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STATS 330: Lecture 4

STATS 330: Lecture 4. Graphics: Doing it in R. Housekeeping. My contact details…. Plus much else on course web page www.stat.auckland.ac.nz/~lee/330/ Or via Cecil. Today’s lecture: R for graphics. Aim of the lecture:

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STATS 330: Lecture 4

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  1. STATS 330: Lecture 4 Graphics: Doing it in R 330 Lecture 4

  2. Housekeeping My contact details…. Plus much else on course web page www.stat.auckland.ac.nz/~lee/330/ Or via Cecil 330 Lecture 4

  3. 330 Lecture 4

  4. Today’s lecture: R for graphics Aim of the lecture: To show you how to use R to produce the plots shown in the last few lectures 330 Lecture 4

  5. Getting data into R • In 330, as in many cases, data comes in 2 main forms • As a text file • As an Excel spreadsheet • Need to convert from these formats to R • Data in R is organized in data frames • Row by column arrangement of data (as in Excel) • Variables are columns • Rows are cases (individuals) 330 Lecture 4

  6. Text files to R • Suppose we have the data in the form of a text file • Edit the text file (use Notepad or similar) so that • The first row consists of the variable names • Each row of data (i.e. data on a complete case) corresponds to one line of the file • Suppose data fields are separated by spaces and/or tabs • Then, to create a data frame containing the data, we use the R function read.table 330 Lecture 4

  7. Example: the cherry tree data Suppose we have a text file called cherry.txt (probably created using Notepad or maybe Word, but saved as a text file) First line: variable names Data for each tree on a separate line, separated by “white space” (spaces or tabs) 330 Lecture 4

  8. Creating the data frame In R, type cherry.df = read.table(file.choose(), header=TRUE) and press the return key Click here to select file This brings up the dialog to select the file cherry.txt containing the data. Click here to load data 330 Lecture 4

  9. Check all is OK! 330 Lecture 4

  10. Getting data from a spreadsheet (1) Create the spreadsheet in Excel Save it as Comma Delimited Text (CSV) This is a text file with all cells separated by commas File is called cherry.csv 330 Lecture 4

  11. Getting data from a spreadsheet (2) In R, type cherry.df = read.table(file.choose(), header=TRUE, sep=“,”) and proceed as before 330 Lecture 4

  12. Getting data from the R330 package • The package R330 contains several data sets used in the course, including the cherry tree data • To access the data frame: • Install the R330 package (see Appendix A.10 of the coursebook) • In R, type > library(R330) > data(cherry.df) 330 Lecture 4

  13. Data frames and variables • Suppose we have read in data and made a data frame • At this point R doesn’t know about the variables in the data frame, so we can’t use e.g. the variable diameter in R commands • We need to say attach(cherry.df) to make the variables in cherry.df visible to R. • Alternatively, say cherry.df$diameter (better) 330 Lecture 4

  14. Scatterplots In R, there are 2 distinct sets of functions for graphics, one for ordinary graphics, one for trellis. Eg for scatterplots, we use either plot (ordinary R) or xyplot (Trellis) In the next few slides, we discuss plot. 330 Lecture 4

  15. Simple plotting plot(cherry.df$height, cherry.df$volume, xlab=“Height (feet)”, ylab=“Volume (cubic feet)”, main = “Volume versus height for 31 black cherry trees”) i.e. label axes (give units if possible), give a title 330 Lecture 4

  16. 330 Lecture 4

  17. Alternative form of plot plot(volume ~ height, xlab=“Height (feet)”, ylab=“Volume (cubic feet)”, main = “Volume versus height for 31 black cherry trees”, data = cherry.df) Don’t need use the $ notation with this form, note reversal of x,y 330 Lecture 4

  18. Colours, points, etc Type ?par for more info par(bg="darkblue") plot(cherry.df$height, cherry.df$volume, xlab="Height (feet)", ylab="Volume (cubic feet)", main = "Volume versus height for 31 black cherry trees", pch=19,fg="white", col.axis=“lightblue",col.main="white", col.lab=“white",col="white",cex=1.3) 330 Lecture 4

  19. 330 Lecture 4

  20. Lines • Suppose we want to join up the rats on the rats plot. (see data next slide) • We could try plot(rats.df$day, rats.df$growth, type=“l”) but this won’t work • Points are plotted in order they appear in the data frame and each point is joined to the next 330 Lecture 4

  21. Rats: the data • > rats.df • growth group rat change day • 1 240 1 1 1 1 • 2 250 1 1 1 8 • 3 255 1 1 1 15 • 4 260 1 1 1 22 • 5 262 1 1 1 29 • 6 258 1 1 1 36 • 7 266 1 1 2 43 • 8 266 1 1 2 44 • 9 265 1 1 2 50 • 10 272 1 1 2 57 • 11 278 1 1 2 64 • 12 225 1 2 1 1 • 12 230 1 2 1 8 • ... More data 330 Lecture 4

  22. Don’t want this! 330 Lecture 4

  23. Solution Various solutions, but one is to plot each line separately, using subsetting plot(day,growth,type="n") lines (day[rat==1],growth[rat==1]) lines (day[rat==2],growth[rat==2]) and so on …. (boring!), or (better) for(j in 1:16){ lines (day[rat==j],growth[rat==j]) } Draw axes, labels only 330 Lecture 4

  24. Indicating groups Want to plot the litters with different colours, add a legend: Rats 1-8 are litter 1, 9-12 litter 2, 13-16 litter 3 plot(day,growth,type="n") for(j in 1:8)lines(day[rat==j], growth[rat==j],col="white") # litter 1 for(j in 9:12)lines (day[rat==j], growth[rat==j],col="yellow") # litter 2 for(j in 13:16)lines (day[rat==j], growth[rat==j],col="purple") # litter 3 Set colour of line 330 Lecture 4

  25. legend legend(13,380, legend = c(“Litter 1”, “Litter 2”, “Litter 3”), col = c("white","yellow","purple"), lwd = c(2,2,2), horiz = TRUE, cex = 0.7) (Type ?legend for a full explanation of these parameters) 330 Lecture 4

  26. 330 Lecture 4

  27. Points and text x=1:25 y=1:25 plot(x,y, type="n") points(x,y,pch=1:25, col="red", cex=1.2) 330 Lecture 4

  28. 330 Lecture 4

  29. Points and text (3) x=1:26 y=1:26 plot(x,y, type="n") text(x,y, letters, col="blue", cex=1.2) 330 Lecture 4

  30. 330 Lecture 4

  31. Use of pos x = 1:10 y = 1:10 plot(x,y) position = rep(c(2,4), 5) mytext = rep(c(“Left",“Right"), 5) text(x,y,mytext, pos=position) 330 Lecture 4

  32. 330 Lecture 4

  33. Trellis • Must load trellis library first library(lattice) • General form of trellis plots xyplot(y~x|W*Z, data=some.df) • Don’t need to use the $ form, , trellis functions can pick out the variables, given the data frame 330 Lecture 4

  34. Main trellis functions • dotplot for dotplots, use when X is categorical, Y is continuous • bwplot for boxplots, use when X is categorical, Y is continuous • xyplot for scatter plots, use when both x and y are continuous • equal.count use to turn continuous conditioning variable into groups 330 Lecture 4

  35. Changing background colour To change trellis background to white trellis.par.set(background = list(col="white")) To change plotting symbols trellis.par.set(plot.symbol = list(pch=16, col="red", cex=1)) 330 Lecture 4

  36. Equal.count xyplot(volume~height|diameter, data=cherry.df) 330 Lecture 4

  37. Equal.count (2) diam.gp<-equal.count(diameter,number=4,overlap=0) xyplot(volume~height|diam.gp, data=cherry.df) 330 Lecture 4

  38. Changing plotting symbols To change plotting symbols trellis.par.set(plot.symbol = list(pch=16, col="red", cex=1)) 330 Lecture 4

  39. 330 Lecture 4

  40. Non-trellis version coplot(volume~height|diameter, data=cherry.df) 330 Lecture 4

  41. Non-trellis version (2) coplot(volume~height|diameter, data=cherry.df,number=4,overlap=0) 330 Lecture 4

  42. Other useful functions • Regular R • scatterplot3d (3d scatter plot, load library scatterplot3d) • contour, persp (draws contour plots, surfaces) • pairs • Trellis • cloud (3d scatter plot) 330 Lecture 4

  43. Rotating plots • You need to install the R330 package Create a data frame e.g. called data.df with the response in the first column • Then, type reg3d(data.df) 330 Lecture 4

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