Introduction to R for Environmental Data Analysis

Interested in climate change, programming, or data visualization? Then, this course is for you!

In this course, you will learn how to graph climate data using the R programming language in Google Colab! Specifically, we'll be looking at how the average annual air temperature changes as the years go by (the x-axis will be the year, and the y-axis will be the average annual temperature). We'll use San Diego climate data from the National Centers for the Environmental Information (NCEI) Global Summary of the Year weather database, but you're welcome to use data from any city. To approximate missing values in the dataset, we'll use linear interpolation and install the necessary packages such as tidyverse, ggplot2, readr, and imputeTS. We'll make basic graphs with ggplot2, including features such as the axes, data points, and lines. Then, we'll make more aesthetic and visual graphs by adding layers, or geoms, with different features such as a title, axes labels, gradient color scale, locally estimated scatterplot smoother, and more! Next, we'll make the graphs with the Fahrenheit system instead of Celsius using a math equation to convert the temperature values. Finally, you'll be provided with some additional resources regarding climate change. No programming experience is needed.