25+ Practical Data Science Projects in R to Build Your Skills

Data science focuses on analyzing data to identify patterns and answer questions and generate predictive models. Among the many tools available, R stands out because it was designed for statistics and visualization. Working on a project bridges the gap between theory and practicality. 

In this blog, you’ll explore 25+ data science projects in R, made for beginner to advanced. These projects will give you hands-on experience and teach you how to apply concepts in real life. 

Now let’s dive deep into the Top Data Science Projects in R with source code. 

If you are someone who is more interested in well-structured learning upGrad’s Data Science Courses offer a mix of theory and hands-on projects, along with mentorship from experienced instructors and industry experts. 

Best Data Science Projects in R With Source Code 

Here is a quick visual representation of some of the best R projects which we will discuss later in this blog.

Beginner Level Data Science Projects in R 

Explore these beginner level projects to build your foundation for the data science journey and to gain hands-on skills. 

1. Uber Data Analysis 

In this project, you will use R to clean Uber trip data, visualize trends, and analyze patterns like peak hours and demand. 

Tools and Technologies Used: 

• R 
• dplyr, tidyr 
• Ggplot2 
• forecast, zoo 
•  lubridate 
• caret, randomForest 
• Recommenderlab 
• janitor 

Project Outcome: 
In this project, you will gain hands-on experience cleaning data, visualizing trends, analyzing patterns, and building basic predictive models using R. 

Check out this Project- How to Build an Uber Data Analysis Project in R 

2. Wine Quality Prediction 

In this project, you will build a model for quality control in the wine industry by using real world dataset. 

Tools and Technologies Used: 

• R 
• dplyr, tidyverse 
• Ggplot2 
• lubridate 
• caret, randomForest 

Project Outcome: 
You will gain hands-on experience in data preprocessing, visualization, feature engineering, and building predictive models in R for real-world quality prediction. 

Check out this Project- Wine Quality Prediction Project in R 

3. Trend Analysis on COVID-19 

In this project we will analyze the trends in confirmed cases, deaths, and vaccinations of COVID-19 globally. 

Tools and Technologies Used: 

• R 
• readr, dplyr, tidyr 
• zoo, xts, lubridate  
• ggplot2, plotly 
• forecast, fable, tseries  
• urca  

Project Outcome: 
You will learn to clean and transform time series data, visualize COVID-19 trends, and build predictive models using ARIMA and ETS in R. 

Check out this Project- Trend Analysis Project on COVID-19 using R 

4. Forest Fire Project 

In this Project you’ll use data related to forest fires and learn how to clean, preprocess, and analyze it using R. 

Tools and Technologies Used: 

• R 
• ggplot2 
• dplyr  
• rpart 
• readr 
• tidyverse 

Project Outcome: 
You will learn to clean and preprocess forest fire data, visualize patterns, and build classification models in R to predict fire risk. 

Check out this Project- Forest Fire Project Using R - A Step-by-Step Guide 

5. Customer Segmentation Project 

This project will help you understand how to divide your customer base into relevant segments, which can further be used for targeted marketing, improved service, and profitable business decisions. 

Tools and Technologies Used: 

• R 
• ggplot2 
• dplyr  
• rpart 
• readr 
• stats (built-in) 
• scales (optional) 

Project Outcome: 
You will learn to clean and analyze customer data, visualize patterns, and perform K-means clustering in R to create meaningful customer segments 

Check out this Project- Customer Segmentation Project Using R: A Step-by-Step Guide 

6. Spam Filter Project 

In this Project you’ll build a spam filter model that will classify text messages as spam or not using the Naive Bayes algorithm. 

Tools and Technologies Used: 

• R 
• tm 
• dplyr  
• e1071 
• caret  
• Stringr 
• Naive Bayes 

Project Outcome: 
You will learn to preprocess text data and build a Naive Bayes model in R to classify messages as spam or not. 

Check out this Project- Spam Filter Project Using R with Naive Bayes – With Code 

7. Car Data Analysis 

In this project, you will use R to clean and explore car data, visualize trends, analyze correlations, and build predictive models to gain insights into automotive performance. 

Tools and Technologies Used: 

• R 
• tidyverse 
• ggplot2  
• corrplot  
• knitr 
• DT 

Project Outcome: 
You will gain hands-on experience in data cleaning, visualization, exploratory analysis, and predictive modeling using R on real-world car data. 

Check out this Project- Car Data Analysis Project Using R 

8. Daily Temperature Forecast 

In this project, you will use R to clean, visualize, and analyze daily temperature data, and apply the ARIMA model to forecast future temperatures. 

Tools and Technologies Used: 

• R 
• tidyverse 
• ggplot2  
• lubridate  
• zoo  
• forecast  
• tseries  

Project Outcome: 
You will gain hands-on experience in time series analysis, data visualization, and building predictive models in R for weather forecasting. 

Check out this Project- Daily Temperature Forecast Analysis Using R 

Once you’re comfortable with beginner projects, these intermediate-level R projects will help you deal with more complex datasets and analysis tasks. 

Intermediate Level Data Science Projects in R 

In these projects, you’ll explore clustering, prediction, and performance analysis to deepen your R skills and apply them to real-world scenarios. 

9. Spotify Music Data Analysis 

In this project, you will use R to clean and analyze Spotify music data, visualize song features, and build a Random Forest model to predict song popularity. 

Tools and Technologies Used: 

• R 
• Logistic Regression 
• Random Forest 
• dplyr , ggplot2  
• readr  
• caret  
• corrplot, GGally  
• plotly , viridis  
• reshape2  
• knitr  

Project Outcome: 
You will gain experience in data cleaning, feature analysis, visualization, and building machine learning models in R to understand factors behind song popularity. 

Check out this Project- Spotify Music Data Analysis Project in R 

10. Movie Rating Analysis 

In this project, you will use R to clean and analyze movie data, visualize trends, engineer features, and build machine learning models to predict and classify movie ratings. 

Tools and Technologies Used: 

• R 
• dplyr  
• ggplot2  
• lubridate 
• janitor 
• pROC  
• tidyverse  

Project Outcome: 
You will gain hands-on experience in data cleaning, visualization, feature engineering, and building predictive and classification models in R for movie rating analysis. 

Check out this Project- Movie Rating Analysis Project in R 

11. Player Performance Analysis & Prediction 

In this project, you will clean and analyze NBA player data, explore key performance metrics, and build machine learning models to predict total points scored. 

Tools and Technologies Used: 

• R 
• dplyr, tidyverse, janitor 
• ggplot2, vip 
• lubridate 
• caret  
• lm, ranger, xgbTree  
• skimr  

Project Outcome: 
You will gain experience in data preprocessing, feature analysis, and building regression models in R to predict player performance and identify influential metrics. 

Check out this Project- Player Performance Analysis & Prediction Using R 

12. Natural Disaster Prediction Analysis 

In this project, you will use R to clean and analyze global disaster data, explore risk indicators, and build regression models to predict disaster risk levels. 

Tools and Technologies Used: 

• R 
• tidyverse 
• skimr  
• caret  
• ggplot2 (part of tidyverse)  

Project Outcome: 
You will gain hands-on experience in data preprocessing, visualization, and building predictive models in R to assess and interpret natural disaster risks. 

Check out this Project- Natural Disaster Prediction Analysis Project in R 

13. Titanic Survival Prediction 

In this project, you will use R to clean and explore the Titanic dataset, visualize survival patterns, and build a Random Forest model to predict passenger survival. 

Tools and Technologies Used: 

• R 
• tidyverse 
• randomForest 
• caret  
• e1071 

Project Outcome: 
You will gain experience in data cleaning, visualization, and building classification models in R to predict survival outcomes. 

Check out this Project- Titanic Survival Prediction in R: Complete Guide with Code 

14. Instagram Fake Profile Detection 

In this project, you will explore Instagram data in R, analyze user behavior patterns, and build a Random Forest model to detect fake profiles. 

Tools and Technologies Used: 

• R 
• tidyverse 
• RandomForest 
• Janitor 
• skimr 
• caret  
• ggplot2, GGally 

Project Outcome: 
You will learn to clean and visualize data, identify key behavioral features, and build a classification model in R to spot suspicious accounts. 

Check out this Project- Instagram Fake Profile Detection Using Machine Learning in R 

15. Loan Approval Classification 

In this project, you will use R to clean and preprocess loan application data and build a logistic regression model to predict loan approval outcomes. 

Tools and Technologies Used: 

• R 
• dplyr 
• RandomForest 
• skimr 
• caret  
• ggplot2 

Project Outcome: 
You will learn to handle missing data, apply classification techniques, and evaluate model performance using accuracy, confusion matrix, and ROC curves in R. 

Check out this Project- Loan Approval Classification Using Logistic Regression in R 

16. Food Delivery Analysis 

In this project, you will use R to clean and analyze food delivery data, explore customer ordering patterns, delivery trends, and payment behaviors. 

Tools and Technologies Used: 

• R 
• tidyverse  
• lubridate  
• janitor  
• ggplot2 
• skimr 

Project Outcome: 
You will gain hands-on experience in data cleaning, visualization, and analyzing operational patterns to uncover insights in food delivery services using R. 

Check out this Project- Food Delivery Analysis Project Using R 

17. Student Performance Analysis 

In this project, you will explore student performance data in R to identify factors that impact final grades and build a regression model to predict outcomes. 

Tools and Technologies Used: 

• R 
• dplyr, tidyverse  
• ggplot2, corrplot  
• skimr (optional)  
• Base R (lm)  

Project Outcome: 
You will learn to clean and visualize data, analyze correlations, and apply linear regression in R to understand and predict student performance. 

Check out this Project- Student Performance Analysis In R With Code and Explanation 

18. World Happiness Report Analysis 

In this project, you will use R to explore the World Happiness Report 2019, analyze factors affecting happiness, and visualize global trends. 

Tools and Technologies Used: 

• R 
• tidyverse  
• ggplot2 
• corrplot  
• readr  

Project Outcome: 
You will learn to clean and visualize data, perform correlation analysis, and uncover patterns that influence national well-being using R. 

Check out this Project- World Happiness Report Analysis in R With Code 

Once you’ve built confidence with intermediate projects, these advanced Data Science Projects in R will challenge you with complex datasets and sophisticated analyses. 

Advanced Level Data Science Projects in R 

These projects focus on advanced modeling, forecasting, and data-driven decision-making to elevate your R skills to a professional level. 

Project Name	Tools and Technologies	Project Outcome
House Price Prediction	R, caret, xgboost, data.table	Predict house prices using advanced regression models and feature engineering.
Stock Market Forecasting	R, quantmod, TTR, prophet	Analyze stock trends and forecast future prices using time series models.
Financial Risk Modeling	R, riskmetrics, fPortfolio, PerformanceAnalytics	Assess financial risks and build risk models for portfolio management.
Voice Gender Recognition	R, tuneR, seewave, caret	Analyze audio features to classify speaker gender using machine learning.
Credit Card Fraud Detection	R, h2o, data.table, mlr	Detect fraudulent transactions using anomaly detection and classification.
Energy Consumption Forecasting	R, tsibble, fable, lubridate	Forecast energy usage patterns with time series and predictive modeling.
Customer Churn Prediction	R, lightgbm, dplyr, ROCR	Predict which customers are likely to churn using advanced classification.
Image Classification with CNN	R, keras, tensorflow, EBImage	Build a convolutional neural network in R to classify images.
Airline Delay Prediction	R, h2o, lubridate, ggplot2	Predict flight delays using regression and classification techniques.
Sentiment Analysis on Reviews	R, text2vec, tm, e1071	Analyze textual reviews and classify sentiment using NLP and ML models.

Why Choose R for Data Science? 

Data scientists widely use the R programming language in their work. It was designed for statistical analysis and visualization which makes it suitable for data handling. Here’s why R is worth learning: 

• Statistical Power: R has in-built functions for statistical tests, linear and nonlinear modeling, and time-series analysis. 
• Data Visualization: Packages like ggplot2, lattice, and plotly make it easy to create clear, professional visualizations. 
• Rich Ecosystem: With packages like tidyverse, caret, and randomForest, R supports everything from data cleaning to advanced machine learning. 
• Community Support: R has a strong academic and research community, making resources, tutorials, and datasets widely available. 
• Integration: R can connect with databases, Python, Hadoop, and Spark, extending its usability beyond just statistical analysis. 

Using R in your projects helps you understand concepts deeply while giving you access to specialized tools built for data science. 

Best Practices for Executing Projects

Good projects follow a clear plan. Great projects are the ones you can explain, repeat, and improve over time. When you work on data science projects in R, focusing on structure, clarity, and documentation makes a big difference.

Here’s how to approach your projects the right way:

1. Keep Your Workflow Organized

• Create folders for data, scripts, and outputs.
• Use clear file names like data_cleaning.R or model_build.R.
• Save intermediate results so you don’t need to rerun everything.
• Always set a working directory and use relative paths.

This helps you find and reuse your work easily, especially for large data science based projects in R.

2. Document Everything

• Add short comments in your code explaining each step.
• Maintain a README.md file that describes the purpose, data source, and method used.
• Keep a small note of challenges and learnings for future reference.
• Save visual outputs (plots, tables) for presentation later.

What to Document	Why It Matters
Data source	Ensures transparency
Cleaning steps	Helps reproduce results
Model details	Simplifies explanation
Evaluation metrics	Shows performance clearly

3. Ensure Reproducibility

• Set random seeds using set.seed() before running models.
• List all required libraries at the start of your script.
• Use tools like renv to save package versions.
• Export your results as CSV or RDS for others to verify.

Reproducibility is a key part of professional data science projects in R.

4. Focus on Data Quality

• Check for missing values using summary() and is.na().
• Handle outliers carefully instead of deleting them blindly.
• Always explore data distributions before modelling.
• Validate data types and formats early.

A clean dataset saves time and improves model accuracy.

5. Communicate Your Findings

• Present key insights visually using ggplot2.
• Summarize models in plain language.
• Share reports via RMarkdown or Shiny dashboards.
• Include a short “key takeaway” section for each analysis.

When you present your data science based projects in R, clarity and visuals matter as much as accuracy.

Tip:
Treat every project like something you’d show a recruiter or teammate. That mindset keeps your work clean, clear, and professional from the start.

Conclusion 

From exploring Uber trip analysis to stock market prediction these Data Science Projects in R convert actual data into operational knowledge. Your skill development will occur through project creation and evaluation which will also reveal hidden data patterns and stories to enhance your R experience. 

Unsure which data science project is good fit for you? Book a free career counseling session with our experts and receive personalized guidance to align your skills, interests, and goals with the right project.