Basic Plotting: Answers

Warning

Make sure that you try the exercises yourself first before looking at the answers

Use R to do the following exercises on the BOD data

Display the built-in dataset called BOD by running BOD.

BOD
  Time demand
1    1    8.3
2    2   10.3
3    3   19.0
4    4   16.0
5    5   15.6
6    7   19.8

What is the data structure of BOD? What are the dimensions?

str(BOD)
'data.frame':   6 obs. of  2 variables:
 $ Time  : num  1 2 3 4 5 7
 $ demand: num  8.3 10.3 19 16 15.6 19.8
 - attr(*, "reference")= chr "A1.4, p. 270"
dim(BOD)
[1] 6 2

What are the names of BOD? Use a function other than str.

names(BOD)
[1] "Time"   "demand"

Make a line graph of demand versus time, where the line is a deep pink dot-dashed line [Hint: run ?par and look for the parameter lty to see the line types]. Add a blue dashed line of 1.1 times the demand and give it a thickness of 2 using the line width parameter lwd. Make sure both lines are entirely visible by adjusting the range of y using the parameter ylim in the original plot.

plot(BOD$Time, BOD$demand, type = "l", lty = 4,
  col = "pink", ylim = c(0, 25))
lines(BOD$Time, 1.1 * BOD$demand, lwd = 2, col = "blue")

Use R to do the following exercises on the chickwts data.

Display the built-in chickwts data.

chickwts
   weight      feed
1     179 horsebean
2     160 horsebean
3     136 horsebean
4     227 horsebean
5     217 horsebean
6     168 horsebean
7     108 horsebean
8     124 horsebean
9     143 horsebean
10    140 horsebean
11    309   linseed
12    229   linseed
13    181   linseed
14    141   linseed
15    260   linseed
16    203   linseed
17    148   linseed
18    169   linseed
19    213   linseed
20    257   linseed
21    244   linseed
22    271   linseed
23    243   soybean
24    230   soybean
25    248   soybean
26    327   soybean
27    329   soybean
28    250   soybean
29    193   soybean
30    271   soybean
31    316   soybean
32    267   soybean
33    199   soybean
34    171   soybean
35    158   soybean
36    248   soybean
37    423 sunflower
38    340 sunflower
39    392 sunflower
40    339 sunflower
41    341 sunflower
42    226 sunflower
43    320 sunflower
44    295 sunflower
45    334 sunflower
46    322 sunflower
47    297 sunflower
48    318 sunflower
49    325  meatmeal
50    257  meatmeal
51    303  meatmeal
52    315  meatmeal
53    380  meatmeal
54    153  meatmeal
55    263  meatmeal
56    242  meatmeal
57    206  meatmeal
58    344  meatmeal
59    258  meatmeal
60    368    casein
61    390    casein
62    379    casein
63    260    casein
64    404    casein
65    318    casein
66    352    casein
67    359    casein
68    216    casein
69    222    casein
70    283    casein
71    332    casein

What are the names of chickwts? Use a function other than str

names(chickwts)
[1] "weight" "feed"

What are the levels of feed?

levels(chickwts$feed)
[1] "casein"    "horsebean" "linseed"   "meatmeal"  "soybean"   "sunflower"

Make the following plots in one 2 x 2 image: - A bar chart of the feed types, each bar a different color. - A bar chart of the proportions of feed types, each bar a different color. - A boxplot of the weights by feed type, each box a different color. - A horizontal boxplot of the weights by feed type, each box a different color.

par(mfrow = c(2, 2))

barplot(table(chickwts$feed),
  col = c("red", "orange", "yellow",
  "green", "blue", "purple"))

barplot(table(chickwts$feed)/length(chickwts$feed),
  col = c("red", "orange", "yellow",
  "green", "blue", "purple"))

boxplot(chickwts$weight~chickwts$feed,
  col = c("red", "orange", "yellow",
  "green", "blue", "purple"))

boxplot(chickwts$weight~chickwts$feed,
  col = c("red", "orange", "yellow",
  "green", "blue", "purple"),
  horizontal = TRUE)

Use R to do the following exercises on the Puromycin data.

Display the built-in Puromycin data.

Puromycin
   conc rate     state
1  0.02   76   treated
2  0.02   47   treated
3  0.06   97   treated
4  0.06  107   treated
5  0.11  123   treated
6  0.11  139   treated
7  0.22  159   treated
8  0.22  152   treated
9  0.56  191   treated
10 0.56  201   treated
11 1.10  207   treated
12 1.10  200   treated
13 0.02   67 untreated
14 0.02   51 untreated
15 0.06   84 untreated
16 0.06   86 untreated
17 0.11   98 untreated
18 0.11  115 untreated
19 0.22  131 untreated
20 0.22  124 untreated
21 0.56  144 untreated
22 0.56  158 untreated
23 1.10  160 untreated

Make a scatterplot of the rate versus the concentration. Describe the relationship.

plot(Puromycin$conc, Puromycin$rate)

The rate increases faster at lower concentrations than at higher concentrations.

Make a scatterplot of the rate versus the log of the concentration. Describe the relationship.

plot(log(Puromycin$conc), Puromycin$rate)

The two variables have a linear relationship

Make a scatterplot of the rate versus the log of the concentration and color the points by treatment group (state). Describe what you see.

plot(log(Puromycin$conc), Puromycin$rate, col = Puromycin$state)

It appears that the the treated group has higher rates than the untreated group, on average. (Note that default colors are black for the first level and red for the second level).

Make a scatterplot of the rate versus the log of the concentration, color the points by treatment group (state), label the x-axis “Concentration” and the y-axis “Rate”, and label the plot “Puromycin”.

plot(log(Puromycin$conc), Puromycin$rate, col = Puromycin$state,
  xlab = "Concentration", ylab = "Rate", main = "Puromycin")

Add a legend to the above plot indicating what the points represent.

plot(log(Puromycin$conc), Puromycin$rate, col = Puromycin$state,
  xlab = "Concentration", ylab = "Rate", main = "Puromycin")

legend("topleft",c("Treated", "Untreated"), col = 1:2, pch = 1)

Make a boxplot of the treated versus untreated rates. Using the function pdf, save the image to a file with a width and height of 7 inches.

pdf("puromycin.pdf",width = 7, height = 7)
boxplot(Puromycin$rate~Puromycin$state)
dev.off()

Make a histogram of the frequency of concentrations. What is the width of the bins?

hist(Puromycin$conc)

The bin width is 0.20.

Make a histogram of the frequency of concentrations with a bin width of 0.10. How is this different from the histogram above?

hist(Puromycin$conc, breaks = seq(0, 1.2, .10))

The bins are narrower, so we see in finer detail the distribution of the concentrations.

Plot the histograms side by side in the same graphic window and make sure they have the same range on the y-axis. Does this make it easier to answer the question of how the two histograms differ?

par(mfrow = c(1, 2))
hist(Puromycin$conc, ylim = c(0, 12))
hist(Puromycin$conc, breaks = seq(0, 1.2, .10), ylim = c(0, 12))

In some situations it may be of use to view plots simultaneously. In this case, on the right we see clearly that more values are between 0 and 0.10 than 0.10 and 0.20 whereas the plot on the left does not display this information. In the histogram on the right we see that no concentrations fall between 0.30 and 0.50, whereas this is not apparent in the histogram on the left.