In this post, I show you how to navigate some common ecological data portals and discuss when you might want to use one or another for accessing data that you can use for practice or your next research project.

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'm going to explain how to create your own functions and provide a few examples.

In this tutorial, we're introduce the structures that R provides to help you organize your data.

In this tutorial, I introduce you to several different types of data, explain how to use and manipulate each of them, and show you how to check what type of data you have.

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 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.

R Markdown is a powerful method for creating reports that combine formatted text with R code. This can help you with accountability, data analysis reproducibility, for making tutorials (like this one), and also for learning R! In part one of this two-part series, you'll learn how to create basic R Markdown documents with all the essential content.

In this tutorial, I'm going to explain what exactly an NA value is, how you can find NAs in your data, and how to remove them.

In this tutorial, I show you how to combine tables (data frames) together using both base R and the `dplyr` package.