Last year we launched R Course Finder, an online directory that helps you to find the right R course quickly. With so many R courses available online, we thought it was a good idea to offer a tool that helps people to compare these courses, before they decide where to ... [Read more...]

A few months ago we launched R Course Finder, an online directory that helps you to find the right R course quickly. With so many R courses available online, we thought it was a good idea to offer a tool that helps people to compare these courses, before they decide ... [Read more...]

To stay on top of R in the news, we’re sharing some stories related to R published last week. Why Data Science ‘Rock Stars’ Earn Big Salaries (Dennis McCafferty) Recent post and slide deck related to the 2016 Data Science Salary Survey (O’Reilly Media), with R mentioned as one ... [Read more...]

Higher order functions are functions that take other functions as arguments or return functions as their result. In this set of exercises we will focus on the former. R has a set of built-in higher order functions: Map, Reduce, Filter, Find, Position, Negate. They enable us to complete complex operations ... [Read more...]

Ready, set, go! On R-exercises, you will find hundreds of exercises that will help you to learn R. 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 ... [Read more...]

In the exercises below we cover the basics of ordering vectors, matrices and data frames. We consider both column-wise and row-wise ordering, single and multiple variables, ascending and descending sorting, and sorting based on numeric, character and factor variables. Before proceeding, it might be helpful to look over the help ... [Read more...]

Binding vectors, matrices and data frames using rbind and cbind is a common R task. However, when dimensions or classes differ between the objects passed to these functions, errors or unexpected results are common as well. Sounds familiar? Time to practice! Answers to the exercises are available here. Exercise 1 Try ... [Read more...]

One of our readers commented on our mode exercises post: “What real world tasks are you using mode to solve?” I think it’s an interesting question, from a somewhat larger perspective. Obviously, it’d be a waste of time to learn all kinds of obscure commands that don’t ... [Read more...]

In the exercises below we cover the basics of R object modes. Understanding mode is important, because mode is a very basic property of any R object. Practically, you’ll use the mode property often to convert e.g. a character vector to a numeric vector or vice versa. Before ... [Read more...]

Today we’re practising functions! In the exercises below, you’re asked to write short R scripts that define functions aimed at specific tasks. The exercises start at an easy level, and gradually move towards slightly more complex functions. Answers to the exercises are available here. If you obtained a ... [Read more...]

In the exercises below we cover the basics of conditional execution. In all previous exercises, the solutions required one or more R statements that were all executed consecutively. In this series of exercises we’re going to use the if, else and ifelse functions, to execute only a subset of ... [Read more...]

In the exercises below we cover the basics of the scan function. Before proceeding, first read section 7.2 of An Introduction to R. Answers to the exercises are available here. For each exercise we provide a data set that can be accessed through the link shown in the exercise. You can ... [Read more...]

In the exercises below we cover the basics of reading delimited data. Before proceeding, first read section 7.1 of An Introduction to R, and the help pages for the read.table function. Answers to the exercises are available here. For each exercise we provide a data set that can be accessed ... [Read more...]

In the exercises below we cover the basics of data frames. Before proceeding, first read section 6.3.1 of An Introduction to R, and the help pages for the cbind, dim, str, order and cut functions. Answers to the exercises are available here. Exercise 1 Create the following data frame, afterwards invert Sex ... [Read more...]

In the exercises below we cover the basics of lists. Before proceeding, first read section 6.1-6.2 of An Introduction to R, and the help pages for the sum, length, strsplit, and setdiff functions. Answers to the exercises are available here. Exercise 1... [Read more...]

In the exercises below we cover the basics of factors. Before proceeding, first read chapter 4 of An Introduction to R, and the help pages for the cut, and table functions. Answers to the exercises are available here. Exercise 1 If x = c(1, 2, 3, 3, 5, 3, 2, 4, NA), what are the […] [Read more...]

In the exercises below we cover the basics of index vectors. Before proceeding, first read section 2.7 of An Introduction to R, and the help pages for the sum, and which functions. Answers to the exercises are available here. Exercise 1 If x [Read more...]

In the exercises below we cover the basics of character vectors. Before proceeding, first read section 2.6 of An Introduction to R, and the help pages for the nchar, substr and sub functions. Answers to the exercises are available here. Exercise 1 If x [Read more...]

Today we’re training how to handle missing values in a data set. Before starting the exercises, please first read section 2.5 of An Introduction to R. Solutions are available here. Exercise 1 If X [Read more...]

Exercise 1 Create an array (3 dimensional) of 24 elements using the  dim()  function. Exercise 2 Create an array (3 dimensional) of 24 elements using the  array()  function. Exercise 3 Assign some dimnames of your choice to the array using the  dimnames()  function. Exercise 4 Assign some dimnames of your choice to the array using […] [Read more...]