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Start here to learn R!

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On R-exercises, you will find more than 4,000 R exercises. We’ve bundled them into exercise sets, where each set covers a specific concept or function. An exercise set typically contains about 10 exercises, progressing from easy to somewhat more difficult. In order to give you a full picture of all the amazing content on this website, we’ve categorized all sets into broader topics below.

Start at the beginning, or anywhere you want

If you’re completely new to R, we suggest you simply start with the first topic, “Vectors”. Once you’ve managed to work through all exercise sets, from top to bottom, you should have a fair amount of knowledge of, and practical experience with, using R. Of course, those of you who are familiar with R already, can jump straight to any of the topics below.

Start Here To Learn R Books

For an even better experience, we recommend our Start Here to Learn R books.

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New exercise sets are added on a daily basis. Read Why exercise and Celebrating our 100th Exercise Set to better understand our philosophy. If you enjoy our exercises, please share this page with your friends. If you’re a college student and enjoy our exercises, you can help your fellow students by asking your TA or professor to add a link to R-exercises on your department’s webpage with R resources.

Have fun!

Exercise sets by Topic

Vectors and sequences

  1. Vectors [creating vectors] [working with vectors] [vectors and functions] [tutorial] [vol. 1] [vol. 2]
  2. Regular sequences [vol. 1] [vol. 2]
  3. Logical vectors and operators
  4. Missing Values
  5. Character vector exercises
  6. Index vectors
  7. [BONUS] Comparing Vectors *

Object modes and attributes

  1. Mode exercises
  2. Practical uses of R objects: Some examples

Factors

  1. Factor Exercises
  2. Using factor variables like a pro [Part 1] [Part 2]
  3. Facing the Facts about Factors
  4. Fighting Factors with Cats
  5. Working with Factors

Arrays and Matrices

  1. Matrix exercises [vol. 1] [vol. 2]
  2. Array exercises
  3. Bind exercises
  4. Matrix operations

Lists and dataframes

  1. List Exercises [vol. 1] [vol. 2]
  2. Know your lists
  3. Data Frames [vol. 1] [vol. 2] [BONUS]
  4. Merging Dataframes Exercises
  5. Accessing Dataframe Objects Exercises
  6. Apply functions to lists

Data Structures

  1. Data Structures
  2. Data Structures [part 1] [part 2]

Importing data

  1. Reading delimited data
  2. Scan exercises
  3. R-SQL
  4. Web scraping
  5. Easy Web Scraping With Rvest
  6. Harvesting Data from the Web with Rvest
  7. [BONUS] Loading data from the web *
  8. [BONUS] Obtaining Twitter Data Exercises *
  9. R with Remote Databases [part 1] [part 2]
  10. Tidy Data Reading

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Exporting data

  1. To Excel using xlsx [part 1] [part 2]
  2. Create XLSX Files with R [part 1] [part 2]

Character strings

  1. Regular expressions part 1
  2. Regular Expressions Fundamentals
  3. String manipulation
  4. [BONUS] Working with tm package and wordclouds *
  5. [BONUS] Character Functions *
  6. [BONUS] Character Functions (intermediate) *
  7. Character Functions (advanced)
  8. [BONUS] Introduction to Text Mining *
  9. Hacking Strings with stringr
  10. Stringr Basic Functions
  11. Pull the Right Strings with Stringr
  12. Hacking Strings with stringi
  13. More string Hacking with Regex and Rebus
  14. Text Data Wrangling

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Tables

  1. data exploration with table
  2. Complex tables
  3. Cross Tabulation with Xtabs
  4. Interactive Data Tables with DT [tutorial] [part 1]

Data manipulation

  1. Get-your-stuff-in-order-exercises
  2. Basic operations
  3. Summary statistics with aggregate()
  4. [BONUS] Introduction to working with categorical variables *
  5. Data Shape Transformation With Reshape()
  6. Interactive Subsetting exercises
  7. Reshape 2 exercises
  8. Efficient data processing with apply
  9. Optimize data processing with sapply
  10. [BONUS] Working with vapply *
  11. Multivariate apply
  12. [BONUS] Data aggregation with tapply *
  13. Sampling exercise [part 1]
  14. Select and query
  15. Let’s get started with dplyr
  16. dplyr basics: More smooth data exploration
  17. dplyr basic functions [tutorial] [part 1]
  18. dplyr non-standard evaluation
  19. Melt and Cast The Shape of Your Data-Frame
  20. [BONUS] Tidy the data up! *
  21. How to Tidy Up your Dataset [tutorial] [part 1]
  22. Data Hacking with RDSTK [part 1] [part 2] [part 3]
  23. Data Wrangling [part 1] [part 2] [part 3] [part 4] [part 5] [part 6] [part 7] [part 8] [part 9] [part 10]
  24. [BONUS] Dummify your Data *

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Data.table

  1. Data table exercises: keys and subsetting
  2. Data Manipulation with data.table [part 1] [part 2]
  3. Basics of data.table: Smooth data exploration
  4. Beyond the basics of data.table: Smooth data exploration
  5. Sharpening the Knives in the data.table Toolbox

Reproducible reporting

  1. RMarkdown [Tutorial] [part 1] [part 2]
  2. Use KableExtra and RMarkdown to Create Tables in PDF Documents [tutorial] [part 1]

Dates/times

  1. As.Date()
  2. Lubridate [part 1] [part 2] [part 3]
  3. zoo time series exercises
  4. Parsing Dates and Time [part 1] [part 2] [part 3]
  5. Tidy Date/Times

Probability distributions

  1. Combinations exercises
  2. Lets Begin with something sample
  3. Generating data
  4. Answer probability questions with simulation [part 1] [part 2]
  5. Probability Functions – Beginner
  6. Probability Functions – Intermediate
  7. Probability Functions – Advanced

Loops and conditional execution

  1. Conditional execution exercises [vol. 1] [vol. 2]
  2. Scripting Loops in R [vol. 1]
  3. Loops in R
  4. [BONUS] Simplifying For loops *

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Functions

  1. Functions exercises [vol. 1] [vol. 2]
  2. Higher order functions
  3. User Defined Functions
  4. A Primer in Functional Programming in R [part 1] [part 2]
  5. [BONUS] Working with Custom Functions *
  6. Well-Behaved Functions

Data visualization

  1. Start plotting data!
  2. Customize a scatterplot exercises
  3. Replicating plots: Boxplot
  4. Advanced base graphics
  5. [BONUS] Statistical Graphics *
  6. Graphics parameters exercises
  7. 3D plotting exercises
  8. GGplot Drilling
  9. Practice Your ggplot Skills
  10. Igraph Network analysis [part 1] [part 2] [part 3]
  11. Graph Theory: Using iGraph [part 1] [part 2]
  12. Shiny Apps[part 1] [part 2] [part 3] [part 4] [part 5] [part 6] [part 7] [part 8] [part 9] [part 10]
  13. Shiny Apps Layout [part 1] [part 2] [part 3] [part 4] [part 5] [part 6] [part 7] [part 8] [part 9] [part 10]
  14. Prettify your Shiny Tables with DT
  15. Modularize your Shiny Apps
  16. GoogleVis [part 1] [part 2] [part 3] [part 4] [part 5] [part 6] [part 7] [part 8] [part 9][part 10]
  17. GGvis [tutorial] [Part 1] [Part 2]
  18. [BONUS] Shiny html tags *
  19. Shapefiles
  20. Getting started with Plotly: basic plots
  21. Plotly: advanced plots and features
  22. Plotly basic charts [tutorial] [part 1]
  23. Lattice graphs [part 1] [part 2]
  24. Graphics with Lattice [part 1] [part 2]
  25. Display multivariate relationship graphs with lattice [tutorial] [part 1]
  26. [BONUS] Rebuilding a Figure *
  27. Multi-panel Graphics
  28. Spatial Analysis with ggmap
  29. iPlots [tutorial] [part 1]
  30. Plotting Interactive Maps With Leaflet [tutorial] [part 1]
  31. [BONUS] Geo Mapping with Leaflet *
  32. Specialize in Geo-Spatial Visualizations With Leaflet [part 1] [part 2]
  33. Visualize Data With Highcharter [tutorial] [part 1]
  34. Plot With Metricsgraphics [tutorial] [part 1]
  35. Plot With Ggiraph [tutorial] [part 1]
  36. Plot with Dygraphs [tutorial] [part 1]
  37. Plot with Patchwork [tutorial] [part 1]
  38. Create a Flexdashboard [tutorial] [part 1]
  39. Step Up Your Dashboard With Shinydashboard [part 1] [part 2]
  40. Visual Data Exploration
  41. Mathematical Expressions in R Plots [tutorial] [part 1]

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Statistical Testing

  1. Independent t-test
  2. Paired t-test
  3. Nonparametric tests
  4. Frequency and chi-square test for independence
  5. Introduction to Statistical Testing and Sampling [part 1] [part 2]

Experimental Design and Analysis

  1. One way ANOVA in R
  2. Two way ANOVA in R
  3. Repeated measures ANOVA in R
  4. Groups Comparison with ANOVA [part 1] [part 2]
  5. One Way MANOVA
  6. Modeling with ANCOVA [part 1] [part 2]
  7. Experimental Design
  8. Bayesian A/B Testing Made Easy
  9. Power Analysis [tutorial] [part 1]

Statistics

  1. Examining data
  2. [BONUS] Basic Data Exploration in Base R Exercises *
  3. [BONUS] Descriptive statistics *
  4. [BONUS] Working with and visualizing a confidence interval *
  5. Model evaluation [part 1] [part 2]
  6. Evaluate your Model with R
  7. Basic tree [part 1] [part 2]
  8. Intermediate tree [part 1] [part 2]
  9. Recursive partitioning and regression trees
  10. Intro to FFTree
  11. [BONUS] Evaluating a linear time series model *
  12. Exploratory Factor Analysis
  13. Hierarchical clustering
  14. Density-based Clustering
  15. K-Means Clustering in R
  16. [BONUS] ROC curves *
  17. Correlation and Correlogram
  18. Introduction to Copulas [part 1] [part 2]
  19. Tidy Modeling
  20. Tidy Model Results
  21. Survival Analysis [part 1] [part 2] [part 3]

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Regression

  1. Multiple regression [part 1] [part 2]
  2. Regression Model Assumptions [tutorial] [part 1]
  3. Predicting Housing Prices with Linear Regression
  4. Generalized Linear Functions – Beginner
  5. Logistic regression in R
  6. Quantile regression
  7. Lasso regression
  8. Ridge regression
  9. Instrumental Variables [part 1] [part 2] [part 3]
  10. Calculating Marginal Effects
  11. Polynomial Model in R – Study Case
  12. Non-linear Models
  13. Basic Generalized Models in Ecology
  14. Basic Generalized Linear Modeling [part 1] [part 2] [part 3] [part 4]

Machine Learning

  1. Machine Learning [tutorial] [Part 1] [Part 2] [Part 3]
  2. Neural Networks [part 1] [part 2] [part 3]
  3. Building a Neural Network Using the Iris Data Set
  4. Call Center Productivity Boosting with ML
  5. eXtremely Boost your machine learning [part 1] [part 2]

Bayesian statistics

  1. Basic Bayesian Inference for MCMC Techniques [part 1] [part 2]
  2. MCMC for Bayesian Inference – Metropolis
  3. MCMC for Bayesian Inference – Gibbs Sampling
  4. Bayesian Inference – MCMC Diagnostics using coda
  5. Bayesian Inference Using the MCMCglmm Package
  6. MCMC Using Stan – An Introduction With The RStan Package
  7. MCMC Using Stan – An Introduction With The RStanarm Package
  8. MCMC Using Stan – Diagnostics with the Bayesplot Package
  9. MCMC Using Stan – Visualization with the Shinystan Package

Forecasting

  1. Time Series Exploration
  2. Intro to Time Series Analysis [part 1] [part 2]
  3. Linear Trend and ARIMA Models
  4. Exponential Smoothing
  5. Multivariate Regression
  6. ARIMAX model
  7. Forecasting for Small Business [part 1] [part 2] [part 3] [part 4]
  8. Basic Time-series Hydro-logical Data Analysis [part 1] [part 2]
  9. Half Hourly Hydrological Dataset Analysis and Visualization
  10. Resampling Time Series Datasets in Hydrology

Investing

  1. Analysis of stock prices [part 1] [[part 2] [part 3]
  2. Shares analysis using Quantmod package
  3. using MANOVA to analysis the banking crisis
  4. Volatility modelling in R [part 1] [part 2] [part 3] [part 4]
  5. Analyzing Crypto-currency Data with R

Parallel Computing

  1. Snowfall
  2. Foreach and DoParallel
  3. Snow and Rmpi

Big Data

  1. Big Data Manipulation
  2. Big Data analytics with RevoScaleR [part 1] [part 2]
  3. Big Data analytics with H2O [part 1] [part 2]
  4. Applying Machine Learning with H2O [part 1] [part 2] [part 3]
  5. Add Some Spark to Your Analysis – Sparklyr [part 1] [part 2] [part 3]

Tensorflow

  1. Tensorflow Basics 1 [tutorial] [part 1]
  2. Tensorflow Basics 2 [tutorial] [part 1] [part 2]
  3. Linear Regression in Tensorflow [tutorial] [part 1]
  4. Logistic Regression in Tensorflow [tutorial] [part 1]
  5. Tensorflow – Neural Network Training

Coding

  1. Best practices while writing R code Exercises
  2. Unit Testing in R using testthat
  3. So You Think Your Code is Efficient
  4. Style-Consistent Code: Exercises

Geospatial data

  1. Use Environmental Data With Ropenaq [tutorial] [part 1]
  2. plot Air Pollution Data with Openair [tutorial] [part 1]
  3. Working with Air Quality and Meteorological Data [part 1] [part 2] [part 3] [part 4] [part 5] [part 6] [part 7] [part 8] [part 9] [part 10]
  4. Spatial Data Analysis: Introduction to Raster Processing [tutorial 1] [tutorial 2] [exercises] [tutorial 3] [tutorial 4]
  5. Advanced Techniques with Raster Data [tutorial 1] [tutorial 2] [exercises]
  6. Advanced Raster Data
  7. Simple Spatial Modeling [part 1] [part 2] [part 3]

Other

  1. Bioinformatics Tutorial with Exercises in R [part 1]
  2. [BONUS] Students’ Achievements Research Project *
  3. Data Science for Operational Excellence [part 1] [part 2] [part 3] [part 4] [part 5]
  4. Accessing and Manipulating Biological Databases [part 1] [part 2] [part 3]
  5. Manipulate Biological Data Using Biostrings Package [part 1] [part 2] [part 3] [part 4]
  6. Canada Labour Market: Future Perspectives
  7. Canada Immigration: Where to Settle In?
  8. Soccer data sparring: Scraping, merging and analyzing exercises
  9. Basic R For Stata Users
  10. Create and Format a Google Sheet Within R
  11. Programmatically Creating Text Outputs
  12. [BONUS] Introduction in working with categorical variables *
  13. [BONUS] Built-in Numerical Functions *
  14. Simple Numerical Modeling in R [part 1] [part 2]
  15. Numerical Analysis using R
  16. Sending Emails from R
  17. Applied Economic Analysis with R
  18. Image Recognition With Google Vision API
  19. Image processing
  20. Word Embedding
  21. Tidy Text Mining
  22. [BONUS] Sentiment Analysis using TidyText *
  23. Functional Programming With Purrr [part 1] [part 2]
  24. UFC Analytics
  25. Automating and Scheduling R Scripts in Windows: Tutorial

* Subscribe for free to access BONUS exercises and receive our weekly newsletter.

Stand alone series: Data preparation made simple

  1. Descriptive analytics, Part 0: Data Exploration
  2. Descriptive analytics, Part 1: Data Formatting
  3. Descriptive analytics, Part 2: Data Imputation
  4. Descriptive analytics, Part 3: Outlier Treatment
  5. Descriptive analytics, Part 4: Data Manipulation
  6. Descriptive analytics, Part 5: Data visualisation (continuous variables)
  7. Descriptive analytics, Part 5: Data visualisation (categorical variables)
  8. Descriptive analytics, Part 5: Data visualisation (spatial data)
  9. Descriptive analytics, Part 6: Interactive dashboard [part 1] [part 2]

Stand alone series: Data Science for Doctors

  1. Data Science for Doctors, Part 1: Data Display
  2. Data Science for Doctors, Part 2: Descriptive Statistics
  3. Data Science for Doctors, Part 3: Distributions
  4. Data Science for Doctors, Part 4: Inferential Statistics [part 1] [part 2] [part 3] [part 4] [part 5]
  5. Data Science for Doctors, Part 5: Cluster Analysis
  6. Data Science for Doctors, Part 6: Variable Importance

Stand alone series: Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises

  1. How I Learned to Stop Worrying About Calculus and Love Stats Exercises [part 1]
  2. How I Learned to Stop Worrying About Calculus and Love Stats Exercises [part 2]
  3. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 3]
  4. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 4]
  5. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 5]
  6. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 6]
  7. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 7]
  8. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 8]
  9. How I Learned to Stop Worrying About Calculus and Love Stats Exercises[part 9]

Standalone Series: R for Hydrologists

  1. Loading and Plotting Data [part 1] [part 2] [part 3]
  2. Correlation and Information Theory Measurements [part 1] [part 2] [part 3]
  3. Seasonality and Trend Decomposition

 

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Comments

  1. Hello,

    The exercises on r programming websites are just fantastic! These exercises help us test our understanding on the subject and more importantly makes us learn! Thank you so much!!

    Reply

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