Rcoding

Rcoding is a teaching package for the R Coding Support Sessions (MSc in Epidemiology). It provides synthetic datasets students use to practise R, from basic manipulation to more advanced workflows.

Every dataset describes one fictional scenario: endemic, seasonal malaria in the fictional Republic of Amani, under surveillance across 12 districts over five years. Students meet the same people, places and pathogen across the weekly problem sets. All data are fictional and simulated purely for teaching; all place names are invented.

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Installation

# install.packages("remotes")
remotes::install_github("mrc-ide/Rcoding")

The single scenario

Almost every object is derived from two master datasets:

• case_linelist — individual-level line-list of reported malaria cases (the master).
• environment_weekly — weekly rainfall and temperature per district (drives transmission).

Supporting masters: testing_weekly, districts, facilities.

Available data objects

• case_linelist — malaria case line-list (~120k cases, 18 variables).
• environment_weekly — weekly rainfall + temperature per district.
• testing_weekly — weekly tests and confirmed cases per district.
• districts / facilities — geography, population, bednet coverage, health facilities.
• incidence_weekly_age — weekly case counts by 5-year age band (matrix).
• incidence_weekly — total weekly case counts (vector).
• patient_records — 150-patient demographic sample with messy names (data frame).
• district_weekly_list — weekly surveillance for the two focus cities (list).
• allele_freq_matrix — parasite SNP allele frequencies, with artefacts and missingness (matrix).
• resistance_trajectories — drug-resistance marker prevalence + modelled bands (list).
• analysis_bundle — bednet coverage vs malaria incidence: data, summary stats, fitted lm (list).
• posterior_density — 2D posterior over transmission-model parameters (list).
• chw_trial — community health worker intervention trial summary (data frame).

Data-import practice

export_examples() writes file copies of these datasets (in various formats, with deliberate "messiness") so students can practise reading data from disk — the file they import is the same data they can also load directly from the package:

dir.create("data_raw")
Rcoding::export_examples("data_raw")