We publish R tutorials from scientists at academic and scientific institutions with a goal to give everyone in the world access to a free knowledge. Our tutorials cover different topics including statistics, data manipulation and visualization!

Basic Statistics

The goal of this course is to give you the skills to do the statistics that are in current published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more on making the connection between the mathematical equation and the R code, and what types of variables fit into each slot of the equation.

Much of the R code will come from the Hadleyverse, including the well-known ggplot2, the less-well known dplyr, and the even less-well known (but still very useful!) purrr. If you already have experience with R, but are less familiar with these packages, this course will help you improve your R pipeline to be more readable and efficient. Moreover, you can read dplyr tutorial and ggplot2 tutorial published here at DataScience+

In addition to statistics and figure making, this course will get you acquainted with other aspects of R and RStudio to allow for more productive data analysis and management, including R Projects, Git, and Bitbucket.

To begin you will need to have a few things pre-installed or set up:

After that you’re ready to go!

The course is set up to follow a certain order with each lesson building on the previous one. However, you can also use the links below to jump to a specific topic. All videos for the lessons provided below. Links to the lab and PDFs of the slides can be found on the primary course website. All of the data sets and code can also be found on my GitHub account. Note, there are separate repositories for each lesson, for example this the Lesson 1 repository.

Find below the video tutorial(s) for this post.

- Lesson 0: Introduction and Set-up 17:23
- Lesson 1: R Basics 08:09
- Lesson 2: Linear Regression 10:22
- Lesson 3: Logistic Regression 17:03
- Lesson 4: Multiple Regression 27:07
- Lesson 5: Analysis of Variance 23:12
- Lesson 6, Part 1: Linear Mixed Effects Models 21:18
- Lesson 6, Part 2: Linear Mixed Effects Models 13:57