The OxCOVID19 Project aims to increase our understanding of the COVID-19 pandemic and elaborate possible strategies to reduce the impact on the society through the combined power of statistical, mathematical modelling, and machine learning techniques. The OxCOVID19 Database is a large, single-centre, multimodal relational database consisting of information (using acknowledged sources) related to COVID-19 pandemic. This package provides an R-specific interface to the OxCOVID19 Database based on widely-used data handling and manipulation approaches in R.
The OxCOVID19 Project team presented to the CoMo Consortium during its weekly meeting on the 1st of July 2020. During this meeting, the CoMo Consortium considered the use of the OxCOVID19 Database for use in filling data and information for country-specific parameters required in the CoMo Consortium model. Given that the CoMo Consortium model is R-centric, it makes logical sense to build an R-specific API to connect with the OxCOVID19 PostgreSQL database. This package aims to facilitate the possible use of the OxCOVID19 database for this purpose through purposefully-written functions that connects an R user to the database directly from R (as opposed to doing a manual download of the data or using separate tools to access PostgreSQL) and processes and structures the available datasets into country-specific tables structured for the requirements of the CoMo Consortium model. A direct link to the PostgreSQL via R is also advantageous as this is updated more frequently than the CSV datasets made available via GitHub.
You can install oxcovid19
from CRAN with:
You can install the development version of oxcovid19
from GitHub with:
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.
The following code demonstrates this workflow:
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 output of the workflow shown above is:
#> # 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 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.
The oxcovid19
package is in active development which will be dictated by the evolution of the OxCOVID19 Database over time. Whilst every attempt will be employed to maintain syntax of current functions, it is possible that current functions may change syntax or operability in order to ensure relevance or maybe deprecated in lieu of a more appropriate and/or more performant function. In either of these cases, any change will be well-documented and explained to users and deprecation will be staged in such a way that users will be informed in good time to allow for transition to using the new functions.
Adam Mahdi, Piotr Błaszczyk, Paweł Dłotko, Dario Salvi, Tak-Shing Chan, John Harvey, Davide Gurnari, Yue Wu, Ahmad Farhat, Niklas Hellmer, Alexander Zarebski, Bernie Hogan, Lionel Tarassenko, Oxford COVID-19 Database: a multimodal data repository for better understanding the global impact of COVID-19. University of Oxford, 2020.