This repository aims to provide a framework for computing social sector indicators in a clean, organized and repeatable manner.

To start contributing, first clone this repository. Then, we primarily work with two branches: 1) main and 2) development.

The development branch is the most current one. When contributing, pull from this branch and create a new personal branch for the specific task you are working on. For instance, if you are reviewing geographical disaggregations, create a branch named "geographic_dis_scl".

Work on the assigned tasks, commit and push the changes to the development branch. Make a pull request so that another team member can review it and accept it as the new version of development.

To calculate indicators, create the folder Outputs and then there are two ways to calculate indicators depending on the amount of countries and years required:

1. Specific country, source of information and year: Open the runningScript.R, and modify the variables pais, year, type and geoLevel after which execute the code runningScript.R.

   • pais: To specify this use the ISO Alpha-3 code for the specific country (e.g. SLV for El Salvador).
   • year: Specify as a string the required year for the indicator
   • tipo: Specify one of the two:"censos" or "encuestas" as strings depending if census or surveys are required.
   • geoLevel1: For census, specify if the indicators to calculate will be disagregated at a country level or first geographical disagreggation.

2. Loop through different countries and years: Open the runningScript.R and define the variable tipo with "encuestas" or "censos" depending on the data source type required. Then execute the code runningScript_loop.R and indicators will be calculated based on all the census or surveys available according to the Inputs/running_survey.csv or Inputs/running_census.csv files.

This repository consists of three main parts.

1. Intermediate Variables: One script per division, each containing all necessary variables for computing the indicators of the corresponding division (var_EDU.R, var_GDI.R, var_LMK.R and var_SOC.R)

2. Indicator Definitions (idef.csv): This file controls the computation of indicators. It contains the definition of each indicator.

3. Running Scripts: runningScript.R runs the function in scl_indicators.R. runningScript_loop.R runs scl_indicators.R for a batch of countries

4. Functions: These scripts stablish the functions required to calculate indicators.

   • functions.R: contains the functions to calculate indicators for ratios (scl_pct), means (scl_mean) and gini (scl_gini). As well as a function to execute all indicators in idef.csv (calculate_indicators) and a function to delimit the disagregations to execute (evaluatingFilter).
   • directory_periods.R: This scrips based on the type of data source survey (encuesta) or census(censos) defined in variable tipo returns the appropiate harmonized.

5. Inputs folder This folder includes important supporting documents such as excels showing the data available (Planeación - Armonización de Encuestas de Hogares.xlsx, Planeación - Population and Housing Censuses.xlsx, running_census.csv and running_survey.csv), dictionary of variable for both census and surveys (D.1.1.4 Diccionario microdatos encuestas de hogares.xlsx and D.7.1.3 Diccionario variables censos.xlsx) and identification files (idef.csv and idefCensos.csv) defining the indicators to calculate.

1. Fork the repository to your GitHub account.

2. Clone the forked repository to your local machine.

3. Create a new branch for your tasks.

4. Make your changes and commit them to your branch.

5. Push the branch to your GitHub repository.

6. From the GitHub page of your forked repository, open a pull request to the development branch of the main repository.