Ease IMPACT’s data and database officers woRk

impactR started as a simple project: mainly a reminder of totally-perfectible functions used and made on the go for the Burkina Faso mission in 2021.

All of this is still a very rough draft, but useable.

Yet, it is becoming somehow bigger to make data officers and database officers daily R work easier as follows:

• utils.R: functions used on an everyday basis: import .csv and .xlsx files, recode NAs, get column names starting with a pattern, etc.
• monitor.R: functions to produce logs and help monitoring data collection (logical tests, outliers, other answers)
• clean.R: use of the same logs to clean the dataset (update values, delete surveys)
• analysis.R: calculate optimums, weighted proportions and interactions, pivot tables reports
• plots.R: plot functions and themes (colors, fonts)
• maps.R: some utils (e.g. ease bbox) and some themes (mainly using tmap)

There is still a need to consolidate parameters and log checks, and (a lot) of documentation to write.

Specs:

• mainly using Kobo collection and the tidyverse, srvyr, janitor, tmap
• it requires R 4.1+ (mostly for the native pipe |>).

You can install the last version of impactR from GitHub with:

# install.packages("devtools")
devtools::install_github("gnoblet/impactR")

From version 0.4, contributions should go with minimal and complete commits as a good practice. The dev branch will be used from there.

• introduce tidy eval wherever is makes sense
• more plotting functions
• add (re) count columns post-cleaning for multiple choices columns and simple choice’s other column
• MSNA analysis tools : roster (education, demography, WGI), weighting functions, analysis functions
• Split this big mess into several consolidated small packages : a viz one, an analysis one and a cleaning one (maybe?).

There will be a Shiny app for cleaning and monitoring (in French for now) whose repo will be collectoR.

These are basics example of daily uses:

box::use(impactR[...])
## basic example codes and uses (not run!)

## Import a csv file with clean names and clean types
#import_csv("data.csv")

## Get colnames for sector foodsec whose variables start with "f_"
#tbl_col_start(data, "f_")

## Bbox with a buffer (useful to add compass, scale_bar, legends to a map)
#buffer_bbox(admin1_sf, buffer = 0.05)

## Make an outlier log for all numeric variables in the data.frame/tibble
#make_log_outlier(rawdata, survey, id_col = "uuid", cols_to_keep = c("uuid", "i_enum_id"))

## Make a log based on logical tests, outliers and "other" answers
#make_all_logs(rawdata, 
#              survey, 
#              check_list,
#              other = "other_", 
#              id_col = "uuid", 
#              cols_to_keep = c("uuid", "i_enum_id"))

## Recode parent "other" from a well-filled cleaning log
#recode_other_parent_from_log(data, log, id_col = "uuid")

## Calculate weigthed proportion for shelter type by group (e.g. administrative areas or population groups)
#svy_prop(design, group_cols = c("admin1", "group_pop"), "s_shelter_type", na.rm = T, level = 0.95)