In epicentre-msf/covidutils: Utility Functions For Epicentre COVID-19 Analysis

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covidutils

Centralised utility functions for covid related projects at Epicentre.

Installation

Install covidutils from github with:

remotes::install_github("epicentre-msf/covidutils")

Development

To include a function in the package, add it to a script in the R/ directory.

Best practices

• Large functions should have their own .R script inside the R/ directory with an information name, get_ecdc.R for example
• Smaller utility functions can be grouped into a single script such as utils.R
• If you are using functions used from external packages inside your own function, they must be fully qualified i.e. dplyr::filter()
• All external packages used must be added to the imports section of the DESCRIPTION file. This can be done via usethis::use_package("name_of_package")
• All variables used within a function should be passed as arguments
• Document your functions using roxygen syntax
• When your changes are ready and documented:
• run devtools::document() to build the appropriate documentation files
• run devtools::check() to check the package for any errors or warnings
• run devtools::install() to install or re-install the package on your system
• consider adding a usage of example of any new functions in this README.Rmd below then knit the document to produce the .md format required for github
• push the changes to github

If using RStudio, you can document, check and install the package with option in the 'Build' pane.

When developing new functions you can run devtools::load_all() at anytime load the current state of all function in the R/ directory.

Further R package development resources:

• Hadley Wickham's R Packages book (free online)
• Karl Broman's R package primer
• Hilary Parker's R package from scratch tutorial

Function examples

library(tidyverse)
library(covidutils)

Import JHU CSSE data

df_covid <- get_owid_jhcsse()
glimpse(df_covid)

Compute 14 and 30 day case and death trends

df_trends <- get_country_summaries(df_covid)
glimpse(df_trends)

# country with fastest growing case numbers in last 14 days
(iso <- df_trends %>% 
  filter(trend_cases_coeff_14d == max(trend_cases_coeff_14d, na.rm = TRUE)) %>% 
  pull(iso_a3))
df_country <- df_covid %>% filter(iso_a3 == iso)
df_trends_country <- df_trends %>% filter(iso_a3 == iso)

country_plot(df_country, df_trends_country, add_title = TRUE)

epicentre-msf/covidutils documentation built on July 22, 2021, 1:03 p.m.