Improve Your Data Science Skills with R Programming Exercises

R Programming Exercises

Working with data and programming languages can be a challenge. But with the help of exercises, you can learn how to use R programming language quickly and effectively! Here are some exercises to get you started in the world of R programming.

Exercise 1: Creating Vectors

Vectors are one of the most important data structures in R programming. In this exercise, you will learn how to create vectors and perform basic operations on them. You will start by creating two vectors, then you'll add them together and calculate the mean of the result. Lastly, you will find out how to access the elements in the vector.

Exercise 2: Manipulating Data Frames

Data frames are an essential data structure in R. In this exercise, you will learn how to work with data frames by accessing, adding, and manipulating data. You'll start by importing an example data frame, then practice changing the column and row names, accessing elements in the data frame, and more.

Exercise 3: Running Basic Statistics

Run basic statistics using R programming! In this exercise, you'll use R commands to complete common statistical tasks, such as calculating the mean, median, mode, range, and standard deviation of a given dataset. You'll also learn how to calculate correlations between variables, set up hypothesis tests, and more.

Exercise 4: Plotting Basics

Visualize data with the help of graphs and charts! This exercise will help you become familiar with plotting data using R programming. You'll start by setting up an example plot, then practice customizing it further to display specific data points. Finally, you'll try creating various types of plots including bar plots, box plots, and scatter plots.

Exercise 5: Writing Functions

Writing functions is an important part of programming! In this exercise, you will learn how to write functions using R. You will start by writing a basic function, then learn to debug it by testing the results. Finally, you will practice writing more complex functions that accept parameters and generate automated output.