{IndicatorCalc}

There is broad consensus around the key indicators used to measure, inform and monitor progress towards global development objectives, as exemplified by the Sustainable Development Goals and related efforts of the MICS, DHS, IHSN, together with national governments. UNHCR’s objectives are largely aligned with these frameworks. UNHCR Results Monitoring Surveys (RMS) are household-level surveys with standard questionnaires following context-appropriate methodological approaches. They can be implemented across UNHCR operations to monitor changes in the lives of all relevant groups of persons of concern (impacts) and in UNHCR’s key areas of engagement (outcomes). RMS help us to calculate impact and outcome indicators in a standardized way to have a global understanding of the results. Both indicators and questionnaire is also largely aligned with MICS, DHS, IHSN, national household surveys and other UNHCR standardized surveys.

The goal of {IndicatorCalc} is to ease the implementation of standard calculations for survey indicators related to Forcibly Displaced Population. Among the objectives is also to avoid duplication of documentation efforts around the information to include in the technical report and the information that is already expected to be gathered and recorded within UNHCR Internal Data Repository which is following Data Documentation Initiative standards.

The package is designed to work based on dataset standard backup format exported from kobotoolbox within UNHCR internal data repository. It is adapted from the initial indicator script version.

Each calculation is implemented as a function with in-built check to identify whether the expected variables and modalities are within the dataset and a mapper to transform the data in the expected format in case of divergence of data structure between what was collected and what is expected. You can check each function reference to see in details all documented calculations

Please check the tutorial here

Usage

The easiest way to use the package is through its shiny Companion App and then follow the instruction from there.

The workflow is described below:

1. Run the function var_mapping( “path/to/myxlsform.xlsx”) in order to create your variable mapping. The variable mapping is designed to check if the expected variables and modalities are present in your dataset.

2. Review manually the variable mapping and recode data manually the variables where the automatic match could not be applied and upload it back

3. Then either generate a dummy dataset or connect your project with RIDL and apply calculation to get you summary report and download your expanded XlsForm to include it within your Kobocruncher automatic data exploration

Console user / Developpers

You can install the development version of {IndicatorCalc} from GitHub with:

install.packages("pak")
pak::pkg_install("unhcr-americas/IndicatorCalc")

The {riddle} package is used to ensure integration with UNHCR Data Repository. It requires you to add your API token and store it for further use. The easiest way to do that is to store your API token in your .Renviron file which is automatically read by R on startup.

You can retrieve your API TOKEN in your user page.

To use the package, you’ll need to store your RIDL API token in the RIDL_API_TOKEN environment variable. The easiest way to do that is by calling usethis::edit_r_environ() and adding the line RIDL_API_TOKEN=xxxxx to the file before saving and restarting your R session.

Contributing

Contributions to the packages are welcome. Please, follow the code of conduct.

If you encounter a bug or have idea for a new feature or check, please fill a ticket on github.