.

How many new on treatment (TX_NEW) for 15+ are in each PSNU?

How do the planned FY60 targets compare to historic results and/or targets?

Enter…`tameDP`

Imports and tidies data from the PEPFAR, Excel-based COP Target Setting Tool (formerly known as the Data Pack)

Imports Target Setting Tool as tidy data frame
Breaks up data elements stored in the indicatorCode column into distinct columns
Cleans up the HTS variables, separating modalities out of the indicator name
Creates a statushiv column
Cleans and separates PSNU and PSNU UID into distinct columns
Adds in mechanism information from DATIM, including operatingunit, funding - agency, partner and mechanism name
Removes any rows with no targets
Allows for aggregate to the PSNU level

How do I use `tameDP`?

Prework

Need to install R (and ideally RStudio); See Appendix A
Need to install a handful of R packages; See Appendix B
Need a Target Setting Tool

Usage

Load the library and run one to two commands

library(tameDP)
library(readr)

#set your filepath
tst_path <- "../tools/TargetSettingTool_Minoria_20590101.xlsx"
#import/tidy & export
tame_dp(tst_path) %>% 
  write_csv("../outputs/TST_Minoria_tidy.csv")

Why is `tameDP` useful?

Working with the data in the TST itself is cumbersome given (1) the level of granularity needed to generate the targets and (2) the wide structure
tameDP allows an analyst to quickly pull and extract a tidy dataset that is usable for analysis and visualization regardless of what software program you end up using
The package creates facsimile PSD structure for your data, making it familiar and giving you calculated indicators useful to analysis (i.e. HTS_TST, HTS_TST_POS, PMTCT_POS) and allows to you bind it to historic MSD files

Why is `tameDP` useful? (cont’d)

When working at the PSNUxIM level, tameDP even applies the agency and mech names which are not available in the TST

Different than GHSD’s package

GHSD’s Shiny App, built off datapackr, creates a validation file and “Flatpack”

Being aware of potential drawbacks

You need to have R installed with tameDP and the dependent packages
No connection to PAW
Only so much we can test without having completed TST files
Results may not 100% match the “Flatpack”

Reporting an issue

If you run into an error, you can either submit a new issue on GitHub (if you have a GitHub account) or you can email us at si.coreanalytics@usaid.gov

Appendix

A. Installing R and RStudio

To install both R and RStudio Desktop on your GFE, go to Software Center on your computer (Start > Microsoft Endpoint manager > Software Center) Once there, you can select the Application called “R for Windows” and click “Install”. After that completes, you can then select “RStudio Desktop” and then “Install”. If you run into any issues, first try restarting your machine and if that fails, you can contact M/CIO Help Desk.

B. Installing R Packages

Within the R Console, you will need to install tameDP along with a few other USAID/OHA custom packages it relies on.

 install.packages(c('gagglr', 'glamr', 'gophr', 'tameDP'), 
                  repos = c('https://usaid-oha-si.r-universe.dev',
                            'https://cloud.r-project.org'))

C. Vignettes

More in depth help/walk throughs can be found on tameDP’s Vignette pages

.

Enter…tameDP

How do I use tameDP?

Why is tameDP useful?

Why is tameDP useful? (cont’d)