📊 Stack Overflow Trends Analysis

This project performs an in-depth analysis of programming language trends on Stack Overflow from 2008 onwards using R. It includes data cleaning, statistical testing, time series visualization, and exploration of language popularity over time.

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🔍 Overview

Stack Overflow is one of the largest Q&A communities for programmers. This analysis focuses on understanding how the popularity of different programming languages has evolved over the years by examining the number of questions posted for each language.

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🛠️ Technologies & Libraries Used

• R Language
• Libraries:
  • tidyverse
  • lubridate
  • ggplot2
  • dplyr
  • forecast
  • readr

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📁 Dataset

The dataset used contains monthly counts of Stack Overflow questions for several programming languages from early 2008. The CSV file includes columns like Date, Python, JavaScript, PHP, SQL, Java, etc.

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📈 Key Analyses Performed

1. Data Cleaning

• Checked and removed missing values
• Renamed and formatted date columns
• Filtered data from 2008 onwards

2. Trend Visualization

• Line plots showing trends of language popularity over time
• Year-wise and month-wise breakdown of question frequency

3. Statistical Analysis

• T-tests to assess the impact of specific timeframes (e.g., before/after June 2020)
• Hypothesis testing for early years (2008 vs 2009)
• Linear regression to detect trend direction for Python questions

4. Exploratory Data Analysis

• Pie chart showing distribution of total questions per language
• Bar chart comparing total question volume by language
• Histogram and density plots for specific languages
• Boxplots for cross-language comparison
• Scatter plots and regression lines for correlation (e.g., Python vs JavaScript)

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📊 Visualizations Included

• 📈 Time Series Plots
• 📊 Bar Charts
• 📦 Boxplots
• 🧠 T-Test & Hypothesis Testing Results
• 📉 Linear Regression Summary
• 🥧 Pie Charts
• 📚 Histograms & Density Plots
• 🔁 Correlation Analysis via Scatter Plots

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🧪 Notable Insights

• Python has consistently grown in popularity and leads in question volume.
• JavaScript and SQL also maintain high levels of engagement.
• Some older languages like Perl and Objective-C show decline over time.
• Clear shift in question trends before and after 2020 (possibly due to ChatGPT or remote learning trends).

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🚀 How to Run

1. Clone the repository
2. Ensure you have R installed with the listed libraries
3. Update the path to the dataset in the file_path variable
4. Run the script to generate all insights and visualizations

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📬 Contact

linkedin.com/in/ayesha-zahid-4a9046301/ For questions or feedback, feel free to reach out!