5/2/ Lecture 41 STATS 330: Lecture 4

5/2/ Lecture 42 Housekeeping My contact details…. Plus much else on course web page Or via Cecil

5/2/ Lecture 43

5/2/ Lecture 44 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

5/2/ Lecture 45 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)

5/2/ Lecture 46 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

5/2/ Lecture 47 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)

5/2/ Lecture 48 Creating the data frame In R, type cherry.df = read.table(file.choose(), header=TRUE) and press the return key This brings up the dialog to select the file cherry.txt containing the data. Click here to select file Click here to load data

5/2/ Lecture 49 Check all is OK!

5/2/ Lecture 410 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

5/2/ Lecture 411 Getting data from a spreadsheet (2) In R, type cherry.df = read.table(file.choose(), header=TRUE, sep=“,”) and proceed as before

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) 5/2/ Lecture 412

5/2/ Lecture 413 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)

5/2/ Lecture 414 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.

5/2/ Lecture 415 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

5/2/ Lecture 416

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 5/2/ Lecture 417

5/2/ Lecture 418 Colours, points, etc 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) Type ?par for more info

5/2/ Lecture 419

5/2/ Lecture 420 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

5/2/ Lecture 421 Rats: the data > rats.df growth group rat change day More data

5/2/ Lecture 422 Don’t want this!

5/2/ Lecture 423 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

5/2/ Lecture 424 Indicating groups Want to plot the litters with different colours, add a legend: Rats 1-8 are litter 1, 9-12 litter 2, 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

5/2/ Lecture 425 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)

5/2/ Lecture 426

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) 5/2/ Lecture 4

5/2/ Lecture 4

Points and text (3) x=1:26 y=1:26 plot(x,y, type="n") text(x,y, letters, col="blue", cex=1.2) 5/2/ Lecture 4

5/2/ Lecture 4

Use of pos 5/2/ Lecture 431 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)

5/2/ Lecture 432

5/2/ Lecture 433 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

5/2/ Lecture 434 Main trellis functions  dotplotfor dotplots, use when X is categorical, Y is continuous  bwplotfor boxplots, use when X is categorical, Y is continuous  xyplotfor scatter plots, use when both x and y are continuous  equal.countuse to turn continuous conditioning variable into groups

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)) 5/2/ Lecture 435

5/2/ Lecture 436 Equal.count xyplot(volume~height|diameter, data=cherry.df)

5/2/ Lecture 437 Equal.count (2) diam.gp<-equal.count(diameter,number=4,overlap=0) xyplot(volume~height|diam.gp, data=cherry.df)

Changing plotting symbols To change plotting symbols trellis.par.set(plot.symbol = list(pch=16, col="red", cex=1)) 5/2/ Lecture 438

5/2/ Lecture 439

5/2/ Lecture 440 Non-trellis version coplot(volume~height|diameter, data=cherry.df)

5/2/ Lecture 441 Non-trellis version (2) coplot(volume~height|diameter, data=cherry.df,number=4,overlap=0)

5/2/ Lecture 442 Other useful functions  Regular R scatterplot3d (3d scatter plot, load library scatterplot3d) contour, persp (draws contour plots, surfaces) pairs  Trellis cloud (3d scatter plot)

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) 5/2/ Lecture 443