leburupalesa22/SA-unemployment-analysis

Analysis of South Africa's unemployment trends using Stats SA QLFS data | R # South African Unemployment Analysis 📊 ## Overview This project analyses South Africa's labour market using official data from Statistics South Africa (Stats SA) Quarterly Labour Force Survey (QLFS) Q4 2024. It explores unemployment trends by time, province, age group, and gender. ## Key Findings - 📈 SA's unemployment rate peaked at **33.5%** in Q2 2024 - **Limpopo** has the highest provincial unemployment rate at 44.2% - **70% of youth aged 15–24** are unemployed the most affected group - Women face consistently higher unemployment than men (34.5% vs 29.7%) ## Data Source - **Source:** Statistics South Africa (Stats SA) - **Survey:** Quarterly Labour Force Survey (QLFS) Q4 2024 - **Coverage:** Persons aged 15–64 years, Q4 2019 – Q4 2024 - **Link:** statssa.gov.za ## Project Structure sa-unemployment-analysis/ *qlfs_by_age.csv *qlfs_by_province.csv *qlfs_by_sex.csv *qlfs_key_indicators.csv *plot1_unemployment_trend.png *plot2_province.png *plot3_age.png *plot4_gender.png *sa_unemployment_analysis.R *README.md ## Visualisations 1. Unemployment Rate Over Time 2. Unemployment by Province 3. Unemployment by Age Group 4. Unemployment by Gender Tools Used - **Language:** R - **Libraries:** tidyverse, ggplot2 - **IDE:** RStudio ## Author Palesa Leburu BSc Mathematical Sciences | Honours Statistics (in progress) [linkedin.com] | [github.com]