on the button below to learn about tapply(): + Going further click here to learn about tapply(): `tapply()` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply()`, but @@ -362,7 +361,7 @@ briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: ```{r eval = FALSE} -tapply(stegen$age, stegen$sex, summary) +tapply(stegen$age, INDEX = stegen$sex, FUN = summary) ``` this literally means: select the age variable in the dataset `stegen` @@ -370,7 +369,7 @@ this literally means: select the age variable in the dataset `stegen` (`summary()`). So for instance, to get the average age by sex (function `mean()`), one could use: ```{r stegen_age_per_sex} -tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) +tapply(stegen$age, INDEX = stegen$sex, FUN = mean, na.rm = TRUE) ``` Here we illustrate that further arguments to the function can be passed as extra arguments; here, `na.rm = TRUE` means "ignore missing data". @@ -399,6 +398,7 @@ ggplot(stegen) + geom_histogram(aes(x = age)) ``` Color can be added using: + ```{r stegen_histogram_sex} ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) ``` @@ -413,18 +413,21 @@ be found online. The [official website](https://ggplot2.tidyverse.org/) is a goo For instance, here we: +- define the width of each bin to be 1 year (`binwidth = 1` to avoid the warning from above) - add white borders to the plot (`color = "white"`) - specify colors manually for male / female, using specified color codes (see [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to define your own colors) (`scale_fill_manual(...)`) -- adding labels for title, *x* and *y* axes (`labs()`) +- add labels for title, *x* and *y* axes (`labs()`) - specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) - move the legend inside the plot (`theme(...)`) +Note, it's best practice to include only one *ggplot2* command per line if you +are using more than two. ```{r stegen_ggplot2_custom} ggplot(stegen) + - geom_histogram(aes(x = age, fill = sex), color = "white") + + geom_histogram(aes(x = age, fill = sex), binwidth = 1, color = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + labs(title = "Age distribution by gender", x = "Age (years)", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + @@ -485,11 +488,11 @@ We can also customise this graphic like other *ggplot2* plots (see [this tutoria ```{r incidence_stratified_custom} plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + - labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + - theme_light(base_family = "Times", base_size = 16) + geom_hline(yintercept = 1:55, color = "white") + + labs(title = "Epicurve by case", x = "Date of onset", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + theme(legend.position = c(0.8, 0.8)) + - theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) ```
      @@ -744,9 +747,23 @@ all_rr head(all_rr) ``` -Finally, we re-shape these results into a numeric `vector` (using `unlist()`) for -further plotting; note that for this graph, the basic system is easier to use -than *ggplot2*: +Finally, we re-shape these results into a sorted data frame for further plotting +with *ggplot2*. Reshaping will take three steps: + +1. convert from a list to a vector using `unlist()` +2. sort the vector using `sort()` +3. convert the named vector to a data frame using `stack()` (from the *utils* package) + +
      +Going Further click here to learn more about stack() + +The first two functions, `unlist()` and `sort()` have self-explanitory names, +but `stack()` may not be so clear. This function takes the elements in a vector +and **stacks** them together to make a data frame with two columns: one column +called "values" containing the data in the vector, and the other column called +"ind" containing the names of those elements as a factor. + +
      ```{r stegen_rr_plot} ## convert results from list to numeric vector @@ -756,21 +773,21 @@ rr_data ## sort results by decreasing order rr_data <- sort(rr_data, decreasing = TRUE) -## plot using the basic system -par(mar = c(5, 7, 4, 1)) # change margins -barplot(rr_data, horiz = TRUE, las = 1, - xlab = "Risk ratio", - col = heat.colors(15), # add colors - main = "Risk ratio by food exposure") -abline(v = 1, lty = 2) # add vertical line x = 1 +## create a data frame from the named vector +rr_df <- stack(rr_data) +rr_df + +ggplot(rr_df, aes(x = values, y = ind)) + + geom_point() + + geom_vline(xintercept = 1, lty = 2) + + geom_segment(aes(xend = 1, yend = ind)) + + theme_light(base_size = 16, base_family = "Times") + + labs(title = "Risk Ratio by Food Exposure", x = "Risk Ratio", y = "Food Item") ``` The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. - - - # Conclusion This case study illustrated how **R** can be used to import data, clean them, @@ -809,8 +826,8 @@ based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. Contributions are welcome via [pull -requests](https://github.com/reconhub/learn/pulls). The source file can be found -[here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). +requests](https://github.com/reconhub/learn/pulls). The [source file for this case study can be found +here](https://github.com/reconhub/learn/blob/master/content/post/practical-stegen.Rmd). diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index a40c42b..f4c6e1d 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -83,23 +83,30 @@ practices for data analysis using **R**, including: The following packages will be used in the case study: - - `here`: to find the path to data or script files - - `readxl`: to read Excel spreadsheets into **R** - - `incidence`: to build epicurves + - *here*: to find the path to data or script files + - *readxl*: to read Excel spreadsheets into **R** + - *incidence*: to build epicurves + - *epitrix*: to clean labels from our spreadsheet + - dplyr: to help with factors + - ggplot2: to create custom visualisations -To avoid having to specify the name of the package whenever we use a -function using the syntax `package_name::function_name()`, we load these -packages using `library`: +If we have these packages installed, we can tell R to load these +packages from our R library: ``` r -library(here) # find data/script files -library(readxl) # read xlsx files -library(incidence) # make epicurves -library(epitrix) # clean labels and variables -library(dplyr) # general data handling -library(ggplot2) # advanced graphics +library("here") # find data/script files +library("readxl") # read xlsx files +library("incidence") # make epicurves +library("epitrix") # clean labels and variables +library("dplyr") # general data handling +library("ggplot2") # advanced graphics ``` +
      + + Package not installed? Did you get an error? + + Note that you will get an error if the packages have not been installed on your system. To install them (you only need to do this once\!), type: @@ -112,10 +119,12 @@ install.packages("dplyr") install.packages("ggplot2") ``` +
      + Loading these packages makes all functions implemented by the packages available, so that `function_name()` can be used directly (without the `package_name::` prefix). This is not a problem here as these packages -do now implement functions with identical names. +do not implement functions with identical names. ## Importing data from Excel @@ -127,8 +136,9 @@ saved in a `data/` folder of your project, and that your current R session is at the root of the project. Here we decompose the steps to read data in: finding the path to the -data (`path_to_data`), using the function `read_xlsx` to read data in, -and saving the output in a new object `stegen`: +data (`path_to_data`), using the function `read_xlsx()` from the +*readxl* package to read data in, and saving the output in a new object +`stegen`: ``` r path_to_data <- here("data", "stegen_raw.xlsx") @@ -259,9 +269,9 @@ summary(stegen) Note that binary variables, when treated as numeric values (0/1), are summarised at such, which may not always be useful. As an alternative, -`table` can be used to list all possible values of a variable, and count -how many time each value appears in the data. For instance, we can -compare the `summary` and `table` for consumption of `tiramisu`: +`table()` can be used to list all possible values of a variable, and +count how many time each value appears in the data. For instance, we can +compare the `summary()` and `table()` for consumption of `tiramisu`: ``` r stegen$tiramisu # all values @@ -318,7 +328,7 @@ in **R** directly. Here, we make a copy of the old data set, and clean stegen_old <- stegen # save 'dirty data' ``` -We use `epitrix`’s function `clean_labels` to standardise the variable +We use *epitrix*’s function `clean_labels()` to standardise the variable names: ``` r @@ -343,7 +353,7 @@ stegen$ill <- factor(stegen$ill) stegen$date_onset <- as.Date(stegen$date_onset) ``` -We use the function `recode` from the `dplyr` package to recode `sex` +We use the function `recode()` from the *dplyr* package to recode `sex` more explicitely: ``` r @@ -352,8 +362,8 @@ stegen$ill <- recode_factor(stegen$ill, "0" = "non case", "1" = "case") ``` Finally we look in more depth into these variables having maximum values -of 9, where we expect 0/1; `table` is useful to list all values taken by -a variable, and listing their frequencies: +of 9, where we expect 0/1; `table()` is useful to list all values taken +by a variable, and listing their frequencies: ``` r @@ -381,11 +391,10 @@ stegen$salmon[stegen$salmon == 9] <- NA stegen$horseradish[stegen$horseradish == 9] <- NA ``` -**Going further** click on the button below for more explanation: -
      - Why does this work? + Going furhter Why does this work? Click here for more +details. There are several things going on in a command like: @@ -441,7 +450,7 @@ summary(stegen$age) # age stats summary(stegen$sex) # gender distribution ## male female ## 139 152 -tapply(stegen$age, stegen$sex, summary) # age stats by gender +tapply(stegen$age, INDEX = stegen$sex, FUN = summary) # age stats by gender ## $male ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 12.00 17.50 19.00 26.38 28.00 80.00 4 @@ -451,29 +460,28 @@ tapply(stegen$age, stegen$sex, summary) # age stats by gender ## 13.00 18.00 20.00 26.93 24.50 65.00 4 ``` -**Going further** click on the button below to learn about `tapply`: -
      - More about tapply + Going further click here to learn about +tapply(): -`tapply` is a very handy function to stratify any kind of analyses. You -will find more details by reading the documentation of the function -`?tapply`, but briefly, the syntax to be used is `tapply(input_data, +`tapply()` is a very handy function to stratify any kind of analyses. +You will find more details by reading the documentation of the function +`?tapply()`, but briefly, the syntax to be used is `tapply(input_data, stratification, function_to_use, optional_arguments)`. In the command used above: ``` r -tapply(stegen$age, stegen$sex, summary) +tapply(stegen$age, INDEX = stegen$sex, FUN = summary) ``` this literally means: select the age variable in the dataset `stegen` (`stegen$age`), stratify it by sex (`stegen$sex`), and summaries each -strata (`summary`). So for instance, to get the average age by sex -(function `mean`), one could use: +strata (`summary()`). So for instance, to get the average age by sex +(function `mean()`), one could use: ``` r -tapply(stegen$age, stegen$sex, mean, na.rm = TRUE) +tapply(stegen$age, INDEX = stegen$sex, FUN = mean, na.rm = TRUE) ## male female ## 26.37778 26.92568 ``` @@ -487,7 +495,7 @@ as extra arguments; here, `na.rm = TRUE` means “ignore missing data”. The summaries above may be useful for reporting purposes, but graphics are usually better for getting a feel for the data. Here, we illustrate -how the package `ggplot2` can be used to derive informative graphics of +how the package *ggplot2* can be used to derive informative graphics of age/sex distribution. It implements an alternative graphics system to the basic **R** plots, in which the plot is built by adding (literally using `+`) different layers of information, using: @@ -519,39 +527,39 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) Here, the age distribution is pretty much identical between male and female. -**Going further** click on the button below to learn about customising -`ggplot2` graphics: -
      - More on ggplot2 + Going further: click here to learn about customising +ggplot2 graphics: -`ggplot2` graphics are highly customisable, and a lot of help and +*ggplot2* graphics are highly customisable, and a lot of help and examples can be found online. The [official website](https://ggplot2.tidyverse.org/) is a good place to start. For instance, here we: + - define the width of each bin to be 1 year (`binwidth = 1` to avoid + the warning from above) - add white borders to the plot (`color = "white"`) - specify colors manually for male / female, using specified color codes (see [html color picker](https://www.w3schools.com/colors/colors_picker.asp) to define your own colors) (`scale_fill_manual(...)`) - - adding labels for title, *x* and *y* axes (`labs()`) + - add labels for title, *x* and *y* axes (`labs()`) - specify a lighter general color theme, using Times font of a larger size by default (`theme_light(...)`) - - move the legend inside the plot -(`theme(...)`) + - move the legend inside the plot (`theme(...)`) - +Note, it’s best practice to include only one *ggplot2* command per line +if you are using more than two. ``` r -ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + +ggplot(stegen) + + geom_histogram(aes(x = age, fill = sex), binwidth = 1, color = "white") + scale_fill_manual(values = c(male = "#4775d1", female = "#cc6699")) + labs(title = "Age distribution by gender", x = "Age (years)", y = "Number of cases") + theme_light(base_family = "Times", base_size = 16) + theme(legend.position = c(0.8, 0.8)) -## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` ![](practical-stegen_files/figure-gfm/stegen_ggplot2_custom-1.png) @@ -560,10 +568,10 @@ ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), color = "white") + ## Epidemic curve -Incidence curves can be built using the package `incidence`, which will +Incidence curves can be built using the package *incidence*, which will compute the number of new cases given a vector of dates (here, onset) -and a time interval (1 day by default). We use the function `incidence` -to achieve this, and then visualise the results: +and a time interval (1 day by default). We use the function +`incidence()` to achieve this, and then visualise the results: ``` r i <- incidence(stegen$date_onset) @@ -648,32 +656,32 @@ The outbreak really only happened over 3 days: onsets reported after did not match the epi case definition. This is compatible with a food-borne outbreak with limited or no person-to-person transmission. -Note that the plots produced by `incidence` are `ggplot2` objects, so +Note that the plots produced by *incidence* are *ggplot2* objects, so that the options seen before can be used for further customisation (see below). -**Going further** click on the button below to learn about `incidence`: -
      - More on incidence + Going further click here to learn about the +incidence package: -More information on the `incidence` package can be found from the +More information on the *incidence* package can be found from the [dedicated website](https://www.repidemicsconsortium.org/incidence/). Here, we illustrate how incidence can be stratified e.g. by case definition: -We can also customise this graphic like other `ggplot2` plots (see [this +We can also customise this graphic like other *ggplot2* plots (see [this tutorial](https://www.repidemicsconsortium.org/incidence/articles/customize_plot.html) for more): ``` r plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#990033")) + - labs(title = "Epicurve by gender", x = "Date of onset", y = "Number of cases") + - theme_light(base_family = "Times", base_size = 16) + geom_hline(yintercept = 1:55, color = "white") + - theme(legend.position = c(0.8, 0.8), axis.text.x = element_text(angle = 45, hjust = 1, vjust = 0.5)) + labs(title = "Epicurve by case", x = "Date of onset", y = "Number of cases") + + theme_light(base_family = "Times", base_size = 16) + + theme(legend.position = c(0.8, 0.8)) + + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) ``` ![](practical-stegen_files/figure-gfm/incidence_stratified_custom-1.png) @@ -685,12 +693,15 @@ plot(i_ill, border = "white", color = c("non case" = "#66cc99", "case" = "#99003 We use the same principles as before to visualise the distribution of illness by age and gender. To split the plot into different panels according to `sex`, we use `facet_grid()` (see previous extra info on -`ggplot2` customisation for further details otions): +*ggplot2* customisation for further details otions): ``` r -ggplot(stegen) + geom_histogram(aes(x = age, fill = ill)) + +ggplot(stegen) + + geom_histogram(aes(x = age, fill = ill)) + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + - facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() + facet_grid(sex ~ .) + + labs(title = "Cases by age and gender") + + theme_light() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` @@ -720,12 +731,12 @@ variable, only approaches of type 2 and 3 will be illustrated here. ### Is illness linked to age? -We can use the function `t.test` to test if the average age is different -across illness status. As this test assumes that the two categories -exhibit similar variation, we first ensure that the variances are -comparable using Bartlett’s test. The syntax`variable ~ group` is used -to indicate the variable of interest (left hand-side), and the group -(right hand-side): +We can use the function `t.test()` to test if the average age is +different across illness status. As this test assumes that the two +categories exhibit similar variation, we first ensure that the variances +are comparable using Bartlett’s test. The syntax `variable ~ group` is +used to indicate the variable of interest (left hand-side), and the +group (right hand-side): ``` r bartlett.test(stegen$age ~ stegen$ill) @@ -817,7 +828,7 @@ will illustrate one of the most common workflows: 2) run a Chi-square test on this table (using `chisq.test`) All of these have been seen so far; the only missing piece is step 2), -which we will cover using a very handy function called `lapply`. +which we will cover using a very handy function called `lapply()`. ### Isolating the variables to test @@ -865,11 +876,11 @@ food There are many variables to test, and having to enter separate command lines for each would be cumbersome and prone to errors. As a workaround, -the function `lapply` can be used. This function allows to repeat an +the function `lapply()` can be used. This function allows to repeat an operation using a vector of inputs, using each element in turn. Its general syntax is `lapply(vector_of_inputs, function_to_use, other_arguments)`, where `other_arguments` can be any secondary -arguments taken by `function_to_use`. Here, we use it to build a +arguments taken by `function_to_use()`. Here, we use it to build a separate contingency table of each variable in `food` crossed with illness status (`stegen$ill`); note that we show only the first few tables: @@ -917,7 +928,7 @@ head(food_tables) ### Realising multiple Chi-squared tests The same principle used to compute several contingency tables can be -used to compute Chi-square tests for each table, using `lapply`; note +used to compute Chi-square tests for each table, using `lapply()`; note that we show only the first few tests: ``` r @@ -992,8 +1003,8 @@ food_tables$pork ## 1 72 48 ``` -This can be turned into proportions using `prop.table`, specifying that -we want proportions by rows (`margin = 1`): +This can be turned into proportions using `prop.table()`, specifying +that we want proportions by rows (`margin = 1`): ``` r pork_prop <- prop.table(food_tables$pork, margin = 1) @@ -1030,15 +1041,15 @@ In the example above, we have computed the risk ratio using a simple recipe: 1. select a contingency table -2. convert raw numbers to proportions (using `prop.table`) +2. convert raw numbers to proportions (using `prop.table()`) 3. isolating the risks in exposed and non-exposed groups, and compute their ratio Repeating these tasks manually for all variables in `food` would be -cumbersome. As an alternative, we can define the steps above as -`function`, i.e. a generic recipe which we can then apply to any -contingency table. This new function will be called `risk_ratio`, and is -defined as +cumbersome. As an alternative, we can define the steps above as a +function, i.e. a generic recipe which we can then apply to any +contingency table. This new function will be called `risk_ratio()`, and +is defined as follows: ``` r @@ -1068,7 +1079,7 @@ risk_ratio(food_tables$pork) ``` And we can now apply this function sequentially to all variables in -`food` as before, using `lapply` (we show only the first few values): +`food` as before, using `lapply()` (we show only the first few values): ``` r all_rr <- lapply(food_tables, risk_ratio) @@ -1092,9 +1103,27 @@ all_rr <- lapply(food_tables, risk_ratio) ## [1] 2.08206 ``` -Finally, we re-shape these results into a numeric `vector` (using -`unlist)` for further plotting; note that for this graph, the basic -system is easier to use than `ggplot2`: +Finally, we re-shape these results into a sorted data frame for further +plotting with *ggplot2*. Reshaping will take three steps: + +1. convert from a list to a vector using `unlist()` +2. sort the vector using `sort()` +3. convert the named vector to a data frame using `stack()` (from the + *utils* package) + +
      + +Going Further click here to learn more about +stack() + +The first two functions, `unlist()` and `sort()` have self-explanitory +names, but `stack()` may not be so clear. This function takes the +elements in a vector and **stacks** them together to make a data frame +with two columns: one column called “values” containing the data in the +vector, and the other column called “ind” containing the names of those +elements as a factor. + +
      ``` r ## convert results from list to numeric vector @@ -1110,13 +1139,31 @@ rr_data ## sort results by decreasing order rr_data <- sort(rr_data, decreasing = TRUE) -## plot using the basic system -par(mar = c(5, 7, 4, 1)) # change margins -barplot(rr_data, horiz = TRUE, las = 1, - xlab = "Risk ratio", - col = heat.colors(15), # add colors - main = "Risk ratio by food exposure") -abline(v = 1, lty = 2) # add vertical line x = 1 +## create a data frame from the named vector +rr_df <- stack(rr_data) +rr_df +## values ind +## 1 18.3116883 tiramisu +## 2 4.9689579 mousse +## 3 4.5010211 dmousse +## 4 2.8472222 wmousse +## 5 2.5006177 fruit_salad +## 6 2.0820602 redjelly +## 7 1.2898653 tomato +## 8 1.2557870 horseradish +## 9 1.2518519 pork +## 10 1.1617347 chickenwin +## 11 1.0568237 mince +## 12 1.0334249 salmon +## 13 0.7607985 roastbeef +## 14 0.6767842 beer + +ggplot(rr_df, aes(x = values, y = ind)) + + geom_point() + + geom_vline(xintercept = 1, lty = 2) + + geom_segment(aes(xend = 1, yend = ind)) + + theme_light(base_size = 16, base_family = "Times") + + labs(title = "Risk Ratio by Food Exposure", x = "Risk Ratio", y = "Food Item") ``` ![](practical-stegen_files/figure-gfm/stegen_rr_plot-1.png) @@ -1128,7 +1175,7 @@ risk factor in this outbreak. This case study illustrated how **R** can be used to import data, clean them, and derive basic summaries for a first glance at the data. It also -showed how to generate graphics using `ggplot2`, and how to detect +showed how to generate graphics using *ggplot2*, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case @@ -1157,9 +1204,9 @@ Berlin, 1998. Zhian N. Kamvar, Thibaut Jombart Contributions are welcome via [pull -requests](https://github.com/reconhub/learn/pulls). The source file can -be found -[here](https://github.com/reconhub/learn/blob/master/static/slides/outbreaker2/outbreaker2.Rmd). +requests](https://github.com/reconhub/learn/pulls). 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z^Nd7kqk_Dh(;9RiAPe*4%!ZV}TUd}HD2QaUfN(ZTEr-Hr%san>{EgHr4|H(|4Q9r> zL?=>xK3)>^_2%INw&{>k(CEM@nzT?z5&-GjrlYj>tfS+!l*ieQ-Ul{{sg` z2ty+yi}!Z!_mDQKi<5@e#_XinS?e-O+@_?3re%u(6($Oa`W9!3oY&Y3ksCJ51$WK$ z)NjGEcrNW6$B3s^M@Ft#P9bs{pLPffV*DE4YV_)nY|C1u(xJ@QzJ1KVX`6%i3=RyS zLC0(A!EXFlpfh_d4NC5#VaWFB5A6PpQIu%Is|)h51f6nd`>5?>(VX7uMJ7nffUR-z#F^S?kY~e zdzSf=*WZr!F($Rd!O{4Uf3h>x6pfB;YDWw7GuSR{$^#7kdnv%BUgi%hc!3)B@qVnC8DGAFg{TU zF0$nkeM^drDy9}sI9+ad zPJuQMqqtTp2LB-e@qTVx)(xZwpf3%_@5MOvsp@KH`{@QU!`vT(3E Date: Wed, 31 Oct 2018 12:24:53 +0000 Subject: [PATCH 35/59] add link to source data file --- content/post/practical-stegen.Rmd | 16 ++++++++-------- content/post/practical-stegen.md | 7 ++++--- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 045c647..d7ccd7b 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -140,17 +140,17 @@ implement functions with identical names. ## Importing data from Excel -Linelist data can be read from various formats, including flat text files -(e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or Excel -spreadsheets. We illustrate the latter, which is probably the most common -format. We assume that the data file `stegen_raw.xlsx` has been saved in a -`data/` folder of your project, and that your current R session is at the root -of the project. +Linelist data can be read from various formats, including flat text files (e.g. +`.txt`, `.csv`), other statistical software (e.g. STATA) or Excel spreadsheets. +We illustrate the latter, which is probably the most common format. We assume +that [the data file `stegen_raw.xlsx`](../../data/stegen_raw.xlsx) has been +saved in a `data/` folder of your project, and that your current R session is +at the root of the project. Here we decompose the steps to read data in: finding the path to the data -(`path_to_data`), using the function `read_xlsx()` from the *readxl* package to read data in, and saving the -output in a new object `stegen`: +(`path_to_data`), using the function `read_xlsx()` from the *readxl* package to +read data in, and saving the output in a new object `stegen`: ```{r eval = FALSE} path_to_data <- here("data", "stegen_raw.xlsx") diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index f4c6e1d..2ba2b00 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -131,9 +131,10 @@ do not implement functions with identical names. Linelist data can be read from various formats, including flat text files (e.g. `.txt`, `.csv`), other statistical software (e.g. STATA) or Excel spreadsheets. We illustrate the latter, which is probably the most -common format. We assume that the data file `stegen_raw.xlsx` has been -saved in a `data/` folder of your project, and that your current R -session is at the root of the project. +common format. We assume that [the data file +`stegen_raw.xlsx`](../../data/stegen_raw.xlsx) has been saved in a +`data/` folder of your project, and that your current R session is at +the root of the project. Here we decompose the steps to read data in: finding the path to the data (`path_to_data`), using the function `read_xlsx()` from the From 8a3973d405f9e1fee65ab1d693f23b3c5aa6fe49 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Wed, 31 Oct 2018 14:02:56 +0000 Subject: [PATCH 36/59] add small data writing section --- content/post/practical-stegen.Rmd | 23 ++++++++++++++++++++++- content/post/practical-stegen.md | 23 ++++++++++++++++++++++- 2 files changed, 44 insertions(+), 2 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index d7ccd7b..1661ce0 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -299,7 +299,7 @@ stegen$horseradish[stegen$horseradish == 9] <- NA

      - Going furhter Why does this work? Click here for more details. + Going further Why does this work? Click here for more details. There are several things going on in a command like: @@ -335,6 +335,27 @@ toy_vector # check outcome
      +## Saving clean data + +Now that we've cleaned our data, we will probably want to re-use it in further +analyses, but we probably don't want to go through the steps of cleaning it +again. Here, we will save our cleaned data into a new folder under `data/` +called `cleaned/`. We will use **R**'s function `dir.create()` to create this +folder under our data folder: + +```{r create-clean-dir, eval = FALSE} +clean_dir <- here("data", "cleaned") +dir.create(clean_dir) +``` + +After this, we can save our cleaned data into a new file called +`stegen_clean.csv`: + +```{r save-clean-data, eval = FALSE} +stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") +write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") +``` + # Data exploration diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 2ba2b00..2a3564e 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -394,7 +394,7 @@ stegen$horseradish[stegen$horseradish == 9] <- NA
      - Going furhter Why does this work? Click here for more + Going further Why does this work? Click here for more details. There are several things going on in a command like: @@ -437,6 +437,27 @@ toy_vector # check outcome
      +## Saving clean data + +Now that we’ve cleaned our data, we will probably want to re-use it in +further analyses, but we probably don’t want to go through the steps of +cleaning it again. Here, we will save our cleaned data into a new folder +under `data/` called `cleaned/`. We will use **R**’s function +`dir.create()` to create this folder under our data folder: + +``` r +clean_dir <- here("data", "cleaned") +dir.create(clean_dir) +``` + +After this, we can save our cleaned data into a new file called +`stegen_clean.csv`: + +``` r +stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") +write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") +``` + # Data exploration ## Summaries of age and sex distribution From e76fa94e0b04b3019079365baa47892fb2cca393 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Wed, 31 Oct 2018 16:11:15 +0000 Subject: [PATCH 37/59] removed figs that don't matter --- .../post/figure-gfm/incidence_stratified-1.png | Bin 22918 -> 0 bytes .../incidence_stratified_custom-1.png | Bin 47315 -> 0 bytes .../post/figure-gfm/stegen_case_age_sex-1.png | Bin 27500 -> 0 bytes .../post/figure-gfm/stegen_ggplot2_custom-1.png | Bin 32944 -> 0 bytes static/post/figure-gfm/stegen_histogram-1.png | Bin 17021 -> 0 bytes .../post/figure-gfm/stegen_histogram_sex-1.png | Bin 19313 -> 0 bytes static/post/figure-gfm/stegen_incidence-1.png | Bin 20016 -> 0 bytes static/post/figure-gfm/stegen_rr_plot-1.png | Bin 35651 -> 0 bytes 8 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 static/post/figure-gfm/incidence_stratified-1.png delete mode 100644 static/post/figure-gfm/incidence_stratified_custom-1.png delete mode 100644 static/post/figure-gfm/stegen_case_age_sex-1.png delete mode 100644 static/post/figure-gfm/stegen_ggplot2_custom-1.png delete mode 100644 static/post/figure-gfm/stegen_histogram-1.png delete mode 100644 static/post/figure-gfm/stegen_histogram_sex-1.png delete mode 100644 static/post/figure-gfm/stegen_incidence-1.png delete mode 100644 static/post/figure-gfm/stegen_rr_plot-1.png diff --git a/static/post/figure-gfm/incidence_stratified-1.png b/static/post/figure-gfm/incidence_stratified-1.png deleted file mode 100644 index 4b39ef08b1c3fe9ced786241634f00914315f49b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 22918 zcmeIacT`i`+b$YVL_xrUvOy^dC`CZ2(m_Ai!5rXtdN zAaqcALMNe@GqZnN@Voc?bMF1dy=R=U_aBZu7AtdQ%{kxqeV*riX5OeO%TkchkwG93 z3b{x3)gch#BnX6P;`AwS#qIq#KLm1;)IGv5BmSjfuU5vAXO% z2;^>1kQUVZsus0Gm4g{GONWLv|0{T=Bn2x)gf@S2Dbjm%RL~|`PH^b@-1Cj4jt|Wp zcloOJlT{w5dfdUpQ-80k@L^)ZQw>X$@xkt9_oYg+Wsvz6?yFIhCxVM$ZY&QY02sA$Km* zAD8*&ZZz08dXSh`@sR!!(+kFDB{P}iuS1AdGPGKqh}?F<7$q@!3>(kpD(*;q=W+Y| 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content/post/practical-stegen.Rmd | 62 +++++++++------- content/post/practical-stegen.md | 99 ++++++++++++++++++-------- static/img/screenshots/map_stegen.png | Bin 0 -> 945831 bytes 3 files changed, 105 insertions(+), 56 deletions(-) create mode 100644 static/img/screenshots/map_stegen.png diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 1661ce0..a2d930f 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -23,34 +23,35 @@ expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students, and some graduates from a nearby school (school B). -A self-service party buffet was supplied by a commercial caterer in + +A self-service party buffet was supplied by a commercial caterer from a the nearby city of Freiburg. Food was prepared on the day of the party and transported in a -refrigerated van to the school. +refrigerated van to the school. Relative locations of the two schools and Freiburg can be seen on image 1 + +![Image 1: Map of Stegen, with annoted school A and B](../../img/screenshots/map_stegen.png) + + + Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet -extended late during the night and alcoholic beverages were quite popular. All +extended late into the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. - - ## The alert On 2nd July 1998, the Freiburg local health office reported to the Robert Koch -Institute (RKI) in Berlin the occurrence of many cases of gastroenteritis +Institute (RKI) in Berlin a significant increase of cases of gastroenteritis in the munipality of Stegen following the graduation party described above. More than 100 cases were -suspected among participants and some of them were admitted to nearby -hospitals. Sick people suffered from fever, nausea, diarrhoea and vomiting -lasting for several days. Most believed that the tiramisu consumed at dinner was -responsible for their illness. *Salmonella enteritidis* was isolated from 19 -stool samples. - -The Freiburg health office sent a team to investigate the kitchen of the -caterer. Food preparation procedures were reviewed. Food samples, except -tiramisu (none was left over), were sent to the laboratory of Freiburg -University. Microbiological analyses were performed on samples of the following: +suspected among participants and some of them were so ill that they were admitted to nearby +hospitals. Sick people showed symptoms like; *fever, nausea, diarrhoea and vomiting* +lasting for several days. Stool samples were collected from several ill cases and send to the laboratory in Freiburg for microbiological identification. *Salmonella enteritidis* was identified in 19 stool samples. + +In response to the large number of cases associated with the dinner the Freiburg health office sent an outbreak control team to investigate the kitchen of the caterer in Freiburg. Food preparation procedures were reviewed. Samples were taken of remaining food items after the graduation ceremony and were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the following food items: brown chocolate mousse, caramel cream, remoulade sauce, yoghurt dill sauce, and -10 raw eggs. +10 raw eggs. Unfortunately no tiramisu was left over after the dinner so no samples were tested of this specific food item. The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify potential vehicle(s) and risk @@ -75,15 +76,16 @@ analysed in this case study was built from these 291 responses. ## This case study -In this case study, we will take you through the analysis of this epidemic. This +In this case study, we will take you through the analysis of this epidemic using R. This will be the occasion to illustrate more generally useful practices for data analysis using **R**, including: -- how to read data from Excel +- how to read and import data from Excel - how to explore data using tables and summaries - how to clean data - how to make graphics to describe the data - how to test if specific food items are linked to the disease +- how to plot a very basic spatial overview of cases @@ -208,7 +210,7 @@ summary(stegen) ``` Note that binary variables, when treated as numeric values (0/1), are summarised -at such, which may not always be useful. As an alternative, `table()` can be used +as such, which may not always be useful. As an alternative, `table()` can be used to list all possible values of a variable, and count how many time each value appears in the data. For instance, we can compare the `summary()` and `table()` for consumption of `tiramisu`: @@ -221,7 +223,7 @@ table(stegen$tiramisu) # table **Good news**: the dataset has the expected dimensions, and all the relevant -variables seem to be present. There are, however, **a few issues**: +variables seem to be present. There are, however, **a few often observed issues**: 1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation @@ -362,7 +364,7 @@ write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") ## Summaries of age and sex distribution -We can have a brief look at age and sex distributions using some basic +It is good practice to first start to explore and familiarise yourself with a dataset before heading into the analyses of your data. We can have a brief look at age and sex distributions using some basic summaries; for instance: ```{r stegen_age_sex} @@ -809,15 +811,25 @@ ggplot(rr_df, aes(x = values, y = ind)) + The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. +# - Plotting a very basic spatial overview of cases +To conclude your report you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals that had symptoms gps coordinates were generated providing each individual with a latitude and longtitude which corresponds with their household. The variables *lat* and *long* include the longitude and latitude which we will use to plot the cases using *ggplot2*. + +
      +Advanced mapping and spatial methodologies? + +Normally you would generate maps in R using more sufisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies + +
      + # Conclusion -This case study illustrated how **R** can be used to import data, clean them, +This case study illustrated how **R** can be used to import data, clean data, and derive basic summaries for a first glance at the data. It also showed how to generate graphics using *ggplot2*, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic -regression in epidemic studies. +regression in epidemic studies. Lastly we plotted a basic overview of the cases in this outbreak in relative distance to each other. More suffisticated mapping and spatial mathodologies will be covered in other case studies. @@ -833,7 +845,7 @@ regression in epidemic studies. ## Source This case study was first designed by Alain Moren and Gilles Desve, EPIET. It is -based on an investigation conducted by Anja Hauri, RKI, Berlin, 1998. +based on an real outbreak investigation conducted by Anja Hauri, RKI, Berlin, 1998. ## Contributors diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 2a3564e..5d045e1 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -19,32 +19,46 @@ were expected. Attendants included graduates from that school, their families and friends, teachers, 12th grade students, and some graduates from a nearby school (school B). -A self-service party buffet was supplied by a commercial caterer in -Freiburg. Food was prepared on the day of the party and transported in a -refrigerated van to the school. +A self-service party buffet was supplied by a commercial caterer from a +the nearby city of Freiburg. Food was prepared on the day of the party +and transported in a refrigerated van to the school. Relative locations +of the two schools and Freiburg can be seen on image 1 + +![Image 1: Map of Stegen, with annoted school A and +B](../../img/screenshots/map_stegen.png) + + Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the -buffet extended late during the night and alcoholic beverages were quite +buffet extended late into the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. ## The alert On 2nd July 1998, the Freiburg local health office reported to the -Robert Koch Institute (RKI) in Berlin the occurrence of many cases of -gastroenteritis following the graduation party described above. More -than 100 cases were suspected among participants and some of them were -admitted to nearby hospitals. Sick people suffered from fever, nausea, -diarrhoea and vomiting lasting for several days. Most believed that the -tiramisu consumed at dinner was responsible for their illness. -*Salmonella enteritidis* was isolated from 19 stool samples. - -The Freiburg health office sent a team to investigate the kitchen of the -caterer. Food preparation procedures were reviewed. Food samples, except -tiramisu (none was left over), were sent to the laboratory of Freiburg +Robert Koch Institute (RKI) in Berlin a significant increase of cases of +gastroenteritis in the munipality of Stegen following the graduation +party described above. More than 100 cases were suspected among +participants and some of them were so ill that they were admitted to +nearby hospitals. Sick people showed symptoms like; *fever, nausea, +diarrhoea and vomiting* lasting for several days. Stool samples were +collected from several ill cases and send to the laboratory in Freiburg +for microbiological identification. *Salmonella enteritidis* was +identified in 19 stool samples. + +In response to the large number of cases associated with the dinner the +Freiburg health office sent an outbreak control team to investigate the +kitchen of the caterer in Freiburg. Food preparation procedures were +reviewed. Samples were taken of remaining food items after the +graduation ceremony and were sent to the laboratory of Freiburg University. Microbiological analyses were performed on samples of the -following: brown chocolate mousse, caramel cream, remoulade sauce, -yoghurt dill sauce, and 10 raw eggs. +following food items: brown chocolate mousse, caramel cream, remoulade +sauce, yoghurt dill sauce, and 10 raw eggs. Unfortunately no tiramisu +was left over after the dinner so no samples were tested of this +specific food item. The Freiburg health office requested help from the RKI in the investigation to assess the magnitude of the outbreak and identify @@ -57,7 +71,7 @@ better control the outbreak. the party at St Sebastian High School and who suffered from *diarrhoea* (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998, or from at least three of the following symptoms: *vomiting*, *fever over -38.5° C*, *nausea*, *abdominal pain*, *headache*. +38.5° C*, *nausea*, *abdominal pain*, *headache*. Students from both schools attending the party were asked through phone interviews to provide names of persons who attended the party. @@ -68,14 +82,15 @@ linelist analysed in this case study was built from these 291 responses. ## This case study In this case study, we will take you through the analysis of this -epidemic. This will be the occasion to illustrate more generally useful -practices for data analysis using **R**, including: +epidemic using R. This will be the occasion to illustrate more generally +useful practices for data analysis using **R**, including: - - how to read data from Excel + - how to read and import data from Excel - how to explore data using tables and summaries - how to clean data - how to make graphics to describe the data - how to test if specific food items are linked to the disease + - how to plot a very basic spatial overview of cases # Initial data processing @@ -269,7 +284,7 @@ summary(stegen) ``` Note that binary variables, when treated as numeric values (0/1), are -summarised at such, which may not always be useful. As an alternative, +summarised as such, which may not always be useful. As an alternative, `table()` can be used to list all possible values of a variable, and count how many time each value appears in the data. For instance, we can compare the `summary()` and `table()` for consumption of `tiramisu`: @@ -299,8 +314,8 @@ table(stegen$tiramisu) # table ``` **Good news**: the dataset has the expected dimensions, and all the -relevant variables seem to be present. There are, however, **a few -issues**: +relevant variables seem to be present. There are, however, **a few often +observed issues**: 1. variable names are a bit messy, and include different separators, spaces, and irregular capitalisation @@ -462,7 +477,9 @@ write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") ## Summaries of age and sex distribution -We can have a brief look at age and sex distributions using some basic +It is good practice to first start to explore and familiarise yourself +with a dataset before heading into the analyses of your data. We can +have a brief look at age and sex distributions using some basic summaries; for instance: ``` r @@ -597,7 +614,6 @@ and a time interval (1 day by default). We use the function ``` r i <- incidence(stegen$date_onset) -## 160 missing observations were removed. i ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -641,7 +657,6 @@ definition will clarify the situation: ``` r i_ill <- incidence(stegen$date_onset, group = stegen$ill) -## 160 missing observations were removed. i_ill ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -1193,16 +1208,38 @@ ggplot(rr_df, aes(x = values, y = ind)) + The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. +# \- Plotting a very basic spatial overview of cases + +To conclude your report you would like to include a very basic spatial +overview of cases. Based on the postal codes of all individuals that had +symptoms gps coordinates were generated providing each individual with a +latitude and longtitude which corresponds with their household. The +variables *lat* and *long* include the longitude and latitude which we +will use to plot the cases using *ggplot2*. + +
      + # Conclusion This case study illustrated how **R** can be used to import data, clean -them, and derive basic summaries for a first glance at the data. It also +data, and derive basic summaries for a first glance at the data. It also showed how to generate graphics using *ggplot2*, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic regression in epidemic -studies. +studies. Lastly we plotted a basic overview of the cases in this +outbreak in relative distance to each other. More suffisticated mapping +and spatial mathodologies will be covered in other case studies.


      @@ -1211,8 +1248,8 @@ studies. ## Source This case study was first designed by Alain Moren and Gilles Desve, -EPIET. It is based on an investigation conducted by Anja Hauri, RKI, -Berlin, 1998. +EPIET. It is based on an real outbreak investigation conducted by Anja +Hauri, RKI, Berlin, 1998. ## Contributors diff --git a/static/img/screenshots/map_stegen.png b/static/img/screenshots/map_stegen.png new file mode 100644 index 0000000000000000000000000000000000000000..f3812a0aa9cb35fa9c7e136f75eaf4396b12e177 GIT binary patch literal 945831 zcmeGEXH?T!`vnZgQBg+$M^w6q2#6pEgc3jykq$wMv=E|%7D9*6iw+7(2uOzzL^?>5 zE*%F6y#^BLDm@X9P7(r7oVm;YUhn(y`S_f*Sdf#oe&k%|oU81;ue^Q))@J>k=XU@A zz^beB&;$UuAOHZImO1+ybH-=-_7d~gDJOM9bpW6;?)>4?)6Cb`;W{RU06>rk0PySu z06=3-JzD_)P2x5|8R&Qr(A2LMoJ|Mhd? z+NOpRb5hV()6!SN+u^A%!o&T#8N$s0Aahq*QAt`_>8|v3X?dl)a!Rs5Td|EH0N~g_ z_o2F3fb9>)svmDp%Gcn@g>^tNXh~68d`Z@RX$_JNYYd(_8f{r`ESW2Q`+h^*_pR+? zE7VrZAK7O+RgfYlK28k>XGB~UPPn8Se<|wNNM!ii&vVzmu|&AdIqHkCFh9?KN1az+ z)_-3y|N4172mL=2KVnvH{Lh54P~+MEJJCM)O7fQ_|1&?w{MgFvw`(iAW=fTJratWp&w2bmg$NuiX1$BrTPMgmJPIF9%Pks z#rAN?iPOsr3FTt%HG9Tx{auoaEK&dbPs< z2N}7+MMcGpg-s9D<6yN5jLDXs-p>z%cu#x{n1nT^H{00q+CfN3vTeyr5HRDS!wsOV zBVMqq4$R{P7k-kdK)f)acRHcef6y^K7o#d(8-wJTzjmJ=)SZ z-grGzd`>J%L90_j!8AOE03r&YxLcs+PK-%t@%JTU^+bqdG8C+9{=u(Q9+Q~+et$Ps zMH#VxSA<$wZJ6q=X2yk+{4m!(s&vWZwH|*bgj)v6MXr>dN8Jg;_|7caKAwpow(ib8 z4Wh6gH{ycq=ydph&#LDQgg}Kf5n;3uky*=QRP$qbdS%C~A?b3nzsL&D{Cj*JmnnFX z+r_OXYVOMK07di2&Kz5AyQw+^_AAg6FOPviAgR6fN{A`>&x$+f+2E$es!>$tzAZc= z1|d4LEv0%q2=Rc0?ze!=Js=w$LJ!dP+8O!g_e@+)3nvq_#6Y^cX6PSw`f@kp^vZS~ z#iyGD-YHR?!ImcHUrTIs4GsMm?7_feAZj7U9lKJkv>%DcaK^T1_z{79F8sJJPHnC= z*hw{OImtf%YbHC7M6FPu1HSX&PxDykQb|ps5Z|3;oXx`Dx6_m7P19Mxv&28AHJPWv z0n;P4{DWutNAnAlm<{uS64T&(yiu z+ph5qR0sRF(YLT(^1EpH7+wH?PlK+n0ACv%5t3di*u?8{7)qq}6x3wvO0$ZAHXfXn z@wr9_k>7Vy&NLW;HU}L2MJ>-zYP(=Oem_f_D|N!%r%YSoo%Z9MvzlPx#iSfOJ3`U5 z!G~_nD%LAksaYtB3FXpu_P*a?D5k~jYLSnY`WzJCA!qNhjxHi2AYQEbf8 zkLds;Zo|9@QO^yvHwAaNR$8Vhav?KH&v8|GcT%=}d@w^lXuM{F-s&?a`e4#O+iHx;brU-wR~LAHv< z%!8VQ&73Fwye-&mHDY$2dvRy5%-ajl4i}0)ZoYQEmRc%Qn=NAeen^>fZiUitudzr` z0PZ_;GkIsWt1VU7A+~hUwUOG#-gb0QEm;w)fLQ|G=9ThcNV{u!EEKH^Y(EwJ$CY3E zAH#eiw@dUW75j14of~Xer52pa-;1HitmLA1JMG-JuCvojO3g;Wxo8$r^A@>}Q&P>T ztiOCjQ#h2dm`bS+W6Q3rYIe2TKj97l42>NP4z)gPfJVj;m2xED5xAxHJ|451wJ+Qs zN4V)pq2tu$tK8=;EX*rxT4k)D(ze@N#XO&d&bvvN=bG*~!dt-}3w*cba^KXQces&a zLfn`gz&_KNj{=@E2J`UTEb09&lV2%m4O^A&g=S>jiW^t7qH6MDhM@)gxCIHMUOQ8l2BP6SF1%rlsqh*#hp z0axv874ne1Bs-#pM}iP99$}6~+HV#AbcxM1^QH3paI$J_h>CMc*#@kSlYC>Z zJ*pHAqppf}mcV8}#nahB$4-=K{M~}O};a~?G zZZ~sNm3yk|lBu++f%)Le^PE}1YZ!&u(BoeHwsULtHtOQ%1!^Ra(K#bZ(gm%@i}Z>x z+IopV8=aP#)!MQ-zpoH}w23#GJEqOEcesY8RV;_O+imE(xVbkYcnNb&yiQ1&x#Vo= zV!4LhhD19 zD|^JmTb5JY{2XQ}v7u@^Ed|mE{4PhitU)2dY)T7QNl|^$3U>uH9T(dvFLLYsrkaf4 z`R>xjzRZQvDA8Sq6?>!%ycDPj)vv!Eht=*B%>?}4H5Z!K5iKpEF(6LJ14002&Ad;H zNUv~#AZv?H0V~m}B(oLO`Sevq>t3TlQbOsrFlDVC7Je_&S3@Aw+hC z033BoU+FUTJa{RO=SHrv%&eo;yR@Rt&Kz}F>C|1#td?+UEoqLiwqYBxhH-arm`;m* zCgPB2rsS=#Jdw$asJn|6S>b#0{&TzAR#O23`u2LuWz)1v`K|GgbUzR=VWB+f$(ALM z&nfpyLZ+O0bSl5=g24N2g}x`X4jiGotx3Ak^}h3u z=3S7DH^gKF{ZaKz@u?*R5-x?}lHG&R+v>R)OVyGu6hD_E7vH+doeE@8=JFB0v%zNp z3G4dLu>gRDA;5}13tqm-%2?&l8_XYbz7ZIL%chFe6n=5}Ge>WOByfWib78W}I@rXG z>rCPa0FJ8?){SVPOzM)Xoa_w5T+)o^w8a81NTX&!Fl|=2N2CfH%`@B7zOH&O5Q#Y73&cBA@yh}>Mw$r;E3TbqOcZ;FkU22jsr+!{y`V(CyS( zWd+(Uiojl;_b5Spg8b&G^~{eJm5^gEhx8@qAx+X3*hI2fq9MmM8@%LIpFlQqTO1D8 z{F#OwZ}d_5>e6q0v)YAgv{DQ6-(}(4ClSSgx5yt9=ArPiJtT3^utj;v?90lm1PB7B z17rNPg+(`9!4odc#y!&a?A~z!+BYx%In=PDkz^Xmfs0K;xD5t|y49sg**|wzYVckC z-dE9jm_eEgTHRIf&66!w##eElg=Z2k%Q>U!TKC2|vyRs)!l{cSSjbuIM)w)&GK5<3 zN!z61^Fh%HF46F9daWA?Q%fnBpc%MWLf`>N>-I#b!zJjce{TfcJzqNCZ zb(!fQumzdElE9$BigdD`EP|Q3?R@vJ4PvZu!h(@O-#~_@_93yl(MvObSf7v>IwUT^ z+K#t2jyI3#3sT`^k~>U^1hqsM{%Gl$y(uQBElLnRW5FZUw_zma4ijErBrm$}xrPv@ z91<5l_4qb6x0%tX4~SmowkOL#sFbk`_rkT9o|AD3|EG?8MsPKd&92m(Otp6EF<9%C zu!=yF5AOEG8m*}i)5UtM(bN!|zz%#))2F((ca(Uj>EmRr; zbo&^xIy$vs6FNVj01MdcT=jLhAecaYvXXmVy{mWn&3DiJy)}XGgKq-iGy#S17LXN` zxoLlnNmd6_&|%ppUpNfJr26Qh?5rYU#ItM@SMylTjnlkBRwHdYf-+^TU~cJ97&Vcm zck%yjh$D}*Ju^K7wQ!&Ob0^A=Mh;K6b~L@!83Z*WTuPV8!#3&AA5e0M6$XX#EDNor zm3(Hb08j51Ul%_MZh1?VO!%S zt9o;JFDR(30x`vmO{8)teLL&;aGV5_T0#%Ms7ofdFPPcs33F0#?5%F0&Y3L4g;Kc~ ziR=+zEncw_6nw2@j5dPs9p3^GvzMZiHW7Uk*+I1tR6&UKyWTU*3*h-3dQ1>VH3a~^ z20%XUW%|#$nGg2(icMH^I_;~r?IDmRdJX5oxN_J9mvdIAboM;Hq>3OIZxr3|ONYfsKs&%J{c?95wz-cQxH5FxDkF5+s#bc*9NdU)1AZJ`

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      + +Advanced mapping and spatial methodologies? + +Normally you would generate maps in R using more sufisticated mapping +tools, but for the scope of this case study we will keep it more basic. +We will explore mapping and spatial analytical methods this in future +case studies + +
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June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] categories: "case studies" -tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] +tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting ases"] date: 2018-10-24 slug: stegen licenses: CC-BY @@ -811,8 +811,8 @@ ggplot(rr_df, aes(x = values, y = ind)) + The results are a lot clearer now: the tiramisu is by far the largest risk factor in this outbreak. -# - Plotting a very basic spatial overview of cases -To conclude your report you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals that had symptoms gps coordinates were generated providing each individual with a latitude and longtitude which corresponds with their household. The variables *lat* and *long* include the longitude and latitude which we will use to plot the cases using *ggplot2*. +# Plotting a very basic spatial overview of cases +To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables *lat* and *long* include the longitude and latitude which we will use to plot the cases using *ggplot2*.

      Advanced mapping and spatial methodologies? From 45d1997f662815b2e00a49ff19f7250cce24a4a6 Mon Sep 17 00:00:00 2001 From: Amrish Baidjoe <32675775+AmmerB@users.noreply.github.com> Date: Thu, 1 Nov 2018 18:09:51 +0000 Subject: [PATCH 40/59] added tag "plotting cases" --- content/post/practical-stegen.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index df3c67a..b6ad01a 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -3,7 +3,7 @@ title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] categories: "case studies" -tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting ases"] +tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting cases"] date: 2018-10-24 slug: stegen licenses: CC-BY From be51a9dfd75e1367b1333b5546d9f69fe57d4b61 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 2 Nov 2018 17:17:48 +0000 Subject: [PATCH 41/59] modified stat section to show confidence interval This still needs to be cleaned up and an explanation of lapply() should be added, but it appears to work for now :triumph: --- content/post/practical-stegen.Rmd | 420 +++++--- content/post/practical-stegen.md | 932 +++++++++++++----- .../figure-gfm/plot-arrange-1.png | Bin 0 -> 27969 bytes .../figure-gfm/stegen_case_age_sex-1.png | Bin 8506 -> 8187 bytes .../figure-gfm/stegen_histogram-bin-1.png | Bin 0 -> 5634 bytes .../figure-gfm/stegen_histogram_sex-1.png | Bin 6289 -> 6384 bytes 6 files changed, 931 insertions(+), 421 deletions(-) create mode 100644 static/post/practical-stegen_files/figure-gfm/plot-arrange-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen_histogram-bin-1.png diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index b6ad01a..4a0a134 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -9,6 +9,7 @@ slug: stegen licenses: CC-BY image: img/highres/graduation-1965.jpg showonlyimage: true +toc: true --- ```{r setup, include=FALSE} @@ -102,7 +103,8 @@ The following packages will be used in the case study: - *incidence*: to build epicurves - *epitrix*: to clean labels from our spreadsheet - dplyr: to help with factors -- ggplot2: to create custom visualisations +- *ggplot2*: to create custom visualisations +- *epitools*: to calculate risk ratios If we have these packages installed, we can tell R to load these packages from our R library: @@ -114,6 +116,7 @@ library("incidence") # make epicurves library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics +library("epitools") # statistics for epi data ```
      @@ -129,6 +132,7 @@ install.packages("incidence") install.packages("epitrix") install.packages("dplyr") install.packages("ggplot2") +install.packages("epitools") ```
      @@ -420,10 +424,36 @@ For instance, to get an histogram of age: ggplot(stegen) + geom_histogram(aes(x = age)) ``` -Color can be added using: +
      + +What does "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`" mean? + +This is a message that came from `geom_histogram()`. If we look at the help +documentation for `geom_histogram()`, we can see this paragraph in the **Details** +section: + +> By default, the underlying computation (‘stat_bin()’) uses 30 +> bins; this is not a good default, but the idea is to get you +> experimenting with different bin widths. You may need to look +> at a few to uncover the full story behind your data. + +Each bar in a histogram is considered a "bin". The height of each bar is the +number of observations that fall within the bounds of that bar. Because we are +looking at age, it may be better for us to define a binwidth of 1 so that each +bar represents a single year. + +```{r stegen_histogram-bin} +ggplot(stegen) + geom_histogram(aes(x = age), binwidth = 1) +``` + +
      + + +We can differentiate between genders by filling the bars with color (n.b. we are +also setting the binwidth to be 1 year): ```{r stegen_histogram_sex} -ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), binwidth = 1) ``` Here, the age distribution is pretty much identical between male and female. @@ -531,45 +561,58 @@ further details otions): ```{r stegen_case_age_sex} ggplot(stegen) + - geom_histogram(aes(x = age, fill = ill)) + + geom_histogram(aes(x = age, fill = ill), binwidth = 1) + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() ``` -Illness does not seem to be depending on age or gender. This can be tested: this -is the topic of the next and final section. - +From these graphics, we can see that illness does not appear to depend on age or +gender, which suggests that the illness was caused by one or more of the foods +consumed. We will explore this with a risk ratio analysis in the next section. +-------------------------------------------------------------------------------- # Looking for the culprits +In order to figure out which food item was responsible for the outbreak, we need +to test the [Risk Ratio](https://en.wikipedia.org/wiki/Risk_ratio) for all of +the food items recorded. Risk ratios are normally computed on *contingency* +tables (commonly known as 2x2 tables). In this part, we will compute contingency +tables for each predictor (food item) against the defined cases, calculate their +risk ratio with confidence intervals and p-values, and plot them as points and +errorbars using *ggplot2*. + +
      + + Going Further: Different types of Univariate tests + ## Univariate tests Methods for testing the association between two variables can be broken down in 3 types, depending on which types these variables are: -1. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar - methods +1. **2 categorical variables**: Chi-squared test on the 2x2 table + (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) 2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with 2 groups: Student's $t$-test -3. **2 categorical variables**: Chi-squared test on the 2x2 table - (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) +3. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar + methods We can use these approaches to test if the disease is linked to any of the other recorded variables. As illness itself is a categorical variable, only approaches -of type 2 and 3 will be illustrated here. +of type 2 and 3 are illustrated in this case study. ### Is illness linked to age? -We can use the function `t.test()` to test if the average age is different across -illness status. As this test assumes that the two categories exhibit similar -variation, we first ensure that the variances are comparable using Bartlett's -test. The syntax `variable ~ group` is used to indicate the variable of interest (left hand-side), and the group - (right hand-side): +We can use the function `t.test()` to test if the average age is different +across illness status. As this test assumes that the two categories exhibit +similar variation, we first ensure that the variances are comparable using +Bartlett's test. The syntax `variable ~ group` is used to indicate the variable +of interest (left hand-side), and the group (right hand-side): ```{r stegen_bartlett_test} bartlett.test(stegen$age ~ stegen$ill) @@ -612,206 +655,281 @@ Once a contingency table has been built, the Chi-square test can be run using `c chisq.test(tab_ill_sex) ``` -Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests illness is not related to sex either. - +Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests illness is not related to gender either. -## Making multiple tests - -The natural next step is to run multiple tests for all potential risk factors -recorded. There are many ways to go about this in **R**, but we will illustrate -one of the most common workflows: - -1. isolate a subset of variables to test (the ones indicative of food consumption) (using `[]`) -2. for each: - i) build contingency table with illness status (using `table`) - ii) run a Chi-square test on this table (using `chisq.test`) - -All of these have been seen so far; the only missing piece is step 2), which we -will cover using a very handy function called `lapply()`. +
      ### Isolating the variables to test -We use the natural structure of the dataset, as variables representing food -consumption are all the last columns: +Because not all of the columns in our data set are food items (i.e. "age", and +"tportion"), we only want to keep the columns that are food items for our +analysis. ```{r stegen_isolate_food} names(stegen) ``` In this case, we need to retain columns 6 to 21, excluding `tportion` and -`mportion`, which are not binary; this can be done using the subsetting operator -`[...]`, where `...` are numbers indicating the columns to retain: +`mportion`, which are not binary; this can be done using the subsetting +brackets `[ ]`, where we will place character vector indicating the columns we +want to keep. ```{r stegen_food} -to_keep <- c(6, 8, 9, 10, 12:21) +to_keep <- c('tiramisu', 'wmousse', 'dmousse', 'mousse', 'beer', 'redjelly', + 'fruit_salad', 'tomato', 'mince', 'salmon', 'horseradish', + 'chickenwin', 'roastbeef', 'pork') to_keep food <- stegen[to_keep] food ``` +## Risk ratios -### Building several contingency tables +### For one variable -There are many variables to test, and having to enter separate command lines for -each would be cumbersome and prone to errors. As a workaround, the function -`lapply()` can be used. This function allows to repeat an operation using a vector -of inputs, using each element in turn. Its general syntax is -`lapply(vector_of_inputs, function_to_use, other_arguments)`, where -`other_arguments` can be any secondary arguments taken by -`function_to_use()`. Here, we use it to build a separate contingency table of each -variable in `food` crossed with illness status (`stegen$ill`); note that we show -only the first few tables: +The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the +ratio between the proportion of illness in one group (typically 'exposed') vs +another ('non-exposed'). For instance, let us consider the contingency table of +pork consumption and illness. One way to calcuate a contingency table is using +the `epitable()` function from the *epitools* package: -```{r stegen_food_tables, echo = c(1,3), eval = -2} -food_tables <- lapply(food, table, stegen$ill) -food_tables -head(food_tables) +```{r stegen_pork_table} +pork_table <- epitable(food$pork, stegen$ill) +pork_table ``` +We can get the risk ratio by using the `riskratio()` function from the *epitools* +package. Here, we want to use Yates' continuity correction and calculate Wald +confidence intervals (more inforamtion about this can be found on the help page +for `riskratio()` by typing `?riskratio` in your R console). -### Realising multiple Chi-squared tests - -The same principle used to compute several contingency tables can be used to -compute Chi-square tests for each table, using `lapply()`; note that we show -only the first few tests: - -```{r stegen_food_tests, echo = c(1,3), eval = -2} -food_tests <- lapply(food_tables, chisq.test) -food_tests -head(food_tests) +```{r stegen_pork_rr} +pork_rr <- riskratio(pork_table, correct = TRUE, method = "wald") +pork_rr ``` -Looking at these results, it seems a large number of food items are -significantly correlated to the illness. Which food item is the biggest suspect? -We will try and address this question using risk ratios. +The documentation of `riskratio()` shows that it returns quite a few things as +a list. However, we only want three things from this list: +1. risk ratio estimate +2. 95% confidence interval +3. P-value -## Risk ratios +
      -### For one variable + Going further: What is a list? -The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as the -ratio between the proportion of illness in one group (typically 'exposed') vs -another ('non-exposed'). For instance, let us consider the contingency table of -pork consumption and illness: +`pork_rr` is an R object called a "list": -```{r stegen_pork_table} -food_tables$pork +```{r stegen_pork-list1} +class(pork_rr) ``` -This can be turned into proportions using `prop.table()`, specifying that we want -proportions by rows (`margin = 1`): +A list is an R vector that can hold other vectors. While this may look strange +and unfamiliar, we've actually seen lists before. If you look close, you can +see that each element of a list starts with a `$`. This is the exact same syntax +as data frames. If we inspect our `stegen` data frame, we can see that it is +actually a list: -```{r stegen_pork_prop} -pork_prop <- prop.table(food_tables$pork, margin = 1) -pork_prop +```{r stegen_is_list} +is.list(stegen) +is.list(pork_rr) ``` -the corresponding risk ratio is: +Each column of a data frame is just a different element of a list. In fact, we +can use the `$` operator to access different elements of the list just as we +would the columns of a data frame: -```{r stegen_pork_rr} -## ill & exposed: row 2, column 2 -pork_prop[2, 2] # row 2, column 2 +```{r stegen_is_list2} +stegen_list <- as.list(stegen) +head(stegen_list) +stegen_list$ill +stegen$ill +``` -## ill & non-exposed: row 1, column 2 -pork_prop[1, 2] +
      -## compute the risk ratio -pork_rr <- pork_prop[2, 2] / pork_prop[1, 2] -pork_rr +All of these are present in the `pork_rr` object, but this output does not lend +itself well to quick visual inspection. What we can do now is to *extract* +elements from this output and place it in a data frame. + +The first thing we want is the risk ratio and the confidence intervals for those +consumption of pork. This is presented in the `$measure` element of the list: + +```{r extract-rr} +pork_rr$measure +class(pork_rr$measure) +``` + +We can see that this is present as a "matrix". A matrix has rows and columns and +we can access them like we do a vector with square brackets (`[ ]`), but we +separate the rows and columns with a comma like so: `[rows, columns]`. For +example, if we wanted the risk ratio for pork consumed, we would take the second +row in the first column: + +```{r pork-rr-mat} +pork_rr$measure[2, 1] +``` + +In our case, we want to take the entire second row, so we can leave the `columns` +portion blank: + +```{r pork-rr-mat2} +pork_est_ci <- pork_rr$measure[2, ] +pork_est_ci +``` + +We also want to get the P-value, of which, we have three choices. In our case, +we will choose the P-value from Fisher's exact test and combine the result into +a vector that we will turn into a data frame: + +```{r pork-rr-p-value} +pork_p <- pork_rr$p.value[2, "fisher.exact"] +pork_p +res <- data.frame(estimate = pork_est_ci["estimate"], + lower = pork_est_ci["lower"], + upper = pork_est_ci["upper"], + p.value = pork_p + ) +res +``` + +Now we have a table that tells us that pork has a risk ratio of +`r round(res$estimate, 3)` CI: (`r round(res$lower, 3)`, +`r round (res$upper, 3)`), P = `r round(res$p.value, 3)`. + +### Consolidating several steps into one + +To recap, to get a risk ratio we've done the following: + +1. created a contingency table +2. estimated the risk ratio +3. extracted estimate into a data frame + +However, we have `r ncol(food)` potential predictors to sort through and we +don't want to have to repeat these procedures manually over and over. One +strategy to help with this is to create our own *function*, which we can think +of as a miniature recipe for a computer to read. If we take the steps above and +place them into individual steps we would get: + +```{r make-fun-1} +et <- epitable(food$pork, stegen$ill) +rr <- riskratio(et) +estimate <- rr$measure[2, ] +res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) +res +``` + +Just like a recipe, a function needs both ingredients and instructions. What we +have above are instructions, but we still need to say what our ingredients are. +If we look at the above code, we can see that we need to define our predictor +(`food$pork`) and outcome (`stegen$ill`); everything else flows from that. We +can then write our function (which we will call `single_risk_ratio()`) like this: + +```{r make-fun-2} +single_risk_ratio <- function(predictor, outcome) { # ingredients defined here + et <- epitable(predictor, outcome) # ingredients used here + rr <- riskratio(et) + estimate <- rr$measure[2, ] + res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) + return(res) # return the data frame +} ``` -Note the use of the syntax `[rows, columns]` to subset specific rows and columns -of 2-dimensional objects. +Now, we can use this `single_risk_ratio()` like any other function: +```{r risk-ratio-df-fun-run} +pork_rr <- single_risk_ratio(predictor = food$pork, outcome = stegen$ill) +pork_rr +``` -### Multiple variables - -In the example above, we have computed the risk ratio using a simple recipe: +If we change the variable, we'll get a different answer: -1. select a contingency table -2. convert raw numbers to proportions (using `prop.table()`) -3. isolating the risks in exposed and non-exposed groups, and compute their - ratio - -Repeating these tasks manually for all variables in `food` would be -cumbersome. As an alternative, we can define the steps above as a function, -i.e. a generic recipe which we can then apply to any contingency table. This new -function will be called `risk_ratio()`, and is defined as follows: - -```{r stegen_risk_ratio_function} -risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table - ## compute table of proportions - tab_prop <- prop.table(tab, margin = 1) - - ## compute the risk ratio - result <- tab_prop[2, 2] / tab_prop[1, 2] - - ## return the output - return(result) # this will be the output of the function -} +```{r risk-ratio-df-fun-run-fruit} +fruit_rr <- single_risk_ratio(predictor = food$fruit_salad, outcome = stegen$ill) +fruit_rr ``` -We can now try this function on any of the tables in `food_tables`, for instance: +We can combine the two using a function from *dplyr* called `bind_rows()`: -```{r rr_demo} -food_tables$pork -risk_ratio(food_tables$pork) +```{r pork-fruit} +bind_rows(pork = pork_rr, fruit = fruit_rr, .id = "predictor") ``` -And we can now apply this function sequentially to all variables in `food` as -before, using `lapply()` (we show only the first few values): +Here we can see that the fruit salad has a higher risk ratio with a lower +P-value, but we want to compare all of the variables and still, copying and +pasting all this code would be a nightmare and potentially lead to errors. We +will see in the next section how we can use R to apply the function +`single_risk_ratio()` to each column of our `food` data frame to give us a table of +risk ratios for all the food items. -```{r stegen_all_rr, echo = 1:2, eval = -2} -all_rr <- lapply(food_tables, risk_ratio) -all_rr -head(all_rr) -``` +### Multiple variables + +In the example above, we have computed the risk ratio using a simple recipe: -Finally, we re-shape these results into a sorted data frame for further plotting -with *ggplot2*. Reshaping will take three steps: +1. create a contingency table +2. estimate the risk ratio +3. extract estimate into a data frame -1. convert from a list to a vector using `unlist()` -2. sort the vector using `sort()` -3. convert the named vector to a data frame using `stack()` (from the *utils* package) +We placed this recipe in a function called `single_risk_ratio()` and we know we can +use it to calcuate the risk ratio for individual variables, but we need to be +able to calculate it over all the variables in our `food` data frame. The solution +is to use an R function called `lapply()`, which takes each element of a list +(or column of a data frame) and uses it as the first ingredient (also known as +argument) of a function. Like `tapply()`, the function can be anything and +additional arguments are placed behind the function. It will then return the +output as a list: -
      -Going Further click here to learn more about stack() -The first two functions, `unlist()` and `sort()` have self-explanitory names, -but `stack()` may not be so clear. This function takes the elements in a vector -and **stacks** them together to make a data frame with two columns: one column -called "values" containing the data in the vector, and the other column called -"ind" containing the names of those elements as a factor. +```{r stegen_all_rr} +all_rr <- lapply(food, FUN = single_risk_ratio, outcome = stegen$ill) +head(all_rr) +``` +Finally, like we did above, we re-shape these results into a data frame -
      +```{r all-food-df} +all_food_df <- bind_rows(all_rr, .id = "predictor") +all_food_df +``` -```{r stegen_rr_plot} -## convert results from list to numeric vector -rr_data <- unlist(all_rr) -rr_data +This is still in the order of our data, but we want it sorted in order of the +risk ratio. For this, we will use the *dplyr* functions `arrange()` and `desc()` +to arrange our data by the risk ratio estimate in descending order: -## sort results by decreasing order -rr_data <- sort(rr_data, decreasing = TRUE) +```{r arrange-desc} +all_food_df <- arrange(all_food_df, desc(estimate)) +all_food_df +``` -## create a data frame from the named vector -rr_df <- stack(rr_data) -rr_df +Now we can use this data frame to plot the results using *ggplot2*: -ggplot(rr_df, aes(x = values, y = ind)) + +```{r plot-arrange} +# first, make sure the predictors are factored in the right order +all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) +# plot +ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_point() + - geom_vline(xintercept = 1, lty = 2) + - geom_segment(aes(xend = 1, yend = ind)) + - theme_light(base_size = 16, base_family = "Times") + - labs(title = "Risk Ratio by Food Exposure", x = "Risk Ratio", y = "Food Item") + geom_errorbarh(aes(xmin = lower, xmax = upper)) + + geom_vline(xintercept = 1, linetype = 2) + + scale_color_viridis_c() ``` The results are a lot clearer now: the tiramisu is by far the largest risk -factor in this outbreak. +factor in this outbreak, but it's wide confidence interval suggests that there +may be confounding factors involved (i.e. the food items were not independent). # Plotting a very basic spatial overview of cases + To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables *lat* and *long* include the longitude and latitude which we will use to plot the cases using *ggplot2*.
      diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 5d045e1..d0bea62 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -3,14 +3,17 @@ title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] categories: "case studies" -tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis"] +tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting cases"] date: 2018-10-24 slug: stegen licenses: CC-BY image: img/highres/graduation-1965.jpg showonlyimage: true +toc: true --- + + # Context On 26 June 1998, the St Sebastian High School in Stegen (school A), @@ -71,7 +74,7 @@ better control the outbreak. the party at St Sebastian High School and who suffered from *diarrhoea* (min. 3 loose stool for 24 hours) between 27 June and 29 June 1998, or from at least three of the following symptoms: *vomiting*, *fever over -38.5° C*, *nausea*, *abdominal pain*, *headache*. +38.5° C*, *nausea*, *abdominal pain*, *headache*. Students from both schools attending the party were asked through phone interviews to provide names of persons who attended the party. @@ -103,7 +106,8 @@ The following packages will be used in the case study: - *incidence*: to build epicurves - *epitrix*: to clean labels from our spreadsheet - dplyr: to help with factors - - ggplot2: to create custom visualisations + - *ggplot2*: to create custom visualisations + - *epitools*: to calculate risk ratios If we have these packages installed, we can tell R to load these packages from our R library: @@ -115,6 +119,7 @@ library("incidence") # make epicurves library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics +library("epitools") # statistics for epi data ```
      @@ -132,6 +137,7 @@ install.packages("incidence") install.packages("epitrix") install.packages("dplyr") install.packages("ggplot2") +install.packages("epitools") ```
      @@ -554,11 +560,40 @@ ggplot(stegen) + geom_histogram(aes(x = age)) ![](practical-stegen_files/figure-gfm/stegen_histogram-1.png) -Color can be added using: +
      + +What does “`stat_bin()` using `bins = 30`. Pick better value +with `binwidth`” mean? + +This is a message that came from `geom_histogram()`. If we look at the +help documentation for `geom_histogram()`, we can see this paragraph in +the **Details** section: + +> By default, the underlying computation (‘stat\_bin()’) uses 30 bins; +> this is not a good default, but the idea is to get you experimenting +> with different bin widths. You may need to look at a few to uncover +> the full story behind your data. + +Each bar in a histogram is considered a “bin”. The height of each bar is +the number of observations that fall within the bounds of that bar. +Because we are looking at age, it may be better for us to define a +binwidth of 1 so that each bar represents a single +year. ``` r -ggplot(stegen) + geom_histogram(aes(x = age, fill = sex)) -## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. +ggplot(stegen) + geom_histogram(aes(x = age), binwidth = 1) +``` + +![](practical-stegen_files/figure-gfm/stegen_histogram-bin-1.png) + +
      + +We can differentiate between genders by filling the bars with color +(n.b. we are also setting the binwidth to be 1 +year): + +``` r +ggplot(stegen) + geom_histogram(aes(x = age, fill = sex), binwidth = 1) ``` ![](practical-stegen_files/figure-gfm/stegen_histogram_sex-1.png) @@ -614,6 +649,7 @@ and a time interval (1 day by default). We use the function ``` r i <- incidence(stegen$date_onset) +## 160 missing observations were removed. i ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -657,6 +693,7 @@ definition will clarify the situation: ``` r i_ill <- incidence(stegen$date_onset, group = stegen$ill) +## 160 missing observations were removed. i_ill ## ## [131 cases from days 1998-06-26 to 1998-07-09] @@ -734,37 +771,54 @@ according to `sex`, we use `facet_grid()` (see previous extra info on ``` r ggplot(stegen) + - geom_histogram(aes(x = age, fill = ill)) + + geom_histogram(aes(x = age, fill = ill), binwidth = 1) + scale_fill_manual("Illness", values = c("non case" = "#66cc99", "case" = "#990033")) + facet_grid(sex ~ .) + labs(title = "Cases by age and gender") + theme_light() -## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` ![](practical-stegen_files/figure-gfm/stegen_case_age_sex-1.png) -Illness does not seem to be depending on age or gender. This can be -tested: this is the topic of the next and final section. +From these graphics, we can see that illness does not appear to depend +on age or gender, which suggests that the illness was caused by one or +more of the foods consumed. We will explore this with a risk ratio +analysis in the next section. + +----- # Looking for the culprits +In order to figure out which food item was responsible for the outbreak, +we need to test the [Risk +Ratio](https://en.wikipedia.org/wiki/Risk_ratio) for all of the food +items recorded. Risk ratios are normally computed on *contingency* +tables (commonly known as 2x2 tables). In this part, we will compute +contingency tables for each predictor (food item) against the defined +cases, calculate their risk ratio with confidence intervals and +p-values, and plot them as points and errorbars using *ggplot2*. + +
      + + Going Further: Different types of Univariate tests + ## Univariate tests Methods for testing the association between two variables can be broken down in 3 types, depending on which types these variables are: -1. **2 quantitative variables**: Pearson’s correlation coefficient - (\(r\)) and similar methods -2. **1 quantitative, 1 categorical**: ANOVA types of approaches; - particular case with 2 groups: Student’s \(t\)-test -3. **2 categorical variables**: Chi-squared test on the 2x2 table +1. **2 categorical variables**: Chi-squared test on the 2x2 table (a.k.a. *contingency* table) and similar methods (e.g. Fisher’s exact test) +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; + particular case with 2 groups: Student’s \(t\)-test +3. **2 quantitative variables**: Pearson’s correlation coefficient + (\(r\)) and similar methods We can use these approaches to test if the disease is linked to any of the other recorded variables. As illness itself is a categorical -variable, only approaches of type 2 and 3 will be illustrated here. +variable, only approaches of type 2 and 3 are illustrated in this case +study. ### Is illness linked to age? @@ -849,28 +903,16 @@ chisq.test(tab_ill_sex) ## X-squared = 0.6562, df = 1, p-value = 0.4179 ``` -Here, the p-value of 0.418 suggests illness is not related to sex +Here, the p-value of 0.418 suggests illness is not related to gender either. -## Making multiple tests - -The natural next step is to run multiple tests for all potential risk -factors recorded. There are many ways to go about this in **R**, but we -will illustrate one of the most common workflows: - -1. isolate a subset of variables to test (the ones indicative of food - consumption) (using `[]`) -2. for each: - 1) build contingency table with illness status (using `table`) - 2) run a Chi-square test on this table (using `chisq.test`) - -All of these have been seen so far; the only missing piece is step 2), -which we will cover using a very handy function called `lapply()`. +
      ### Isolating the variables to test -We use the natural structure of the dataset, as variables representing -food consumption are all the last columns: +Because not all of the columns in our data set are food items (i.e. +“age”, and “tportion”), we only want to keep the columns that are +food items for our analysis. ``` r names(stegen) @@ -883,13 +925,18 @@ names(stegen) In this case, we need to retain columns 6 to 21, excluding `tportion` and `mportion`, which are not binary; this can be done using the -subsetting operator `[...]`, where `...` are numbers indicating the -columns to retain: +subsetting brackets `[ ]`, where we will place character vector +indicating the columns we want to +keep. ``` r -to_keep <- c(6, 8, 9, 10, 12:21) +to_keep <- c('tiramisu', 'wmousse', 'dmousse', 'mousse', 'beer', 'redjelly', + 'fruit_salad', 'tomato', 'mince', 'salmon', 'horseradish', + 'chickenwin', 'roastbeef', 'pork') to_keep -## [1] 6 8 9 10 12 13 14 15 16 17 18 19 20 21 +## [1] "tiramisu" "wmousse" "dmousse" "mousse" "beer" +## [6] "redjelly" "fruit_salad" "tomato" "mince" "salmon" +## [11] "horseradish" "chickenwin" "roastbeef" "pork" food <- stegen[to_keep] food ## # A tibble: 291 x 14 @@ -909,313 +956,658 @@ food ## # horseradish , chickenwin , roastbeef , pork ``` -### Building several contingency tables +## Risk ratios -There are many variables to test, and having to enter separate command -lines for each would be cumbersome and prone to errors. As a workaround, -the function `lapply()` can be used. This function allows to repeat an -operation using a vector of inputs, using each element in turn. Its -general syntax is `lapply(vector_of_inputs, function_to_use, -other_arguments)`, where `other_arguments` can be any secondary -arguments taken by `function_to_use()`. Here, we use it to build a -separate contingency table of each variable in `food` crossed with -illness status (`stegen$ill`); note that we show only the first few -tables: +### For one variable + +The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as +the ratio between the proportion of illness in one group (typically +‘exposed’) vs another (‘non-exposed’). For instance, let us consider +the contingency table of pork consumption and illness. One way to +calcuate a contingency table is using the `epitable()` function from the +*epitools* package: ``` r -food_tables <- lapply(food, table, stegen$ill) -head(food_tables) -## $tiramisu -## -## non case case -## 0 158 7 -## 1 27 94 -## -## $wmousse -## -## non case case -## 0 156 49 -## 1 23 49 +pork_table <- epitable(food$pork, stegen$ill) +pork_table +## Outcome +## Predictor non case case +## 0 115 54 +## 1 72 48 +``` + +We can get the risk ratio by using the `riskratio()` function from the +*epitools* package. Here, we want to use Yates’ continuity correction +and calculate Wald confidence intervals (more inforamtion about this can +be found on the help page for `riskratio()` by typing `?riskratio` in +your R console). + +``` r +pork_rr <- riskratio(pork_table, correct = TRUE, method = "wald") +pork_rr +## $data +## Outcome +## Predictor non case case Total +## 0 115 54 169 +## 1 72 48 120 +## Total 187 102 289 ## -## $dmousse -## -## non case case -## 0 148 26 -## 1 37 76 +## $measure +## risk ratio with 95% C.I. +## Predictor estimate lower upper +## 0 1.000000 NA NA +## 1 1.251852 0.9176849 1.707703 ## -## $mousse -## -## non case case -## 0 144 22 -## 1 42 81 +## $p.value +## two-sided +## Predictor midp.exact fisher.exact chi.square +## 0 NA NA NA +## 1 0.1620037 0.1708777 0.198536 ## -## $beer -## -## non case case -## 0 96 69 -## 1 76 30 +## $correction +## [1] TRUE ## -## $redjelly -## -## non case case -## 0 154 58 -## 1 34 45 +## attr(,"method") +## [1] "Unconditional MLE & normal approximation (Wald) CI" ``` -### Realising multiple Chi-squared tests +The documentation of `riskratio()` shows that it returns quite a few +things as a list. However, we only want three things from this list: + +1. risk ratio estimate +2. 95% confidence interval +3. P-value + +
      + + Going further: What is a list? -The same principle used to compute several contingency tables can be -used to compute Chi-square tests for each table, using `lapply()`; note -that we show only the first few tests: +`pork_rr` is an R object called a “list”: ``` r -food_tests <- lapply(food_tables, chisq.test) -head(food_tests) -## $tiramisu -## -## Pearson's Chi-squared test with Yates' continuity correction -## -## data: X[[i]] -## X-squared = 161.64, df = 1, p-value < 2.2e-16 -## -## -## $wmousse -## -## Pearson's Chi-squared test with Yates' continuity correction -## -## data: X[[i]] -## X-squared = 43.526, df = 1, p-value = 4.183e-11 -## -## -## $dmousse -## -## Pearson's Chi-squared test with Yates' continuity correction -## -## data: X[[i]] -## X-squared = 79.574, df = 1, p-value < 2.2e-16 -## -## -## $mousse -## -## Pearson's Chi-squared test with Yates' continuity correction -## -## data: X[[i]] -## X-squared = 82.943, df = 1, p-value < 2.2e-16 -## -## -## $beer -## -## Pearson's Chi-squared test with Yates' continuity correction +class(pork_rr) +## [1] "list" +``` + +A list is an R vector that can hold other vectors. While this may look +strange and unfamiliar, we’ve actually seen lists before. If you look +close, you can see that each element of a list starts with a `$`. This +is the exact same syntax as data frames. If we inspect our `stegen` data +frame, we can see that it is actually a list: + +``` r +is.list(stegen) +## [1] TRUE +is.list(pork_rr) +## [1] TRUE +``` + +Each column of a data frame is just a different element of a list. In +fact, we can use the `$` operator to access different elements of the +list just as we would the columns of a data frame: + +``` r +stegen_list <- as.list(stegen) +head(stegen_list) +## $unique_key +## [1] "210" "12" "288" "186" "20" "148" "201" "106" "272" "50" "216" +## [12] "141" "91" "98" "200" "109" "117" "281" "269" "77" "196" "16" +## [23] "168" "102" "204" "205" "271" "48" "287" "25" "15" "45" "125" +## [34] "113" "284" "121" "52" "207" "63" "43" "175" "214" "251" "213" +## [45] "65" "159" "29" "14" "165" "145" "202" "255" "169" "274" "254" +## [56] "61" "2" "86" "59" "74" "133" "115" "103" "138" "70" "173" +## [67] "144" "212" "234" "156" "146" "152" "31" "279" "36" "75" "286" +## [78] "215" "56" "199" "154" "27" "42" "49" "96" "66" "104" "13" +## [89] "221" "51" "177" "111" "242" "143" "278" "62" "176" "134" "256" +## [100] "55" "235" "58" "194" "282" "137" "118" "220" "24" "187" "190" +## [111] "189" "195" "231" "239" "289" "184" "126" "209" "290" "67" "170" +## [122] "230" "151" "283" "211" "69" "35" "233" "208" "155" "198" "40" +## [133] "119" "139" "180" "188" "157" "80" "203" "280" "37" "193" "53" +## [144] "22" "85" "232" "258" "265" "54" "237" "266" "236" "88" "10" +## [155] "11" "174" "185" "161" "226" "273" "227" "260" "223" "107" "183" +## [166] "250" "253" "44" "182" "228" "285" "83" "248" "136" "172" "46" +## [177] "114" "166" "164" "101" "21" "158" "108" "34" "276" "222" "130" +## [188] "275" "72" "218" "267" "76" "241" "171" "142" "89" "105" "39" +## [199] "167" "140" "124" "131" "17" "73" "97" "5" "123" "9" "99" +## [210] "84" "229" "116" "238" "217" "122" "240" "95" "110" "41" "206" +## [221] "23" "257" "163" "64" "100" "120" "160" "224" "94" "60" "263" +## [232] "191" "147" "179" "93" "57" "112" "268" "243" "219" "247" "38" +## [243] "33" "68" "4" "150" "79" "178" "127" "153" "261" "92" "90" +## [254] "264" "277" "181" "197" "225" "18" "1" "252" "47" "245" "78" +## [265] "162" "81" "3" "82" "32" "71" "30" "28" "135" "246" "149" +## [276] "7" "19" "249" "128" "6" "192" "270" "262" "259" "87" "8" +## [287] "129" "26" "132" "244" "291" ## -## data: X[[i]] -## X-squared = 4.519, df = 1, p-value = 0.03352 +## $ill +## [1] case case case case case case case +## [8] case case case case case case case +## [15] case case case case case case case +## [22] case case case case case case case +## [29] case case case case case case case +## [36] case case case case case case case +## [43] case case case case case case case +## [50] case case case case case case case +## [57] case case case case case case case +## [64] case case case case case case case +## [71] case case case case case case case +## [78] case case case case case case case +## [85] case case case case case case case +## [92] case case case case case case case +## [99] non case non case non case non case case non case non case +## [106] non case non case non case non case non case non case non case +## [113] non case non case non case non case non case non case non case +## [120] non case non case non case case non case non case non case +## [127] non case non case non case non case non case non case non case +## [134] non case non case non case non case non case non case non case +## [141] non case non case non case non case non case non case non case +## [148] non case non case non case non case non case non case non case +## [155] non case non case non case non case non case non case non case +## [162] non case non case non case non case non case non case non case +## [169] non case non case non case non case non case non case non case +## [176] non case non case non case non case non case non case non case +## [183] non case non case non case non case non case non case non case +## [190] non case non case non case non case non case non case non case +## [197] non case non case non case non case non case non case non case +## [204] non case non case non case non case non case non case non case +## [211] non case non case case non case non case non case non case +## [218] non case non case non case non case non case non case non case +## [225] non case non case non case non case non case non case non case +## [232] non case non case non case non case non case non case non case +## [239] non case non case non case non case non case case non case +## [246] case non case non case non case non case non case non case +## [253] non case non case non case non case non case non case non case +## [260] non case non case non case non case non case non case non case +## [267] non case non case non case non case non case non case non case +## [274] non case non case non case non case non case non case non case +## [281] non case non case non case non case non case non case non case +## [288] non case non case non case non case +## Levels: non case case ## +## $date_onset +## [1] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [6] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [11] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [16] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [21] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [26] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [31] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [36] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [41] "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" "1998-06-27" +## [46] "1998-06-27" "1998-06-27" "1998-06-28" "1998-06-28" "1998-06-28" +## [51] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [56] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [61] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [66] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [71] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [76] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [81] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [86] "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" "1998-06-28" +## [91] "1998-06-29" "1998-06-29" "1998-06-29" "1998-06-29" "1998-06-29" +## [96] "1998-06-29" "1998-06-29" "1998-06-29" NA NA +## [101] "1998-07-05" NA "1998-06-27" NA NA +## [106] NA NA "1998-07-02" NA NA +## [111] NA "1998-06-28" NA NA NA +## [116] NA NA NA NA "1998-07-01" +## [121] NA NA "1998-06-28" NA NA +## [126] NA NA NA "1998-06-30" NA +## [131] NA NA NA "1998-06-28" NA +## [136] NA NA NA NA NA +## [141] NA NA NA NA NA +## [146] NA NA NA NA "1998-07-09" +## [151] NA NA "1998-06-29" NA "1998-07-02" +## [156] NA NA NA NA NA +## [161] "1998-07-06" NA NA NA NA +## [166] NA NA NA NA NA +## [171] NA NA "1998-06-27" NA NA +## [176] NA NA NA "1998-06-27" NA +## [181] NA NA NA "1998-06-28" NA +## [186] NA NA NA NA "1998-06-30" +## [191] NA NA NA NA NA +## [196] "1998-06-27" NA NA "1998-06-28" NA +## [201] NA NA NA NA NA +## [206] NA NA NA NA NA +## [211] "1998-06-26" NA "1998-06-28" "1998-06-27" NA +## [216] NA NA NA "1998-06-27" NA +## [221] NA NA "1998-06-27" "1998-07-01" NA +## [226] NA NA NA NA NA +## [231] NA NA NA NA NA +## [236] NA NA NA NA NA +## [241] NA NA NA NA NA +## [246] "1998-06-28" "1998-07-02" NA NA NA +## [251] NA "1998-06-27" NA NA NA +## [256] NA NA NA NA NA +## [261] NA NA NA "1998-06-29" "1998-07-01" +## [266] "1998-06-27" NA "1998-06-28" NA NA +## [271] NA "1998-07-04" NA NA NA +## [276] NA NA NA NA NA +## [281] NA NA NA NA NA +## [286] NA NA "1998-06-30" NA NA +## [291] NA ## -## $redjelly +## $sex +## [1] female male female male female male male male female male +## [11] female male male female male male male male female female +## [21] male female male male male female male female male male +## [31] female female female male female female male male female male +## [41] male female male male female female male female female female +## [51] male male male female female female male male female male +## [61] male male male female female female female male female female +## [71] female female male female male male male female male female +## [81] female female male male female male female male female male +## [91] female female male female male male female male female male +## [101] male female male male male female male female female female +## [111] female male female male male male male male female male +## [121] male female female female female male female female male female +## [131] male female male male female female female female male male +## [141] female male female male male female male female female female +## [151] female female female female female female female male male male +## [161] male female female male female female female male male male +## [171] female male male male female male male female male female +## [181] male male male male female female male male female male +## [191] male female female male female female male female female female +## [201] male male male female female male female female female male +## [211] male female male female male female male male male male +## [221] male male female male male male female female male female +## [231] female female male male male male female male female female +## [241] female female female female female female male female female female +## [251] female female male female female female female male female male +## [261] female male female male male female male female female female +## [271] female male female female male male female male male female +## [281] female female female male female female female female male female +## [291] male +## Levels: male female ## -## Pearson's Chi-squared test with Yates' continuity correction +## $age +## [1] 18 57 56 17 19 16 19 19 40 53 20 23 17 19 15 19 57 17 47 16 17 19 17 +## [24] 17 18 18 29 14 13 21 19 20 57 38 18 64 57 27 23 21 21 20 20 18 24 24 +## [47] 19 58 19 18 27 20 19 54 23 18 16 26 16 14 20 56 46 18 19 21 18 80 23 +## [70] 20 50 18 48 21 47 18 47 17 NA 52 20 32 17 16 17 20 19 19 NA 19 19 19 +## [93] 19 48 19 52 19 NA 21 18 20 19 39 17 22 13 20 18 17 17 62 17 21 54 20 +## [116] 16 17 18 22 18 18 NA 19 17 20 57 20 20 48 19 44 19 46 51 17 16 22 18 +## [139] 19 20 47 15 19 20 13 20 21 20 18 20 45 20 44 18 22 58 17 14 NA 20 20 +## [162] NA 19 19 16 26 20 19 17 19 57 18 19 17 18 18 23 19 45 15 20 19 25 20 +## [185] 20 20 48 23 17 17 46 19 21 18 24 18 52 50 23 55 19 16 18 19 50 49 65 +## [208] 19 43 43 20 59 21 19 20 56 14 55 47 19 42 22 49 45 42 60 16 20 18 18 +## [231] 21 19 19 17 17 18 45 58 20 24 57 19 20 56 46 18 17 37 44 19 21 19 18 +## [254] 28 53 17 19 19 19 17 20 18 24 15 12 16 48 18 21 59 57 15 NA 20 18 18 +## [277] 18 NA 16 20 16 51 18 21 22 18 18 21 17 21 22 ## -## data: X[[i]] -## X-squared = 20.781, df = 1, p-value = 5.148e-06 +## $tiramisu +## [1] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +## [24] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 +## [47] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 NA 1 +## [70] 0 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 +## [93] 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 +## [116] 0 0 0 NA 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 +## [139] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 +## [162] 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 +## [185] 0 0 0 NA 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 +## [208] 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 +## [231] 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 +## [254] 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 NA 0 1 0 0 0 +## [277] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 +stegen_list$ill +## [1] case case case case case case case +## [8] case case case case case case case +## [15] case case case case case case case +## [22] case case case case case case case +## [29] case case case case case case case +## [36] case case case case case case case +## [43] case case case case case case case +## [50] case case case case case case case +## [57] case case case case case case case +## [64] case case case case case case case +## [71] case case case case case case case +## [78] case case case case case case case +## [85] case case case case case case case +## [92] case case case case case case case +## [99] non case non case non case non case case non case non case +## [106] non case non case non case non case non case non case non case +## [113] non case non case non case non case non case non case non case +## [120] non case non case non case case non case non case non case +## [127] non case non case non case non case non case non case non case +## [134] non case non case non case non case non case non case non case +## [141] non case non case non case non case non case non case non case +## [148] non case non case non case non case non case non case non case +## [155] non case non case non case non case non case non case non case +## [162] non case non case non case non case non case non case non case +## [169] non case non case non case non case non case non case non case +## [176] non case non case non case non case non case non case non case +## [183] non case non case non case non case non case non case non case +## [190] non case non case non case non case non case non case non case +## [197] non case non case non case non case non case non case non case +## [204] non case non case non case non case non case non case non case +## [211] non case non case case non case non case non case non case +## [218] non case non case non case non case non case non case non case +## [225] non case non case non case non case non case non case non case +## [232] non case non case non case non case non case non case non case +## [239] non case non case non case non case non case case non case +## [246] case non case non case non case non case non case non case +## [253] non case non case non case non case non case non case non case +## [260] non case non case non case non case non case non case non case +## [267] non case non case non case non case non case non case non case +## [274] non case non case non case non case non case non case non case +## [281] non case non case non case non case non case non case non case +## [288] non case non case non case non case +## Levels: non case case +stegen$ill +## [1] case case case case case case case +## [8] case case case case case case case +## [15] case case case case case case case +## [22] case case case case case case case +## [29] case case case case case case case +## [36] case case case case case case case +## [43] case case case case case case case +## [50] case case case case case case case +## [57] case case case case case case case +## [64] case case case case case case case +## [71] case case case case case case case +## [78] case case case case case case case +## [85] case case case case case case case +## [92] case case case case case case case +## [99] non case non case non case non case case non case non case +## [106] non case non case non case non case non case non case non case +## [113] non case non case non case non case non case non case non case +## [120] non case non case non case case non case non case non case +## [127] non case non case non case non case non case non case non case +## [134] non case non case non case non case non case non case non case +## [141] non case non case non case non case non case non case non case +## [148] non case non case non case non case non case non case non case +## [155] non case non case non case non case non case non case non case +## [162] non case non case non case non case non case non case non case +## [169] non case non case non case non case non case non case non case +## [176] non case non case non case non case non case non case non case +## [183] non case non case non case non case non case non case non case +## [190] non case non case non case non case non case non case non case +## [197] non case non case non case non case non case non case non case +## [204] non case non case non case non case non case non case non case +## [211] non case non case case non case non case non case non case +## [218] non case non case non case non case non case non case non case +## [225] non case non case non case non case non case non case non case +## [232] non case non case non case non case non case non case non case +## [239] non case non case non case non case non case case non case +## [246] case non case non case non case non case non case non case +## [253] non case non case non case non case non case non case non case +## [260] non case non case non case non case non case non case non case +## [267] non case non case non case non case non case non case non case +## [274] non case non case non case non case non case non case non case +## [281] non case non case non case non case non case non case non case +## [288] non case non case non case non case +## Levels: non case case ``` -Looking at these results, it seems a large number of food items are -significantly correlated to the illness. Which food item is the biggest -suspect? We will try and address this question using risk ratios. +
      -## Risk ratios +All of these are present in the `pork_rr` object, but this output does +not lend itself well to quick visual inspection. What we can do now is +to *extract* elements from this output and place it in a data frame. -### For one variable - -The [risk ratio](https://en.wikipedia.org/wiki/Risk_ratio) is defined as -the ratio between the proportion of illness in one group (typically -‘exposed’) vs another (‘non-exposed’). For instance, let us consider -the contingency table of pork consumption and illness: +The first thing we want is the risk ratio and the confidence intervals +for those consumption of pork. This is presented in the `$measure` +element of the list: ``` r -food_tables$pork -## -## non case case -## 0 115 54 -## 1 72 48 +pork_rr$measure +## risk ratio with 95% C.I. +## Predictor estimate lower upper +## 0 1.000000 NA NA +## 1 1.251852 0.9176849 1.707703 +class(pork_rr$measure) +## [1] "matrix" ``` -This can be turned into proportions using `prop.table()`, specifying -that we want proportions by rows (`margin = 1`): +We can see that this is present as a “matrix”. A matrix has rows and +columns and we can access them like we do a vector with square brackets +(`[ ]`), but we separate the rows and columns with a comma like so: +`[rows, columns]`. For example, if we wanted the risk ratio for pork +consumed, we would take the second row in the first column: ``` r -pork_prop <- prop.table(food_tables$pork, margin = 1) -pork_prop -## -## non case case -## 0 0.6804734 0.3195266 -## 1 0.6000000 0.4000000 +pork_rr$measure[2, 1] +## [1] 1.251852 ``` -the corresponding risk ratio is: +In our case, we want to take the entire second row, so we can leave the +`columns` portion blank: ``` r -## ill & exposed: row 2, column 2 -pork_prop[2, 2] # row 2, column 2 -## [1] 0.4 +pork_est_ci <- pork_rr$measure[2, ] +pork_est_ci +## estimate lower upper +## 1.2518519 0.9176849 1.7077027 +``` -## ill & non-exposed: row 1, column 2 -pork_prop[1, 2] -## [1] 0.3195266 +We also want to get the P-value, of which, we have three choices. In our +case, we will choose the P-value from Fisher’s exact test and combine +the result into a vector that we will turn into a data frame: -## compute the risk ratio -pork_rr <- pork_prop[2, 2] / pork_prop[1, 2] -pork_rr -## [1] 1.251852 +``` r +pork_p <- pork_rr$p.value[2, "fisher.exact"] +pork_p +## [1] 0.1708777 +res <- data.frame(estimate = pork_est_ci["estimate"], + lower = pork_est_ci["lower"], + upper = pork_est_ci["upper"], + p.value = pork_p + ) +res +## estimate lower upper p.value +## estimate 1.251852 0.9176849 1.707703 0.1708777 ``` -Note the use of the syntax `[rows, columns]` to subset specific rows and -columns of 2-dimensional objects. +Now we have a table that tells us that pork has a risk ratio of 1.252 +CI: (0.918, 1.708), P = 0.171. -### Multiple variables +### Consolidating several steps into one -In the example above, we have computed the risk ratio using a simple -recipe: +To recap, to get a risk ratio we’ve done the following: -1. select a contingency table -2. convert raw numbers to proportions (using `prop.table()`) -3. isolating the risks in exposed and non-exposed groups, and compute - their ratio +1. created a contingency table +2. estimated the risk ratio +3. extracted estimate into a data frame -Repeating these tasks manually for all variables in `food` would be -cumbersome. As an alternative, we can define the steps above as a -function, i.e. a generic recipe which we can then apply to any -contingency table. This new function will be called `risk_ratio()`, and -is defined as -follows: +However, we have 14 potential predictors to sort through and we don’t +want to have to repeat these procedures manually over and over. One +strategy to help with this is to create our own *function*, which we can +think of as a miniature recipe for a computer to read. If we take the +steps above and place them into individual steps we would get: ``` r -risk_ratio <- function(tab) { # 'tab' is a placeholder for the input 2x2 table - ## compute table of proportions - tab_prop <- prop.table(tab, margin = 1) - - ## compute the risk ratio - result <- tab_prop[2, 2] / tab_prop[1, 2] - - ## return the output - return(result) # this will be the output of the function +et <- epitable(food$pork, stegen$ill) +rr <- riskratio(et) +estimate <- rr$measure[2, ] +res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) +res +## estimate lower upper p.value +## estimate 1.251852 0.9176849 1.707703 0.1708777 +``` + +Just like a recipe, a function needs both ingredients and instructions. +What we have above are instructions, but we still need to say what our +ingredients are. If we look at the above code, we can see that we need +to define our predictor (`food$pork`) and outcome (`stegen$ill`); +everything else flows from that. We can then write our function (which +we will call `single_risk_ratio()`) like +this: + +``` r +single_risk_ratio <- function(predictor, outcome) { # ingredients defined here + et <- epitable(predictor, outcome) # ingredients used here + rr <- riskratio(et) + estimate <- rr$measure[2, ] + res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) + return(res) # return the data frame } ``` -We can now try this function on any of the tables in `food_tables`, for -instance: +Now, we can use this `single_risk_ratio()` like any other +function: ``` r -food_tables$pork -## -## non case case -## 0 115 54 -## 1 72 48 -risk_ratio(food_tables$pork) -## [1] 1.251852 +pork_rr <- single_risk_ratio(predictor = food$pork, outcome = stegen$ill) +pork_rr +## estimate lower upper p.value +## estimate 1.251852 0.9176849 1.707703 0.1708777 ``` -And we can now apply this function sequentially to all variables in -`food` as before, using `lapply()` (we show only the first few values): +If we change the variable, we’ll get a different +answer: ``` r -all_rr <- lapply(food_tables, risk_ratio) -## all_rr +fruit_rr <- single_risk_ratio(predictor = food$fruit_salad, outcome = stegen$ill) +fruit_rr +## estimate lower upper p.value +## estimate 2.500618 1.886773 3.314171 9.998203e-09 +``` + +We can combine the two using a function from *dplyr* called +`bind_rows()`: + +``` r +bind_rows(pork = pork_rr, fruit = fruit_rr, .id = "predictor") +## predictor estimate lower upper p.value +## 1 pork 1.251852 0.9176849 1.707703 1.708777e-01 +## 2 fruit 2.500618 1.8867735 3.314171 9.998203e-09 +``` + +Here we can see that the fruit salad has a higher risk ratio with a +lower P-value, but we want to compare all of the variables and still, +copying and pasting all this code would be a nightmare and potentially +lead to errors. We will see in the next section how we can use R to +apply the function `single_risk_ratio()` to each column of our `food` +data frame to give us a table of risk ratios for all the food items. + +### Multiple variables + +In the example above, we have computed the risk ratio using a simple +recipe: + +1. create a contingency table +2. estimate the risk ratio +3. extract estimate into a data frame + +We placed this recipe in a function called `single_risk_ratio()` and we +know we can use it to calcuate the risk ratio for individual variables, +but we need to be able to calculate it over all the variables in our +`food` data frame. The solution is to use an R function called +`lapply()`, which takes each element of a list (or column of a data +frame) and uses it as the first ingredient (also known as argument) of a +function. Like `tapply()`, the function can be anything and additional +arguments are placed behind the function. It will then return the output +as a list: + +``` r +all_rr <- lapply(food, FUN = single_risk_ratio, outcome = stegen$ill) +head(all_rr) ## $tiramisu -## [1] 18.31169 +## estimate lower upper p.value +## estimate 18.31169 8.814202 38.04291 1.794084e-41 ## ## $wmousse -## [1] 2.847222 +## estimate lower upper p.value +## estimate 2.847222 2.128267 3.809049 5.825494e-11 ## ## $dmousse -## [1] 4.501021 +## estimate lower upper p.value +## estimate 4.501021 3.086945 6.562862 1.167009e-19 ## ## $mousse -## [1] 4.968958 +## estimate lower upper p.value +## estimate 4.968958 3.299403 7.483336 1.257341e-20 ## ## $beer -## [1] 0.6767842 +## estimate lower upper p.value +## estimate 0.6767842 0.4757688 0.96273 0.02806394 ## ## $redjelly -## [1] 2.08206 +## estimate lower upper p.value +## estimate 2.08206 1.555917 2.786123 4.415074e-06 ``` -Finally, we re-shape these results into a sorted data frame for further -plotting with *ggplot2*. Reshaping will take three steps: +Finally, like we did above, we re-shape these results into a data frame -1. convert from a list to a vector using `unlist()` -2. sort the vector using `sort()` -3. convert the named vector to a data frame using `stack()` (from the - *utils* package) - -
      +``` r +all_food_df <- bind_rows(all_rr, .id = "predictor") +all_food_df +## predictor estimate lower upper p.value +## 1 tiramisu 18.3116883 8.8142022 38.042913 1.794084e-41 +## 2 wmousse 2.8472222 2.1282671 3.809049 5.825494e-11 +## 3 dmousse 4.5010211 3.0869446 6.562862 1.167009e-19 +## 4 mousse 4.9689579 3.2994031 7.483336 1.257341e-20 +## 5 beer 0.6767842 0.4757688 0.962730 2.806394e-02 +## 6 redjelly 2.0820602 1.5559166 2.786123 4.415074e-06 +## 7 fruit_salad 2.5006177 1.8867735 3.314171 9.998203e-09 +## 8 tomato 1.2898653 0.9379601 1.773799 1.368934e-01 +## 9 mince 1.0568237 0.7571858 1.475036 7.893882e-01 +## 10 salmon 1.0334249 0.7452531 1.433026 8.976422e-01 +## 11 horseradish 1.2557870 0.9008457 1.750578 2.026013e-01 +## 12 chickenwin 1.1617347 0.8376217 1.611261 4.176598e-01 +## 13 roastbeef 0.7607985 0.4129094 1.401795 4.172932e-01 +## 14 pork 1.2518519 0.9176849 1.707703 1.708777e-01 +``` -Going Further click here to learn more about -stack() +This is still in the order of our data, but we want it sorted in order +of the risk ratio. For this, we will use the *dplyr* functions +`arrange()` and `desc()` to arrange our data by the risk ratio estimate +in descending order: -The first two functions, `unlist()` and `sort()` have self-explanitory -names, but `stack()` may not be so clear. This function takes the -elements in a vector and **stacks** them together to make a data frame -with two columns: one column called “values” containing the data in the -vector, and the other column called “ind” containing the names of those -elements as a factor. +``` r +all_food_df <- arrange(all_food_df, desc(estimate)) +all_food_df +## predictor estimate lower upper p.value +## 1 tiramisu 18.3116883 8.8142022 38.042913 1.794084e-41 +## 2 mousse 4.9689579 3.2994031 7.483336 1.257341e-20 +## 3 dmousse 4.5010211 3.0869446 6.562862 1.167009e-19 +## 4 wmousse 2.8472222 2.1282671 3.809049 5.825494e-11 +## 5 fruit_salad 2.5006177 1.8867735 3.314171 9.998203e-09 +## 6 redjelly 2.0820602 1.5559166 2.786123 4.415074e-06 +## 7 tomato 1.2898653 0.9379601 1.773799 1.368934e-01 +## 8 horseradish 1.2557870 0.9008457 1.750578 2.026013e-01 +## 9 pork 1.2518519 0.9176849 1.707703 1.708777e-01 +## 10 chickenwin 1.1617347 0.8376217 1.611261 4.176598e-01 +## 11 mince 1.0568237 0.7571858 1.475036 7.893882e-01 +## 12 salmon 1.0334249 0.7452531 1.433026 8.976422e-01 +## 13 roastbeef 0.7607985 0.4129094 1.401795 4.172932e-01 +## 14 beer 0.6767842 0.4757688 0.962730 2.806394e-02 +``` -
      +Now we can use this data frame to plot the results using *ggplot2*: ``` r -## convert results from list to numeric vector -rr_data <- unlist(all_rr) -rr_data -## tiramisu wmousse dmousse mousse beer redjelly -## 18.3116883 2.8472222 4.5010211 4.9689579 0.6767842 2.0820602 -## fruit_salad tomato mince salmon horseradish chickenwin -## 2.5006177 1.2898653 1.0568237 1.0334249 1.2557870 1.1617347 -## roastbeef pork -## 0.7607985 1.2518519 - -## sort results by decreasing order -rr_data <- sort(rr_data, decreasing = TRUE) - -## create a data frame from the named vector -rr_df <- stack(rr_data) -rr_df -## values ind -## 1 18.3116883 tiramisu -## 2 4.9689579 mousse -## 3 4.5010211 dmousse -## 4 2.8472222 wmousse -## 5 2.5006177 fruit_salad -## 6 2.0820602 redjelly -## 7 1.2898653 tomato -## 8 1.2557870 horseradish -## 9 1.2518519 pork -## 10 1.1617347 chickenwin -## 11 1.0568237 mince -## 12 1.0334249 salmon -## 13 0.7607985 roastbeef -## 14 0.6767842 beer - -ggplot(rr_df, aes(x = values, y = ind)) + +# first, make sure the predictors are factored in the right order +all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) +# plot +ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_point() + - geom_vline(xintercept = 1, lty = 2) + - geom_segment(aes(xend = 1, yend = ind)) + - theme_light(base_size = 16, base_family = "Times") + - labs(title = "Risk Ratio by Food Exposure", x = "Risk Ratio", y = "Food Item") + geom_errorbarh(aes(xmin = lower, xmax = upper)) + + geom_vline(xintercept = 1, linetype = 2) + + scale_color_viridis_c() ``` -![](practical-stegen_files/figure-gfm/stegen_rr_plot-1.png) +![](practical-stegen_files/figure-gfm/plot-arrange-1.png) The results are a lot clearer now: the tiramisu is by far the largest -risk factor in this outbreak. - -# \- Plotting a very basic spatial overview of cases - -To conclude your report you would like to include a very basic spatial -overview of cases. Based on the postal codes of all individuals that had -symptoms gps coordinates were generated providing each individual with a -latitude and longtitude which corresponds with their household. The -variables *lat* and *long* include the longitude and latitude which we -will use to plot the cases using *ggplot2*. +risk factor in this outbreak, but it’s wide confidence interval suggests +that there may be confounding factors involved (i.e. the food items were +not independent). + +# Plotting a very basic spatial overview of cases + +To complete your report, you would like to include a very basic spatial +overview of cases. Based on the postal codes of all individuals with a +date of symptom onset spatial coordinates were generated providing +everyone with a latitude and longitude which corresponds with their +household (in relation to their postal code). The variables *lat* and +*long* include the longitude and latitude which we will use to plot the +cases using *ggplot2*.
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zqJ*>9`650wZ{1!?oLJw=amzJ#h4Fz~y1G;DDuKQYg7|Q+H~Fta&e*QqCWr7{@_Lz| z0kR(6`nbtDnSBNN)Fxx$?Umg3N5yI{*=obAOFww@H=YL#jguB6kDE`q7o^z5dm02p z?ccJlKaXd z10yFccuiK`DZiW^)6wGHR-8 zA2!aAYmV9q`rUW$bH{H&6s)RHBQC`6vP4p>a;H#%CAEFtqVuizj>JMOaT|cYO?ytg zGyA6Hb3yMGD!B3{<)N?W+8{+`p`7-%sJm3Z&Tb3V@_8~fy}snlO-h`WSZ(iVX3W=s z#h7Qql$OsKTE5BWuT}1*Rx5U?*5V3ZZ;q_MT;2s6ZMHUV8<2MRbBnEN$ZB=6Qz-mY zzm1vy*W9Fin^q?wKSJgT<%^fc^d^T|U{_dAfp^`;xv!(S;ii{sM}1`w0L5 From 61ac7b716491e740add4984288766e7d3cd3a77a Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 5 Nov 2018 12:19:57 +0000 Subject: [PATCH 42/59] update learn package for diff pandoc In pandoc 2, gfm is favored over markdown_github This fixes that issue and also changes the Rproj to a package instead of a website so the Rmd files don't get re-rendered every time. --- R/render_new_rmds_to_md.R | 6 +++--- learn.Rproj | 4 +++- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/R/render_new_rmds_to_md.R b/R/render_new_rmds_to_md.R index ea546f1..1b95013 100644 --- a/R/render_new_rmds_to_md.R +++ b/R/render_new_rmds_to_md.R @@ -44,14 +44,14 @@ render_new_rmds_to_md <- function(dir = "content/post", if(build == "new"){ to_build <- unbuilt } - + pv <- rmarkdown::pandoc_version() >= package_version("2.0.0") + variant <- if(pv) "gfm" else "markdown_github" # build only the ones to be built if(length(to_build) > 0){ - for (b in to_build) { rmd <- b rmarkdown::render(rmd, - rmarkdown::md_document(variant = "gfm", + rmarkdown::md_document(variant = variant, preserve_yaml = TRUE )) } }else{ diff --git a/learn.Rproj b/learn.Rproj index 1b166b5..d1d41d5 100644 --- a/learn.Rproj +++ b/learn.Rproj @@ -12,4 +12,6 @@ Encoding: UTF-8 RnwWeave: knitr LaTeX: XeLaTeX -BuildType: Website +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source From 77e8d123720f461f85c6c782fe21cba1bcc89737 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 5 Nov 2018 14:00:51 +0000 Subject: [PATCH 43/59] attempt to use embedded map of stegen --- content/post/practical-stegen.Rmd | 6 +++++- content/post/practical-stegen.md | 14 +++++++++++--- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 4a0a134..d75924c 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -29,13 +29,17 @@ A self-service party buffet was supplied by a commercial caterer from a the near Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. Relative locations of the two schools and Freiburg can be seen on image 1 -![Image 1: Map of Stegen, with annoted school A and B](../../img/screenshots/map_stegen.png) + + + + + Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late into the night and alcoholic beverages were quite popular. All diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index d0bea62..caeb531 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -25,15 +25,23 @@ from a nearby school (school B). A self-service party buffet was supplied by a commercial caterer from a the nearby city of Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. Relative locations -of the two schools and Freiburg can be seen on image 1 +of the two schools and Freiburg can be seen on image +1 -![Image 1: Map of Stegen, with annoted school A and -B](../../img/screenshots/map_stegen.png) + + + + + + + Festivities started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late into the night and alcoholic beverages were quite From 51643deb72782c243985a08c7f926c6857fe579d Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 5 Nov 2018 14:08:08 +0000 Subject: [PATCH 44/59] add widget --- content/post/practical-stegen.Rmd | 6 +- content/post/practical-stegen.md | 12 +- static/post/widgets/stegen-casemap.html | 4833 +++++++++++++++++++++++ 3 files changed, 4848 insertions(+), 3 deletions(-) create mode 100755 static/post/widgets/stegen-casemap.html diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index d75924c..d06dfb6 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -27,7 +27,7 @@ friends, teachers, 12th grade students, and some graduates from a nearby school A self-service party buffet was supplied by a commercial caterer from a the nearby city of Freiburg. Food was prepared on the day of the party and transported in a -refrigerated van to the school. Relative locations of the two schools and Freiburg can be seen on image 1 +refrigerated van to the school. Relative locations of the two schools and Freiburg can be seen below: @@ -941,6 +941,10 @@ To complete your report, you would like to include a very basic spatial overview Normally you would generate maps in R using more sufisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies + + + +
      # Conclusion diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index caeb531..88fc219 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -25,8 +25,8 @@ from a nearby school (school B). A self-service party buffet was supplied by a commercial caterer from a the nearby city of Freiburg. Food was prepared on the day of the party and transported in a refrigerated van to the school. Relative locations -of the two schools and Freiburg can be seen on image -1 +of the two schools and Freiburg can be seen +below: @@ -1626,6 +1626,14 @@ tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies + + + + + +
      # Conclusion diff --git a/static/post/widgets/stegen-casemap.html b/static/post/widgets/stegen-casemap.html new file mode 100755 index 0000000..4d641cd --- /dev/null +++ b/static/post/widgets/stegen-casemap.html @@ -0,0 +1,4833 @@ + + + + + + + + + + + + + + + + + + + + + + + +
      +
      +
      + + + + From b4fd9c9a62d60be2267a73e4f8ffeb8f4ea5d9ee Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Mon, 5 Nov 2018 16:15:16 +0000 Subject: [PATCH 45/59] add ggmap --- content/post/practical-stegen.Rmd | 31 +++++++- content/post/practical-stegen.md | 70 +++++++++++++----- static/data/stegen-map/background.rds | Bin 0 -> 430044 bytes static/data/stegen_raw.xlsx | Bin 31189 -> 35748 bytes .../figure-gfm/incidence_stratified-1.png | Bin 12633 -> 22918 bytes .../incidence_stratified_custom-1.png | Bin 34504 -> 47180 bytes .../figure-gfm/plot-arrange-1.png | Bin 27969 -> 38583 bytes .../figure-gfm/stegen-ill-plot-1-1.png | Bin 0 -> 41100 bytes .../figure-gfm/stegen_case_age_sex-1.png | Bin 8187 -> 23763 bytes .../figure-gfm/stegen_ggplot2_custom-1.png | Bin 24139 -> 34637 bytes .../figure-gfm/stegen_histogram-1.png | Bin 5636 -> 17021 bytes .../figure-gfm/stegen_histogram-bin-1.png | Bin 5634 -> 17349 bytes .../figure-gfm/stegen_histogram_sex-1.png | Bin 6384 -> 19345 bytes .../figure-gfm/stegen_incidence-1.png | Bin 6171 -> 20016 bytes .../figure-gfm/unnamed-chunk-4-1.png | Bin 0 -> 137173 bytes 15 files changed, 79 insertions(+), 22 deletions(-) create mode 100755 static/data/stegen-map/background.rds create mode 100644 static/post/practical-stegen_files/figure-gfm/stegen-ill-plot-1-1.png create mode 100644 static/post/practical-stegen_files/figure-gfm/unnamed-chunk-4-1.png diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index d06dfb6..cc68b8c 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -109,6 +109,7 @@ The following packages will be used in the case study: - dplyr: to help with factors - *ggplot2*: to create custom visualisations - *epitools*: to calculate risk ratios +- *ggmap*: to plot googlemaps If we have these packages installed, we can tell R to load these packages from our R library: @@ -121,6 +122,7 @@ library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics library("epitools") # statistics for epi data +library("ggmap") # plotting googlemaps ```
      @@ -934,12 +936,37 @@ may be confounding factors involved (i.e. the food items were not independent). # Plotting a very basic spatial overview of cases -To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables *lat* and *long* include the longitude and latitude which we will use to plot the cases using *ggplot2*. +To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables `latitude` and `longitude` include the longitude and latitude which we will use to plot the cases using *ggplot2*. Here, we want to plot the data to see if +there is any spatial component to the outbreak. Since we have the coordinates +for each household, it becomes straightforward to plot them with *ggplot2*. Here +we will use points and color them based on whether or not the student was ill: + +```{r stegen-ill-plot-1} +ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) + + geom_point() + + coord_map() +``` + + +
      Advanced mapping and spatial methodologies? -Normally you would generate maps in R using more sufisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies +Normally you would generate maps in R using more sophisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies + +```{r, eval = -2, echo = -4} +stegen_coord <- c(longitude = 7.96301545767211, latitude = 47.9816228184647) +smap <- get_googlemap(center = stegen_coord, zoom = 15) +#> Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=47.981623,7.963015&zoom=15&size=640x640&scale=2&maptype=terrain&sensor=false +smap <- readRDS(here::here("static", "data", "stegen-map", "background.rds")) +tiramisu_labels <- c(`0` = "No Tiramisu", `1` = "Had Tiramisu") +ggmap(smap) + + geom_point(aes(x = longitude, y = latitude, color = ill), data = stegen) + + facet_wrap(~tiramisu, labeller = as_labeller(tiramisu_labels)) + + coord_map() +``` + diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 88fc219..c39fdbb 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -116,6 +116,7 @@ The following packages will be used in the case study: - dplyr: to help with factors - *ggplot2*: to create custom visualisations - *epitools*: to calculate risk ratios + - *ggmap*: to plot googlemaps If we have these packages installed, we can tell R to load these packages from our R library: @@ -128,6 +129,7 @@ library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics library("epitools") # statistics for epi data +library("ggmap") # plotting googlemaps ```
      @@ -180,7 +182,7 @@ commands: ``` r stegen -## # A tibble: 291 x 21 +## # A tibble: 291 x 23 ## `Unique key` ill `Date-onset` SEX Age tiramisu tportion wmousse ## ## 1 210 1 1998-06-27 1 18 1 3 0 @@ -193,10 +195,11 @@ stegen ## 8 106 1 1998-06-27 0 19 1 2 1 ## 9 272 1 1998-06-27 1 40 1 2 1 ## 10 50 1 1998-06-27 0 53 1 1 1 -## # ... with 281 more rows, and 13 more variables: dmousse , +## # ... with 281 more rows, and 15 more variables: dmousse , ## # mousse , mportion , Beer , redjelly , `Fruit ## # salad` , tomato , mince , salmon , -## # horseradish , chickenwin , roastbeef , PORK +## # horseradish , chickenwin , roastbeef , PORK , +## # latitude , longitude ## View(stegen) ``` @@ -234,13 +237,13 @@ the variables: ``` r dim(stegen) # rows x columns -## [1] 291 21 +## [1] 291 23 names(stegen) # column labels ## [1] "Unique key" "ill" "Date-onset" "SEX" "Age" ## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" ## [11] "mportion" "Beer" "redjelly" "Fruit salad" "tomato" ## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" -## [21] "PORK" +## [21] "PORK" "latitude" "longitude" ``` We can now try and summarise the dataset using: @@ -287,14 +290,14 @@ summary(stegen) ## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.00000 ## Max. :9.0000 Max. :9.0000 Max. :1.0000 Max. :1.00000 ## -## PORK -## Min. :0.0000 -## 1st Qu.:0.0000 -## Median :0.0000 -## Mean :0.4742 -## 3rd Qu.:1.0000 -## Max. :9.0000 -## +## PORK latitude longitude +## Min. :0.0000 Min. :47.98 Min. :7.952 +## 1st Qu.:0.0000 1st Qu.:47.98 1st Qu.:7.959 +## Median :0.0000 Median :47.98 Median :7.962 +## Mean :0.4742 Mean :47.98 Mean :7.962 +## 3rd Qu.:1.0000 3rd Qu.:47.98 3rd Qu.:7.965 +## Max. :9.0000 Max. :47.99 Max. :7.974 +## NA's :160 NA's :160 ``` Note that binary variables, when treated as numeric values (0/1), are @@ -368,7 +371,7 @@ new_labels # check the result ## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" ## [11] "mportion" "beer" "redjelly" "fruit_salad" "tomato" ## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" -## [21] "pork" +## [21] "pork" "latitude" "longitude" names(stegen) <- new_labels ``` @@ -928,7 +931,7 @@ names(stegen) ## [6] "tiramisu" "tportion" "wmousse" "dmousse" "mousse" ## [11] "mportion" "beer" "redjelly" "fruit_salad" "tomato" ## [16] "mince" "salmon" "horseradish" "chickenwin" "roastbeef" -## [21] "pork" +## [21] "pork" "latitude" "longitude" ``` In this case, we need to retain columns 6 to 21, excluding `tportion` @@ -1613,18 +1616,45 @@ To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their -household (in relation to their postal code). The variables *lat* and -*long* include the longitude and latitude which we will use to plot the -cases using *ggplot2*. +household (in relation to their postal code). The variables `latitude` +and `longitude` include the longitude and latitude which we will use to +plot the cases using *ggplot2*. Here, we want to plot the data to see if +there is any spatial component to the outbreak. Since we have the +coordinates for each household, it becomes straightforward to plot them +with *ggplot2*. Here we will use points and color them based on whether +or not the student was ill: + +``` r +ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) + + geom_point() + + coord_map() +``` + +![](practical-stegen_files/figure-gfm/stegen-ill-plot-1-1.png)
      Advanced mapping and spatial methodologies? -Normally you would generate maps in R using more sufisticated mapping +Normally you would generate maps in R using more sophisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future -case studies +case +studies + +``` r +stegen_coord <- c(longitude = 7.96301545767211, latitude = 47.9816228184647) +## smap <- get_googlemap(center = stegen_coord, zoom = 15) +#> Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=47.981623,7.963015&zoom=15&size=640x640&scale=2&maptype=terrain&sensor=false +tiramisu_labels <- c(`0` = "No Tiramisu", `1` = "Had Tiramisu") +ggmap(smap) + + geom_point(aes(x = longitude, y = latitude, color = ill), data = stegen) + + facet_wrap(~tiramisu, labeller = as_labeller(tiramisu_labels)) + + coord_map() +## Coordinate system already present. Adding new coordinate system, which will replace the existing one. +``` + +![](practical-stegen_files/figure-gfm/unnamed-chunk-4-1.png) diff --git a/static/data/stegen-map/background.rds b/static/data/stegen-map/background.rds new file mode 100755 index 0000000000000000000000000000000000000000..67d285b71829b62bc903bf5630ffda42aa615209 GIT binary patch literal 430044 zcmZs?2RPOJ8$bR$B_tyxo6PD+WoK7rR+Qt|r|cE7N1P-(gzQZu2glwyMjT)St+em@ghd!W2!%I4AN++&zo^R!ZorK27fyYMhT1Tv0r}I*pA=Auc2|^ zez`hapT$10^W#{URI(y%s^vJSA z8fI$MTFdd_LrGN01lk!hvRZS}CYuIt_~vUkg+`+>JD1V2_&2^jsc#pzk}@Z$LW|`0 zf^cunR%YQSd|?nyw`yJ07jbsd7+=NiDlF#Tdhm_)w0D}>w}wuJ&6^psV|y3BGyVgQ zIX;}TrQ$-suF)4BsTLtFzFvRiBK?ZO_CAdOG3Q`5)P{&F>ckVXvwt|yLIG<_V$h|+ z9iPsj*zprB-z(-Jh82O~1#(=rKih3eUKCBj{|=yf+0maJ?nmp~Ztc7rxdf+eLU?)H zH(oHeg>~Qd9O*>+;*NS{>yFzZhuQB*V|Lc`@H=H;n_f*88h7hY#-3JQiE;B1Tq!WG zxls1|bc-EIk^CtNgBtX$_LqewAC$IA_B%z%6%o z*EOnM?*=k1-dR#t@@%=N1Mho!CW}C<%`|z*MrS$+-0R#wIzp%8eN`SEsRjP(_2GXk 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b/content/post/practical-stegen.Rmd @@ -160,12 +160,21 @@ saved in a `data/` folder of your project, and that your current R session is at the root of the project. -Here we decompose the steps to read data in: finding the path to the data -(`path_to_data`), using the function `read_xlsx()` from the *readxl* package to -read data in, and saving the output in a new object `stegen`: +Here we decompose the steps to read data in: -```{r eval = FALSE} +1. finding the path to the data (`path_to_data`) with the *here* package +2. using the function `read_xlsx()` from the *readxl* package to read data in, + and saving the output in a new object `stegen`. + +```{r stegen_show_data} path_to_data <- here("data", "stegen_raw.xlsx") +path_to_data +``` + +> n.b. the value of `path_to_data` will not be the what you see in this +tutorial. It will be the location of this data set on your computer. + +```{r fake_read_Data, eval = FALSE} stegen <- read_xlsx(path_to_data) ``` @@ -173,7 +182,7 @@ stegen <- read_xlsx(path_to_data) stegen <- readxl::read_xlsx(here::here("static", "data", "stegen_raw.xlsx")) ``` -To print the content of the dataset, we can use either of these commands: +To look at the content of the dataset, we can use either of these commands: ```{r stegen_print_data, eval = 1} stegen @@ -191,7 +200,7 @@ If the above fails, you should check: - that your data file has been saved in the right folder, with the right name (lower case and upper case do matter) - that your R session was started from the right project - if unsure, close R -and re-open Rstudio by double-clicking on the `.Rproj` file + and re-open Rstudio by double-clicking on the `.Rproj` file - that all packages are installed and loaded (see installation guidelines above)

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      @@ -320,13 +329,17 @@ There are several things going on in a command like: stegen$pork[stegen$pork == 9] <- NA ``` -let us break them down: +let us break them down from the inside out. 1. `stegen$pork` means "get the variable called `pork` in the dataset `stegen`" -2. `[...]` subset the vector according to `...`; if `...` is a series of - `TRUE/FALSE` values, only entries corresponding to `TRUE` are retained -3. `... == 9` for each value of `...` test if it is equal to `9`, and return `TRUE` if so (`FALSE` otherwise) -4. `... <- NA` replace `...` with `NA` (missing value) +2. The `==` is a logical test for equality. `stegen$pork == 9` tests each + element in `stegen$pork` if it's equal to `9`, returning `TRUE` if an element of + `stegen$pork` is equal to `9` and `FALSE` if it doesn't. +3. The square brackets (`[ ]`) subset the vector `stegen$pork` according to + whatever is between them; In this case, it's the test `stegen$pork == 9`, which + evaluates to `r head(stegen_old$PORK == 9)`... This will return only the cases + in `stegen$pork` that have `9`s recorded. +4. The replacement: `... <- NA` replace `...` with `NA` (missing value) in other words: "isolate the entries of `stegen$pork` which equal `9`, and replace them with `NA`; here is another toy example to illustrate the procedure: @@ -360,6 +373,8 @@ clean_dir <- here("data", "cleaned") dir.create(clean_dir) ``` +> STOP: open your file browser and confirm that file called "data/cleaned" has been created + After this, we can save our cleaned data into a new file called `stegen_clean.csv`: @@ -391,7 +406,7 @@ tapply(stegen$age, INDEX = stegen$sex, FUN = summary) # age stats by gender `tapply()` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply()`, but briefly, the syntax to be used is `tapply(input_data, stratification, -function_to_use, optional_arguments)`. In the command used above: +function_to_use, further_arguments)`. In the command used above: ```{r eval = FALSE} tapply(stegen$age, INDEX = stegen$sex, FUN = summary) @@ -404,8 +419,8 @@ this literally means: select the age variable in the dataset `stegen` ```{r stegen_age_per_sex} tapply(stegen$age, INDEX = stegen$sex, FUN = mean, na.rm = TRUE) ``` -Here we illustrate that further arguments to the function can be passed as extra -arguments; here, `na.rm = TRUE` means "ignore missing data". +Here we illustrate that further arguments to the function `mean()` can be passed +on; here, `na.rm = TRUE` means "ignore missing data".
      @@ -942,8 +957,8 @@ for each household, it becomes straightforward to plot them with *ggplot2*. Here we will use points and color them based on whether or not the student was ill: ```{r stegen-ill-plot-1} -ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) + - geom_point() + +ggplot(stegen) + + geom_point(aes(x = longitude, y = latitude, color = ill)) + coord_map() ``` @@ -953,23 +968,31 @@ ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) +
      Advanced mapping and spatial methodologies? -Normally you would generate maps in R using more sophisticated mapping tools, but for the scope of this case study we will keep it more basic. We will explore mapping and spatial analytical methods this in future case studies +If we wanted to get a sense of where these cases were in relation to the +buildings, one option is to use the *ggmap* package. This provides the ability +to download maps from google or open street maps to use as a background for your +data. Note that here, the first call is to `ggmap()` instead of `ggplot()` and +in the `geom_point()`, we use an extra parameter called `data` to tell the geom +where the data for the points are coming from: + +> **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE** -```{r, eval = -2, echo = -4} +```{r ggmap, eval = -2, echo = -4} stegen_coord <- c(longitude = 7.96301545767211, latitude = 47.9816228184647) smap <- get_googlemap(center = stegen_coord, zoom = 15) #> Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=47.981623,7.963015&zoom=15&size=640x640&scale=2&maptype=terrain&sensor=false smap <- readRDS(here::here("static", "data", "stegen-map", "background.rds")) -tiramisu_labels <- c(`0` = "No Tiramisu", `1` = "Had Tiramisu") ggmap(smap) + geom_point(aes(x = longitude, y = latitude, color = ill), data = stegen) + - facet_wrap(~tiramisu, labeller = as_labeller(tiramisu_labels)) + coord_map() ``` +The downside of using *ggmap* is that you are limited by your internet connection +and how many times you can download data from the API. In most situations, you +would be using shapefiles. - +
      diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index c39fdbb..bd9fc5a 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -167,17 +167,29 @@ common format. We assume that [the data file `data/` folder of your project, and that your current R session is at the root of the project. -Here we decompose the steps to read data in: finding the path to the -data (`path_to_data`), using the function `read_xlsx()` from the -*readxl* package to read data in, and saving the output in a new object -`stegen`: +Here we decompose the steps to read data in: + +1. finding the path to the data (`path_to_data`) with the *here* + package +2. using the function `read_xlsx()` from the *readxl* package to read + data in, and saving the output in a new object `stegen`. + + ``` r path_to_data <- here("data", "stegen_raw.xlsx") +path_to_data +## [1] "/Users/zhian/Documents/Imperial/Websites/recon-learn/data/stegen_raw.xlsx" +``` + +> n.b. the value of `path_to_data` will not be the what you see in this +> tutorial. It will be the location of this data set on your computer. + +``` r stegen <- read_xlsx(path_to_data) ``` -To print the content of the dataset, we can use either of these +To look at the content of the dataset, we can use either of these commands: ``` r @@ -435,16 +447,20 @@ There are several things going on in a command like: stegen$pork[stegen$pork == 9] <- NA ``` -let us break them down: +let us break them down from the inside out. 1. `stegen$pork` means “get the variable called `pork` in the dataset `stegen`” -2. `[...]` subset the vector according to `...`; if `...` is a series - of `TRUE/FALSE` values, only entries corresponding to `TRUE` are - retained -3. `... == 9` for each value of `...` test if it is equal to `9`, and - return `TRUE` if so (`FALSE` otherwise) -4. `... <- NA` replace `...` with `NA` (missing value) +2. The `==` is a logical test for equality. `stegen$pork == 9` tests + each element in `stegen$pork` if it’s equal to `9`, returning `TRUE` + if an element of `stegen$pork` is equal to `9` and `FALSE` if it + doesn’t. +3. The square brackets (`[ ]`) subset the vector `stegen$pork` + according to whatever is between them; In this case, it’s the test + `stegen$pork == 9`, which evaluates to FALSE, FALSE, FALSE, TRUE, + FALSE, FALSE… This will return only the cases in `stegen$pork` that + have `9`s recorded. +4. The replacement: `... <- NA` replace `...` with `NA` (missing value) in other words: "isolate the entries of `stegen$pork` which equal `9`, and replace them with `NA`; here is another toy example to illustrate @@ -482,6 +498,9 @@ clean_dir <- here("data", "cleaned") dir.create(clean_dir) ``` +> STOP: open your file browser and confirm that file called +> “data/cleaned” has been created + After this, we can save our cleaned data into a new file called `stegen_clean.csv`: @@ -524,7 +543,7 @@ tapply(stegen$age, INDEX = stegen$sex, FUN = summary) # age stats by gender `tapply()` is a very handy function to stratify any kind of analyses. You will find more details by reading the documentation of the function `?tapply()`, but briefly, the syntax to be used is `tapply(input_data, -stratification, function_to_use, optional_arguments)`. In the command +stratification, function_to_use, further_arguments)`. In the command used above: ``` r @@ -542,8 +561,8 @@ tapply(stegen$age, INDEX = stegen$sex, FUN = mean, na.rm = TRUE) ## 26.37778 26.92568 ``` -Here we illustrate that further arguments to the function can be passed -as extra arguments; here, `na.rm = TRUE` means “ignore missing data”. +Here we illustrate that further arguments to the function `mean()` can +be passed on; here, `na.rm = TRUE` means “ignore missing data”.
      @@ -1625,8 +1644,8 @@ with *ggplot2*. Here we will use points and color them based on whether or not the student was ill: ``` r -ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) + - geom_point() + +ggplot(stegen) + + geom_point(aes(x = longitude, y = latitude, color = ill)) + coord_map() ``` @@ -1636,31 +1655,37 @@ ggplot(stegen, aes(x = longitude, y = latitude, color = ill)) +
      Advanced mapping and spatial methodologies? -Normally you would generate maps in R using more sophisticated mapping -tools, but for the scope of this case study we will keep it more basic. -We will explore mapping and spatial analytical methods this in future -case -studies +If we wanted to get a sense of where these cases were in relation to the +buildings, one option is to use the *ggmap* package. This provides the +ability to download maps from google or open street maps to use as a +background for your data. Note that here, the first call is to `ggmap()` +instead of `ggplot()` and in the `geom_point()`, we use an extra +parameter called `data` to tell the geom where the data for the points +are coming from: + +> **If you are currently in a workshop: PLEASE DO NOT RUN THIS +CODE** ``` r stegen_coord <- c(longitude = 7.96301545767211, latitude = 47.9816228184647) ## smap <- get_googlemap(center = stegen_coord, zoom = 15) #> Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=47.981623,7.963015&zoom=15&size=640x640&scale=2&maptype=terrain&sensor=false -tiramisu_labels <- c(`0` = "No Tiramisu", `1` = "Had Tiramisu") ggmap(smap) + geom_point(aes(x = longitude, y = latitude, color = ill), data = stegen) + - facet_wrap(~tiramisu, labeller = as_labeller(tiramisu_labels)) + coord_map() ## Coordinate system already present. Adding new coordinate system, which will replace the existing one. ``` -![](practical-stegen_files/figure-gfm/unnamed-chunk-4-1.png) +![](practical-stegen_files/figure-gfm/ggmap-1.png) - +The downside of using *ggmap* is that you are limited by your internet +connection and how many times you can download data from the API. In +most situations, you would be using +shapefiles. - + diff --git a/static/post/practical-stegen_files/figure-gfm/ggmap-1.png b/static/post/practical-stegen_files/figure-gfm/ggmap-1.png new file mode 100644 index 0000000000000000000000000000000000000000..7b420f2d8e387cb88e85fed80f97c65af600bcd1 GIT binary patch literal 238095 zcmZs?Wl$VW@UFWoF2N&`!$?o}GcoF_@^7#xumAwSn=cA7ng9T>2mnCZ#6bJc;~T%h2LPau z+eu5S+e^z!J3BbLX}OqNg5*I?AU8WpO?fE*;B!KPwy`ahHnCWzyUn}zQ(6vu5gxz9 z-+XwJro&g<)Dg6{#_yE*g@2jh!0PsFDtmnDGjHc}v6`&9Klf2C@lbDTFdfqk-ilZg zX9m`X<-c@HVomp9>IZRU$2r2hId&cO>!36LtNilC8`1-Qrc3|UUJ7ek<)}&MTxl&D_kLkV zCm~&wYeT(|d>>P2#g7bVZY>U4xg~};ecQGy7Y5zM0u$`4ciFb*qEWTAzC~x@Cru3B z%i%n6*Ug(2seC)GJb;y0O8>Z4U@#3!gNv$r1|KWGe3k@*+;NAOpL1ogaW#6)Gv=8EdZ zQqe;uuws%x+aO)Etq8+K;~}mgCXY2OwkD(8?-PLwHRVk9_Y9=JjE$LwJ+w_Vu~bEMp_HiY`86v@1V?2sK(uh5z~63LpA}8mTmI< z4&A8-(}!aO&n+wB+t1CQUERx8w^4(M&IaYd69+waZ*2-kv>nomKRLrO!_-%*&6*t!`5n4U+vCj%e>lL1o!{l>r) zVQBtN&lZOZt-I;S?YAomgAG4Y@Y`0K!%X{XMf!IRBs8RM1N>VW<~(pl91Ke-jm416 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z=_j}2v75MlYxwf%8j?eIrk9dYn=K6}=NOz?DlT)vrYnlCCY-o@0?(dqlQj{{U2 z8f2y=3+2T>q3y4wi-r!1yhtj+zQsR{OJ26fT-4IUlX7RGf3zqvq=WZbVjiXBWM_|* z!k?M(>9<-Wn|3hg$A& Date: Tue, 6 Nov 2018 11:47:36 +0700 Subject: [PATCH 47/59] minor changes We might wants to clarify a few things slightly better to make sure we catch beginning R users better. Especially around setting paths, importing data. Reason from 'classical data import' in excel/STATA --- content/post/practical-stegen.Rmd | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index f4bde02..0b46078 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -40,7 +40,7 @@ Please find the HTML for the iframe of the embedded interactive map: -Festivities started with a dinner buffet open from 8.30 pm onwards and were +Festivities at school A started with a dinner buffet open from 8.30 pm onwards and were followed by a dessert buffet offered from 10 pm. The party and the buffet extended late into the night and alcoholic beverages were quite popular. All agreed it was a party to be remembered. @@ -174,6 +174,9 @@ path_to_data > n.b. the value of `path_to_data` will not be the what you see in this tutorial. It will be the location of this data set on your computer. + + ```{r fake_read_Data, eval = FALSE} stegen <- read_xlsx(path_to_data) ``` @@ -945,9 +948,9 @@ ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + scale_color_viridis_c() ``` -The results are a lot clearer now: the tiramisu is by far the largest risk -factor in this outbreak, but it's wide confidence interval suggests that there -may be confounding factors involved (i.e. the food items were not independent). +The results are a lot clearer now: the tiramisu has by far the highest risk +ratio in this outbreak, but it's wide confidence interval suggests that there +may be confounding factors involved (i.e. the food items were not independent or in this case, were potentially sharing contaminated ingredients). # Plotting a very basic spatial overview of cases From f4c785f83735f0aced463ef837d97d8aa718ca32 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Tue, 6 Nov 2018 09:12:41 +0000 Subject: [PATCH 48/59] start on changes to using sf --- content/post/practical-stegen.Rmd | 26 ++++++++++++------- static/data/stegen-map/stegen_households.cpg | 1 + static/data/stegen-map/stegen_households.prj | 1 + static/data/stegen-map/stegen_households.qpj | 1 + static/data/stegen-map/stegen_households.shp | Bin 0 -> 218596 bytes static/data/stegen-map/stegen_households.shx | Bin 0 -> 10052 bytes 6 files changed, 20 insertions(+), 9 deletions(-) create mode 100755 static/data/stegen-map/stegen_households.cpg create mode 100755 static/data/stegen-map/stegen_households.prj create mode 100755 static/data/stegen-map/stegen_households.qpj create mode 100755 static/data/stegen-map/stegen_households.shp create mode 100755 static/data/stegen-map/stegen_households.shx diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index f4bde02..3ba9e8e 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -122,7 +122,8 @@ library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics library("epitools") # statistics for epi data -library("ggmap") # plotting googlemaps +library("sf") +library("leaflet") ```

      @@ -962,7 +963,9 @@ ggplot(stegen) + coord_map() ``` +## Using shapefile data +It's not uncommon to have community-level s
      @@ -975,18 +978,23 @@ data. Note that here, the first call is to `ggmap()` instead of `ggplot()` and in the `geom_point()`, we use an extra parameter called `data` to tell the geom where the data for the points are coming from: +```{r read-shapefiles, eval = 2, echo = 1} +stegen_shp <- read_sf(here("data", "stegen-map", "stegen_households.shp")) +stegen_shp <- read_sf(here("static/data/stegen-map/stegen_households.shp")) +``` + + > **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE** -```{r ggmap, eval = -2, echo = -4} -stegen_coord <- c(longitude = 7.96301545767211, latitude = 47.9816228184647) -smap <- get_googlemap(center = stegen_coord, zoom = 15) -#> Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=47.981623,7.963015&zoom=15&size=640x640&scale=2&maptype=terrain&sensor=false -smap <- readRDS(here::here("static", "data", "stegen-map", "background.rds")) -ggmap(smap) + - geom_point(aes(x = longitude, y = latitude, color = ill), data = stegen) + - coord_map() +```{r sf_map} + +ggplot(stegen) + + geom_point(aes(x = longitude, y = latitude, color = ill)) + + geom_sf(data = stegen_shp) ``` + + The downside of using *ggmap* is that you are limited by your internet connection and how many times you can download data from the API. In most situations, you would be using shapefiles. diff --git a/static/data/stegen-map/stegen_households.cpg b/static/data/stegen-map/stegen_households.cpg new file mode 100755 index 0000000..3ad133c --- /dev/null +++ b/static/data/stegen-map/stegen_households.cpg @@ -0,0 +1 @@ +UTF-8 \ No newline at end of file diff --git a/static/data/stegen-map/stegen_households.prj b/static/data/stegen-map/stegen_households.prj new file mode 100755 index 0000000..a30c00a --- /dev/null +++ b/static/data/stegen-map/stegen_households.prj @@ -0,0 +1 @@ +GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]] \ No newline at end of file diff --git a/static/data/stegen-map/stegen_households.qpj b/static/data/stegen-map/stegen_households.qpj new file mode 100755 index 0000000..5fbc831 --- /dev/null +++ b/static/data/stegen-map/stegen_households.qpj @@ -0,0 +1 @@ +GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 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Kamvar" Date: Tue, 6 Nov 2018 10:30:01 +0000 Subject: [PATCH 49/59] update with spatial silliness --- content/post/practical-stegen.Rmd | 69 +- content/post/practical-stegen.md | 111 +- .../figure-gfm/sf_map-1.png | Bin 0 -> 84133 bytes static/post/widgets/stegen-casemap.html | 8725 ++++++++--------- 4 files changed, 4013 insertions(+), 4892 deletions(-) create mode 100644 static/post/practical-stegen_files/figure-gfm/sf_map-1.png mode change 100755 => 100644 static/post/widgets/stegen-casemap.html diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 9c4731e..3a2200c 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -10,6 +10,7 @@ licenses: CC-BY image: img/highres/graduation-1965.jpg showonlyimage: true toc: true +always_allow_html: yes --- ```{r setup, include=FALSE} @@ -958,7 +959,7 @@ may be confounding factors involved (i.e. the food items were not independent or To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables `latitude` and `longitude` include the longitude and latitude which we will use to plot the cases using *ggplot2*. Here, we want to plot the data to see if there is any spatial component to the outbreak. Since we have the coordinates for each household, it becomes straightforward to plot them with *ggplot2*. Here -we will use points and color them based on whether or not the student was ill: +we will use points and color them based on whether or not the person was ill: ```{r stegen-ill-plot-1} ggplot(stegen) + @@ -966,47 +967,69 @@ ggplot(stegen) + coord_map() ``` -## Using shapefile data - -It's not uncommon to have community-level s - +Here, we can see that there is no clear correlation between illness and location. - # Conclusion diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index bd9fc5a..0fff56c 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -10,6 +10,7 @@ licenses: CC-BY image: img/highres/graduation-1965.jpg showonlyimage: true toc: true +always_allow_html: yes --- @@ -42,10 +43,11 @@ Please find the HTML for the iframe of the embedded interactive map: --> +``` r +# create the map +lmap <- leaflet() +# add open street map tiles +lmap <- addTiles(lmap) +# set the coordinates for Stegen +lmap <- setView(lmap, lng = 7.963, lat = 47.982, zoom = 15) +# Add the shapefile +lmap <- addPolygons(lmap, data = st_transform(stegen_shp, '+proj=longlat +ellps=GRS80')) +# Add the cases +lmap <- addMarkers(lmap, label = ~ill, data = stegen_sub) +# show the map +lmap +``` -
      -Advanced mapping and spatial methodologies? +## Using shapefile data -If we wanted to get a sense of where these cases were in relation to the -buildings, one option is to use the *ggmap* package. This provides the ability -to download maps from google or open street maps to use as a background for your -data. Note that here, the first call is to `ggmap()` instead of `ggplot()` and -in the `geom_point()`, we use an extra parameter called `data` to tell the geom -where the data for the points are coming from: +It's not uncommon to have community-level shapefile data. These data give us a +better idea of how the cases relate to each other. In this instance, we have +shapefiles of the households located in `data/stegen-map/stegen_households.shp`. +We will use the `read_sf()` function from the *sf* package to read the +shapefiles in: ```{r read-shapefiles, eval = 2, echo = 1} stegen_shp <- read_sf(here("data", "stegen-map", "stegen_households.shp")) stegen_shp <- read_sf(here("static/data/stegen-map/stegen_households.shp")) ``` - -> **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE** +Now we can use the same ggplot2 code as above, but we place a call to `geom_sf()` +right after the ggplot call to place the shapes underneath the points. Note that +we are removing the `coord_map()` because the shapefile ensures that the +projection is correct. ```{r sf_map} - ggplot(stegen) + - geom_point(aes(x = longitude, y = latitude, color = ill)) + - geom_sf(data = stegen_shp) + geom_sf(data = stegen_shp) + + geom_point(aes(x = longitude, y = latitude, color = ill)) ``` +## Interactive maps + +It is also possible to create interactive maps that allow you to zoom in and get +information about individual cases. This uses a different system than *ggplot2*, +but behaves in a similar idea of building up the map layer by layer. Here, we +use the package *leaflet*, which uses image data from open street maps. Because +mapping could take up an entire workshop in and of itself, we will present the +code as is. One thing that we must do first is to subset the data to all cases +with non-missing coordinates with the command `!is.na()` where the `!` means +"not" and `is.na()` checks each element of a vector if it's missing. +```{r subset_coords} +stegen_sub <- stegen[!is.na(stegen$longitude), ] +``` -The downside of using *ggmap* is that you are limited by your internet connection -and how many times you can download data from the API. In most situations, you -would be using shapefiles. +> **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE**. It may +> overload the servers + +```{r interactive_map, eval = FALSE} +# create the map +lmap <- leaflet() +# add open street map tiles +lmap <- addTiles(lmap) +# set the coordinates for Stegen +lmap <- setView(lmap, lng = 7.963, lat = 47.982, zoom = 15) +# Add the shapefile +lmap <- addPolygons(lmap, data = st_transform(stegen_shp, '+proj=longlat +ellps=GRS80')) +# Add the cases +lmap <- addMarkers(lmap, label = ~ill, data = stegen_sub) +# show the map +lmap +``` - + -
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b/static/post/widgets/stegen-casemap.html old mode 100755 new mode 100644 index 4d641cd..1ef7698 --- a/static/post/widgets/stegen-casemap.html +++ b/static/post/widgets/stegen-casemap.html @@ -1,4833 +1,3892 @@ - - - - - - - - - - - - - - - - - - - - - - - -
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      + + + + From 2b2d6acc28264690d00811cedcfbd7333b4cc540 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Tue, 6 Nov 2018 10:42:24 +0000 Subject: [PATCH 50/59] fix stupid echo thing --- content/post/practical-stegen.Rmd | 5 ++++- content/post/practical-stegen.md | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 3a2200c..fe096e7 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -977,8 +977,11 @@ shapefiles of the households located in `data/stegen-map/stegen_households.shp`. We will use the `read_sf()` function from the *sf* package to read the shapefiles in: -```{r read-shapefiles, eval = 2, echo = 1} +```{r read-shapefiles_fake, eval = FALSE} stegen_shp <- read_sf(here("data", "stegen-map", "stegen_households.shp")) +``` + +```{r read-shapefiles, echo = FALSE} stegen_shp <- read_sf(here("static/data/stegen-map/stegen_households.shp")) ``` diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 0fff56c..aad6bea 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -1672,7 +1672,7 @@ function from the *sf* package to read the shapefiles in: ``` r -## stegen_shp <- read_sf(here("data", "stegen-map", "stegen_households.shp")) +stegen_shp <- read_sf(here("data", "stegen-map", "stegen_households.shp")) ``` Now we can use the same ggplot2 code as above, but we place a call to From d61860743810eb4e87e43c0a446f51150e67cf49 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Wed, 7 Nov 2018 11:54:31 +0000 Subject: [PATCH 51/59] fix a couple of things in the stegen practical - make risk ratio plot log 10 - add details tag for creating second function --- content/post/practical-stegen.Rmd | 258 +++++++++----- content/post/practical-stegen.md | 328 +++++++++++------- .../figure-gfm/incidence_stratified-1.png | Bin 22918 -> 12633 bytes .../incidence_stratified_custom-1.png | Bin 47180 -> 34504 bytes .../figure-gfm/plot-arrange-1.png | Bin 38583 -> 28283 bytes .../figure-gfm/sf_map-1.png | Bin 84133 -> 80093 bytes .../figure-gfm/stegen-ill-plot-1-1.png | Bin 41100 -> 27676 bytes .../figure-gfm/stegen_case_age_sex-1.png | Bin 23763 -> 8187 bytes .../figure-gfm/stegen_ggplot2_custom-1.png | Bin 34637 -> 24139 bytes .../figure-gfm/stegen_histogram-1.png | Bin 17021 -> 5636 bytes .../figure-gfm/stegen_histogram-bin-1.png | Bin 17349 -> 5634 bytes .../figure-gfm/stegen_histogram_sex-1.png | Bin 19345 -> 6384 bytes .../figure-gfm/stegen_incidence-1.png | Bin 20016 -> 6171 bytes 13 files changed, 384 insertions(+), 202 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index fe096e7..fc72a5f 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -93,10 +93,6 @@ analysis using **R**, including: - how to test if specific food items are linked to the disease - how to plot a very basic spatial overview of cases - - - - # Initial data processing ## Loading required packages @@ -110,7 +106,8 @@ The following packages will be used in the case study: - dplyr: to help with factors - *ggplot2*: to create custom visualisations - *epitools*: to calculate risk ratios -- *ggmap*: to plot googlemaps +- *sf*: To read in shape files +- *leaflet*: to demonstrate interactive maps If we have these packages installed, we can tell R to load these packages from our R library: @@ -123,8 +120,8 @@ library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics library("epitools") # statistics for epi data -library("sf") -library("leaflet") +library("sf") # shapefile handling +library("leaflet") # interactive maps ```
      @@ -141,6 +138,8 @@ install.packages("epitrix") install.packages("dplyr") install.packages("ggplot2") install.packages("epitools") +install.packages("sf") +install.packages("leaflet") ```
      @@ -610,80 +609,6 @@ tables for each predictor (food item) against the defined cases, calculate their risk ratio with confidence intervals and p-values, and plot them as points and errorbars using *ggplot2*. -
      - - Going Further: Different types of Univariate tests - -## Univariate tests - -Methods for testing the association between two variables can be broken down in 3 types, -depending on which types these variables are: - -1. **2 categorical variables**: Chi-squared test on the 2x2 table - (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) -2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with - 2 groups: Student's $t$-test -3. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar - methods - - -We can use these approaches to test if the disease is linked to any of the other -recorded variables. As illness itself is a categorical variable, only approaches -of type 2 and 3 are illustrated in this case study. - - -### Is illness linked to age? - -We can use the function `t.test()` to test if the average age is different -across illness status. As this test assumes that the two categories exhibit -similar variation, we first ensure that the variances are comparable using -Bartlett's test. The syntax `variable ~ group` is used to indicate the variable -of interest (left hand-side), and the group (right hand-side): - -```{r stegen_bartlett_test} -bartlett.test(stegen$age ~ stegen$ill) -``` - -The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)[["p.value"]], 3)`) suggests the variances are indeed comparable. Wecan thus proceed to a $t$-test: - - -```{r stegen_t_test} -t.test(stegen$age ~ stegen$ill, var.equal = TRUE) -``` - -The p-value of `r round(t.test(stegen$age ~ stegen$ill, var.equal = TRUE)[["p.value"]], 3)` confirms the previous graphics: the illness does not seem to be linked to age. - - - -### Is illness linked to gender? - -To test the association between gender and illness (2 categorical variables), we -first build a 2-by-2 (contingency) table, using: - -```{r stegen_2by2} -tab_ill_sex <- table(stegen$ill, stegen$sex) -tab_ill_sex -``` - -Note that proportions can be obtained using `prop.table`: - -```{r stegen_prop_table} -## basic proportions -prop.table(tab_ill_sex) - -## expressed in %, rounded: -round(100 * prop.table(tab_ill_sex)) -``` - -Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: - -```{r stegen_chisq} -chisq.test(tab_ill_sex) -``` - -Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests illness is not related to gender either. - -
      ### Isolating the variables to test @@ -826,7 +751,82 @@ Now we have a table that tells us that pork has a risk ratio of `r round(res$estimate, 3)` CI: (`r round(res$lower, 3)`, `r round (res$upper, 3)`), P = `r round(res$p.value, 3)`. -### Consolidating several steps into one +
      + + Going Further: Different types of Univariate tests + +## Univariate tests + +Methods for testing the association between two variables can be broken down in 3 types, +depending on which types these variables are: + +1. **2 categorical variables**: Chi-squared test on the 2x2 table + (a.k.a. *contingency* table) and similar methods (e.g. Fisher's exact test) +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; particular case with + 2 groups: Student's $t$-test +3. **2 quantitative variables**: Pearson's correlation coefficient ($r$) and similar + methods + + +We can use these approaches to test if the disease is linked to any of the other +recorded variables. As illness itself is a categorical variable, only approaches +of type 2 and 3 are illustrated in this case study. + + +### Is illness linked to age? + +We can use the function `t.test()` to test if the average age is different +across illness status. As this test assumes that the two categories exhibit +similar variation, we first ensure that the variances are comparable using +Bartlett's test. The syntax `variable ~ group` is used to indicate the variable +of interest (left hand-side), and the group (right hand-side): + +```{r stegen_bartlett_test} +bartlett.test(stegen$age ~ stegen$ill) +``` + +The resulting p-value (`r round(bartlett.test(stegen$age ~ stegen$ill)[["p.value"]], 3)`) suggests the variances are indeed comparable. We can thus proceed to a $t$-test: + + +```{r stegen_t_test} +t.test(stegen$age ~ stegen$ill, var.equal = TRUE) +``` + +The p-value of `r round(t.test(stegen$age ~ stegen$ill, var.equal = TRUE)[["p.value"]], 3)` confirms the previous graphics: the illness does not seem to be linked to age. + + + +### Is illness linked to gender? + +To test the association between gender and illness (2 categorical variables), we +first build a 2-by-2 (contingency) table, using: + +```{r stegen_2by2} +tab_ill_sex <- table(stegen$ill, stegen$sex) +tab_ill_sex +``` + +Note that proportions can be obtained using `prop.table`: + +```{r stegen_prop_table} +## basic proportions +prop.table(tab_ill_sex) + +## expressed in %, rounded: +round(100 * prop.table(tab_ill_sex)) +``` + +Once a contingency table has been built, the Chi-square test can be run using `chisq.test`: + +```{r stegen_chisq} +chisq.test(tab_ill_sex) +``` + +Here, the p-value of `r round(chisq.test(tab_ill_sex)[["p.value"]], 3)` suggests illness is not related to gender either. + +
      + +### Consolidating several steps into one (writing functions) To recap, to get a risk ratio we've done the following: @@ -943,20 +943,104 @@ Now we can use this data frame to plot the results using *ggplot2*: # first, make sure the predictors are factored in the right order all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) # plot -ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + +p <- ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_point() + geom_errorbarh(aes(xmin = lower, xmax = upper)) + geom_vline(xintercept = 1, linetype = 2) + + scale_x_log10() + scale_color_viridis_c() +p ``` The results are a lot clearer now: the tiramisu has by far the highest risk ratio in this outbreak, but it's wide confidence interval suggests that there -may be confounding factors involved (i.e. the food items were not independent or in this case, were potentially sharing contaminated ingredients). +may be confounding factors involved (i.e. the food items were not independent +or in this case, were potentially sharing contaminated ingredients). + +
      + + Going Further: Creating a function for multiple risk ratios + +Just like we created the function `single_risk_ratio()` to calculate the risk +ratio of a single variable, we can create another function that will calculcate +the risk ratio for all variables in a data frame. We can do it the same way we +did above. First, define the recipe: + +```{r multi-risk-recipe} +all_rr <- lapply(food, FUN = single_risk_ratio, outcome = stegen$ill) +all_food_df <- bind_rows(all_rr, .id = "predictor") +all_food_df <- arrange(all_food_df, desc(estimate)) +all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) +``` + +Now, find the ingredients. The first step is `lapply()` which needs `food` and +`stegen$ill`, which are a data frame of predictors and a vector of outcomes, +resepectively, so we will add these as *arguments* called `predictors` and +`outcome`: + +```{r multi-risk-df} +multi_risk_ratio <- function(predictors, outcome) { + all_rr <- lapply(predictors, FUN = single_risk_ratio, outcome = outcome) + all_food_df <- bind_rows(all_rr, .id = "predictor") + all_food_df <- arrange(all_food_df, desc(estimate)) + all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) + return(all_food_df) +} +``` + +And we can call the function like so: + +```{r multi-risk-run} +multi_risk_ratio(predictors = food, outcome = stegen$ill) +``` + + +Note that we have defined arguments for `predictors` and `outcome`, but we +didn't define an agrument for the `single_risk_ratio()` function. This is +because we know that we've defined it above, but this also means that if we +want to use the `multi_risk_ratio()` function in other scripts, we have to also +define `single_risk_ratio()` as well. One way of keeping these organised is to +always write the functions together: + +```{r funwrite} +single_risk_ratio <- function(predictor, outcome) { # ingredients defined here + et <- epitools::epitable(predictor, outcome) # ingredients used here + rr <- epitools::riskratio(et) + estimate <- rr$measure[2, ] + res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) + return(res) # return the data frame +} + +multi_risk_ratio <- function(predictors, outcome) { + all_rr <- lapply(predictors, FUN = single_risk_ratio, outcome = outcome) + all_food_df <- dplyr::bind_rows(all_rr, .id = "predictor") + all_food_df <- dplyr::arrange(all_food_df, dplyr::desc(estimate)) + all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) + return(all_food_df) +} +``` + +You might notice, however that these functions look a bit different. The +`epitable()` function is now written as `epitools::epitable()`. This is no +accident. This is a way for us to tell R to use a function *even if the package +hasn't been loaded*, which makes it easier to share these functions. + + +
      # Plotting a very basic spatial overview of cases -To complete your report, you would like to include a very basic spatial overview of cases. Based on the postal codes of all individuals with a date of symptom onset spatial coordinates were generated providing everyone with a latitude and longitude which corresponds with their household (in relation to their postal code). The variables `latitude` and `longitude` include the longitude and latitude which we will use to plot the cases using *ggplot2*. Here, we want to plot the data to see if +To complete your report, you would like to include a very basic spatial +overview of cases. Based on the postal codes of all individuals with a date of +symptom onset spatial coordinates were generated providing everyone with a +latitude and longitude which corresponds with their household (in relation to +their postal code). The variables `latitude` and `longitude` include the +longitude and latitude which we will use to plot the cases using *ggplot2*. +Here, we want to plot the data to see if there is any spatial component to the outbreak. Since we have the coordinates for each household, it becomes straightforward to plot them with *ggplot2*. Here we will use points and color them based on whether or not the person was ill: @@ -1042,7 +1126,9 @@ generate graphics using *ggplot2*, and how to detect associations between several potential risk factors and the occurrence of illness. One major caveat here is that we are not accounting for potential confounding factors. These will be treated in a separate case study, which will focus on the use of logistic -regression in epidemic studies. Lastly we plotted a basic overview of the cases in this outbreak in relative distance to each other. More suffisticated mapping and spatial mathodologies will be covered in other case studies. +regression in epidemic studies. Lastly we plotted a basic overview of the cases +in this outbreak in relative distance to each other. More suffisticated mapping +and spatial mathodologies will be covered in other case studies. diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index aad6bea..d0631cf 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -118,7 +118,8 @@ The following packages will be used in the case study: - dplyr: to help with factors - *ggplot2*: to create custom visualisations - *epitools*: to calculate risk ratios - - *ggmap*: to plot googlemaps + - *sf*: To read in shape files + - *leaflet*: to demonstrate interactive maps If we have these packages installed, we can tell R to load these packages from our R library: @@ -131,8 +132,8 @@ library("epitrix") # clean labels and variables library("dplyr") # general data handling library("ggplot2") # advanced graphics library("epitools") # statistics for epi data -library("sf") -library("leaflet") +library("sf") # shapefile handling +library("leaflet") # interactive maps ```
      @@ -151,6 +152,8 @@ install.packages("epitrix") install.packages("dplyr") install.packages("ggplot2") install.packages("epitools") +install.packages("sf") +install.packages("leaflet") ```
      @@ -182,7 +185,7 @@ Here we decompose the steps to read data in: ``` r path_to_data <- here("data", "stegen_raw.xlsx") path_to_data -## [1] "/Users/zhian/Documents/Imperial/Websites/recon-learn/data/stegen_raw.xlsx" +## [1] "/home/zkamvar/Documents/Websites/recon-learn/data/stegen_raw.xlsx" ``` > n.b. the value of `path_to_data` will not be the what you see in this @@ -835,116 +838,6 @@ contingency tables for each predictor (food item) against the defined cases, calculate their risk ratio with confidence intervals and p-values, and plot them as points and errorbars using *ggplot2*. -
      - - Going Further: Different types of Univariate tests - -## Univariate tests - -Methods for testing the association between two variables can be broken -down in 3 types, depending on which types these variables are: - -1. **2 categorical variables**: Chi-squared test on the 2x2 table - (a.k.a. *contingency* table) and similar methods (e.g. Fisher’s - exact test) -2. **1 quantitative, 1 categorical**: ANOVA types of approaches; - particular case with 2 groups: Student’s \(t\)-test -3. **2 quantitative variables**: Pearson’s correlation coefficient - (\(r\)) and similar methods - -We can use these approaches to test if the disease is linked to any of -the other recorded variables. As illness itself is a categorical -variable, only approaches of type 2 and 3 are illustrated in this case -study. - -### Is illness linked to age? - -We can use the function `t.test()` to test if the average age is -different across illness status. As this test assumes that the two -categories exhibit similar variation, we first ensure that the variances -are comparable using Bartlett’s test. The syntax `variable ~ group` is -used to indicate the variable of interest (left hand-side), and the -group (right hand-side): - -``` r -bartlett.test(stegen$age ~ stegen$ill) -## -## Bartlett test of homogeneity of variances -## -## data: stegen$age by stegen$ill -## Bartlett's K-squared = 0.82311, df = 1, p-value = 0.3643 -``` - -The resulting p-value (0.364) suggests the variances are indeed -comparable. Wecan thus proceed to a \(t\)-test: - -``` r -t.test(stegen$age ~ stegen$ill, var.equal = TRUE) -## -## Two Sample t-test -## -## data: stegen$age by stegen$ill -## t = -0.55979, df = 281, p-value = 0.5761 -## alternative hypothesis: true difference in means is not equal to 0 -## 95 percent confidence interval: -## -4.509729 2.512679 -## sample estimates: -## mean in group non case mean in group case -## 26.31148 27.31000 -``` - -The p-value of 0.576 confirms the previous graphics: the illness does -not seem to be linked to age. - -### Is illness linked to gender? - -To test the association between gender and illness (2 categorical -variables), we first build a 2-by-2 (contingency) table, using: - -``` r -tab_ill_sex <- table(stegen$ill, stegen$sex) -tab_ill_sex -## -## male female -## non case 86 102 -## case 53 50 -``` - -Note that proportions can be obtained using `prop.table`: - -``` r -## basic proportions -prop.table(tab_ill_sex) -## -## male female -## non case 0.2955326 0.3505155 -## case 0.1821306 0.1718213 - -## expressed in %, rounded: -round(100 * prop.table(tab_ill_sex)) -## -## male female -## non case 30 35 -## case 18 17 -``` - -Once a contingency table has been built, the Chi-square test can be run -using `chisq.test`: - -``` r -chisq.test(tab_ill_sex) -## -## Pearson's Chi-squared test with Yates' continuity correction -## -## data: tab_ill_sex -## X-squared = 0.6562, df = 1, p-value = 0.4179 -``` - -Here, the p-value of 0.418 suggests illness is not related to gender -either. - -
      - ### Isolating the variables to test Because not all of the columns in our data set are food items (i.e. @@ -1435,7 +1328,117 @@ res Now we have a table that tells us that pork has a risk ratio of 1.252 CI: (0.918, 1.708), P = 0.171. -### Consolidating several steps into one +
      + + Going Further: Different types of Univariate tests + +## Univariate tests + +Methods for testing the association between two variables can be broken +down in 3 types, depending on which types these variables are: + +1. **2 categorical variables**: Chi-squared test on the 2x2 table + (a.k.a. *contingency* table) and similar methods (e.g. Fisher’s + exact test) +2. **1 quantitative, 1 categorical**: ANOVA types of approaches; + particular case with 2 groups: Student’s \(t\)-test +3. **2 quantitative variables**: Pearson’s correlation coefficient + (\(r\)) and similar methods + +We can use these approaches to test if the disease is linked to any of +the other recorded variables. As illness itself is a categorical +variable, only approaches of type 2 and 3 are illustrated in this case +study. + +### Is illness linked to age? + +We can use the function `t.test()` to test if the average age is +different across illness status. As this test assumes that the two +categories exhibit similar variation, we first ensure that the variances +are comparable using Bartlett’s test. The syntax `variable ~ group` is +used to indicate the variable of interest (left hand-side), and the +group (right hand-side): + +``` r +bartlett.test(stegen$age ~ stegen$ill) +## +## Bartlett test of homogeneity of variances +## +## data: stegen$age by stegen$ill +## Bartlett's K-squared = 0.82311, df = 1, p-value = 0.3643 +``` + +The resulting p-value (0.364) suggests the variances are indeed +comparable. We can thus proceed to a \(t\)-test: + +``` r +t.test(stegen$age ~ stegen$ill, var.equal = TRUE) +## +## Two Sample t-test +## +## data: stegen$age by stegen$ill +## t = -0.55979, df = 281, p-value = 0.5761 +## alternative hypothesis: true difference in means is not equal to 0 +## 95 percent confidence interval: +## -4.509729 2.512679 +## sample estimates: +## mean in group non case mean in group case +## 26.31148 27.31000 +``` + +The p-value of 0.576 confirms the previous graphics: the illness does +not seem to be linked to age. + +### Is illness linked to gender? + +To test the association between gender and illness (2 categorical +variables), we first build a 2-by-2 (contingency) table, using: + +``` r +tab_ill_sex <- table(stegen$ill, stegen$sex) +tab_ill_sex +## +## male female +## non case 86 102 +## case 53 50 +``` + +Note that proportions can be obtained using `prop.table`: + +``` r +## basic proportions +prop.table(tab_ill_sex) +## +## male female +## non case 0.2955326 0.3505155 +## case 0.1821306 0.1718213 + +## expressed in %, rounded: +round(100 * prop.table(tab_ill_sex)) +## +## male female +## non case 30 35 +## case 18 17 +``` + +Once a contingency table has been built, the Chi-square test can be run +using `chisq.test`: + +``` r +chisq.test(tab_ill_sex) +## +## Pearson's Chi-squared test with Yates' continuity correction +## +## data: tab_ill_sex +## X-squared = 0.6562, df = 1, p-value = 0.4179 +``` + +Here, the p-value of 0.418 suggests illness is not related to gender +either. + +
      + +### Consolidating several steps into one (writing functions) To recap, to get a risk ratio we’ve done the following: @@ -1622,11 +1625,13 @@ Now we can use this data frame to plot the results using *ggplot2*: # first, make sure the predictors are factored in the right order all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) # plot -ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + +p <- ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_point() + geom_errorbarh(aes(xmin = lower, xmax = upper)) + geom_vline(xintercept = 1, linetype = 2) + + scale_x_log10() + scale_color_viridis_c() +p ``` ![](practical-stegen_files/figure-gfm/plot-arrange-1.png) @@ -1637,6 +1642,97 @@ that there may be confounding factors involved (i.e. the food items were not independent or in this case, were potentially sharing contaminated ingredients). +
      + + Going Further: Creating a function for multiple risk +ratios + +Just like we created the function `single_risk_ratio()` to calculate the +risk ratio of a single variable, we can create another function that +will calculcate the risk ratio for all variables in a data frame. We can +do it the same way we did above. First, define the recipe: + +``` r +all_rr <- lapply(food, FUN = single_risk_ratio, outcome = stegen$ill) +all_food_df <- bind_rows(all_rr, .id = "predictor") +all_food_df <- arrange(all_food_df, desc(estimate)) +all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) +``` + +Now, find the ingredients. The first step is `lapply()` which needs +`food` and `stegen$ill`, which are a data frame of predictors and a +vector of outcomes, resepectively, so we will add these as *arguments* +called `predictors` and `outcome`: + +``` r +multi_risk_ratio <- function(predictors, outcome) { + all_rr <- lapply(predictors, FUN = single_risk_ratio, outcome = outcome) + all_food_df <- bind_rows(all_rr, .id = "predictor") + all_food_df <- arrange(all_food_df, desc(estimate)) + all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) + return(all_food_df) +} +``` + +And we can call the function like so: + +``` r +multi_risk_ratio(predictors = food, outcome = stegen$ill) +## predictor estimate lower upper p.value +## 1 tiramisu 18.3116883 8.8142022 38.042913 1.794084e-41 +## 2 mousse 4.9689579 3.2994031 7.483336 1.257341e-20 +## 3 dmousse 4.5010211 3.0869446 6.562862 1.167009e-19 +## 4 wmousse 2.8472222 2.1282671 3.809049 5.825494e-11 +## 5 fruit_salad 2.5006177 1.8867735 3.314171 9.998203e-09 +## 6 redjelly 2.0820602 1.5559166 2.786123 4.415074e-06 +## 7 tomato 1.2898653 0.9379601 1.773799 1.368934e-01 +## 8 horseradish 1.2557870 0.9008457 1.750578 2.026013e-01 +## 9 pork 1.2518519 0.9176849 1.707703 1.708777e-01 +## 10 chickenwin 1.1617347 0.8376217 1.611261 4.176598e-01 +## 11 mince 1.0568237 0.7571858 1.475036 7.893882e-01 +## 12 salmon 1.0334249 0.7452531 1.433026 8.976422e-01 +## 13 roastbeef 0.7607985 0.4129094 1.401795 4.172932e-01 +## 14 beer 0.6767842 0.4757688 0.962730 2.806394e-02 +``` + +Note that we have defined arguments for `predictors` and `outcome`, but +we didn’t define an agrument for the `single_risk_ratio()` function. +This is because we know that we’ve defined it above, but this also means +that if we want to use the `multi_risk_ratio()` function in other +scripts, we have to also define `single_risk_ratio()` as well. One way +of keeping these organised is to always write the functions +together: + +``` r +single_risk_ratio <- function(predictor, outcome) { # ingredients defined here + et <- epitools::epitable(predictor, outcome) # ingredients used here + rr <- epitools::riskratio(et) + estimate <- rr$measure[2, ] + res <- data.frame(estimate = estimate["estimate"], + lower = estimate["lower"], + upper = estimate["upper"], + p.value = rr$p.value[2, "fisher.exact"] + ) + return(res) # return the data frame +} + +multi_risk_ratio <- function(predictors, outcome) { + all_rr <- lapply(predictors, FUN = single_risk_ratio, outcome = outcome) + all_food_df <- dplyr::bind_rows(all_rr, .id = "predictor") + all_food_df <- dplyr::arrange(all_food_df, dplyr::desc(estimate)) + all_food_df$predictor <- factor(all_food_df$predictor, unique(all_food_df$predictor)) + return(all_food_df) +} +``` + +You might notice, however that these functions look a bit different. The +`epitable()` function is now written as `epitools::epitable()`. This is +no accident. This is a way for us to tell R to use a function *even if +the package hasn’t been loaded*, which makes it easier to share these +functions. + +
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Kamvar" Date: Wed, 7 Nov 2018 12:09:39 +0000 Subject: [PATCH 52/59] update main plot with proper labels --- content/post/practical-stegen.Rmd | 5 ++++- content/post/practical-stegen.md | 5 ++++- .../figure-gfm/plot-arrange-1.png | Bin 28283 -> 32804 bytes 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index fc72a5f..902ffe4 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -948,7 +948,10 @@ p <- ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_errorbarh(aes(xmin = lower, xmax = upper)) + geom_vline(xintercept = 1, linetype = 2) + scale_x_log10() + - scale_color_viridis_c() + scale_color_viridis_c() + + labs(x = "Risk Ratio (log scale)", + y = "Predictor", + title = "Risk Ratio for gastroenteritis in Stegen, Germany") p ``` diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index d0631cf..323b136 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -1630,7 +1630,10 @@ p <- ggplot(all_food_df, aes(x = estimate, y = predictor, color = p.value)) + geom_errorbarh(aes(xmin = lower, xmax = upper)) + geom_vline(xintercept = 1, linetype = 2) + scale_x_log10() + - scale_color_viridis_c() + scale_color_viridis_c() + + labs(x = "Risk Ratio (log scale)", + y = "Predictor", + title = "Risk Ratio for gastroenteritis in Stegen, Germany") p ``` diff --git a/static/post/practical-stegen_files/figure-gfm/plot-arrange-1.png b/static/post/practical-stegen_files/figure-gfm/plot-arrange-1.png index f70c368888f291ea858ede63f114d6c4046ecf02..b7d507e58a6d916e91db4b395c428b2122f27be4 100644 GIT binary patch literal 32804 zcmagG1z45s)-8;Jgpz`QgoH?!gi7P0k?t;OL_&}TX#pt_kdl(_ZV-_Y5s;FSmPWec zpUZcD-}%no=Rap%dtd8CSZm$Sz3w^Zm}89jgeWOU;an%Xj)H=MBO@)Yf`W224F%=O z4CYn%m;FuYr|^VjFRlFo1qHVa`Tr}OcDbe~DAXu2;*Zr_lh>!L^aw}CQMS0b$F!~n 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changed, 4 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 902ffe4..714a0b5 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -1098,8 +1098,6 @@ with non-missing coordinates with the command `!is.na()` where the `!` means stegen_sub <- stegen[!is.na(stegen$longitude), ] ``` -> **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE**. It may -> overload the servers ```{r interactive_map, eval = FALSE} # create the map diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 323b136..d23eed5 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -1803,8 +1803,6 @@ non-missing coordinates with the command `!is.na()` where the `!` means stegen_sub <- stegen[!is.na(stegen$longitude), ] ``` -> **If you are currently in a workshop: PLEASE DO NOT RUN THIS CODE**. -> It may overload the servers ``` r # create the map From 2ec8752830bd736b1b9d7d428e08a62a4a1b1be8 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Wed, 7 Nov 2018 17:29:19 +0000 Subject: [PATCH 54/59] add saving RDS files; add Amrish --- content/post/practical-stegen.Rmd | 41 ++++++++++++++++++---- content/post/practical-stegen.md | 58 +++++++++++++++++++++++++++---- 2 files changed, 87 insertions(+), 12 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 714a0b5..25406a3 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -1,10 +1,9 @@ --- title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" -author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" -authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +authors: ["Amrish Baidjoe", "Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] categories: "case studies" tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting cases"] -date: 2018-10-24 +date: 2018-11-07 slug: stegen licenses: CC-BY image: img/highres/graduation-1965.jpg @@ -364,7 +363,7 @@ toy_vector # check outcome
    6. -## Saving clean data +## Saving clean data {#clean-data} Now that we've cleaned our data, we will probably want to re-use it in further analyses, but we probably don't want to go through the steps of cleaning it @@ -379,14 +378,34 @@ dir.create(clean_dir) > STOP: open your file browser and confirm that file called "data/cleaned" has been created -After this, we can save our cleaned data into a new file called -`stegen_clean.csv`: +We will store two different files in this directory that represent the same data: + +1. a flat text file (spreadsheet) that can be read by any program +2. a binary file that can only be accessed from R + +### Saving as a flat file + +A flat file is a text file that can be read by any program. The most common type +of flat file is a called a **csv** (comma separated values) file. This is a +text version of a spreadsheet with commas denoting the columns. ```{r save-clean-data, eval = FALSE} stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") ``` +### Saving as a binary file + +When you save as a flat file, you will have to re-define which varaibles are +dates and which are factors when you re-read in the clean data. One way to +avoid doing this is to save your data into a file called an **RDS** file, which +is a data file that only R can read. This will preserve your column +definitions. + +```{r save-clean-rds, eval = FALSE} +stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") +saveRDS(stegen, file = stegen_clean_rds) +``` # Data exploration @@ -609,7 +628,17 @@ tables for each predictor (food item) against the defined cases, calculate their risk ratio with confidence intervals and p-values, and plot them as points and errorbars using *ggplot2*. +## Reading in clean data +In the section on saving clean data, we saved a binary file with our stegen +data set. If you are starting here, you will want to read this file in with +`readRDS()` + +```{r read-clean-data, eval = 3} +stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") +stegen <- readRDS(stegen_clean_rds) +head(stegen) +``` ### Isolating the variables to test diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index d23eed5..3433aa0 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -1,10 +1,9 @@ --- title: "An outbreak of gastroenteritis in Stegen, Germany, June 1998" -author: "Janetta Skarp, Zhian N. Kamvar, Alexander Spina, Patrick Keating and Thibaut Jombart" -authors: ["Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] +authors: ["Amrish Baidjoe", "Thibaut Jombart", "Janetta Skarp", "Zhian N. Kamvar", "Alexander Spina", "Patrick Keating"] categories: "case studies" tags: ["level: beginner", "epicurve", "single variable analysis", "2x2 tables", "gastroenteritis", "plotting cases"] -date: 2018-10-24 +date: 2018-11-07 slug: stegen licenses: CC-BY image: img/highres/graduation-1965.jpg @@ -511,14 +510,37 @@ dir.create(clean_dir) > STOP: open your file browser and confirm that file called > “data/cleaned” has been created -After this, we can save our cleaned data into a new file called -`stegen_clean.csv`: +We will store two different files in this directory that represent the +same data: + +1. a flat text file (spreadsheet) that can be read by any program +2. a binary file that can only be accessed from R + +### Saving as a flat file + +A flat file is a text file that can be read by any program. The most +common type of flat file is a called a **csv** (comma separated values) +file. This is a text version of a spreadsheet with commas denoting the +columns. ``` r stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") ``` +### Saving as a binary file + +When you save as a flat file, you will have to re-define which varaibles +are dates and which are factors when you re-read in the clean data. One +way to avoid doing this is to save your data into a file called an +**RDS** file, which is a data file that only R can read. This will +preserve your column definitions. + +``` r +stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") +saveRDS(stegen, file = stegen_clean_rds) +``` + # Data exploration ## Summaries of age and sex distribution @@ -838,6 +860,31 @@ contingency tables for each predictor (food item) against the defined cases, calculate their risk ratio with confidence intervals and p-values, and plot them as points and errorbars using *ggplot2*. +## Reading in clean data + +In the section on saving clean data, we saved a binary file with our +stegen data set. If you are starting here, you will want to read this +file in with `readRDS()` + +``` r +## stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") +## stegen <- readRDS(stegen_clean_rds) +head(stegen) +## # A tibble: 6 x 23 +## unique_key ill date_onset sex age tiramisu tportion wmousse dmousse +## +## 1 210 case 1998-06-27 fema… 18 1 3 0 1 +## 2 12 case 1998-06-27 male 57 1 1 0 1 +## 3 288 case 1998-06-27 fema… 56 0 0 0 0 +## 4 186 case 1998-06-27 male 17 1 1 1 0 +## 5 20 case 1998-06-27 fema… 19 1 2 0 0 +## 6 148 case 1998-06-27 male 16 1 2 1 1 +## # ... with 14 more variables: mousse , mportion , beer , +## # redjelly , fruit_salad , tomato , mince , +## # salmon , horseradish , chickenwin , roastbeef , +## # pork , latitude , longitude +``` + ### Isolating the variables to test Because not all of the columns in our data set are food items (i.e. @@ -1803,7 +1850,6 @@ non-missing coordinates with the command `!is.na()` where the `!` means stegen_sub <- stegen[!is.na(stegen$longitude), ] ``` - ``` r # create the map lmap <- leaflet() From 526ef26f87eb61db10d15516d672bb0359959e2f Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Wed, 7 Nov 2018 17:34:17 +0000 Subject: [PATCH 55/59] friggen echo options --- content/post/practical-stegen.Rmd | 6 +++++- content/post/practical-stegen.md | 31 ++++++++++++++++--------------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 25406a3..1fca396 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -634,11 +634,15 @@ In the section on saving clean data, we saved a binary file with our stegen data set. If you are starting here, you will want to read this file in with `readRDS()` -```{r read-clean-data, eval = 3} +```{r read-clean-data, eval = FALSE} stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") stegen <- readRDS(stegen_clean_rds) head(stegen) ``` +```{r read-clean-data-show, echo = FALSE} +head(stegen) +``` + ### Isolating the variables to test diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 3433aa0..2feca26 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -867,24 +867,25 @@ stegen data set. If you are starting here, you will want to read this file in with `readRDS()` ``` r -## stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") -## stegen <- readRDS(stegen_clean_rds) +stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") +stegen <- readRDS(stegen_clean_rds) head(stegen) -## # A tibble: 6 x 23 -## unique_key ill date_onset sex age tiramisu tportion wmousse dmousse -## -## 1 210 case 1998-06-27 fema… 18 1 3 0 1 -## 2 12 case 1998-06-27 male 57 1 1 0 1 -## 3 288 case 1998-06-27 fema… 56 0 0 0 0 -## 4 186 case 1998-06-27 male 17 1 1 1 0 -## 5 20 case 1998-06-27 fema… 19 1 2 0 0 -## 6 148 case 1998-06-27 male 16 1 2 1 1 -## # ... with 14 more variables: mousse , mportion , beer , -## # redjelly , fruit_salad , tomato , mince , -## # salmon , horseradish , chickenwin , roastbeef , -## # pork , latitude , longitude ``` + ## # A tibble: 6 x 23 + ## unique_key ill date_onset sex age tiramisu tportion wmousse dmousse + ## + ## 1 210 case 1998-06-27 fema… 18 1 3 0 1 + ## 2 12 case 1998-06-27 male 57 1 1 0 1 + ## 3 288 case 1998-06-27 fema… 56 0 0 0 0 + ## 4 186 case 1998-06-27 male 17 1 1 1 0 + ## 5 20 case 1998-06-27 fema… 19 1 2 0 0 + ## 6 148 case 1998-06-27 male 16 1 2 1 1 + ## # ... with 14 more variables: mousse , mportion , beer , + ## # redjelly , fruit_salad , tomato , mince , + ## # salmon , horseradish , chickenwin , roastbeef , + ## # pork , latitude , longitude + ### Isolating the variables to test Because not all of the columns in our data set are food items (i.e. From c525cff5ba988231df77b174bdf21b89e820ca31 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 9 Nov 2018 10:50:22 +0000 Subject: [PATCH 56/59] update stegen practical to use readr I also tweaked a few things visually --- content/post/practical-stegen.Rmd | 23 ++++++++++++++++------- content/post/practical-stegen.md | 23 ++++++++++++++++++----- 2 files changed, 34 insertions(+), 12 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 1fca396..09e9562 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -100,6 +100,7 @@ The following packages will be used in the case study: - *here*: to find the path to data or script files - *readxl*: to read Excel spreadsheets into **R** +- *readr*: to write (and read) spreadsheets as text files - *incidence*: to build epicurves - *epitrix*: to clean labels from our spreadsheet - dplyr: to help with factors @@ -114,6 +115,7 @@ our R library: ```{r stegen_libraries, warning=FALSE, message=FALSE} library("here") # find data/script files library("readxl") # read xlsx files +library("readr") # read and write text spreadsheets library("incidence") # make epicurves library("epitrix") # clean labels and variables library("dplyr") # general data handling @@ -132,6 +134,7 @@ system. To install them (you only need to do this once!), type: ```{r stegen_install_libraries, eval = FALSE} install.packages("here") install.packages("readxl") +install.packages("readr") install.packages("incidence") install.packages("epitrix") install.packages("dplyr") @@ -163,7 +166,7 @@ at the root of the project. Here we decompose the steps to read data in: 1. finding the path to the data (`path_to_data`) with the *here* package -2. using the function `read_xlsx()` from the *readxl* package to read data in, +2. using the function `read_excel()` from the *readxl* package to read data in, and saving the output in a new object `stegen`. ```{r stegen_show_data} @@ -178,21 +181,22 @@ tutorial. It will be the location of this data set on your computer. --> ```{r fake_read_Data, eval = FALSE} -stegen <- read_xlsx(path_to_data) +stegen <- read_excel(path_to_data) ``` ```{r stegen_read_data, results = 'hide', echo = FALSE} -stegen <- readxl::read_xlsx(here::here("static", "data", "stegen_raw.xlsx")) +stegen <- readxl::read_excel(here::here("static", "data", "stegen_raw.xlsx")) ``` To look at the content of the dataset, we can use either of these commands: -```{r stegen_print_data, eval = 1} +```{r stegen_print_data} stegen +``` +```{r, stegen_view, eval = FALSE} View(stegen) ``` -
      Problems? In case of trouble, click here to toggle additional help: @@ -387,13 +391,16 @@ We will store two different files in this directory that represent the same data A flat file is a text file that can be read by any program. The most common type of flat file is a called a **csv** (comma separated values) file. This is a -text version of a spreadsheet with commas denoting the columns. +text version of a spreadsheet with commas denoting the columns. We can use the +`write_csv()` function from the *readr* package to do this: ```{r save-clean-data, eval = FALSE} stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") -write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") +write_csv(stegen, path = stegen_clean_file) ``` +When you want to read in the file later, you can use the `read_csv()` function. + ### Saving as a binary file When you save as a flat file, you will have to re-define which varaibles are @@ -407,6 +414,8 @@ stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") saveRDS(stegen, file = stegen_clean_rds) ``` +To read in the clean data for later use, we would use the `readRDS()` function. + # Data exploration diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index 2feca26..eee3acd 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -112,6 +112,7 @@ The following packages will be used in the case study: - *here*: to find the path to data or script files - *readxl*: to read Excel spreadsheets into **R** + - *readr*: to write (and read) spreadsheets as text files - *incidence*: to build epicurves - *epitrix*: to clean labels from our spreadsheet - dplyr: to help with factors @@ -126,6 +127,7 @@ packages from our R library: ``` r library("here") # find data/script files library("readxl") # read xlsx files +library("readr") # read and write text spreadsheets library("incidence") # make epicurves library("epitrix") # clean labels and variables library("dplyr") # general data handling @@ -146,6 +148,7 @@ on your system. To install them (you only need to do this once\!), type: ``` r install.packages("here") install.packages("readxl") +install.packages("readr") install.packages("incidence") install.packages("epitrix") install.packages("dplyr") @@ -176,7 +179,7 @@ Here we decompose the steps to read data in: 1. finding the path to the data (`path_to_data`) with the *here* package -2. using the function `read_xlsx()` from the *readxl* package to read +2. using the function `read_excel()` from the *readxl* package to read data in, and saving the output in a new object `stegen`. @@ -195,7 +198,7 @@ computer. --> ``` r -stegen <- read_xlsx(path_to_data) +stegen <- read_excel(path_to_data) ``` To look at the content of the dataset, we can use either of these @@ -221,7 +224,10 @@ stegen ## # salad` , tomato , mince , salmon , ## # horseradish , chickenwin , roastbeef , PORK , ## # latitude , longitude -## View(stegen) +``` + +``` r +View(stegen) ```
      @@ -521,13 +527,17 @@ same data: A flat file is a text file that can be read by any program. The most common type of flat file is a called a **csv** (comma separated values) file. This is a text version of a spreadsheet with commas denoting the -columns. +columns. We can use the `write_csv()` function from the *readr* package +to do this: ``` r stegen_clean_file <- here("data", "cleaned", "stegen_clean.csv") -write.csv(stegen, file = stegen_clean_file, rownames = FALSE, sep = ",") +write_csv(stegen, path = stegen_clean_file) ``` +When you want to read in the file later, you can use the `read_csv()` +function. + ### Saving as a binary file When you save as a flat file, you will have to re-define which varaibles @@ -541,6 +551,9 @@ stegen_clean_rds <- here("data", "cleaned", "stegen_clean.rds") saveRDS(stegen, file = stegen_clean_rds) ``` +To read in the clean data for later use, we would use the `readRDS()` +function. + # Data exploration ## Summaries of age and sex distribution From 899afce024a80ee59576f1303081b4db23423220 Mon Sep 17 00:00:00 2001 From: "Zhian N. Kamvar" Date: Fri, 9 Nov 2018 10:51:01 +0000 Subject: [PATCH 57/59] remove test-buttons from my site --- content/post/test-buttons.Rmd | 2 +- content/post/test-buttons.md | 43 ----------------------------------- 2 files changed, 1 insertion(+), 44 deletions(-) delete mode 100644 content/post/test-buttons.md diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd index 581047a..3c2f2a8 100644 --- a/content/post/test-buttons.Rmd +++ b/content/post/test-buttons.Rmd @@ -2,7 +2,7 @@ title: "Test collapse button" author: "Thibaut Jombart" authors: ["Thibaut Jombart"] -date: 2018-10-24 +date: 2918-10-24 licenses: CC-BY categories: "case study" slug: button-test diff --git a/content/post/test-buttons.md b/content/post/test-buttons.md deleted file mode 100644 index 49c80d1..0000000 --- a/content/post/test-buttons.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -title: "Test collapse button" -author: "Thibaut Jombart" -authors: ["Thibaut Jombart"] -date: 2018-10-24 -licenses: CC-BY -categories: "case study" -slug: button-test -image: img/highres/graduation-1965.jpg -showonlyimage: true ---- - -Here is an attempt at collapse button, taken from: - - -
      - -

      - -Simple Collapsible - -

      - -

      - -Click on the button to toggle between showing and hiding -content. - -

      - - - -
      - -Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod -tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim -veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea -commodo consequat. - -
      - -
      From a21033e4ced1ac9f85df5a40fa4fc4c2153b4515 Mon Sep 17 00:00:00 2001 From: "Zhian N. 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zAsi|ou78*zj6zo6q;ULoo%o!C%Y=E-$ Date: Fri, 9 Nov 2018 12:20:17 +0000 Subject: [PATCH 59/59] add instructions for project setup --- content/post/practical-stegen.Rmd | 70 ++++++++++++++++++++++++++----- content/post/practical-stegen.md | 67 +++++++++++++++++++++++++++-- content/post/test-buttons.Rmd | 1 - 3 files changed, 123 insertions(+), 15 deletions(-) diff --git a/content/post/practical-stegen.Rmd b/content/post/practical-stegen.Rmd index 09e9562..8e5d647 100644 --- a/content/post/practical-stegen.Rmd +++ b/content/post/practical-stegen.Rmd @@ -94,6 +94,61 @@ analysis using **R**, including: # Initial data processing +## Project setup + +When using R, it's a very good idea to keep your data and scripts organised +together. One way of doing this is to keep them in a single folder that hosts +and RStudio project file. For this project, your folder structure will start +out looking like this: + +``` +. +├── 01-stegen-analysis.R +├── data/ +│   ├── stegen-map/ +│   │   ├── stegen_households.cpg +│   │   ├── stegen_households.prj +│   │   ├── stegen_households.qpj +│   │   ├── stegen_households.shp +│   │   └── stegen_households.shx +│   └── stegen_raw.xlsx +├── LICENSE +├── README.md +└── stegen.Rproj +``` + +To create this, do the following steps: + +1. Open RStudio and [create a new RStudio project in a new directory called + "stegen"](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects) +2. Once you have your Project set up, make a new folder called "data/" and + [download `stegen_raw.xlsx` and save it to that folder](../../data/stegen_raw.xlsx) +3. [Download `stegen-map.zip`](../../data/stegen-map.zip) and extract it to the + "data/" folder +4. In RStudio, click on the following menu path: File > New File > R + Script and save it as `01-stegen-analysis.R`. This will be the R + script where you will save the code of the analysis. + +

      + +Going Further: What are the `LICENSE` and `README.md` files? + +Both of these files are plain text files (edited via programs such as notepad +or textedit) that tell people how they can use the data and code. + +The `README.md` file is a text file that describes in plain language what this +project is about and what the components are. It's called README with the idea +that anyone who comes across this project should be able to read this file +first and understand how to work with the project. + +The `LICENSE` file describes how the data and code are to be used. For example, +this case study is licensed under the [Creative Commons Attribution 4.0 +International license](https://creativecommons.org/licenses/by/4.0/) which +gives people the freedom to reuse this in any way as long as they attribute the +original authors. + +
      + ## Loading required packages The following packages will be used in the case study: @@ -1174,12 +1229,7 @@ in this outbreak in relative distance to each other. More suffisticated mapping and spatial mathodologies will be covered in other case studies. - -
      -
      -
      - - +-------------------------------------------------------------------------------- # About this document @@ -1197,7 +1247,7 @@ based on an real outbreak investigation conducted by Anja Hauri, RKI, Berlin, 19 - adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis -- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart +- rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, Zhian N. Kamvar, Thibaut Jombart, Amrish Baidjoe Contributions are welcome via [pull @@ -1208,8 +1258,8 @@ here](https://github.com/reconhub/learn/blob/master/content/post/practical-stege ## Legal stuff -**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) +**License**: [CC-BY](https://creativecommons.org/licenses/by/4.0/) **Copyright**: 2018 -
      -
      +-------------------------------------------------------------------------------- + diff --git a/content/post/practical-stegen.md b/content/post/practical-stegen.md index eee3acd..3f5e939 100644 --- a/content/post/practical-stegen.md +++ b/content/post/practical-stegen.md @@ -106,6 +106,65 @@ useful practices for data analysis using **R**, including: # Initial data processing +## Project setup + +When using R, it’s a very good idea to keep your data and scripts +organised together. One way of doing this is to keep them in a single +folder that hosts and RStudio project file. For this project, your +folder structure will start out looking like this: + + . + ├── 01-stegen-analysis.R + ├── data/ + │   ├── stegen-map/ + │   │   ├── stegen_households.cpg + │   │   ├── stegen_households.prj + │   │   ├── stegen_households.qpj + │   │   ├── stegen_households.shp + │   │   └── stegen_households.shx + │   └── stegen_raw.xlsx + ├── LICENSE + ├── README.md + └── stegen.Rproj + +To create this, do the following steps: + +1. Open RStudio and [create a new RStudio project in a new directory + called + “stegen”](https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects) +2. Once you have your Project set up, make a new folder called “data/” + and [download `stegen_raw.xlsx` and save it to that + folder](../../data/stegen_raw.xlsx) +3. [Download `stegen-map.zip`](../../data/stegen-map.zip) and extract + it to the “data/” folder +4. In RStudio, click on the following menu path: File \> New File + \> R Script and save it as `01-stegen-analysis.R`. This will + be the R script where you will save the code of the analysis. + +
      + +Going Further: What are the `LICENSE` and `README.md` +files? + +Both of these files are plain text files (edited via programs such as +notepad or textedit) that tell people how they can use the data and +code. + +The `README.md` file is a text file that describes in plain language +what this project is about and what the components are. It’s called +README with the idea that anyone who comes across this project should be +able to read this file first and understand how to work with the +project. + +The `LICENSE` file describes how the data and code are to be used. For +example, this case study is licensed under the [Creative Commons +Attribution 4.0 International +license](https://creativecommons.org/licenses/by/4.0/) which gives +people the freedom to reuse this in any way as long as they attribute +the original authors. + +
      + ## Loading required packages The following packages will be used in the case study: @@ -1900,7 +1959,7 @@ studies. Lastly we plotted a basic overview of the cases in this outbreak in relative distance to each other. More suffisticated mapping and spatial mathodologies will be covered in other case studies. -


      +----- # About this document @@ -1919,7 +1978,7 @@ Hauri, RKI, Berlin, 1998. - adaptation for EPIET module: Alicia Barrasa, Ioannis Karagiannis - rewriting for R: Alexander Spina, Patrick Keating, Janetta Skarp, - Zhian N. Kamvar, Thibaut Jombart + Zhian N. Kamvar, Thibaut Jombart, Amrish Baidjoe Contributions are welcome via [pull requests](https://github.com/reconhub/learn/pulls). The [source file for @@ -1928,7 +1987,7 @@ here](https://github.com/reconhub/learn/blob/master/content/post/practical-stege ## Legal stuff -**License**: [CC-BY](https://creativecommons.org/licenses/by/3.0/) +**License**: [CC-BY](https://creativecommons.org/licenses/by/4.0/) **Copyright**: 2018 -

      +----- diff --git a/content/post/test-buttons.Rmd b/content/post/test-buttons.Rmd index 3c2f2a8..bfe62b3 100644 --- a/content/post/test-buttons.Rmd +++ b/content/post/test-buttons.Rmd @@ -4,7 +4,6 @@ author: "Thibaut Jombart" authors: ["Thibaut Jombart"] date: 2918-10-24 licenses: CC-BY -categories: "case study" slug: button-test image: img/highres/graduation-1965.jpg showonlyimage: true