ICCWPMBB Workshop 2023.Rmd

---
title: "_Introduction to R_"
subtitle: '_ICCWPMBB 2023_'
author: "Dahrii Paul"
date: "2023-01-24"
output:
  word_document: default
  html_document: default
---
# _R_
* R is a programming and statistical language for analytical purposes
* It is also one of the most popular tools used for data visualization
* It is simple and easy to learn, read & write
* Free and open source

## _1. Installation_ 
* Download R: <https://cran.r-project.org/bin/windows/base/>
* Download Rstudio: <https://www.rstudio.com/>
* R cloud: <https://rstudio.cloud/>

## _2. Variables in R_
* Variables are reserved memory location to store values.
  + i.e When you create a variable you reserved some space in memory
  
![](D:/1_obj/2_R_datasets 2023/R_Basic/Image/1.png)
```{r}
x = 25;x
```
```{r}
y <- "Hello";y
```

---
## _3. Operators in R_

**R-Manuals link:** <https://cran.r-project.org/doc/manuals/>

**a). Assignment Operators**

Operators||Example|
---------|--|-------|------
Right assignment| = | x = 5
| |<- |x <- 5 
| |<<-|x <<- 5
Left assignment| -> | 3 -> y
| | ->>| 3 ->> y

_Examples_
```{r}
x = 5
x <- 5
x <<- 5
x <- x + 3 
3 -> y
3 ->> y
```

**b). Arithmetic Operators**

Operators||Example|
---------|--|-------|------
|||input|return
Addition | + | 2 + 3| 5
Subtraction | - | 5 - 2|3
Multiplication| * |  3 * 4|12 
Division| / | 6/2|3
Exponentiation | ^ | 2^3|8
Integer division |%/%| 7 %/% 2 |3
Modulus| %% |7 %% 2|1

_Example_
```{r}
a <- 2
b <- 3
c <- a + b # c will return 5
d <- a - b # d will return -1
e <- a * b # e will return 6
f <- a / b # f will return 0.66667
g <- a^b  # g will return 8
h <- 7 %/% 2 # h will return 3
i <- 7 %% 2 # i will return 1
```

**c). Relational Operators**

Operators||Example|
---------|--|-------|------
|||input|return
less than |<|2<3|TRUE
greater than|>|2>3|FALSE
greater than or equal to|>=|2>=3|FALSE
less than or equal to | <= |2<=3|TRUE
equal to |==|2==2|TRUE
not equal to|!=|2!=2|TRUE

**d). Logical Operators**

Operators|
---------|--
And|&
Or|
Not|! 

_Example '&'_
```{r}
a <- 5
b <- 10
ifelse(a > 3 & b < 15,
       "Both conditions are true", "Either condition is false") 
```

_Example '|'_
```{r}
a <- 5
b <- 10
ifelse(a < 3 | b > 15,
       "Either conditions are true", "Both condition are false") 
```
_Example '!'_
```{r}
a <- 5
b <- 10
ifelse(!(a > 3 & b < 15),"Either condition is false", "Both conditions are true") 
```

**e). Special Operators**

Operators||Example
---------|--|---
Help|?| ?vector
Sequence|:| x=1:3
Matching|%in%|x=1:3;y=2;y%in%x 
List subset|$|

## _4. Data Types_

a) **Vectors**
      + A vector is a sequence of data elements of the same basic type
      + It can contain elements of different data types, such as numeric, character, or logical values

_Create a numeric vector_
```{r}
# vector_name <- c(list of values separated by comma)
v1<-c(1,2,3,4,5,6)
v1
```
```{r}
#vector_name<-c(range)
v2<-c(5:11)
v2
```
_Create a string vector_
```{r}
v3 <- c("A","A","G","T","C","G")
v3
#mix vector type
v_mix <- c("new",1,2,3,"four")
v_mix 
```

```{r}
typeof(v1)
typeof(v3)
typeof(v_mix)
```
_Create an integer vector_
```{r}
v4<-c(8L,16L,64L,128L)
v4
```

b) **Factors**
    +  Factor is a data structure which are used to categorize the data and store it as levels
    + Can store both integers and strings

```{r}
v3
v5 <- as.factor(v3) 
v5
class(v5)
```

c) **Array**
    + A multi-dimensional data structure that can store data in more than two dimensions
    + Arrays hold multidimensional rectangular data
    + “Rectangular” means that each row is the same length, and likewise for each column and other dimensions
   + Arrays can store only values having similar kinds of data, i.e. variables / elements having similar data type

_Create an Array 1-D_
```{r}
array_1<-array(c(v1))
array_1
class(array_1)
```

_Create an Array 2-D_
```{r}
array_2<-array(1:12,c(4,3))
array_2
```

_Create multiple-D array_
```{r}
array_multi<- array(1:24,c(3,4,2))
array_multi
```

d) **Matrices**
    + They are 2-dimensional data structures arranged in a rectangular layout
    + Can have only homogeneous element type

```{r}
length(v1)
#Copy the vector
mat1 <- v1
dim(mat1) <- c(3,2)
mat1
class(mat1)
dim(mat1)

mat2 <- cbind(v1,v2)
mat2
mat3 <- rbind(v1,v2)
mat3
```

_Create a matrix using 'matrix' function_
```{r}
mat4 <-  matrix(c(v1, v2), nrow = 6, ncol = 2)
mat4

# Create a matrix – by range
mat5 <- matrix(c(1:5), nrow = 4, ncol = 4)
mat5
mat5 <- matrix(c(1:5), nrow = 4, byrow = TRUE)
mat5
#
```


e) **Lists**
    + Objects which contain elements of different types such as strings, numbers, vectors or another list inside under one name

```{r}
ls1 <- list(v1,v2,v3,v4,array_1,array_2,array_multi,mat1,mat2,mat3,mat5)
ls1[[3]]
ls1[[6]][2,2]
```
f) **Data Frame**
    + A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column
    + **_Characteristics of a data frame_**
        + The column names should be non-empty
        + The row names should be unique
        + The data stored in a data frame can be of numeric, factor or character type
        + Each column should contain same number of data items

```{r}
dim(mat4);length(v3)
df1 <-data.frame(mat4,v3)
df1
colnames(df1)[1:3] <- c("var1","var2","DNA")
colnames(df1)
names(df1)[1] <- "col1"
colnames(df1)
```
    
## _4. Data Wrangling_

```{r}
#install.packages("MASS")
library(MASS)
data(package = "MASS")
```

_Load the data_
```{r}
data(cats)
head(cats)
tail(cats)
dim(cats)
str(cats)
summary(cats)
```
_Select subset_
```{r}
cats[,1]
cats$Sex
cats$Sex[1]
```

```{r}
males <- subset(cats, cats$Sex == "M")
females <- subset(cats, cats$Sex == "F")
summary(males)
summary(females)
sd(males$Hwt)
```

```{r}
cats1 <-cats
cats1$Sex <- as.character(cats1$Sex)
str(cats1)
cats1$Sex[cats1$Sex == "F"] = 1
cats1$Sex[cats1$Sex == "M"] = 2
table(cats$Sex)
```

UCI Machine Learning Repository **"Census Income"** [link](https://archive.ics.uci.edu/ml/datasets/adult)

[Data link github]("https://raw.githubusercontent.com/Dahrii-Paul/R_Basic/d1f0be2d9bc12bfd1df3093723db9c40f8865a78/adult.csv")

```{r}
#install.packages("dplyr")
library(dplyr)
df <- read.csv("https://raw.githubusercontent.com/Dahrii-Paul/R_Basic/d1f0be2d9bc12bfd1df3093723db9c40f8865a78/adult.csv")
head(df,2)
dim(df)
```

a) _**'filter()'**_ function
    + The _filter()_ function is used to subset a data frame, retaining all rows that satisfy your conditions i.e based on a logical condition 
    + It is part of the _'dplyr'_ package
```{r}
colnames(df)
df$native.country <- as.factor(df$native.country)
levels(df$native.country)
filter(df, native.country %in% "Scotland")
filter(df,native.country %in% c("Scotland","Honduras"))
filter(df,native.country %in% c("Scotland","Honduras"), hours.per.week > 50 )
```

b) **_'select()'_** function
      + The _select()_ function in R is used to pick specific variables or features of a DataFrame
      +  It is part of the _'dplyr'_ package
      + _select(data, column1, column2, ...)_
      + _select(data, -column1, -column2, ...)_
```{r}
dplyr::select(df, age, income)
dplyr::select(df, -age, -income)
```
This error occur because 'dplyr' and 'MASS' packages have a naming conflict i.e. _select() function_

```{r}
detach("package:MASS", unload = TRUE)
select(df, age, income)
```

c) **Pipe operator %>%**
    +  Pipe operator _%>%_ is a special operator commonly used in _'dplyr package'_, which allow multiple sequence of operations _(function/argument)_ on a data frame
    + **syntax** _data %>% function1() %>% function2() %>% function3() %>% argument_

```{r}
df %>% 
  filter(native.country %in% c("Scotland","Honduras"), sex == "Male", hours.per.week > 50) %>% 
  select(age, native.country, sex, hours.per.week)
```

**Summary**
```{r}
df %>%
  select(-workclass, -education, -occupation, -marital.status, -relationship,-race,-sex, -native.country, -income) %>%
  summarise_all(list(mn=mean, stdev=sd))
```

**Group Level**
```{r}
df %>%
  select(age, race, sex, hours.per.week) %>%
  group_by(race)%>%
  summarise(sampSz=n(), Avg =mean(hours.per.week), stDev = sd(hours.per.week))
```

**Sub-setting data population sample size**
```{r}
df2 <-df %>%
  select(age, native.country, sex, hours.per.week) %>%
  group_by(native.country)%>%
  mutate(samplSz=n())%>%
  filter(samplSz >50) %>%
  ungroup()
df2
```

## _5. Data Visualization_

scatter plot
```{r}
library(MASS)
data(cats)
males <- subset(cats, cats$Sex == "M")
females <- subset(cats, cats$Sex == "F")

plot(males$Bwt,males$Hwt, 
     pch = 8, 
     xlab = "Bwt", ylab = "Hwt",
     col = "green", main = "scatter plot", las =0)
points(females$Bwt,females$Hwt, 
       pch = 8, 
       xlab = "Bwt", ylab = "Hwt",
       col = "blue", main = "scatter plot", las =0)
malesReg <- lm(Hwt ~ Bwt ,data = males)
abline(malesReg, col = "red" , lwd = 2)
femaleReg <- lm(Hwt ~ Bwt,data = females)
abline(femaleReg, col = "black",lwd =2)
legend("bottomright",legend = c("Males cats","Female cats"), 
       pch = c(8,8), col = c("green","blue"))
```
_Identify point using name_
```{r}

data(mammals)
plot(mammals$body,mammals$brain , 
     pch =  16, 
     col = "blue", 
     las = 0, 
     xlab = "body weight in Kg",ylab = "brain weight in gm")
#identify(mammals$body,mammals$brain, labels = rownames(mammals))
```
```{r}
boxplot(cats$Bwt,cats$Hwt, col = "pink", ylab = "residues", main = "box plot")
```