Table of Contents
Welcome to our collection of R practice exercises. Our exercises are designed to help you master the R programming language and build your skills as a data analyst or statistician. Whether you’re a beginner just starting out or an experienced professional looking to brush up on your skills, our exercises will provide you with the knowledge and hands-on experience you need to succeed. Each exercise includes detailed explanations and sample code to help you understand the concepts and techniques involved. With our exercises, you’ll be able to perform statistical analysis, create data visualizations, and work with large datasets. Start practicing today and take your R skills to the next level!
R Basic Exercises
R Advance Exercises
Here are some advanced R programming exercises to challenge your skills:
- Regular Expressions: Use regular expressions to extract and manipulate text data.
- Advanced Plotting: Use the ggplot2 library to create advanced plots, including heat maps, histograms, and density plots.
- Statistical Modeling: Build and evaluate statistical models, such as linear regression, logistic regression, and decision trees, using the caret and randomForest packages.
- Text Mining: Use the tm and tidytext packages to perform text mining tasks, such as text classification, sentiment analysis, and term frequency-inverse document frequency (TF-IDF) analysis.
- Time Series Analysis: Use the forecast package to perform time series analysis, such as trend analysis, seasonality analysis, and forecasting.
- Data Wrangling: Use the dplyr and tidyr packages to perform data wrangling tasks, such as filtering, grouping, and aggregating.
- Map Visualization: Use the ggmap package to create geographical maps and perform spatial analysis.
- Web Scraping: Use the rvest package to scrape data from websites, and to extract information such as text, images, and links.
- Machine Learning: Use the caret and randomForest packages to build and evaluate machine learning models, such as decision trees, random forests, and support vector machines (SVMs).
- Deep Learning: Use the keras and tensorflow packages to build and evaluate deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
These exercises will help you gain a deeper understanding of R programming and data analysis, and prepare you for complex and challenging projects.