cleanstart

During data cleaning for a survey, analysts typically must create countless scripts to inspect and clean every variable in each survey data files. To a large degree, this involves:

• Creating an empty cleaning program for a given scope of cleaning (per data file, survey module, analyst area of expertise, etc.)
• Copying information for each variable from Survey Solutions Designer (e.g., enablement conditions, validation conditions, etc from Survey Solutions Designer)
• Pasting information into the empty Stata file in a consistent, readable format
• Transforming copied information following known rules/procedures (e.g., translate Survey Solutions’ expressions into Stata, write the same form of check(s) for variables of a given question type, etc.)
• Repeating (ad naseaum) for each desired cleaning program

Convinced that computers are better at copy-paste-transform operations than humans, {cleanstart} provides analysts an interactive graphical for creating a template cleaning program. From there, the analyst can dedicate their time, skill, and judgment to a task that computers aren’t (yet) good at doing: cleaning data.

To get started, the analyst simply:

• Installs the package–a one-time operation
• Downloads a JSON file containing questionnaire metadata–a one-time-per-survey operation.
• Opens the app’s graphical interface
• Selects the range of variables to clean (e.g., from s02q01 to s02q31a)
• Provides optional information on desired customizations (e.g., replace @rowcode with members__id, identify “other (specify)” variables as those ending in _os, etc.)
• Provides a file name for the Stata .do file
• Downloads the Stata .do file template

Installation

Since susometa is not yet available on CRAN, it can be installed from GitHub as follows:

if (!require("pak")) install.packages("pak")
pak::pak("lsms-worldbank/cleanstart")

Usage

To open the app’s graphical interface, simply run this command in the R console:

library(cleanstart)
run_app()