The motivation for such an R package and the plots it produces is that most people don’t know if X deaths caused by something is a lot or not. Indeed, unless you are a demographer, you probably have no idea how many people die in the world or even in your city, over a year, or over a day. (If you want to know, a good rule of thumb is that over a given year, in a “rich” country, you can expect roughly 1% of its whole population to die.)
Here I express the number of reported COVID19 deaths compared to 100 “normal” deaths.
For example, a value of 50 would imply that if 100 normal deaths used to occur in a city, then we would observe 150 deaths in total (100 normal + 50 COVID19 ones) assuming that normal deaths have not changed.
This is more informative than expressing the result as a percentage: if the COVID deaths reach very large numbers, we will still see on this graph such an increase progressing linearly. Instead, expressed as a percentage, the death toll would just slowly converge towards 100%.
If you still want to express such number as a percentage, just do:
If you still want to express such number as a percentage, just do
100 * X/(100 + X). For example, with 50 COVID death per 100 normal
ones, the percentage of COVID death is thus 100 * 50/150 = 33.333%.
If instead you want to instead express values along the x-axis as the
odd of dying from COVID, just divide the value X you read on the axis by
100: (X / (100 + X))/(100 / (100 + X)) = X/100. For example, with 50
COVID death per 100 normal ones, the odd of COVID death is thus 50/100 =
0.5.
The package allows to explore or plot either daily deaths or cumulated deaths since a given country had reached a total of 10 deaths. Daily deaths are susceptible to vary due to lags in reporting alone. For this reason, I try to discount the lump reports from nursing homes when they correspond to several days, but I cannot keep track of all of them.
Cumulative deaths are thus probably better since when the deaths are being reported matters less.
When deaths are counted on a daily basis, the baseline is also computed on a daily basis.
When deaths are cumulated, the baseline is computed using normal mortality that would have occurred during the period between the date when the country reached 10 cumulative total deaths and the date of the report being analysed.
To count normal deaths and produce a baseline, the package allows you to either use the most recent mortality data for the same country that is analysed (2018), or to use the average mortality data from the entire world (which is much higher than the country level one in the healthier countries). The choice of the baseline impacts on the ranking.
It is because the cumulative deaths are expressed relatively to the baseline mortality that occurs during the same period. Therefore, if the deaths caused by COVID19 pile up more slowly that the normal deaths, then the relative measure shown in the plot can go down.
You can install this package using {remotes} (or {devtools}):
remotes::install_github("courtiol/coronaR")library(coronaR)today <- Sys.Date() ## note: you can change the date, to rebuild plots retrospectively
data_COVID <- prepare_data_ECDC(path_save_data = "~/Downloads/COVID19",
date_of_report = today)
#> The source of the COVID data have been stored in~/Downloads/COVID19/COVID-19-geographic-disbtribution-worldwide-2020-11-02.xlsx!
#> Warning: Problem with `mutate()` input `continent`.
#> ℹ Some values were not matched unambiguously: XK
#>
#> ℹ Input `continent` is `dplyr::if_else(...)`.
#> Warning in countrycode::countrycode(.data$iso2c, origin = "iso2c", destination = "continent"): Some values were not matched unambiguously: XK
data_COVID
#> # A tibble: 53,676 x 9
#> country iso2c continent date_report date_report_last cases deaths_daily
#> <chr> <chr> <fct> <date> <date> <dbl> <dbl>
#> 1 Afghan… AF Asia 2020-11-02 2020-11-02 132 5
#> 2 Afghan… AF Asia 2020-11-01 2020-11-02 76 0
#> 3 Afghan… AF Asia 2020-10-31 2020-11-02 157 4
#> 4 Afghan… AF Asia 2020-10-30 2020-11-02 123 3
#> 5 Afghan… AF Asia 2020-10-29 2020-11-02 0 0
#> 6 Afghan… AF Asia 2020-10-28 2020-11-02 113 7
#> 7 Afghan… AF Asia 2020-10-27 2020-11-02 199 8
#> 8 Afghan… AF Asia 2020-10-26 2020-11-02 65 3
#> 9 Afghan… AF Asia 2020-10-25 2020-11-02 81 4
#> 10 Afghan… AF Asia 2020-10-24 2020-11-02 61 2
#> # … with 53,666 more rows, and 2 more variables: deaths_cumul <dbl>,
#> # date_first_10_cumul_deaths <date>
## NOTE: the following Warning is expected:
#Warning message:
# In countrycode::countrycode(.data$iso2c, origin = "iso2c", destination = "continent") :
# Some values were not matched unambiguously: XKFor daily plots, you may want to remove manually lump report of deaths from nursing homes:
data_COVID[data_COVID$country == "France" & data_COVID$date_report == "2020-04-04", "deaths_daily"] <- NA
data_COVID[data_COVID$country == "Belgium" & data_COVID$date_report == "2020-04-08", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-04-26") & data_COVID$country == "Ireland", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-09-07") & data_COVID$country == "Ecuador", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-07-24") & data_COVID$country == "Peru", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-08-14") & data_COVID$country == "Peru", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-07-18") & data_COVID$country == "Kyrgyzstan", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-09-07") & data_COVID$country == "Bolivia", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-10-02") & data_COVID$country == "Argentina", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-07-18") & data_COVID$country == "Chile", "deaths_daily"] <- NA
data_COVID[data_COVID$date_report == c("2020-06-08") & data_COVID$country == "Chile", "deaths_daily"] <- NAWe also solve some other issues that show up from time to time (I use www.worldometers.com to figure this out and/or official national reports):
## Date from Spain have one day lag:
if (data_COVID[data_COVID$country == "Spain" & data_COVID$date_report == Sys.Date(), "deaths_daily"] == 0) {
data_COVID[data_COVID$country == "Spain" & data_COVID$date_report == Sys.Date(), "deaths_daily"] <- data_COVID[data_COVID$country == "Spain" & data_COVID$date_report == Sys.Date() - 1, "deaths_daily"]
}data_baseline_mortality <- prepare_data_WB() ## the data do sometimes change from day to day!
#> Downloading fresh data from World Bank, be patient...
data_baseline_mortality
#> # A tibble: 147 x 9
#> country iso2c year_mortality total_death_year total_death_day
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Afghan… AF 2018 238758. 654.
#> 2 Albania AL 2018 22639. 62.0
#> 3 Algeria DZ 2018 199149. 546.
#> 4 Angola AO 2018 252332. 691.
#> 5 Argent… AR 2018 338559. 928.
#> 6 Armenia AM 2018 29096. 79.7
#> 7 Austra… AU 2018 157391. 431.
#> 8 Austria AT 2018 83985. 230.
#> 9 Azerba… AZ 2018 57651. 158.
#> 10 Bangla… BD 2018 892138. 2444.
#> # … with 137 more rows, and 4 more variables: total_death_year_world <dbl>,
#> # total_death_day_world <dbl>, country_pop <dbl>, world_pop <dbl>To look at daily deaths, using the baseline mortality from each country:
plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "daily",
baseline_major = "country",
select_major = "worst_day",
type_minor = "daily",
baseline_minor = "country",
select_minor = "last_day",
title = "Deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline country mortality")
To look at daily deaths, using the baseline mortality from the world:
plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "daily",
baseline_major = "world",
select_major = "worst_day",
type_minor = "daily",
baseline_minor = "world",
select_minor = "last_day",
title = "Deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline worldwide mortality")
To look at cumulative deaths, using the baseline mortality from each country:
plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "cumul",
baseline_major = "country",
select_major = "worst_day",
type_minor = "cumul",
baseline_minor = "country",
select_minor = "last_day",
title = "Cumulative deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline country mortality")
To look at cumulative deaths, using the baseline mortality from the world:
plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "cumul",
baseline_major = "world",
select_major = "worst_day",
type_minor = "cumul",
baseline_minor = "world",
select_minor = "last_day",
title = "Cumulative deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline worldwide mortality")
The workhorse function that lead to tidy longitudinal series is
merge_datasets(). You can for example use it like that:
full_data <- merge_datasets(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type = "daily",
baseline = "country",
select = "worst_day")str(full_data)
#> tibble [37,907 × 24] (S3: tbl_df/tbl/data.frame)
#> $ country : chr [1:37907] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
#> $ continent : Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
#> $ date_report : Date[1:37907], format: "2020-11-02" "2020-11-01" ...
#> $ date_report_last : Date[1:37907], format: "2020-11-02" "2020-11-02" ...
#> $ cases : num [1:37907] 132 76 157 123 0 113 199 65 81 61 ...
#> $ deaths_daily : num [1:37907] 5 0 4 3 0 7 8 3 4 2 ...
#> $ deaths_cumul : num [1:37907] 1541 1536 1536 1532 1529 ...
#> $ date_first_10_cumul_deaths : Date[1:37907], format: "2020-04-08" "2020-04-08" ...
#> $ year_mortality : num [1:37907] 2018 2018 2018 2018 2018 ...
#> $ total_death_year : num [1:37907] 238758 238758 238758 238758 238758 ...
#> $ total_death_day : num [1:37907] 654 654 654 654 654 ...
#> $ total_death_year_world : num [1:37907] 57158343 57158343 57158343 57158343 57158343 ...
#> $ total_death_day_world : num [1:37907] 156598 156598 156598 156598 156598 ...
#> $ country_pop : num [1:37907] 37172386 37172386 37172386 37172386 37172386 ...
#> $ world_pop : num [1:37907] 7.59e+09 7.59e+09 7.59e+09 7.59e+09 7.59e+09 ...
#> $ days_since_first_10_cumul_deaths: 'difftime' num [1:37907] 208 207 206 205 ...
#> ..- attr(*, "units")= chr "days"
#> $ extra_mortality_daily_country : num [1:37907] 0.764 0 0.611 0.459 0 ...
#> $ extra_mortality_cumul_country : num [1:37907] 1.12 1.12 1.13 1.13 1.13 ...
#> $ country_weight : num [1:37907] 0.0049 0.0049 0.0049 0.0049 0.0049 ...
#> $ extra_mortality_daily_world : num [1:37907] 0.652 0 0.522 0.391 0 ...
#> $ extra_mortality_cumul_world : num [1:37907] 0.956 0.957 0.962 0.964 0.967 ...
#> $ extra_mortality : num [1:37907] 8.56 8.56 8.56 8.56 8.56 ...
#> $ date : Date[1:37907], format: "2020-07-07" "2020-07-07" ...
#> $ days_since_date : 'difftime' num [1:37907] 118 118 118 118 ...
#> ..- attr(*, "units")= chr "days"plot_data <- plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "daily",
baseline_major = "country",
select_major = "worst_day",
type_minor = "daily",
baseline_minor = "country",
select_minor = "last_day",
title = "Deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline mortality")class(plot_data)
#> [1] "tbl_df" "tbl" "data.frame"Just add return_plot = TRUE, when calling plot_deaths() and store
the output in an object. The object created will be a plot.
plot_plot <- plot_deaths(data_ECDC = data_COVID,
data_WB = data_baseline_mortality,
type_major = "daily",
baseline_major = "country",
select_major = "worst_day",
type_minor = "daily",
baseline_minor = "country",
select_minor = "last_day",
title = "Deaths by COVID19 on the worst and last day (light & dark colour)\nrelative to baseline mortality",
return_plot = TRUE)class(plot_plot)
#> [1] "gg" "ggplot"There are many limitation that directly stem from the data. For example:
-
some countries (seem to under-report death by COVID19. This is because for many deaths occurying outside hospitals the exact cause of death is not known. (We will be able to look at that when overall death rates will be known.)
-
some countries are not included because either we have no data for COVID19, or the population and mortality data are not in the database I am using. The latter is for example the case of Taiwan.
-
the baseline mortality is based on average daily mortality from 2018.
-
comorbidities are not accounted for.
Here is my current R/computer configuration:
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os Arch Linux
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_GB.UTF-8
#> ctype en_GB.UTF-8
#> tz Europe/Berlin
#> date 2020-11-02
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
#> backports 1.1.10 2020-09-15 [1] CRAN (R 4.0.2)
#> callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3)
#> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
#> cli 2.1.0 2020-10-12 [1] CRAN (R 4.0.3)
#> colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.2)
#> coronaR * 0.0.0.9000 2020-11-02 [1] local
#> countrycode 1.2.0 2020-05-22 [1] CRAN (R 4.0.2)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.2)
#> curl 4.3 2019-12-02 [1] CRAN (R 4.0.2)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.2)
#> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.2)
#> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3)
#> dplyr 1.0.2.9000 2020-11-01 [1] Github (tidyverse/dplyr@20e768c)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.2)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.2)
#> farver 2.0.3 2020-01-16 [1] CRAN (R 4.0.2)
#> forcats 0.5.0 2020-03-01 [1] CRAN (R 4.0.2)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.3)
#> ggplot2 3.3.2 2020-06-19 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
#> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
#> httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
#> jsonlite 1.7.1 2020-09-07 [1] CRAN (R 4.0.2)
#> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.2)
#> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.2)
#> lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.2)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.2)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
#> pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2)
#> pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
#> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
#> processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2)
#> ps 1.4.0 2020-10-07 [1] CRAN (R 4.0.2)
#> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
#> R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.3)
#> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2)
#> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.2)
#> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2)
#> rlang 0.4.8 2020-10-08 [1] CRAN (R 4.0.3)
#> rmarkdown 2.5 2020-10-21 [1] CRAN (R 4.0.3)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.2)
#> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
#> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> testthat 3.0.0 2020-10-31 [1] CRAN (R 4.0.3)
#> tibble 3.0.4 2020-10-12 [1] CRAN (R 4.0.3)
#> tidyr 1.1.2 2020-08-27 [1] CRAN (R 4.0.2)
#> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
#> usethis 1.6.3 2020-09-17 [1] CRAN (R 4.0.2)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.2)
#> vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2)
#> wbstats 1.0.1.9000 2020-11-02 [1] Github (nset-ornl/wbstats@310de68)
#> withr 2.3.0 2020-09-22 [1] CRAN (R 4.0.2)
#> xfun 0.19 2020-10-30 [1] CRAN (R 4.0.3)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
#>
#> [1] /home/alex/R/x86_64-pc-linux-gnu-library/4.0
#> [2] /usr/lib/R/libraryIf you find that this project interesting an idea worth pursuing, please let me know by liking, RT or messaging on Twitter (@alexcourtiol).
Developing is always more fun when it becomes a collaborative work, so please also email me (or leave an issue) if you want to get involved!