This repository contains both a Python notebook and an R script for the same data cleaning and wrangling task to demonstrate the equivalent code structures in these two languages. Pre-processing task includes but not limited to:

• Reading in .sav data files
• Dealing with labelled data and value labels
• Basic frequency tables
• Filtering by group
• Removing missing data
• Creating new variables or recoding them into the same ones
• Calculating group-centered/scaled variables
• Removing outliers based on within-group quartiles
• Replacing missing values with group means
• Exporting data into csv

Data used: The U.S. public-use PIAAC data (2012-2014) (https://nces.ed.gov/surveys/piaac/datafiles.asp)