An R API to the Oxford COVID-19 Database

The primary use case for the oxcovid19 package is for facilitating a simplified, R-based workflow for 1) connecting to the OxCOVID19 PostgreSQL server; 2) accessing table/s available from the PostgreSQL server; and, 3) querying the PostgreSQL server with specific parameters to customise table/s output for intended use. This workflow is illustrated in the workflow diagram below:

library(oxcovid19)

## Step 1: Create a connection to OxCOVID19 PostgreSQL server
con <- connect_oxcovid19()

## Step 2: Access epidemiology table from OxCOVID19 PostgreSQL server
epi_tab <- get_table(con = con, tbl_name = "epidemiology")

## Step 3: Query the epidemiology table to show data for Great Britain
gbr_epi_tab <- dplyr::filter(.data = epi_tab, countrycode == "GBR")

Step 1 and Step 2 above are facilitated by the connect_oxcovid19 and the get_table functions provided by the oxcovid19 package. These functions are basically low-level wrappers to functions in the DBI and RPostgres packages applied specifically to work with the OxCOVID19 PostgreSQL. These functions facilitate convenient access to the server for general R users without having to learn to use the DBI and RPostgres packages.

Step 3, on the other hand, is facilitated by the dplyr package functions which were designed to work with different types of tables including those from various database server connections such as PostgreSQL.

The oxcovid19 package functions are also designed to allow pipe operations using the magrittr package. The workflow above can be done using piped operations as follows:

## Load magrittr to use pipe operator %>%
library(magrittr)

connect_oxcovid19() %>%
  get_table(tbl_name = "epidemiology") %>%
  dplyr::filter(countrycode == "GBR")
#> # Source:   lazy query [?? x 15]
#> # Database: postgres [covid19@covid19db.org:5432/covid19]
#>    source date       country countrycode adm_area_1 adm_area_2 adm_area_3 tested
#>    <chr>  <date>     <chr>   <chr>       <chr>      <chr>      <chr>       <int>
#>  1 GBR_P… 2020-05-19 United… GBR         Wales      Vale of G… Vale of G…   3088
#>  2 GBR_P… 2020-03-25 United… GBR         Wales      Wrexham    Wrexham       138
#>  3 WRD_E… 2020-06-28 United… GBR         <NA>       <NA>       <NA>           NA
#>  4 GBR_P… 2020-06-23 United… GBR         Wales      Monmouths… Monmouths…   2888
#>  5 GBR_P… 2020-06-23 United… GBR         Wales      Pembrokes… Pembrokes…   4922
#>  6 GBR_P… 2020-06-23 United… GBR         Wales      Torfaen    Torfaen      3313
#>  7 WRD_W… 2020-07-12 United… GBR         Bermuda    <NA>       <NA>           NA
#>  8 WRD_W… 2020-07-12 United… GBR         Cayman Is… <NA>       <NA>           NA
#>  9 WRD_W… 2020-07-12 United… GBR         Channel I… <NA>       <NA>           NA
#> 10 WRD_W… 2020-07-12 United… GBR         Gibraltar  <NA>       <NA>           NA
#> # … with more rows, and 7 more variables: confirmed <int>, recovered <int>,
#> #   dead <int>, hospitalised <int>, hospitalised_icu <int>, quarantined <int>,
#> #   gid <chr>

The workflow using the piped workflow outputs the same result as the earlier workflow but with a much streamlined use of code.