TaSTy

COP23 TaST Analytics

The purpose of this project is to process the TaST and historic results/targets from the MER Structured Dataset to leverage during the COP23 Target setting process. The outputs from this project will be added to a Tableau workbook, that allows the user to explore the targets and validations easily.

There are 2 primary scripts into this repo.

The first script, 00_prep_msd.R will process the historic results and targets from the most recent PSNUxIM MSD, into a tidy and cleaned up file for import into the Tableau tool. This step will only need to be run once, as this data will not change with the updates to the TaST.

The second script, 01_prep_dp.R will process the Target Setting Tool using the tameDP package to get a tidy extract of the TaST to pull into the Tableau tool. As such, whenever you have a new version of the TaST ready to explore, simply run this script over the new version of the TaST and swap out the TaST file in the Tableau tool.

Setting up

To get started, you will want to make sure all of the required R packages are installed on your machine. If you do not have these packages installed, run the following code:

#install OHA packages
  remotes::install_github("USAID-OHA-SI/gagglr", build_vignettes = TRUE)
  remotes::install_github("USAID-OHA-SI/glamr", build_vignettes = TRUE)
  remotes::install_github("USAID-OHA-SI/gophr", build_vignettes = TRUE)
  remotes::install_github("USAID-OHA-SI/tameDP", build_vignettes = TRUE)

#install packages from CRAN
  install.packages("tidyverse")
  install.packages("glue")
  install.packages("googlesheets4")
  install.packages("readxl")
  install.packages("janitor")

Please also ensure that you have your si_paths set up, as the R scripts rely on this logic. If you do not have these set up, please follow the following steps:

1. Open the TaSTy project in an RProject
2. Load the glamr package and run the function si_setup(). Documentation can be found here.
3. Load the glamr package and follow the steps outlined here to set up your si_path() using glamr::set_paths()

R and Tableau Workflow

Once all your packages and folders are set up, you are good to go and should not need to repeat these steps.

Using si_setup(), there will be series of folders created in your R Project folder. For the South Africa team, please save the following files in the Data folder in your R Project as resources for the scripts to call on:

1. dsp_attributes_2022-05-17.xlsx
2. PSNU ID_02032023.xlsx
3. psnu_agency_ref.xlsx
4. age_mapping.xlsx
5. msd_disagg_mapping.xlsx

Now, you can begin to run the 00_prep_msd.R and 01_prep_dp.R scripts. Once these data are processed, the tidy files will be saved to the Dataout folder. As a reminder, you will only need to run the MSD processing script once.

From here, open up the Tableau tool and click on the Data Source tab. The data source will open with a prompt to update the union. Simply, switch out the new TaST file for the old version (or add both files into Tableau and convert to union if this is the first time). Refresh the union and the dashboard should be updated with the new data.

Note: The outputs from the scripts are in .csv format and sometimes are ingested into Tableau with the incorrect data types. Please ensure that the cumulative and targets columns are both numeric columns.

Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development. All errors remain our own.