To install covidAgeData run the following code in your favourite R console:
install.packages("remotes")
remotes::install_github("https://github.com/eshom/covid-age-data")You can download and read the newest versions of data in one command:
library(covidAgeData)
df <- download_covid(data = "Output_10")
head(df)Downloading Output_10.zip
|======================================================================| 100%
Reading /tmp/RtmpuvKg50/Data/Output_10.csv
Country Region Code Date Sex Age AgeInt Cases Deaths Tests
1 Afghanistan All AF01.07.2020 01.07.2020 b 0 10 169 0 NA
2 Afghanistan All AF01.07.2020 01.07.2020 b 10 10 1525 5 NA
3 Afghanistan All AF01.07.2020 01.07.2020 b 20 10 7948 18 NA
4 Afghanistan All AF01.07.2020 01.07.2020 b 30 10 7592 54 NA
5 Afghanistan All AF01.07.2020 01.07.2020 b 40 10 5362 117 NA
6 Afghanistan All AF01.07.2020 01.07.2020 b 50 10 3747 161 NA
By default download_covid() saves the downloaded dataset in the current
working directory, so it can be read again later:
df <- read_covid(zippath = "Output_10.zip", data = "Output_10")Downloading specific versions of COVerAge-DB datasets is possible:
download_covid_version(data = "inputDB", version = 1, return = "tibble")Downloading inputDB_v1.zip (timeout set to 60)
trying URL 'https://osf.io/9dsfk/download?version=1'
Content type 'application/octet-stream' length 10287585 bytes (9.8 MB)
==================================================
downloaded 9.8 MB
Reading /tmp/RtmpuvKg50/Data/inputDB.csv
# A tibble: 2,494,617 x 11
Country Region Code Date Sex Age AgeInt Metric Measure Value Short
<chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <dbl> <chr>
1 Afghanis… All AF01.0… 01.07… b 0 10 Count Cases 169 AF
2 Afghanis… All AF01.0… 01.07… b 10 10 Count Cases 1525 AF
3 Afghanis… All AF01.0… 01.07… b 20 10 Count Cases 7948 AF
4 Afghanis… All AF01.0… 01.07… b 30 10 Count Cases 7592 AF
5 Afghanis… All AF01.0… 01.07… b 40 10 Count Cases 5362 AF
6 Afghanis… All AF01.0… 01.07… b 50 10 Count Cases 3747 AF
7 Afghanis… All AF01.0… 01.07… b 60 10 Count Cases 2267 AF
8 Afghanis… All AF01.0… 01.07… b 70 10 Count Cases 840 AF
9 Afghanis… All AF01.0… 01.07… b 80 25 Count Cases 301 AF
10 Afghanis… All AF01.0… 01.07… b TOT NA Count Cases 29751 AF
# … with 2,494,607 more rows
For very large data, it may be advantageous to subset it before completely reading it to memory. This is done with read_subset_covid():
download_covid(data = "inputDB", download_only = TRUE)
df <- read_subset_covid("inputDB.zip", data = "inputDB", return = "tibble",
Country = "USA",
Region = "New York City",
Sex = "f",
Date = "01.10.2020")
dfDownloading inputDB.zip
|======================================================================| 100%
# A tibble: 12 x 11
Country Region Code Date Sex Age AgeInt Metric Measure Value Short
<chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <dbl> <chr>
1 USA New Yor… US_CDC_… 28.1… f 0 1 Count Deaths 1 US_C…
2 USA New Yor… US_CDC_… 28.1… f 1 4 Count Deaths 1 US_C…
3 USA New Yor… US_CDC_… 28.1… f 5 10 Count Deaths 2 US_C…
4 USA New Yor… US_CDC_… 28.1… f 15 10 Count Deaths 10 US_C…
5 USA New Yor… US_CDC_… 28.1… f 25 10 Count Deaths 59 US_C…
6 USA New Yor… US_CDC_… 28.1… f 35 10 Count Deaths 119 US_C…
7 USA New Yor… US_CDC_… 28.1… f 45 10 Count Deaths 401 US_C…
8 USA New Yor… US_CDC_… 28.1… f 55 10 Count Deaths 1072 US_C…
9 USA New Yor… US_CDC_… 28.1… f 65 10 Count Deaths 1800 US_C…
10 USA New Yor… US_CDC_… 28.1… f 75 10 Count Deaths 2341 US_C…
11 USA New Yor… US_CDC_… 28.1… f 85 20 Count Deaths 2811 US_C…
12 USA New Yor… US_CDC_… 28.1… f TOT NA Count Deaths 8617 US_C…
If memory efficiency is not an issue, subset_covid() is the in-memory version:
df <- read_covid("Output_10.zip", "Output_10", return = "tibble")
subset_covid(df, Country = "Sweden")Reading /tmp/Rtmptv6DoX/Data/Output_10.csv
# A tibble: 2,255 x 10
Country Region Code Date Sex Age AgeInt Cases Deaths Tests
<chr> <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 Sweden All SE_ECDC_01.11… 01.11.2… b 0 10 1046 10 NA
2 Sweden All SE_ECDC_01.11… 01.11.2… b 10 10 8818 0 NA
3 Sweden All SE_ECDC_01.11… 01.11.2… b 20 10 24930 10 NA
4 Sweden All SE_ECDC_01.11… 01.11.2… b 30 10 21989 17 NA
5 Sweden All SE_ECDC_01.11… 01.11.2… b 40 10 22242 46 NA
6 Sweden All SE_ECDC_01.11… 01.11.2… b 50 10 22537 165 NA
7 Sweden All SE_ECDC_01.11… 01.11.2… b 60 10 12418 414 NA
8 Sweden All SE_ECDC_01.11… 01.11.2… b 70 10 7519 1279 NA
9 Sweden All SE_ECDC_01.11… 01.11.2… b 80 10 6164. 2092. NA
10 Sweden All SE_ECDC_01.11… 01.11.2… b 90 10 4914. 1794. NA
# … with 2,245 more rows
For repeated read and subset operations, it's recommended to cache the output. One way to do so is with memoise:
library(memoise)
mem_read_subset_covid <- memoise(read_subset_covid)
microbenchmark::microbenchmark(no_cache = read_subset_covid("inputDB.zip",
"inputDB",
Country = "Brazil"),
cache = mem_read_subset_covid("inputDB.zip",
"inputDB",
Country = "Brazil"),
times = 10)Unit: microseconds
expr min lq mean median uq max
no_cache 7015553.357 7383738.955 18515155.4 8034852.2075 20785961.351 64893443
cache 593.748 621.755 734404.8 672.4395 804.345 7337885
neval
10
10
The package itself can be freely used in any context, however if you use the data itself please cite:
Tim Riffe, Enrique Acosta, the COVerAGE-DB team, Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths, International Journal of Epidemiology, Volume 50, Issue 2, April 2021, Pages 390–390f, https://doi.org/10.1093/ije/dyab027 Data downloaded from [DATE] (https://doi.org/10.17605/OSF.IO/MPWJQ)[https://doi.org/10.17605/OSF.IO/MPWJQ]
The 'COVerAGE-DB' Project page: https://github.com/timriffe/covid_age