R.COVID.19

The goal of R.COVID.19 is to simply aquire data for the disease COVID 19 from sources that make the data readily available. No promises are made to the validaty of the data as their are many people at the sources working on that. Because these functions link to the sources, the data should update as the sources update. If data is not being generated, please open an issue so that I can look into the broken link. The sources for the data is listed below.

Finally, the John Hopkins data was transposed to be tidy, instead of having dates as columns in a wide dataset. The New York Times data was already in a tidy format. See GitPage site for information of other functions and sources of data.

GitPage Site: https://fredo-xvii.github.io/R.COVID.19/

Installation

You can install the released version of R.COVID.19 from CRAN with: (NOT ON CRAN)

install.packages("R.COVID.19")

And the development version from GitHub with:

install.packages("devtools")
devtools::install_github("Fredo-XVII/R.COVID.19")

Examples

Load libraries:

library(R.COVID.19)
library(dplyr)
#> 
#> 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(magrittr)

COVID-19 data from John Hopkins University Data

• source: GITHUB

Global COVID-19 Data:

confirmed <- R.COVID.19::global_confirmed_daily()
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   `Province/State` = col_character(),
#>   `Country/Region` = col_character()
#> )
#> See spec(...) for full column specifications.

deaths <- R.COVID.19::global_deaths_daily()
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   `Province/State` = col_character(),
#>   `Country/Region` = col_character()
#> )
#> See spec(...) for full column specifications.

recovered <- R.COVID.19::global_recovered_daily()
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   `Province/State` = col_character(),
#>   `Country/Region` = col_character()
#> )
#> See spec(...) for full column specifications.

combo <- confirmed %>% 
  dplyr::left_join(deaths) %>%
  dplyr::left_join(recovered) %>% 
  dplyr::mutate(mortality_rate = round((.$deaths_cases / .$confirmed_cases)*100,2))
#> Joining, by = c("Province/State", "Country/Region", "Lat", "Long", "greg_d")
#> Joining, by = c("Province/State", "Country/Region", "Lat", "Long", "greg_d")

knitr::kable(combo %>% dplyr::filter(`Country/Region` == "US") %>% tail(10), format = "html") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped"))

Province/State	Country/Region	Lat	Long	greg_d	confirmed_cases	deaths_cases	recovered_cases	mortality_rate
NA	US	40	-100	2/24/21	28309085	506335	0	1.79
NA	US	40	-100	2/25/21	28386492	508673	0	1.79
NA	US	40	-100	2/26/21	28463190	510764	0	1.79
NA	US	40	-100	2/27/21	28527344	512252	0	1.80
NA	US	40	-100	2/28/21	28578548	513291	0	1.80
NA	US	40	-100	3/1/21	28637313	514810	0	1.80
NA	US	40	-100	3/2/21	28694071	516737	0	1.80
NA	US	40	-100	3/3/21	28759980	519205	0	1.81
NA	US	40	-100	3/4/21	28827755	521119	0	1.81
NA	US	40	-100	3/5/21	28894541	522877	0	1.81

US COVID-19 Data with Geographic Data:

us_confirmed <- R.COVID.19::us_geo_confirmed_daily()
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   iso2 = col_character(),
#>   iso3 = col_character(),
#>   Admin2 = col_character(),
#>   Province_State = col_character(),
#>   Country_Region = col_character(),
#>   Combined_Key = col_character()
#> )
#> See spec(...) for full column specifications.

us_deaths <- R.COVID.19::us_geo_deaths_daily()
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   iso2 = col_character(),
#>   iso3 = col_character(),
#>   Admin2 = col_character(),
#>   Province_State = col_character(),
#>   Country_Region = col_character(),
#>   Combined_Key = col_character()
#> )
#> See spec(...) for full column specifications.

combo <- us_confirmed %>% dplyr::left_join(us_deaths) %>% 
  dplyr::mutate(mortality_rate = round((.$deaths_cases / .$confirmed_cases)*100,2))
#> Joining, by = c("UID", "iso2", "iso3", "code3", "FIPS", "Admin2", "Province_State", "Country_Region", "Lat", "Long_", "Combined_Key", "greg_d")

knitr::kable(combo %>% 
               dplyr::filter(Province_State == "New York", Admin2 == "New York") %>% 
               tail(5), format = "html") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped"))

UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key	greg_d	confirmed_cases	Population	deaths_cases	mortality_rate
84036061	US	USA	840	36061	New York	New York	US	40.76727	-73.97153	New York, New York, US	3/1/21	103922	1628706	3906	3.76
84036061	US	USA	840	36061	New York	New York	US	40.76727	-73.97153	New York, New York, US	3/2/21	104444	1628706	3917	3.75
84036061	US	USA	840	36061	New York	New York	US	40.76727	-73.97153	New York, New York, US	3/3/21	104874	1628706	3940	3.76
84036061	US	USA	840	36061	New York	New York	US	40.76727	-73.97153	New York, New York, US	3/4/21	105491	1628706	3954	3.75
84036061	US	USA	840	36061	New York	New York	US	40.76727	-73.97153	New York, New York, US	3/5/21	106144	1628706	3964	3.73

---

COVID-19 data from The New York Times, based on reports from state and local health agencies.

• source: GITHUB

US COVID-19 County Data with Geographic Data:

us_co_cases <- R.COVID.19::us_counties_daily() %>% 
  dplyr::mutate(mortality_rate = round((.$deaths / .$cases)*100,2))
#> Parsed with column specification:
#> cols(
#>   date = col_date(format = ""),
#>   county = col_character(),
#>   state = col_character(),
#>   fips = col_character(),
#>   cases = col_double(),
#>   deaths = col_double()
#> )
knitr::kable(us_co_cases %>% dplyr::filter(state == "New York") %>% tail(10), format = "html") %>% 
  kableExtra::kable_styling(bootstrap_options = "striped")

date	county	state	fips	cases	deaths	mortality_rate
2021-02-24	Yates	New York	36123	1011	26	2.57
2021-02-25	Yates	New York	36123	1011	26	2.57
2021-02-26	Yates	New York	36123	1011	26	2.57
2021-02-27	Yates	New York	36123	1014	26	2.56
2021-02-28	Yates	New York	36123	1014	26	2.56
2021-03-01	Yates	New York	36123	1014	26	2.56
2021-03-02	Yates	New York	36123	1015	26	2.56
2021-03-03	Yates	New York	36123	1015	26	2.56
2021-03-04	Yates	New York	36123	1017	26	2.56
2021-03-05	Yates	New York	36123	1022	26	2.54

US COVID-19 State Data with Geographic Data:

us_st_cases <- R.COVID.19::us_states_daily() %>% 
  dplyr::mutate(mortality_rate = round((.$deaths / .$cases)*100,2))
#> Parsed with column specification:
#> cols(
#>   date = col_date(format = ""),
#>   state = col_character(),
#>   fips = col_character(),
#>   cases = col_double(),
#>   deaths = col_double()
#> )
knitr::kable(us_st_cases %>% dplyr::filter(state == "New York") %>% tail(10), format = "html") %>% 
  kableExtra::kable_styling(bootstrap_options = "striped")

date	state	fips	cases	deaths	mortality_rate
2021-02-24	New York	36	1611288	46680	2.90
2021-02-25	New York	36	1620181	46790	2.89
2021-02-26	New York	36	1628255	46914	2.88
2021-02-27	New York	36	1636297	47025	2.87
2021-02-28	New York	36	1644124	47143	2.87
2021-03-01	New York	36	1650560	47247	2.86
2021-03-02	New York	36	1656941	47345	2.86
2021-03-03	New York	36	1663505	47464	2.85
2021-03-04	New York	36	1670973	47565	2.85
2021-03-05	New York	36	1679124	47672	2.84