Learn about the four challenges when learning R and the key strategies for how to overcome those challenges so that nothing can stop you from mastering R
In this tutorial, I go over the basics of how to prototype, save, and export your plots from R.
In this blog post, I show you how to reshape data (i.e. how to use pivot tables) so that the data are in the correct form for data analysis in R.
In this tutorial, I'm going to explain how to create your own functions and provide a few examples.
In this tutorial I show you everything you need to know about boxplots and how to make them look nice using the built-in functions in R
Here I describe the functions called `grep()`, `grepl()`, and `sub()`, which allow you to find strings in your data that match particular patterns.
Here I show you a useful family of functions that allows you to repetitively perform a specified function (e.g., sum, mean) across a vector, matrix, or data frame.
In this tutorial, I'm going to give an explanation of what pipes are and when they can be used, and then I'm going to demonstrate how useful they can be for writing neat and clear R code.
In this tutorial, I discuss how to use a handy function called group_by() for organizing and preparing your data for analysis and visualization.
In part two of my series on R Markdown, I'll go over how to use R Markdown for learning R by documenting your journey into your own guide to R.