B Covid 19
An example of an R Notebook, rendered at 2023-12-04 12:23:18 JST
B.1 Introduction
The following site of Johns Hopkins University is famous:
In this article, we study a coronavirus data collected by Johns Hopkins University called “JHU Covid-19 global time series data”. Since the original data requires reshaping, we use the data provided by RamiKrispin in the following site.
See also the R package coronavirus at
- https://CRAN.R-project.org/package=coronavirus
- For installation:
install.packages("coronavirus")
library(tidyverse)
#> ── Attaching core tidyverse packages ──── tidyverse 2.0.0 ──
#> ✔ dplyr 1.1.3 ✔ readr 2.1.4
#> ✔ forcats 1.0.0 ✔ stringr 1.5.0
#> ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
#> ✔ purrr 1.0.2
#> ── Conflicts ────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(coronavirus)
library(owidR)We can directly download and read the data from:
It is updated daily.
In this note, we use the original JHU data and transform it using dplyr in the form similar to the Krispin’s.
Our world in data also provides vairous data related to covid-19. We will study a little by using owid_covid.
B.2 Krispin’s Package
coronavirus_tv <- read_csv("https://github.com/RamiKrispin/coronavirus/raw/master/csv/coronavirus.csv")
write_csv(coronavirus_tv, "data/coronavirus_tv.csv")#> Rows: 919308 Columns: 15
#> ── Column specification ────────────────────────────────────
#> Delimiter: ","
#> chr (8): province, country, type, iso2, iso3, combined_...
#> dbl (6): lat, long, cases, uid, code3, population
#> date (1): date
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
COUNTRY <- "Japan"
df_tv <- select(coronavirus_tv, c(date, country, type, cases, population))
df_tv0 <- filter(df_tv, country %in% COUNTRY)
df_tv_confirmed <- filter(df_tv0, type == "confirmed")
df_tv_confirmed_pp <- mutate(df_tv_confirmed, confirmed_pp = cases*100000/population)
ggplot(df_tv_confirmed_pp) + geom_line(aes(x = date, y = confirmed_pp), col = "red") +
geom_smooth(aes(x = date, y = confirmed_pp), formula = y~x, method="loess", span = 0.1, se=FALSE) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = paste("Number of Confirmed Cases per 100,000 ", COUNTRY))
COUNTRIES <- c("US", "Germany", "India", "South Africa","Korea, South", "Japan")
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
coronavirus_tv %>% select(c(date, country, type, cases, population)) %>%
filter(country %in% COUNTRIES) %>%
filter(date >=start_date & df_tv0$date <= end_date) %>%
filter(type == "confirmed") %>%
mutate(confirmed_pp = cases*100000/population) %>%
ggplot() +
geom_smooth(aes(x = date, y = confirmed_pp, color = country), formula=y~x, method="loess", se=FALSE, span=0.1) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = "Number of Confirmed Cases per 100,000")
COUNTRIES <- c("US", "Germany", "India", "South Africa","Korea, South", "Japan")
start_date <- as.Date("2021-07-01")
end_date <- Sys.Date()
df_tv <- select(coronavirus_tv, c(date, country, type, cases, population))
df_tv0 <- filter(df_tv, country %in% COUNTRIES)
df_tv1 <- filter(df_tv0, date >=start_date & df_tv0$date <= end_date)
df_tv1_confirmed <- filter(df_tv1, type == "confirmed")
df_tv1_confirmed_pp <- mutate(df_tv1_confirmed, confirmed_pp = cases*100000/population)
ggplot(df_tv1_confirmed_pp) +
geom_smooth(aes(x = date, y = confirmed_pp, color = country), formula=y~x, method="loess", se=FALSE, span=0.1) +
labs(x = "Date", y = "Number of Confirmed Cases per 100,000",
title = "Number of Confirmed Cases per 100,000")
B.3 Data of Johns Hopkins Universiy
B.3.1 Importing Raw Data
We import the original Johns Hopkins Github data.
- COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/CSSEGISandData/COVID-19
- We use time series data
# IMPORT RAW DATA: Johns Hopkins Github data
confirmedraw <- 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")
#> Rows: 289 Columns: 1147
#> ── Column specification ────────────────────────────────────
#> Delimiter: ","
#> chr (2): Province/State, Country/Region
#> dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/2...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(confirmedraw)
#> Rows: 289
#> Columns: 1,147
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#> $ `Country/Region` <chr> "Afghanistan", "Albania", "Algeri…
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#> $ `7/25/20` <dbl> 36122, 4637, 26764, 897, 916, 0, …
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#> $ `7/28/20` <dbl> 36454, 4997, 28615, 907, 1000, 0,…
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#> $ `8/25/22` <dbl> 191967, 327607, 270097, 46027, 10…
#> $ `8/26/22` <dbl> 191967, 327961, 270145, 46027, 10…
#> $ `8/27/22` <dbl> 191967, 328299, 270175, 46027, 10…
#> $ `8/28/22` <dbl> 192463, 328515, 270194, 46027, 10…
#> $ `8/29/22` <dbl> 192906, 328571, 270235, 46027, 10…
#> $ `8/30/22` <dbl> 193004, 329017, 270272, 46027, 10…
#> $ `8/31/22` <dbl> 193250, 329352, 270304, 46027, 10…
#> $ `9/1/22` <dbl> 193520, 329615, 270359, 46027, 10…
#> $ `9/2/22` <dbl> 193520, 329862, 270405, 46027, 10…
#> $ `9/3/22` <dbl> 193912, 330062, 270426, 46027, 10…
#> $ `9/4/22` <dbl> 194163, 330193, 270443, 46027, 10…
#> $ `9/5/22` <dbl> 194355, 330221, 270461, 46027, 10…
#> $ `9/6/22` <dbl> 194614, 330283, 270476, 46027, 10…
#> $ `9/7/22` <dbl> 195012, 330516, 270489, 46113, 10…
#> $ `9/8/22` <dbl> 195298, 330687, 270507, 46113, 10…
#> $ `9/9/22` <dbl> 195471, 330842, 270522, 46113, 10…
#> $ `9/10/22` <dbl> 195631, 330948, 270532, 46113, 10…
#> $ `9/11/22` <dbl> 195925, 331036, 270539, 46113, 10…
#> $ `9/12/22` <dbl> 196182, 331053, 270551, 46113, 10…
#> $ `9/13/22` <dbl> 196404, 331191, 270551, 46113, 10…
#> $ `9/14/22` <dbl> 196751, 331295, 270570, 46147, 10…
#> $ `9/15/22` <dbl> 196870, 331384, 270584, 46147, 10…
#> $ `9/16/22` <dbl> 196992, 331459, 270599, 46147, 10…
#> $ `9/17/22` <dbl> 197066, 331540, 270606, 46147, 10…
#> $ `9/18/22` <dbl> 197240, 331583, 270609, 46147, 10…
#> $ `9/19/22` <dbl> 197434, 331601, 270612, 46147, 10…
#> $ `9/20/22` <dbl> 197608, 331715, 270612, 46147, 10…
#> $ `9/21/22` <dbl> 197788, 331810, 270619, 46147, 10…
#> $ `9/22/22` <dbl> 198023, 331861, 270625, 46147, 10…
#> $ `9/23/22` <dbl> 198163, 331908, 270631, 46147, 10…
#> $ `9/24/22` <dbl> 198244, 331953, 270637, 46147, 10…
#> $ `9/25/22` <dbl> 198416, 331976, 270641, 46147, 10…
#> $ `9/26/22` <dbl> 198543, 331987, 270649, 46147, 10…
#> $ `9/27/22` <dbl> 198750, 332066, 270654, 46147, 10…
#> $ `9/28/22` <dbl> 198876, 332129, 270662, 46227, 10…
#> $ `9/29/22` <dbl> 199067, 332173, 270668, 46227, 10…
#> $ `9/30/22` <dbl> 199188, 332221, 270673, 46227, 10…
#> $ `10/1/22` <dbl> 199310, 332263, 270676, 46227, 10…
#> $ `10/2/22` <dbl> 199386, 332285, 270679, 46227, 10…
#> $ `10/3/22` <dbl> 199545, 332290, 270682, 46227, 10…
#> $ `10/4/22` <dbl> 199690, 332337, 270690, 46227, 10…
#> $ `10/5/22` <dbl> 199845, 332372, 270693, 46227, 10…
#> $ `10/6/22` <dbl> 199994, 332410, 270697, 46275, 10…
#> $ `10/7/22` <dbl> 200130, 332443, 270701, 46275, 10…
#> $ `10/8/22` <dbl> 200202, 332472, 270701, 46275, 10…
#> $ `10/9/22` <dbl> 200372, 332494, 270707, 46275, 10…
#> $ `10/10/22` <dbl> 200469, 332503, 270713, 46275, 10…
#> $ `10/11/22` <dbl> 200626, 332534, 270716, 46275, 10…
#> $ `10/12/22` <dbl> 200729, 332555, 270722, 46366, 10…
#> $ `10/13/22` <dbl> 200846, 332579, 270722, 46366, 10…
#> $ `10/14/22` <dbl> 201014, 332598, 270734, 46366, 10…
#> $ `10/15/22` <dbl> 201096, 332619, 270734, 46366, 10…
#> $ `10/16/22` <dbl> 201212, 332638, 270740, 46366, 10…
#> $ `10/17/22` <dbl> 201276, 332645, 270757, 46366, 10…
#> $ `10/18/22` <dbl> 201503, 332673, 270766, 46366, 10…
#> $ `10/19/22` <dbl> 201557, 332701, 270768, 46449, 10…
#> $ `10/20/22` <dbl> 201750, 332719, 270769, 46449, 10…
#> $ `10/21/22` <dbl> 201949, 332739, 270771, 46449, 10…
#> $ `10/22/22` <dbl> 202026, 332754, 270771, 46449, 10…
#> $ `10/23/22` <dbl> 202108, 332772, 270783, 46449, 10…
#> $ `10/24/22` <dbl> 202199, 332776, 270788, 46449, 10…
#> $ `10/25/22` <dbl> 202347, 332816, 270800, 46449, 10…
#> $ `10/26/22` <dbl> 202509, 332847, 270810, 46535, 10…
#> $ `10/27/22` <dbl> 202608, 332889, 270817, 46535, 10…
#> $ `10/28/22` <dbl> 202756, 332911, 270826, 46535, 10…
#> $ `10/29/22` <dbl> 202834, 332949, 270829, 46535, 10…
#> $ `10/30/22` <dbl> 202966, 332966, 270836, 46535, 10…
#> $ `10/31/22` <dbl> 203063, 332966, 270838, 46535, 10…
#> $ `11/1/22` <dbl> 203167, 332969, 270839, 46535, 10…
#> $ `11/2/22` <dbl> 203265, 332996, 270840, 46588, 10…
#> $ `11/3/22` <dbl> 203395, 332996, 270847, 46588, 10…
#> $ `11/4/22` <dbl> 203497, 333027, 270856, 46588, 10…
#> $ `11/5/22` <dbl> 203574, 333046, 270862, 46588, 10…
#> $ `11/6/22` <dbl> 203681, 333055, 270873, 46588, 10…
#> $ `11/7/22` <dbl> 203829, 333058, 270881, 46588, 10…
#> $ `11/8/22` <dbl> 203942, 333071, 270891, 46588, 10…
#> $ `11/9/22` <dbl> 204094, 333088, 270906, 46664, 10…
#> $ `11/10/22` <dbl> 204287, 333103, 270917, 46664, 10…
#> $ `11/11/22` <dbl> 204392, 333125, 270924, 46664, 10…
#> $ `11/12/22` <dbl> 204417, 333138, 270929, 46664, 10…
#> $ `11/13/22` <dbl> 204510, 333156, 270939, 46664, 10…
#> $ `11/14/22` <dbl> 204610, 333161, 270952, 46664, 10…
#> $ `11/15/22` <dbl> 204724, 333197, 270969, 46664, 10…
#> $ `11/16/22` <dbl> 204820, 333215, 270981, 46824, 10…
#> $ `11/17/22` <dbl> 204982, 333233, 270996, 46824, 10…
#> $ `11/18/22` <dbl> 205009, 333233, 270996, 46824, 10…
#> $ `11/19/22` <dbl> 205039, 333246, 271011, 46824, 10…
#> $ `11/20/22` <dbl> 205146, 333256, 271023, 46824, 10…
#> $ `11/21/22` <dbl> 205229, 333257, 271028, 46824, 10…
#> $ `11/22/22` <dbl> 205324, 333282, 271035, 46824, 10…
#> $ `11/23/22` <dbl> 205391, 333293, 271041, 46824, 10…
#> $ `11/24/22` <dbl> 205506, 333305, 271050, 46824, 10…
#> $ `11/25/22` <dbl> 205541, 333316, 271057, 46824, 10…
#> $ `11/26/22` <dbl> 205612, 333322, 271061, 46824, 10…
#> $ `11/27/22` <dbl> 205612, 333330, 271061, 46824, 10…
#> $ `11/28/22` <dbl> 205802, 333330, 271079, 46824, 10…
#> $ `11/29/22` <dbl> 205830, 333338, 271082, 46824, 10…
#> $ `11/30/22` <dbl> 205907, 333343, 271090, 47219, 10…
#> $ `12/1/22` <dbl> 206073, 333360, 271096, 47219, 10…
#> $ `12/2/22` <dbl> 206133, 333381, 271100, 47219, 10…
#> $ `12/3/22` <dbl> 206145, 333391, 271102, 47219, 10…
#> $ `12/4/22` <dbl> 206206, 333408, 271107, 47219, 10…
#> $ `12/5/22` <dbl> 206273, 333413, 271113, 47219, 10…
#> $ `12/6/22` <dbl> 206331, 333455, 271122, 47219, 10…
#> $ `12/7/22` <dbl> 206414, 333472, 271128, 47446, 10…
#> $ `12/8/22` <dbl> 206465, 333490, 271135, 47446, 10…
#> $ `12/9/22` <dbl> 206504, 333491, 271140, 47446, 10…
#> $ `12/10/22` <dbl> 206543, 333521, 271146, 47446, 10…
#> $ `12/11/22` <dbl> 206603, 333533, 271146, 47446, 10…
#> $ `12/12/22` <dbl> 206702, 333535, 271147, 47446, 10…
#> $ `12/13/22` <dbl> 206743, 333567, 271149, 47446, 10…
#> $ `12/14/22` <dbl> 206788, 333591, 271156, 47606, 10…
#> $ `12/15/22` <dbl> 206879, 333613, 271156, 47606, 10…
#> $ `12/16/22` <dbl> 206912, 333635, 271156, 47606, 10…
#> $ `12/17/22` <dbl> 206943, 333635, 271168, 47606, 10…
#> $ `12/18/22` <dbl> 207037, 333650, 271174, 47606, 10…
#> $ `12/19/22` <dbl> 207084, 333653, 271179, 47606, 10…
#> $ `12/20/22` <dbl> 207146, 333686, 271182, 47686, 10…
#> $ `12/21/22` <dbl> 207190, 333708, 271186, 47686, 10…
#> $ `12/22/22` <dbl> 207239, 333708, 271190, 47686, 10…
#> $ `12/23/22` <dbl> 207262, 333731, 271193, 47686, 10…
#> $ `12/24/22` <dbl> 207310, 333749, 271194, 47686, 10…
#> $ `12/25/22` <dbl> 207399, 333749, 271198, 47686, 10…
#> $ `12/26/22` <dbl> 207438, 333751, 271198, 47686, 10…
#> $ `12/27/22` <dbl> 207460, 333751, 271202, 47686, 10…
#> $ `12/28/22` <dbl> 207493, 333776, 271208, 47751, 10…
#> $ `12/29/22` <dbl> 207511, 333776, 271217, 47751, 10…
#> $ `12/30/22` <dbl> 207550, 333806, 271223, 47751, 10…
#> $ `12/31/22` <dbl> 207559, 333806, 271228, 47751, 10…
#> $ `1/1/23` <dbl> 207616, 333811, 271229, 47751, 10…
#> $ `1/2/23` <dbl> 207627, 333812, 271229, 47751, 10…
#> $ `1/3/23` <dbl> 207654, 333812, 271230, 47751, 10…
#> $ `1/4/23` <dbl> 207715, 333818, 271236, 47751, 10…
#> $ `1/5/23` <dbl> 207748, 333850, 271244, 47781, 10…
#> $ `1/6/23` <dbl> 207766, 333887, 271250, 47781, 10…
#> $ `1/7/23` <dbl> 207766, 333916, 271254, 47781, 10…
#> $ `1/8/23` <dbl> 207819, 333947, 271254, 47781, 10…
#> $ `1/9/23` <dbl> 207841, 333948, 271255, 47781, 10…
#> $ `1/10/23` <dbl> 207866, 333995, 271262, 47781, 10…
#> $ `1/11/23` <dbl> 207900, 333995, 271268, 47781, 10…
#> $ `1/12/23` <dbl> 207900, 334018, 271277, 47781, 10…
#> $ `1/13/23` <dbl> 207900, 334018, 271286, 47781, 10…
#> $ `1/14/23` <dbl> 207900, 334029, 271287, 47781, 10…
#> $ `1/15/23` <dbl> 207900, 334037, 271287, 47781, 10…
#> $ `1/16/23` <dbl> 207993, 334037, 271287, 47781, 10…
#> $ `1/17/23` <dbl> 208009, 334064, 271292, 47781, 10…
#> $ `1/18/23` <dbl> 208034, 334084, 271296, 47781, 10…
#> $ `1/19/23` <dbl> 208062, 334084, 271307, 47820, 10…
#> $ `1/20/23` <dbl> 208084, 334084, 271316, 47820, 10…
#> $ `1/21/23` <dbl> 208084, 334097, 271328, 47820, 10…
#> $ `1/22/23` <dbl> 208084, 334101, 271335, 47820, 10…
#> $ `1/23/23` <dbl> 208097, 334101, 271346, 47820, 10…
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#> $ `1/25/23` <dbl> 208324, 334124, 271360, 47820, 10…
#> $ `1/26/23` <dbl> 208324, 334135, 271364, 47820, 10…
#> $ `1/27/23` <dbl> 208432, 334144, 271369, 47820, 10…
#> $ `1/28/23` <dbl> 208435, 334153, 271369, 47839, 10…
#> $ `1/29/23` <dbl> 208435, 334157, 271376, 47839, 10…
#> $ `1/30/23` <dbl> 208502, 334157, 271376, 47839, 10…
#> $ `1/31/23` <dbl> 208545, 334167, 271378, 47839, 10…
#> $ `2/1/23` <dbl> 208552, 334177, 271385, 47839, 10…
#> $ `2/2/23` <dbl> 208669, 334187, 271386, 47839, 10…
#> $ `2/3/23` <dbl> 208669, 334203, 271394, 47850, 10…
#> $ `2/4/23` <dbl> 208621, 334204, 271394, 47850, 10…
#> $ `2/5/23` <dbl> 208627, 334211, 271394, 47850, 10…
#> $ `2/6/23` <dbl> 208704, 334211, 271395, 47850, 10…
#> $ `2/7/23` <dbl> 208721, 334211, 271399, 47850, 10…
#> $ `2/8/23` <dbl> 208771, 334222, 271403, 47850, 10…
#> $ `2/9/23` <dbl> 208771, 334229, 271406, 47860, 10…
#> $ `2/10/23` <dbl> 208943, 334229, 271406, 47860, 10…
#> $ `2/11/23` <dbl> 208971, 334234, 271409, 47860, 10…
#> $ `2/12/23` <dbl> 208982, 334255, 271409, 47860, 10…
#> $ `2/13/23` <dbl> 209011, 334255, 271409, 47860, 10…
#> $ `2/14/23` <dbl> 209036, 334264, 271409, 47860, 10…
#> $ `2/15/23` <dbl> 209056, 334264, 271421, 47860, 10…
#> $ `2/16/23` <dbl> 209072, 334273, 271424, 47866, 10…
#> $ `2/17/23` <dbl> 209083, 334291, 271424, 47866, 10…
#> $ `2/18/23` <dbl> 209084, 334305, 271425, 47866, 10…
#> $ `2/19/23` <dbl> 209107, 334314, 271425, 47866, 10…
#> $ `2/20/23` <dbl> 209153, 334315, 271426, 47866, 10…
#> $ `2/21/23` <dbl> 209181, 334336, 271428, 47866, 10…
#> $ `2/22/23` <dbl> 209181, 334336, 271431, 47866, 10…
#> $ `2/23/23` <dbl> 209215, 334345, 271432, 47866, 10…
#> $ `2/24/23` <dbl> 209230, 334356, 271436, 47866, 10…
#> $ `2/25/23` <dbl> 209246, 334373, 271439, 47866, 10…
#> $ `2/26/23` <dbl> 209274, 334378, 271440, 47866, 10…
#> $ `2/27/23` <dbl> 209308, 334380, 271440, 47866, 10…
#> $ `2/28/23` <dbl> 209322, 334391, 271441, 47866, 10…
#> $ `3/1/23` <dbl> 209340, 334408, 271448, 47875, 10…
#> $ `3/2/23` <dbl> 209358, 334408, 271463, 47875, 10…
#> $ `3/3/23` <dbl> 209362, 334427, 271469, 47875, 10…
#> $ `3/4/23` <dbl> 209369, 334427, 271469, 47875, 10…
#> $ `3/5/23` <dbl> 209390, 334427, 271477, 47875, 10…
#> $ `3/6/23` <dbl> 209406, 334427, 271477, 47875, 10…
#> $ `3/7/23` <dbl> 209436, 334427, 271490, 47875, 10…
#> $ `3/8/23` <dbl> 209451, 334443, 271494, 47890, 10…
#> $ `3/9/23` <dbl> 209451, 334457, 271496, 47890, 10…
confirmedraw %>% slice(1:10)# %>% datatable() # Check latest date at the end of data as tibble
#> # A tibble: 10 × 1,147
#> `Province/State` `Country/Region` Lat Long `1/22/20`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 <NA> Afghanistan 33.9 67.7 0
#> 2 <NA> Albania 41.2 20.2 0
#> 3 <NA> Algeria 28.0 1.66 0
#> 4 <NA> Andorra 42.5 1.52 0
#> 5 <NA> Angola -11.2 17.9 0
#> 6 <NA> Antarctica -71.9 23.3 0
#> 7 <NA> Antigua and Bar… 17.1 -61.8 0
#> 8 <NA> Argentina -38.4 -63.6 0
#> 9 <NA> Armenia 40.1 45.0 0
#> 10 Australian Capit… Australia -35.5 149. 0
#> # ℹ 1,142 more variables: `1/23/20` <dbl>, `1/24/20` <dbl>,
#> # `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
#> # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>,
#> # `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
#> # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>,
#> # `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
#> # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, …We need to tranform data. The other data are similar.
deathsraw <- 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")
#> Rows: 289 Columns: 1147
#> ── Column specification ────────────────────────────────────
#> Delimiter: ","
#> chr (2): Province/State, Country/Region
#> dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/2...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
deathsraw %>% slice(1)
#> # A tibble: 1 × 1,147
#> `Province/State` `Country/Region` Lat Long `1/22/20`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 <NA> Afghanistan 33.9 67.7 0
#> # ℹ 1,142 more variables: `1/23/20` <dbl>, `1/24/20` <dbl>,
#> # `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
#> # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>,
#> # `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
#> # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>,
#> # `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
#> # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, …
recoveredraw <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
#> Rows: 274 Columns: 1147
#> ── Column specification ────────────────────────────────────
#> Delimiter: ","
#> chr (2): Province/State, Country/Region
#> dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/2...
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
recoveredraw %>% slice(1)
#> # A tibble: 1 × 1,147
#> `Province/State` `Country/Region` Lat Long `1/22/20`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 <NA> Afghanistan 33.9 67.7 0
#> # ℹ 1,142 more variables: `1/23/20` <dbl>, `1/24/20` <dbl>,
#> # `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
#> # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>,
#> # `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
#> # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>,
#> # `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
#> # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, …
# Note differences in the number of rows/columnsB.3.2 Tidying and Combining: To create country level and global combined data
B.3.2.1 Convert each data set from wide to long
confirmed <- confirmedraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = "Lat", long = "Long") %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="confirmed") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(confirmed = confirmed - lag(confirmed)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
group_by(country, province) %>%
arrange(province, date) Check the data.
df_tv %>% filter(country == "Japan") %>% filter(type == "confirmed") %>% ggplot() + geom_line(aes(x = date, y = cases))
The dplyr::rename seems to have conflict with other rename function.
deaths <- deathsraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = Lat, long = Long) %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="death") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(death = death - lag(death)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
arrange(province, date)
recovered <- recoveredraw %>%
dplyr::rename(province = "Province/State", country = "Country/Region", lat = Lat, long = Long) %>%
pivot_longer(-c(province, country, lat, long), names_to = "date", values_to ="recovered") %>%
mutate(date = as.Date(date, "%m/%d/%y")) %>%
group_by(province, country) %>% arrange(date) %>%
mutate(recovered = recovered - lag(recovered)) %>%
slice(-1) %>% ungroup() %>%
relocate(date, .before = province) %>%
arrange(province, date)B.3.2.2 Final data: combine all three
coronavirus_jhu <- full_join(confirmed, deaths) %>% full_join(recovered) %>%
pivot_longer(c(confirmed, death, recovered), names_to = "cases") %>%
arrange(cases, province, country, date)
#> Joining with `by = join_by(date, province, country, lat,
#> long)`
#> Joining with `by = join_by(date, province, country, lat,
#> long)`
coronavirus_jhu %>% slice(1:10) # %>% datatable()
#> # A tibble: 2,900 × 7
#> # Groups: country, province [290]
#> date province country lat long cases value
#> <date> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 2020-01-23 <NA> Afghanistan 33.9 67.7 confir… 0
#> 2 2020-01-24 <NA> Afghanistan 33.9 67.7 confir… 0
#> 3 2020-01-25 <NA> Afghanistan 33.9 67.7 confir… 0
#> 4 2020-01-26 <NA> Afghanistan 33.9 67.7 confir… 0
#> 5 2020-01-27 <NA> Afghanistan 33.9 67.7 confir… 0
#> 6 2020-01-28 <NA> Afghanistan 33.9 67.7 confir… 0
#> 7 2020-01-29 <NA> Afghanistan 33.9 67.7 confir… 0
#> 8 2020-01-30 <NA> Afghanistan 33.9 67.7 confir… 0
#> 9 2020-01-31 <NA> Afghanistan 33.9 67.7 confir… 0
#> 10 2020-02-01 <NA> Afghanistan 33.9 67.7 confir… 0
#> # ℹ 2,890 more rowsB.3.3 Aggregated by Countries
The list of countries classified in provinces.
coronavirus_jhu %>% filter(!is.na(province)) %>% distinct(country)
#> # A tibble: 91 × 2
#> # Groups: country, province [91]
#> province country
#> <chr> <chr>
#> 1 Alberta Canada
#> 2 Anguilla United Kingdom
#> 3 Anhui China
#> 4 Aruba Netherlands
#> 5 Australian Capital Territory Australia
#> 6 Beijing China
#> 7 Bermuda United Kingdom
#> 8 Bonaire, Sint Eustatius and Saba Netherlands
#> 9 British Columbia Canada
#> 10 British Virgin Islands United Kingdom
#> # ℹ 81 more rowsCheck the data associated with provinces.
If we are only interested in countries, the following is a possibility.
coronavirus_jhu_country <- coronavirus_jhu %>%
group_by(date, country, cases) %>%
summarize(value = sum(value)) %>%
arrange(cases, country, date)
#> `summarise()` has grouped output by 'date', 'country'. You
#> can override using the `.groups` argument.
coronavirus_jhu_country # %>% datatable()
#> # A tibble: 688,626 × 4
#> # Groups: date, country [229,542]
#> date country cases value
#> <date> <chr> <chr> <dbl>
#> 1 2020-01-23 Afghanistan confirmed 0
#> 2 2020-01-24 Afghanistan confirmed 0
#> 3 2020-01-25 Afghanistan confirmed 0
#> 4 2020-01-26 Afghanistan confirmed 0
#> 5 2020-01-27 Afghanistan confirmed 0
#> 6 2020-01-28 Afghanistan confirmed 0
#> 7 2020-01-29 Afghanistan confirmed 0
#> 8 2020-01-30 Afghanistan confirmed 0
#> 9 2020-01-31 Afghanistan confirmed 0
#> 10 2020-02-01 Afghanistan confirmed 0
#> # ℹ 688,616 more rows
B.4 owid_covid
owid_covid: Get the Our World in Data covid-19 dataset
owid_covid()See the detail at the GitHub site.
- Example
covid <- owid_covid()
glimpse(covid)
#> Rows: 360,669
#> Columns: 67
#> $ iso_code <chr> "AFG", …
#> $ continent <chr> "Asia",…
#> $ location <chr> "Afghan…
#> $ date <date> 2020-0…
#> $ total_cases <dbl> NA, NA,…
#> $ new_cases <dbl> 0, 0, 0…
#> $ new_cases_smoothed <dbl> NA, NA,…
#> $ total_deaths <dbl> NA, NA,…
#> $ new_deaths <dbl> 0, 0, 0…
#> $ new_deaths_smoothed <dbl> NA, NA,…
#> $ total_cases_per_million <dbl> NA, NA,…
#> $ new_cases_per_million <dbl> 0, 0, 0…
#> $ new_cases_smoothed_per_million <dbl> NA, NA,…
#> $ total_deaths_per_million <dbl> NA, NA,…
#> $ new_deaths_per_million <dbl> 0, 0, 0…
#> $ new_deaths_smoothed_per_million <dbl> NA, NA,…
#> $ reproduction_rate <dbl> NA, NA,…
#> $ icu_patients <dbl> NA, NA,…
#> $ icu_patients_per_million <dbl> NA, NA,…
#> $ hosp_patients <dbl> NA, NA,…
#> $ hosp_patients_per_million <dbl> NA, NA,…
#> $ weekly_icu_admissions <dbl> NA, NA,…
#> $ weekly_icu_admissions_per_million <dbl> NA, NA,…
#> $ weekly_hosp_admissions <dbl> NA, NA,…
#> $ weekly_hosp_admissions_per_million <dbl> NA, NA,…
#> $ total_tests <dbl> NA, NA,…
#> $ new_tests <dbl> NA, NA,…
#> $ total_tests_per_thousand <dbl> NA, NA,…
#> $ new_tests_per_thousand <dbl> NA, NA,…
#> $ new_tests_smoothed <dbl> NA, NA,…
#> $ new_tests_smoothed_per_thousand <dbl> NA, NA,…
#> $ positive_rate <dbl> NA, NA,…
#> $ tests_per_case <dbl> NA, NA,…
#> $ tests_units <chr> NA, NA,…
#> $ total_vaccinations <dbl> NA, NA,…
#> $ people_vaccinated <dbl> NA, NA,…
#> $ people_fully_vaccinated <dbl> NA, NA,…
#> $ total_boosters <dbl> NA, NA,…
#> $ new_vaccinations <dbl> NA, NA,…
#> $ new_vaccinations_smoothed <dbl> NA, NA,…
#> $ total_vaccinations_per_hundred <dbl> NA, NA,…
#> $ people_vaccinated_per_hundred <dbl> NA, NA,…
#> $ people_fully_vaccinated_per_hundred <dbl> NA, NA,…
#> $ total_boosters_per_hundred <dbl> NA, NA,…
#> $ new_vaccinations_smoothed_per_million <dbl> NA, NA,…
#> $ new_people_vaccinated_smoothed <dbl> NA, NA,…
#> $ new_people_vaccinated_smoothed_per_hundred <dbl> NA, NA,…
#> $ stringency_index <dbl> 0, 0, 0…
#> $ population_density <dbl> 54.422,…
#> $ median_age <dbl> 18.6, 1…
#> $ aged_65_older <dbl> 2.581, …
#> $ aged_70_older <dbl> 1.337, …
#> $ gdp_per_capita <dbl> 1803.98…
#> $ extreme_poverty <dbl> NA, NA,…
#> $ cardiovasc_death_rate <dbl> 597.029…
#> $ diabetes_prevalence <dbl> 9.59, 9…
#> $ female_smokers <dbl> NA, NA,…
#> $ male_smokers <dbl> NA, NA,…
#> $ handwashing_facilities <dbl> 37.746,…
#> $ hospital_beds_per_thousand <dbl> 0.5, 0.…
#> $ life_expectancy <dbl> 64.83, …
#> $ human_development_index <dbl> 0.511, …
#> $ population <dbl> 4112877…
#> $ excess_mortality_cumulative_absolute <dbl> NA, NA,…
#> $ excess_mortality_cumulative <dbl> NA, NA,…
#> $ excess_mortality <dbl> NA, NA,…
#> $ excess_mortality_cumulative_per_million <dbl> NA, NA,…
tdpm <- covid %>% drop_na(`total_deaths_per_million`) %>%
group_by(location) %>%
summarize(`total_deaths_per_million` = max(`total_deaths_per_million`)) %>%
arrange(desc(`total_deaths_per_million`))
DT::datatable(tdpm)
covid %>% drop_na(`total_deaths_per_million`) %>%
group_by(location) %>%
summarize(`total_deaths_per_million` = max(`total_deaths_per_million`), .groups = "drop") %>%
arrange(desc(`total_deaths_per_million`)) %>%
ggplot(aes(x = `total_deaths_per_million`)) + geom_histogram(bins = 30)
covid %>% filter(location == "Japan") %>% arrange(desc(date))
#> # A tibble: 1,428 × 67
#> iso_code continent location date total_cases
#> <chr> <chr> <chr> <date> <dbl>
#> 1 JPN Asia Japan 2023-11-30 33803572
#> 2 JPN Asia Japan 2023-11-29 33803572
#> 3 JPN Asia Japan 2023-11-28 33803572
#> 4 JPN Asia Japan 2023-11-27 33803572
#> 5 JPN Asia Japan 2023-11-26 33803572
#> 6 JPN Asia Japan 2023-11-25 33803572
#> 7 JPN Asia Japan 2023-11-24 33803572
#> 8 JPN Asia Japan 2023-11-23 33803572
#> 9 JPN Asia Japan 2023-11-22 33803572
#> 10 JPN Asia Japan 2023-11-21 33803572
#> # ℹ 1,418 more rows
#> # ℹ 62 more variables: new_cases <dbl>,
#> # new_cases_smoothed <dbl>, total_deaths <dbl>,
#> # new_deaths <dbl>, new_deaths_smoothed <dbl>,
#> # total_cases_per_million <dbl>,
#> # new_cases_per_million <dbl>,
#> # new_cases_smoothed_per_million <dbl>, …
covid %>% filter(location == "Japan") %>% drop_na(new_deaths_smoothed) %>%
ggplot(aes(date, new_deaths_smoothed)) + geom_col() +
labs(title=paste("Covid-19 Death/Day in Japan as of ", Sys.Date()))