data analysis

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.

In this tutorial, I go over the basics of how to prototype, save, and export your plots from R.

In this tutorial, I'm going to explain how to create your own functions and provide a few examples.

Here I describe the functions called `grep()`, `grepl()`, and `sub()`, which allow you to find strings in your data that match particular patterns.

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.

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 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.