AntsaVerse/rrminfo

This package includes the tools used by REACH Mali to produce analyses within the framework of RRM and post-RRM information management.The analytical models were designed by the Post-RRM team at IMPACT Initiatives Mali. Sharing this package aims to ensure transparency in calculation processes and facilitate their understanding by all users. # rrminfo This package includes the tools used by REACH Mali to produce analyses within the framework of RRM and post-RRM information management.The analytical models were designed by the Post-RRM team at IMPACT Initiatives Mali. Sharing this package aims to ensure transparency in calculation processes and facilitate their understanding by all users. ## Installation You can install the development version of rrminfo from GitHub with: ``` r # install.packages("pak") pak::pak("AntsaVerse/rrminfo") ``` ## Load packages ``` r library(rrminfo) library(tidyverse) #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr 1.1.4 ✔ readr 2.1.5 #> ✔ forcats 1.0.0 ✔ stringr 1.5.1 #> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 #> ✔ lubridate 1.9.4 ✔ tidyr 1.3.1 #> ✔ purrr 1.0.4 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ℹ Use the conflicted package ( ) to force all conflicts to become errors ``` ## Data Cleaning and Processing for RRM Analysis ### Clean and Format the Alert Data - Convert date columns to the proper format. - Handle missing values in household and individual numbers. - Adjust outliers in date variables. ``` r alert_clean % clean_HH_number(hh_number = "menage_estimes", ind_number = "personne_estimees", hhsize = 6) %>% format_date(dates_vector = c("date_incident", "date_validation"), date_format = "%d/%m/%Y") %>% clean_dates(start_date = "date_incident", end_date = "date_validation") ``` ### Process the Evaluation Data - Convert date columns to the correct format. - Discretize multiple-choice categorical variables (priority needs) into individual sector columns. ``` r secteur_besoin % format_date(dates_vector = c("date_debut_evaluation", "date_fin_evaluation"), date_format = "%Y-%m-%d") %>% discretize_multiple_choice_variables(multi_choice_column = "Besoins.p …