01 Intro
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The document provides an introduction to the Stat405 statistical computing and graphics course taught by Hadley Wickham. It includes the syllabus, an introduction to using Linux and R, and examples of basic graphics in R like scatterplots and faceting. Students are encouraged to experiment with different aesthetics and geoms in their plots and to keep code organized in a text editor.
01 Intro
- 1. Stat405 Statistical computing & graphics Hadley Wickham
- 2. 1. Introductions 2. Syllabus 3. Introduction to linux 4. Introduction to R 5. Basic graphics
- 3. HE LL O my name is Hadley
- 4. had.co.nz/stat405 (if you can’t remember just google stat405) hadley@rice.edu
- 5. About me From New Zealand Divisional advisor for McMurtry Major advisor for statistics
- 6. Syllabus
- 8. Essential tools The terminal to run R. gedit to edit your R code. To load the terminal, right-click on the desktop. To load R, type R in the terminal. To load gedit, type gedit & in the terminal (the & tells it to run separately). To open a file in gedit, type gedit filename &
- 9. Setup Work through the instructions at http:// had.co.nz/stat405/linux.html. I’ll circulate and make sure everyone gets set up right.
- 10. Terminal essentials Mouse select = Copy Middle button = Paste Ctrl + A = home Ctrl + D = end Alt + tab = change applications Press tab to complete file names
- 12. Learning a new language is hard!
- 13. Scatterplot basics install.packages("ggplot2") library(ggplot2) ?mpg head(mpg) str(mpg) summary(mpg) qplot(displ, hwy, data = mpg)
- 14. Scatterplot basics install.packages("ggplot2") library(ggplot2) ?mpg head(mpg) str(mpg) Always explicitly summary(mpg) specify the data qplot(displ, hwy, data = mpg)
- 15. ● ● 40 ● ● 35 ● ● ● ● ● ● ● ●● 30 ● ● ● ● ● ● ● ●● ● hwy ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● 25 ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● 15 ● ●● ●●● ● ● ● ● ● 2 3 4 5 6 7 qplot(displ, hwy, data = mpg) displ
- 16. Additional variables Can display additional variables with aesthetics (like shape, colour, size) or facetting (small multiples displaying different subsets)
- 17. ● ● 40 ● ● 35 ● ● ● ● class ● ● ● ●● ● 2seater 30 ● ● ● ● compact ● ● ● ● ●● ● ● midsize hwy ● ● ● ● ● ● ● ●● ● ● ● ● ● minivan ● ● ● ●● ● ●● ●● ● ● ● ● 25 ● ● ● ●● ● ● ● ● ● pickup ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● subcompact ● ● ● ● ● ● suv ● ● 20 ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● 15 ● ●● ●●● ● ● ● ● ● 2 3 4 5 6 7 qplot(displ, hwy, colour = class, data = mpg) displ
- 18. ● ● 40 ● ● 35 ● ● ● ● class ● ● ● ●● ● 2seater 30 ● ● ● ● compact ● ● ● ● ●● ● ● midsize hwy ● ● ● ● ● ● ● ●● ● ● ● ● ● minivan ● ● ● ●● ● ●● ●● ● ● ● ● 25 ● ● ● ●● ● ● ● ● ● pickup ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● subcompact ● ● ● ● ● ● suv ● ● 20 ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● Legend chosen and displayed automatically. ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● 15 ● ●● ●●● ● ● ● ● ● 2 3 4 5 6 7 qplot(displ, hwy, colour = class, data = mpg) displ
- 19. Your turn Experiment with colour, size, and shape aesthetics. What’s the difference between discrete or continuous variables? What happens when you combine multiple aesthetics?
- 20. Discrete Continuous Rainbow of Gradient from Colour colours red to blue Linear mapping Discrete size Size between radius steps and value Different shape Shape Doesn’t work for each
- 21. Faceting Small multiples displaying different subsets of the data. Useful for exploring conditional relationships. Useful for large data.
- 22. Your turn qplot(displ, hwy, data = mpg) + facet_grid(. ~ cyl) qplot(displ, hwy, data = mpg) + facet_grid(drv ~ .) qplot(displ, hwy, data = mpg) + facet_grid(drv ~ cyl) qplot(displ, hwy, data = mpg) + facet_wrap(~ class)
- 23. Summary facet_grid(): 2d grid, rows ~ cols, . for no split facet_wrap(): 1d ribbon wrapped into 2d
- 24. Aside: workflow Keep a copy of the slides open so that you can copy and paste the code. For complicated commands, write them in gedit and then copy and paste.
- 25. What’s the ● ● 40 problem with ● this plot? ● ● ● 35 ● ● ● ● ● ● ● ● ● ● ● 30 ● ● ● ● ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● 10 15 20 25 30 35 qplot(cty, hwy, data = mpg) cty
- 26. ● ● ● 40 ● ● ● ●● 35 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 30 ● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ●● ●● ● ● ●● ● hwy ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● 25 ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● 20 ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●●●●●● ● ● ●● ● ● ● ● ●●● ● ● ● ●●● ● ● 15 ●● ●● ●● ● ● ● ● ● ●● ●● 10 15 20 25 30 35 qplot(cty, hwy, data = mpg, geom = "jitter") cty
- 27. ● ● ● 40 ● ● ● ●● 35 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 30 ● ● ● ● ●● ●● ● ● ●● ● ●● ● ● ●● ●● ● ● ●● ● hwy ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● 25 ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● 20 ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●●●●●● ● ● ●● ● ● ● ● ●●● ● ● ● ●●● ● ● 15 ●● ●● ●● ● ● ● ● ● ●● geom controls ●● “type” of plot 10 15 20 25 30 35 qplot(cty, hwy, data = mpg, geom = "jitter") cty
- 28. ● ● ● 40 ● ● 35 ● ● ● ● ● ● ● ● 30 ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● ● ● 2seater compact midsize minivan pickup subcompact suv qplot(class, hwy, data = mpg) class
- 29. How could ● ● we improve ● 40 this plot? ● ● 35 ● ● Brainstorm ● ● ● ● ● ● 30 for 1 minute. ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● ● ● 2seater compact midsize minivan pickup subcompact suv qplot(class, hwy, data = mpg) class
- 30. ● ● ● 40 ● ● 35 ● ● ● ● ● ● ● ● 30 ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● ● ● pickup suv minivan 2seater midsize subcompact compact reorder(class, hwy)
- 31. ● ● ● 40 ● ● 35 ● ● ● ● ● ● ● ● 30 ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● Incredibly useful ● ● technique! ● pickup suv minivan 2seater midsize subcompact compact qplot(reorder(class, hwy), hwy, data = mpg) reorder(class, hwy)
- 32. ● ● ● 40 ● ● ● ● ● 35 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 30 ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● hwy ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ● ● 25 ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ●● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● 15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● pickup suv minivan 2seater midsize subcompact compact qplot(reorder(class, hwy),reorder(class, hwy) = mpg, geom = "jitter") hwy, data
- 33. ● ● ● 40 ● 35 ● 30 hwy ● ● 25 ● ● ● ● 20 ● 15 ● ● pickup suv minivan 2seater midsize subcompact compact qplot(reorder(class, hwy), hwy, data hwy)mpg, geom = "boxplot") reorder(class, =
- 34. ● ● ● ● ● ● 40 ● ● ● ● 35 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 30 ● ● ● ● ●● ●● ● ● ● ● ●● ● ●●● ●● ●● ● ●● ● ● ● ● ● hwy ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● 25 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● qplot(reorder(class,minivan pickup suv hwy), 2seater data = subcompact hwy, midsize mpg, compact geom = c("jitter", "boxplot")) reorder(class, hwy)
- 35. Your turn Read the help for reorder. Redraw the previously plots with class ordered by median hwy. How would you put the jittered points on top of the boxplots?
- 36. Aside: coding strategy At the end of each interactive session, you want a summary of everything you did. Two options: 1. Save everything you did with savehistory() then remove the unimportant bits. 2. Build up the important bits as you go. (this is how I work)