README.md

In Fredo-XVII/R.COVID.19: COVID-19 Daily Global and US Counts

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

Fredo-XVII/R.COVID.19 documentation built on Aug. 6, 2022, 2:46 p.m.