R, ggplot, and Simple Linear Regression
- You will need to install both R and RStudio on your computer. We will, however, cover this in the first lecture.
Data science skills are in much demand today, but it is not just the mathematicians, statisticians, and the computer scientists who can benefit from acquiring them. Data science skills are for everyone!
In this course, I help you to begin using R, one of the most important tools in data science, and the excellent graphics package for R, ggplot2. Along the way, I also show you the basics of simple linear regression.
There are no prerequisites. We begin with installation of R and RStudio, and I introduce R and ggplot skills as they are needed as we progress toward an understanding of linear regression.
Students should be able to complete the course within two weeks, working at an easy pace.
Linear regression is a machine learning technique. I hope to create more courses like this one in the future, teaching machine learning, R, ggplot, dplyr, and programming, all at the same time.
- This course is for beginners interested in using R.
- This course is for beginners interested in learning about the graphics package ggplot2.
- This course is for beginners interested in learning some basics of linear regression.
- This course is NOT for those with a background in statistics who use R and are familiar with ggplot2.
- Installing R and RStudio02:57
- A Tour of RStudio02:06
- Vectors in R06:32
- Data Frames03:35
- Installing ggplot202:52
- Plotting a point with ggplot08:31
- Controlling axis properties09:00
- More with color and shape04:22
- Graphing lines with ggplot05:11
- More with lines06:10
- Normal populations05:20
- Plotting a vertical sample06:11
- Plotting several vertical samples08:47
- Samples along a line08:59
- Cloud of points10:04
- Father and son heights05:42
- Equation of a line02:19
- Residual visualization12:09
- Sum of squared residuals04:57
- The least squares line05:07
- Reading in Excel files02:35
- Course wrap-up01:08
Dr. Charles Redmond is a professor in the Tom Ridge School of Intelligence Studies and Information Science at Mercyhurst University. He has been a member of the Department of Mathematics and Computer Systems at Mercyhurst for 21 years and has recently completed a term as chair of the department. Dr. Redmond received his PhD in mathematics from Lehigh University in 1993 and has published in the Annals of Applied Probability, the Journal of Stochastic Processes and Their Applications, Mathematics Magazine, the College Mathematics Journal, and Mathematics Teacher. In his spare time he enjoys making music and computer generated art, reading, and owning a Clumber Spaniel.