# Graphics problems #------------------ # Load the classroom data you saved in the Reading data exercises, # or just load() it from our website. # Plot 1 #------- # Create a histogram of the mathkind (mathematics score in # kindergarten) variable. Add an xlab= to better explain # what the graph is about. # Plot 2 #------- # Create an empirical density plot of mathkind. # Plot 3 #------- # Create an empirical density plot of mathkind by sex using # the optional argument groups = sex. It is probably a good # idea to suppress the points in the "rug", with the plot.points=FALSE # option. Remember to use auto.key so you can tell which curve is which. # Plot(s) 4 #------- # In xyplot(), the default it to plot points. You can plot an # empirical "smoother" instead, with option type="smooth". You # can overlay to "types" that use the same data with # type=c("p", "smooth"). # Create a scatter-plot of the mathgain versus the kindergarten score. # Add a smoother curve and a ylab (y axis label). # Repeat this plot using type "r" # instead of "smooth" to add a reference (or "regression") # line. # Plot 5 #------- # Find the correlation between mathkind and mathgain. The negative # result is not surprising because mathgain is the grade 1 score minus # the kindergarten score. Create a new variable math1 which is # the sum of mathkind and mathgain. # Plot math1 versus mathkind. Try using the # optional argument aspect="iso" to ensure that a # unit change on the x axis corresponds to a unit change on the y axis. # Plot 6 #------- # Create a multi-panel scatterplot of math1 versus # mathkind with separate panels for males and females. # Plot 7 #------- # Create a multi-panel scatterplot of math1 versus # mathkind classified according to sex and minority. # Plot 8 #------- # Consider only the students in school 11. xyplot has a subset option # that allows you to specify, for instance, subset=(schoolid==11). # Show that the study includes students from nine different classrooms # in that school. # Create a dot-plot of the mathgain by classroom for students # in school 11 only. Label the x and y axis. # Plot 9 #------- # Repeat the plot, joining the classroom averages by overlaying # type "a". # Plot 10 #-------- # Finally, you can reorder the classrooms with the # reorder() function (which relevels a factor). # reorder(classid, mathgain) will # do the trick (the default function applied to mathgain is # the mean() ). # Redo the plot reordering the classrooms according # to increasing mean gain and joining the classroom averages. # Bonus plots #------------ # You can reorder() the levels of a factor by other functions. How # would the previous example change if we reordered by max(mathgain)? # by mean(ses)?