Today we’ll learn how to take an old statistics test (logistic regression) but expand it to when you have two variables (multiple regression). The package purrr is introduced and, as always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 4.
Here you can revisit Lesson 0, Lesson 1, Lesson 2, and Lesson 3.
Guest post by Page Piccinini
With some basics under our belt from Lesson 1, in this lesson we’ll continue working with dplyr and ggplot2, while also learning about the math behind linear regression and how to implement it in R. Plus you get to finish with a report about how the popularity of your name changes over time.
For full materials, see the course website for Lesson 2.
Guest post by Page Piccinini
In this lesson you will be introduced to the basics of R, including how to read in and manipulate data with dplyr, and how to make figures with ggplot2. You’ll also get experience writing up your results in an R Markdown document. Finally, we’ll put all of that initial set-up to good use by saving everything to Git and pushing it up to Bitbucket for better version control management.
Guest post by Page Piccinini
After telling you about my R imposter syndrome two weeks ago, I will start sharing my R course material with you here, beginning with the first lesson (well, Lesson 0) today.
This lesson is meant to set up your R working environment and get you familiar with RStudio, Git, and Bitbucket. This lesson does not include any substantial coding in R, but will situate you for productive R coding, including creating separate working environments for different projects and version control for any scripts. With these practices in place you will be a more productive R programmer in the long run, whether you are working alone or with other researchers.
CogTales 