clean-survey-data

To handle missing values and recode variables in survey data using R, this Skill automatically identifies special codes and replaces them with NA. It transforms coded categorical variables into meaningful factors and collapsed categories for analysis-ready datasets.

Yes, you can convert Stata value labels into R factors for survey data analysis. This Skill uses the haven package to convert value labels directly into R factors, aiding interpretability during statistical modeling and social science research.

The best way to clean large survey datasets with coded missing values is to automate the replacement of special codes with NA. This Skill streamlines survey data cleaning by managing variable labels and collapsing categories to minimize manual preprocessing effort.

Yes, this survey data cleaning workflow supports data.table and rio for data transformation. It utilizes these R packages to manage missing values, variable labels, and category collapsing, ensuring robust data preparation for health and social science research.

You need to collapse categories in coded survey datasets when transforming raw categorical variables into meaningful factors for statistical modeling. This process recodes variables and groups responses, ensuring your data is analysis-ready and interpretable.