WVS Cleaning & GDP Merge

Version: 1.1 (2025-05-25)
Course: Data Analysis with AI (MA, BA)
Author: Gábor’s Data Analysis (gabors-data-analysis.com)

Overview

This script cleans and subsets World Values Survey (WVS) Wave 7 data, generates a random subsample, aggregates by country & year, and merges with World Bank GDP indicators.

Prerequisites

Packages:
- osfr (download from OSF)
- dplyr (data manipulation)
- readr (CSV I/O)
- WDI (World Bank API)

Directory Structure

project-root/
├─ data/
│  ├─ raw/        ← input CSVs
│  └─ clean/      ← outputs
└─ scripts/
   └─ cleaning.R ← this script

Input

data/raw/WVS_Cross-National_Wave_7_csv_v6_0.csv
Downloaded automatically from OSF (ID: 36dgb).

Output

WVS_subset.csv
Selected variables and respondents, wave 1–7.
WVS_random_subset2000.csv
Random sample of 2 000 respondents (≈ per country).
WVS_GDP_merged_data.csv
Aggregated (mean & mode) by country & year for wave 7, merged with GDP & population (2017–2023).

Processing Steps

Setup
- Clear environment (rm(list=ls()))
- Load libraries
- Define data_in and data_out folders
Import & Subset
- Download raw CSV via OSF
- Select key demographics (country codes, interview date, weights) and survey items (Q1–Q89, Q260–Q290)
- Save to WVS_subset.csv
- Note: This file contains answers from all respondents from the data.
Random Subsample
- In this step, we create a random subsample to reduce sample size.
- Seed: 20250124
- Sample ~2 000 respondents stratified by country
- Count the resulting number of respondents in each country
- Save to WVS_random_subset2000.csv
Aggregate & Clean
- In this step, we aggregate the full data (step 2 data) to country-level, then join with GDP data.
- Recode negative codes (–1…–5) to NA
- Count the number of respondents in each country
- Compute country–year means for numeric items, modes for categorical
- Download GDP & population (2017–2023) via WDI
- Merge on ISO3 country code & year
- Save to WVS_GDP_merged_data.csv

Usage

Rscript scripts/cleaning.R

Ensure your working directory is set to project root.
Raw data and outputs will live under data/raw and data/clean.

Contact

gabors-data-analysis.com | MA (BA) Data Analysis with AI course

# WVS Cleaning & GDP Merge **Version:** 1.1 (2025-05-25) **Course:** Data Analysis with AI (MA, BA) **Author:** Gábor’s Data Analysis (gabors-data-analysis.com) --- ## Overview This script cleans and subsets World Values Survey (WVS) Wave 7 data, generates a random subsample, aggregates by country & year, and merges with World Bank GDP indicators. --- ## Prerequisites - Packages: - **osfr** (download from OSF) - **dplyr** (data manipulation) - **readr** (CSV I/O) - **WDI** (World Bank API) --- ## Directory Structure ``` project-root/ ├─ data/ │ ├─ raw/ ← input CSVs │ └─ clean/ ← outputs └─ scripts/ └─ cleaning.R ← this script ``` --- ## Input - `data/raw/WVS_Cross-National_Wave_7_csv_v6_0.csv` Downloaded automatically from OSF (ID: 36dgb). --- ## Output 1. **WVS_subset.csv** Selected variables and respondents, wave 1–7. 2. **WVS_random_subset2000.csv** Random sample of 2 000 respondents (≈ per country). 3. **WVS_GDP_merged_data.csv** Aggregated (mean & mode) by country & year for wave 7, merged with GDP & population (2017–2023). --- ## Processing Steps 1. **Setup** - Clear environment (`rm(list=ls())`) - Load libraries - Define `data_in` and `data_out` folders 2. **Import & Subset** - Download raw CSV via OSF - Select key demographics (country codes, interview date, weights) and survey items (Q1–Q89, Q260–Q290) - Save to `WVS_subset.csv` - Note: This file contains answers from all respondents from the data. 3. **Random Subsample** - In this step, we create a random subsample to reduce sample size. - Seed: `20250124` - Sample ~2 000 respondents stratified by country - Count the resulting number of respondents in each country - Save to `WVS_random_subset2000.csv` 4. **Aggregate & Clean** - In this step, we aggregate the full data (step 2 data) to country-level, then join with GDP data. - Recode negative codes (`–1…–5`) to `NA` - Count the number of respondents in each country - Compute country–year means for numeric items, modes for categorical - Download GDP & population (2017–2023) via `WDI` - Merge on ISO3 country code & year - Save to `WVS_GDP_merged_data.csv` --- ## Usage ```sh Rscript scripts/cleaning.R ``` Ensure your working directory is set to project root. Raw data and outputs will live under `data/raw` and `data/clean`. --- --- ## Contact gabors-data-analysis.com | MA (BA) Data Analysis with AI course