R/get_covid_sources.R
In randyyee/ITFAnalytics: CDC-ITF Analytics
Defines functions
get_covid_sources
Documented in
get_covid_sources
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_covid_sources
#' @description Get a JHU and WHO prepared dataset.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_covid_sources <- function(){
df <- rbind(get_jhu_covid() %>% dplyr::mutate(data_source = "JHU"),
get_who_covid() %>% dplyr::mutate(data_source = "WHO")) %>%
dplyr::mutate(ou_date_src_match = paste(ou_date_match, data_source, sep="_")) %>%
dplyr::mutate(country_code = iso3code) %>%
dplyr::mutate(`Population 2018.x` = `pop_2020yr`) %>%
dplyr::select(Country = "country",
Date = "date",
`New Cases` = "cases_new",
`Cumulative Cases` = "cases_cum",
`New Deaths` = "deaths_new",
`Cumulative Deaths` = "deaths_cum",
`WHO Region` = "who_region",
`Population 2020` = "pop_2020yr",
`Population 2018.x`,
`Country Code` = "iso3code",
country_code,
ou_date_match,
ou_date_src_match,
data_source,
iso2code
) %>%
dplyr::mutate_if(is.numeric, ~replace(., is.na(.), 0))
return(df)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_jhu_covid
#' @description Get and prepare JHU data.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_jhu_covid <- function(countries_dates){
cases <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv", as.is=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
deaths <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv", as.is=TRUE, stringsAsFactors = FALSE, check.names=FALSE)
if (missing(countries_dates)) {
countries_dates <- add_country_dates(countries_data)
}
#Convert to Long Data
cases.long <- cases %>%
tidyr::gather("Date.Orig", "Cumulative Cases", c(5:length(names(cases))))
deaths.long <- deaths %>%
tidyr::gather("Date.Orig", "Cumulative Deaths", c(5:length(names(cases))))
#Clean up Date and Time
cases.long$Date <- as.Date(as.character(cases.long$Date.Orig), format="%m/%d/%y")
deaths.long$Date <- as.Date(as.character(deaths.long$Date.Orig), format="%m/%d/%y")
#Combine Case and Death Data
data.long <- merge(cases.long, deaths.long, by=c("Province/State","Country/Region","Lat","Long","Date"), all=TRUE)
# Remove the redundant date column
data.long[c("Date.Orig.x","Date.Orig.y")] <- NULL
# Calculate Daily New Cases and New Deaths, Then Make 0 if negative)
data.long$Country.Province <- paste0(data.long$`Country/Region`," - ", data.long$`Province/State`)
data.long$`New Cases` <- ave(data.long$`Cumulative Cases`, factor(data.long$Country.Province), FUN=function(x) c(NA,diff(x)))
data.long$`New Deaths` <- ave(data.long$`Cumulative Deaths`, factor(data.long$Country.Province), FUN=function(x) c(NA,diff(x)))
data.long$`New Cases`[data.long$`New Cases` <0] <- 0
data.long$`New Deaths`[data.long$`New Deaths` <0] <- 0
# Aggregate data to the country level
# Keep Taiwan, Hong Kong, and Macao separate from Mainland China
data.long$Country <- data.long$`Country/Region`
data.long$Country[data.long$`Province/State` %in% c("Hong Kong","Macau")] <- data.long$`Province/State`[data.long$`Province/State` %in% c("Hong Kong","Macau")]
data.long$Country <- gsub("*","",data.long$Country, fixed=TRUE)
data.countries <- aggregate(data.long[c("New Cases","New Deaths","Cumulative Cases","Cumulative Deaths")], by=list(Country=data.long$Country, Date=data.long$Date), sum)
data.countries$Country.clean <- passport::parse_country(data.countries$Country, to="en-iso")
data.countries$Country.clean[is.na(data.countries$Country.clean)] <- data.countries$Country[is.na(data.countries$Country.clean)]
data.countries$Country <- data.countries$Country.clean
data.countries$Country[data.countries$Country %in% c("Diamond Princess", "MS Zaandam")] <- "International Conveyance"
data.countries <- data.countries[c("Country","Date","New Cases","Cumulative Cases","New Deaths","Cumulative Deaths")]
data.countries <- data.countries[order(data.countries$Country, data.countries$Date),]
df <- data.countries %>%
dplyr::rename(country = Country,
date = Date,
cases_new = `New Cases`,
cases_cum = `Cumulative Cases`,
deaths_new = `New Deaths`,
deaths_cum = `Cumulative Deaths`) %>%
dplyr::mutate(iso3code = dplyr::case_when(country == "International Conveyance" ~ "OTH",
country == "Eswatini" ~passport::parse_country("Swaziland", to = "iso3c", language = c("en")),
country == "Kosovo" ~ "XKX",
TRUE ~passport::parse_country(country, to = "iso3c", language = c("en")))) %>%
dplyr::filter(!is.na(iso3code)) %>%
dplyr::select(-country) %>%
dplyr::group_by(iso3code) %>%
# Adding back all the first cases
dplyr::mutate(firstcase = dplyr::if_else(date == min(date), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::mutate(cases_new = dplyr::if_else(firstcase == 1, cases_cum, as.integer(cases_new))) %>%
dplyr::mutate(deaths_new = dplyr::if_else(firstcase == 1, deaths_cum, as.integer(deaths_new))) %>%
dplyr::select(-firstcase) %>%
# Sum over cruise ships
dplyr::group_by(iso3code,date) %>%
dplyr::summarise(cases_new = sum(cases_new),
deaths_new = sum(deaths_new),
cases_cum = sum(cases_cum),
deaths_cum = sum(deaths_cum)) %>%
dplyr::ungroup()
# Adding population from the World Bank and JHU
countries_dates <- countries_dates %>%
dplyr::filter(date <= max(df$date)) %>%
dplyr::filter(date >= min(df$date)) %>%
dplyr::filter(iso3code %in% unique(df$iso3code))
# Getting the basic final dataset
base_data <- dplyr::left_join(countries_dates, df) %>%
dplyr::select(country,
date,
cases_new,
cases_cum,
deaths_new,
deaths_cum,
who_region,
pop_2020yr,
iso3code,
ou_date_match,
iso2code) %>%
dplyr::mutate_if(is.numeric, ~ replace(., is.na(.), 0))
return(base_data)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_who_covid
#' @description Get and prepare WHO COVID data.
#' @param countries_dates Optional.
#' If not provided, will use the package provided countries_data and add_country_dates to make the most current dataset.
#' See get_countries for description of required input.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_who_covid <- function(countries_dates){
if (missing(countries_dates)) {
countries_dates <- add_country_dates(countries_data)
}
df <- read.csv("https://covid19.who.int/WHO-COVID-19-global-data.csv", stringsAsFactors=FALSE, encoding="UTF-8") %>%
dplyr::mutate(Country = dplyr::case_when(Country == "Kosovo[1]" ~"Kosovo",
Country %in% c("Bonaire", "Sint Eustatius", "Saba") ~"Bonaire, Sint Eustatius, and Saba",
TRUE ~Country)) %>%
dplyr::mutate(Country_code = dplyr::case_when(Country == "Namibia" ~"NA",
Country == "Other" ~"OT",
Country == "Bonaire, Sint Eustatius, and Saba" ~"BQ",
TRUE ~Country_code)) %>%
dplyr::rename("date" = names(.)[1]) %>%
dplyr::group_by_if(is.character) %>%
dplyr::summarize_all(list(~sum(., na.rm=T))) %>%
dplyr::ungroup() %>%
dplyr::mutate(date = as.Date(date))%>%
dplyr::select(iso2code = Country_code,
date,
New_cases,
New_deaths,
Cumulative_cases,
Cumulative_deaths)
# Expand the time series so all countries have the same number of records
# Create data frame with all countries and all dates
countries_dates <- countries_dates %>%
dplyr::filter(date <= max(df$date)) %>%
dplyr::filter(date >= min(df$date)) %>%
dplyr::filter(iso2code %in% unique(df$iso2code)) %>%
dplyr::right_join(df) %>%
dplyr::select(country,
date,
cases_new = New_cases,
cases_cum = Cumulative_cases,
deaths_new = New_deaths,
deaths_cum = Cumulative_deaths,
who_region,
pop_2020yr,
iso3code,
ou_date_match,
iso2code) %>%
dplyr::mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
dplyr::filter(date != Sys.Date()) %>%
# There is no Taiwan data in the WHO data set but it is in the JHU dataset
dplyr::filter(iso3code != "TWN")
return(countries_dates)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_covid_df
#' @description Get and prepare COVID data.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_covid_df <- function(){
df <- read.csv("https://covid19.who.int/WHO-COVID-19-global-data.csv", stringsAsFactors=FALSE, encoding="UTF-8") %>%
dplyr::rename_all(tolower) %>%
dplyr::mutate(country = dplyr::case_when(country == "Kosovo[1]" ~"Kosovo",
country %in% c("Bonaire", "Sint Eustatius", "Saba") ~"Bonaire, Sint Eustatius, and Saba",
TRUE ~country)) %>%
dplyr::mutate(country_code = dplyr::case_when(country == "Namibia" ~"NA",
country == "Other" ~"OT",
country == "Bonaire, Sint Eustatius, and Saba" ~"BQ",
TRUE ~country_code)) %>%
dplyr::rename("date" = names(.)[1]) %>%
dplyr::group_by_if(is.character) %>%
dplyr::summarize_all(list(~sum(., na.rm=T))) %>%
dplyr::ungroup() %>%
dplyr::mutate(date = as.Date(date),
source = "WHO") %>%
dplyr::bind_rows(
read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
as.is=TRUE, stringsAsFactors=FALSE, check.names=FALSE) %>%
dplyr::rename_all(tolower) %>%
dplyr::filter(`country/region` %in% c("Taiwan*", "China")) %>%
dplyr::mutate(`country/region` = dplyr::case_when(`province/state` == "Hong Kong" ~ "Hong Kong",
`province/state` == "Macau" ~ "Macau",
TRUE ~ `country/region`)) %>%
dplyr::select(-lat,-long) %>%
dplyr::group_by(`country/region`) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE) %>%
dplyr::ungroup() %>%
tidyr::pivot_longer(cols = where(is.numeric), names_to = "date", values_to = "cumulative_cases") %>%
dplyr::mutate(date = lubridate::mdy(date)) %>%
dplyr::mutate(`country/region` = dplyr::recode(`country/region`, "Taiwan*" = "Taiwan")) %>%
dplyr::group_by(`country/region`) %>%
dplyr::mutate(new_cases = cumulative_cases - dplyr::lag(cumulative_cases)) %>%
dplyr::ungroup() %>%
dplyr::left_join(
read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
as.is=TRUE, stringsAsFactors = FALSE, check.names=FALSE) %>%
dplyr::rename_all(tolower) %>%
dplyr::filter(`country/region` %in% c("Taiwan*", "China")) %>%
dplyr::mutate(`country/region` = dplyr::case_when(`province/state` == "Hong Kong" ~ "Hong Kong",
`province/state` == "Macau" ~ "Macau",
TRUE ~ `country/region`)) %>%
dplyr::select(-lat,-long) %>%
dplyr::group_by(`country/region`) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE) %>%
dplyr::ungroup() %>%
tidyr::pivot_longer(cols = where(is.numeric), names_to = "date", values_to = "cumulative_deaths") %>%
dplyr::mutate(date = lubridate::mdy(date)) %>%
dplyr::mutate(`country/region` = dplyr::recode(`country/region`, "Taiwan*" = "Taiwan")) %>%
dplyr::group_by(`country/region`) %>%
dplyr::mutate(new_deaths = cumulative_deaths - dplyr::lag(cumulative_deaths)) %>%
dplyr::ungroup()
) %>%
dplyr::rename(country = `country/region`) %>%
dplyr::mutate(who_region = "WPRO",
country_code = dplyr::case_when(country == "China" ~"CN",
country == "Taiwan" ~"TW",
country == "Hong Kong" ~"HK",
country == "Macau" ~"MO"),
source = "JHU")%>%
dplyr::arrange(country, date)
) %>%
dplyr::mutate(country = dplyr::recode(country, "Côte d’Ivoire" = "Cote d'Ivoire")) %>%
dplyr::mutate(who_region = factor(who_region, levels= c("AMRO","EURO","SEARO","EMRO","AFRO","WPRO"))) %>%
dplyr::mutate(region = dplyr::case_when(who_region == "AMRO" ~"Americas",
who_region == "EURO" ~"Europe",
who_region == "SEARO" ~"Southeast Asia",
who_region == "EMRO" ~"Eastern Mediterranean",
who_region == "AFRO" ~"Africa",
who_region == "WPRO" ~"Western Pacific"),
region = factor(region, levels= c("Americas","Europe","Southeast Asia","Eastern Mediterranean","Africa","Western Pacific")))
}
randyyee/ITFAnalytics documentation built on Dec. 22, 2021, 12:56 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions get_covid_sources
Documented in get_covid_sources
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_covid_sources
#' @description Get a JHU and WHO prepared dataset.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_covid_sources <- function(){
df <- rbind(get_jhu_covid() %>% dplyr::mutate(data_source = "JHU"),
get_who_covid() %>% dplyr::mutate(data_source = "WHO")) %>%
dplyr::mutate(ou_date_src_match = paste(ou_date_match, data_source, sep="_")) %>%
dplyr::mutate(country_code = iso3code) %>%
dplyr::mutate(`Population 2018.x` = `pop_2020yr`) %>%
dplyr::select(Country = "country",
Date = "date",
`New Cases` = "cases_new",
`Cumulative Cases` = "cases_cum",
`New Deaths` = "deaths_new",
`Cumulative Deaths` = "deaths_cum",
`WHO Region` = "who_region",
`Population 2020` = "pop_2020yr",
`Population 2018.x`,
`Country Code` = "iso3code",
country_code,
ou_date_match,
ou_date_src_match,
data_source,
iso2code
) %>%
dplyr::mutate_if(is.numeric, ~replace(., is.na(.), 0))
return(df)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_jhu_covid
#' @description Get and prepare JHU data.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_jhu_covid <- function(countries_dates){
cases <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv", as.is=TRUE, stringsAsFactors=FALSE, check.names=FALSE)
deaths <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv", as.is=TRUE, stringsAsFactors = FALSE, check.names=FALSE)
if (missing(countries_dates)) {
countries_dates <- add_country_dates(countries_data)
}
#Convert to Long Data
cases.long <- cases %>%
tidyr::gather("Date.Orig", "Cumulative Cases", c(5:length(names(cases))))
deaths.long <- deaths %>%
tidyr::gather("Date.Orig", "Cumulative Deaths", c(5:length(names(cases))))
#Clean up Date and Time
cases.long$Date <- as.Date(as.character(cases.long$Date.Orig), format="%m/%d/%y")
deaths.long$Date <- as.Date(as.character(deaths.long$Date.Orig), format="%m/%d/%y")
#Combine Case and Death Data
data.long <- merge(cases.long, deaths.long, by=c("Province/State","Country/Region","Lat","Long","Date"), all=TRUE)
# Remove the redundant date column
data.long[c("Date.Orig.x","Date.Orig.y")] <- NULL
# Calculate Daily New Cases and New Deaths, Then Make 0 if negative)
data.long$Country.Province <- paste0(data.long$`Country/Region`," - ", data.long$`Province/State`)
data.long$`New Cases` <- ave(data.long$`Cumulative Cases`, factor(data.long$Country.Province), FUN=function(x) c(NA,diff(x)))
data.long$`New Deaths` <- ave(data.long$`Cumulative Deaths`, factor(data.long$Country.Province), FUN=function(x) c(NA,diff(x)))
data.long$`New Cases`[data.long$`New Cases` <0] <- 0
data.long$`New Deaths`[data.long$`New Deaths` <0] <- 0
# Aggregate data to the country level
# Keep Taiwan, Hong Kong, and Macao separate from Mainland China
data.long$Country <- data.long$`Country/Region`
data.long$Country[data.long$`Province/State` %in% c("Hong Kong","Macau")] <- data.long$`Province/State`[data.long$`Province/State` %in% c("Hong Kong","Macau")]
data.long$Country <- gsub("*","",data.long$Country, fixed=TRUE)
data.countries <- aggregate(data.long[c("New Cases","New Deaths","Cumulative Cases","Cumulative Deaths")], by=list(Country=data.long$Country, Date=data.long$Date), sum)
data.countries$Country.clean <- passport::parse_country(data.countries$Country, to="en-iso")
data.countries$Country.clean[is.na(data.countries$Country.clean)] <- data.countries$Country[is.na(data.countries$Country.clean)]
data.countries$Country <- data.countries$Country.clean
data.countries$Country[data.countries$Country %in% c("Diamond Princess", "MS Zaandam")] <- "International Conveyance"
data.countries <- data.countries[c("Country","Date","New Cases","Cumulative Cases","New Deaths","Cumulative Deaths")]
data.countries <- data.countries[order(data.countries$Country, data.countries$Date),]
df <- data.countries %>%
dplyr::rename(country = Country,
date = Date,
cases_new = `New Cases`,
cases_cum = `Cumulative Cases`,
deaths_new = `New Deaths`,
deaths_cum = `Cumulative Deaths`) %>%
dplyr::mutate(iso3code = dplyr::case_when(country == "International Conveyance" ~ "OTH",
country == "Eswatini" ~passport::parse_country("Swaziland", to = "iso3c", language = c("en")),
country == "Kosovo" ~ "XKX",
TRUE ~passport::parse_country(country, to = "iso3c", language = c("en")))) %>%
dplyr::filter(!is.na(iso3code)) %>%
dplyr::select(-country) %>%
dplyr::group_by(iso3code) %>%
# Adding back all the first cases
dplyr::mutate(firstcase = dplyr::if_else(date == min(date), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::mutate(cases_new = dplyr::if_else(firstcase == 1, cases_cum, as.integer(cases_new))) %>%
dplyr::mutate(deaths_new = dplyr::if_else(firstcase == 1, deaths_cum, as.integer(deaths_new))) %>%
dplyr::select(-firstcase) %>%
# Sum over cruise ships
dplyr::group_by(iso3code,date) %>%
dplyr::summarise(cases_new = sum(cases_new),
deaths_new = sum(deaths_new),
cases_cum = sum(cases_cum),
deaths_cum = sum(deaths_cum)) %>%
dplyr::ungroup()
# Adding population from the World Bank and JHU
countries_dates <- countries_dates %>%
dplyr::filter(date <= max(df$date)) %>%
dplyr::filter(date >= min(df$date)) %>%
dplyr::filter(iso3code %in% unique(df$iso3code))
# Getting the basic final dataset
base_data <- dplyr::left_join(countries_dates, df) %>%
dplyr::select(country,
date,
cases_new,
cases_cum,
deaths_new,
deaths_cum,
who_region,
pop_2020yr,
iso3code,
ou_date_match,
iso2code) %>%
dplyr::mutate_if(is.numeric, ~ replace(., is.na(.), 0))
return(base_data)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_who_covid
#' @description Get and prepare WHO COVID data.
#' @param countries_dates Optional.
#' If not provided, will use the package provided countries_data and add_country_dates to make the most current dataset.
#' See get_countries for description of required input.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_who_covid <- function(countries_dates){
if (missing(countries_dates)) {
countries_dates <- add_country_dates(countries_data)
}
df <- read.csv("https://covid19.who.int/WHO-COVID-19-global-data.csv", stringsAsFactors=FALSE, encoding="UTF-8") %>%
dplyr::mutate(Country = dplyr::case_when(Country == "Kosovo[1]" ~"Kosovo",
Country %in% c("Bonaire", "Sint Eustatius", "Saba") ~"Bonaire, Sint Eustatius, and Saba",
TRUE ~Country)) %>%
dplyr::mutate(Country_code = dplyr::case_when(Country == "Namibia" ~"NA",
Country == "Other" ~"OT",
Country == "Bonaire, Sint Eustatius, and Saba" ~"BQ",
TRUE ~Country_code)) %>%
dplyr::rename("date" = names(.)[1]) %>%
dplyr::group_by_if(is.character) %>%
dplyr::summarize_all(list(~sum(., na.rm=T))) %>%
dplyr::ungroup() %>%
dplyr::mutate(date = as.Date(date))%>%
dplyr::select(iso2code = Country_code,
date,
New_cases,
New_deaths,
Cumulative_cases,
Cumulative_deaths)
# Expand the time series so all countries have the same number of records
# Create data frame with all countries and all dates
countries_dates <- countries_dates %>%
dplyr::filter(date <= max(df$date)) %>%
dplyr::filter(date >= min(df$date)) %>%
dplyr::filter(iso2code %in% unique(df$iso2code)) %>%
dplyr::right_join(df) %>%
dplyr::select(country,
date,
cases_new = New_cases,
cases_cum = Cumulative_cases,
deaths_new = New_deaths,
deaths_cum = Cumulative_deaths,
who_region,
pop_2020yr,
iso3code,
ou_date_match,
iso2code) %>%
dplyr::mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
dplyr::filter(date != Sys.Date()) %>%
# There is no Taiwan data in the WHO data set but it is in the JHU dataset
dplyr::filter(iso3code != "TWN")
return(countries_dates)
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' @title get_covid_df
#' @description Get and prepare COVID data.
#'
#' @importFrom magrittr `%>%`
#'
#' @export
get_covid_df <- function(){
df <- read.csv("https://covid19.who.int/WHO-COVID-19-global-data.csv", stringsAsFactors=FALSE, encoding="UTF-8") %>%
dplyr::rename_all(tolower) %>%
dplyr::mutate(country = dplyr::case_when(country == "Kosovo[1]" ~"Kosovo",
country %in% c("Bonaire", "Sint Eustatius", "Saba") ~"Bonaire, Sint Eustatius, and Saba",
TRUE ~country)) %>%
dplyr::mutate(country_code = dplyr::case_when(country == "Namibia" ~"NA",
country == "Other" ~"OT",
country == "Bonaire, Sint Eustatius, and Saba" ~"BQ",
TRUE ~country_code)) %>%
dplyr::rename("date" = names(.)[1]) %>%
dplyr::group_by_if(is.character) %>%
dplyr::summarize_all(list(~sum(., na.rm=T))) %>%
dplyr::ungroup() %>%
dplyr::mutate(date = as.Date(date),
source = "WHO") %>%
dplyr::bind_rows(
read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
as.is=TRUE, stringsAsFactors=FALSE, check.names=FALSE) %>%
dplyr::rename_all(tolower) %>%
dplyr::filter(`country/region` %in% c("Taiwan*", "China")) %>%
dplyr::mutate(`country/region` = dplyr::case_when(`province/state` == "Hong Kong" ~ "Hong Kong",
`province/state` == "Macau" ~ "Macau",
TRUE ~ `country/region`)) %>%
dplyr::select(-lat,-long) %>%
dplyr::group_by(`country/region`) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE) %>%
dplyr::ungroup() %>%
tidyr::pivot_longer(cols = where(is.numeric), names_to = "date", values_to = "cumulative_cases") %>%
dplyr::mutate(date = lubridate::mdy(date)) %>%
dplyr::mutate(`country/region` = dplyr::recode(`country/region`, "Taiwan*" = "Taiwan")) %>%
dplyr::group_by(`country/region`) %>%
dplyr::mutate(new_cases = cumulative_cases - dplyr::lag(cumulative_cases)) %>%
dplyr::ungroup() %>%
dplyr::left_join(
read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
as.is=TRUE, stringsAsFactors = FALSE, check.names=FALSE) %>%
dplyr::rename_all(tolower) %>%
dplyr::filter(`country/region` %in% c("Taiwan*", "China")) %>%
dplyr::mutate(`country/region` = dplyr::case_when(`province/state` == "Hong Kong" ~ "Hong Kong",
`province/state` == "Macau" ~ "Macau",
TRUE ~ `country/region`)) %>%
dplyr::select(-lat,-long) %>%
dplyr::group_by(`country/region`) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE) %>%
dplyr::ungroup() %>%
tidyr::pivot_longer(cols = where(is.numeric), names_to = "date", values_to = "cumulative_deaths") %>%
dplyr::mutate(date = lubridate::mdy(date)) %>%
dplyr::mutate(`country/region` = dplyr::recode(`country/region`, "Taiwan*" = "Taiwan")) %>%
dplyr::group_by(`country/region`) %>%
dplyr::mutate(new_deaths = cumulative_deaths - dplyr::lag(cumulative_deaths)) %>%
dplyr::ungroup()
) %>%
dplyr::rename(country = `country/region`) %>%
dplyr::mutate(who_region = "WPRO",
country_code = dplyr::case_when(country == "China" ~"CN",
country == "Taiwan" ~"TW",
country == "Hong Kong" ~"HK",
country == "Macau" ~"MO"),
source = "JHU")%>%
dplyr::arrange(country, date)
) %>%
dplyr::mutate(country = dplyr::recode(country, "Côte d’Ivoire" = "Cote d'Ivoire")) %>%
dplyr::mutate(who_region = factor(who_region, levels= c("AMRO","EURO","SEARO","EMRO","AFRO","WPRO"))) %>%
dplyr::mutate(region = dplyr::case_when(who_region == "AMRO" ~"Americas",
who_region == "EURO" ~"Europe",
who_region == "SEARO" ~"Southeast Asia",
who_region == "EMRO" ~"Eastern Mediterranean",
who_region == "AFRO" ~"Africa",
who_region == "WPRO" ~"Western Pacific"),
region = factor(region, levels= c("Americas","Europe","Southeast Asia","Eastern Mediterranean","Africa","Western Pacific")))
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.