Here are some ggplot basics that I’ll start with.

I’ll use examples from our AP stats book and the IB book. We are using The Practice of Statistics 4th edition  by Starnes, Yates and Moore (TPS4e) for AP Statistics class. I want to recreate some of the plots in the textbook so I can teach my students how they can create these same plots. We can probably improve in some way on these plots and at the same time, teach them the basics of regression and R programming.

Here is my general plan:

• Enter the data into the TI nspire cx.
• Generate a scatter plot on the TI.
• Use the Smartboard to show the code in R using RStudio.
• On the first day use an R Script for the R code.
• All following days, use R Markdown to create and annotate the scatter plots.
• Publish to our Moodle page or maybe saturnscience website.

Making a scatter plot

Now let’s make a scatter plot with the example in the TPS4e book Chapter 3, page 145.

The general form of a command will look like this:

myGraph <- ggplot(myData, aes(variable for x axis, variable for y axis)) + geom()

Here is the data from page 145 in the TPS 4e textbook and how we enter it in. We use the “c” command to combine or concatenate into a vector. We then turn these two vectors into a data frame.

body.wt=c(120,187,109,103,131,165,158,116) 
backpack.wt=c(26,30,26,24,29,35,31,28)
TPS145= data.frame(body.wt,backpack.wt)
TPS145

##   body.wt backpack.wt
## 1     120          26
## 2     187          30
## 3     109          26
## 4     103          24
## 5     131          29
## 6     165          35
## 7     158          31
## 8     116          28

Now we put this data frame into the ggplot object and name it scatter145 and call the ggplot2 package.

library(ggplot2)
scatter145=ggplot(data=TPS145, aes(body.wt,backpack.wt)) +   
  geom_point()

Here is the scatterplot below produced from the above code:
This is a starting point and we can add to this plot to really spruce it up.

I added some blue color to the plot based on the body weight

scatter145=ggplot(data=TPS145, aes(body.wt,backpack.wt,colour=body.wt)) +   
  geom_point() 
scatter145

Adding Labels And Adjusting The Size Of The Data

To add the x, y and main labels, I add on to my plot with the xlab, ylab, and main arguments inside ggplot’s scatter plot. I also increased the size of the plotted data to make it easier to see.

scatter145 = scatter145+ geom_point(size=2) +  
  xlab("Body Weight (lb)") +  
  ylab("Pack weight (lb)") +   
  ggtitle("Backpack Weight")

scatter145

How To Add The Regression Line.

I will keep adding to the plot by plotting the regression line. The function for adding a liner model is “lm”. The gray shaded area is the confidence interval.

Here is the final code for creating the scatter plot with the regression line.

scatter145=scatter145+ geom_point(size=3) + 
  xlab("Body Weight (lb)") +  
  ylab("Pack weight (lb)")+ 
  ggtitle("Backpack Weight")+ 
  geom_smooth(method = "lm")

Here is the scatter plot with the regression line.

scatter145 + geom_smooth(method = "lm")

This is how you make a scatter plot in ggplot2. It is a little involved but I think it is much better than the base graphics.This will teach the basics of working with R and RStudio, ggplot2, and R Markdown files. This entire article was written in R markdown in RStudio and knitted to an HTML file. It would be just as easy to knit to a Word file.

My motivation for working in R Markdown is that I want to teach my students that R Markdown is an excellent way to intergrate their R code, writing, plots and output. This is the way of the near future in Introductory Statistics. Two research papers I read recently support this view.

Some Recent Research On Reproducible Research And Intro Statistics

The authors Deborah Nolan and Jamis Perrett in their paper Teaching and Learning Data Visualization: Ideas and Assignments paper here argue that statistical graphics should have a more prominent role in an introductory statistics course.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into `pictures’, and develop interactive visualizations with, e.g., the virtual earth as a plotting canvas.

Another paper R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics by Ben Baumer, Mine Cetinkaya-Rundel, Andrew Bray,Linda Loi and Nicholas J. Horton argue that teaching students R Markdown helps them to grasp the concept of reproducible research.

R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. We present evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation.

I’ll have more to say in a future post about the latest research in this area.

saturnscience.com