Read and inspect data

snakeData <- read.csv(url("https://whitlockschluter3e.zoology.ubc.ca/Data/chapter03/chap03e1GlidingSnakes.csv"), stringsAsFactors = FALSE)

head(snakeData)

##   snake undulationRateHz
## 1     1              0.9
## 2     2              1.2
## 3     3              1.2
## 4     4              1.3
## 5     5              1.4
## 6     6              1.4

Histogram

This is the histogram of Figure 3.1-1.

ggplot(data = snakeData, aes(x = undulationRateHz)) + 
    geom_histogram(fill = "firebrick", col = "black", 
          breaks = seq(0.8, 2.2, by = 0.2), boundary = 0, closed = "left") + 
    labs(x = "Undulation rate (Hz)", y = "Frequency") + 
    theme_classic()

Mean, standard deviation, variance

Calculate the mean, standard deviation and variance of the undulation rates.

mean(snakeData$undulationRate)

## [1] 1.375

sd(snakeData$undulationRate)

## [1] 0.324037

var(snakeData$undulationRate)

## [1] 0.105

Coefficient of variation.

100 * sd(snakeData$undulationRate)/mean(snakeData$undulationRate)

## [1] 23.56633

Read and inspect the data.

The data are stored as a frequency table of the number of convictions.

convictionsFreq <- read.csv(url("https://whitlockschluter3e.zoology.ubc.ca/Data/chapter03/chap03t1_2ConvictionsFreq.csv"), stringsAsFactors = FALSE)
head(convictionsFreq)

##   convictions frequency
## 1           0       265
## 2           1        49
## 3           2        21
## 4           3        19
## 5           4        10
## 6           5        10

rep() command

First, use the rep() command to repeat each value in the table, the number of times according to frequency. Here, we store the result in convictions.

convictions <- rep(convictionsFreq$convictions, convictionsFreq$frequency)

Mean and standard deviation

mean(convictions)

## [1] 1.126582

sd(convictions)

## [1] 2.456562

These operations won’t work if there are missing values in the data set. If missing values are present it is necessary to add an argument to the command as follows.

mean(convictions, na.rm = TRUE)

## [1] 1.126582

sd(convictions, na.rm = TRUE)

## [1] 2.456562

Read and inspect data

The data are in “long” format. One variable indicates running speed, and a second variable gives treatment (before vs after amputation). Therefore, every individual spider is on two rows, once for its before-amputation measurement and one for its after-amputation measurement.

spiderData <- read.csv(url("https://whitlockschluter3e.zoology.ubc.ca/Data/chapter03/chap03e2SpiderAmputation.csv"), stringsAsFactors = FALSE)
head(spiderData)

##   spider speed treatment
## 1      1  1.25    before
## 2      2  2.94    before
## 3      3  2.38    before
## 4      4  3.09    before
## 5      5  3.41    before
## 6      6  3.00    before

Box plot

Begin by ordering the treatment levels so that the “before” amputation measurements come before the “after” measurements in the plot.

spiderData$treatment <- factor(spiderData$treatment, levels = c("before", "after"))

ggplot(spiderData, aes(x = treatment, y = speed)) + 
    geom_boxplot(fill = "goldenrod1") +
    theme_classic()

Sample median

First, extract the before-amputation data using subset(). Note the double equal sign “==” needed in the logical statement, which indicates “is equal to” in R. Save the result in a new data frame named speedBefore.

speedBefore <- subset(spiderData, treatment == "before") 
speedBefore

##    spider speed treatment
## 1       1  1.25    before
## 2       2  2.94    before
## 3       3  2.38    before
## 4       4  3.09    before
## 5       5  3.41    before
## 6       6  3.00    before
## 7       7  2.31    before
## 8       8  2.93    before
## 9       9  2.98    before
## 10     10  3.55    before
## 11     11  2.84    before
## 12     12  1.64    before
## 13     13  3.22    before
## 14     14  2.87    before
## 15     15  2.37    before
## 16     16  1.91    before

Sample median

median(speedBefore$speed)

## [1] 2.9

First and third quartiles

Calculate the first and third quartiles of before-amputation running speed (0.25 and 0.75 quantiles). Type = 5 reproduces the method we use in the book to calculate quartiles.

quantile(speedBefore$speed, probs = c(0.25, 0.75), type = 5)

##   25%   75% 
## 2.340 3.045

Interquartile range

Calculate the interquartile range of before-amputation running speed. The argument type = 5 reproduces the method we use in the book to calculate quartiles.

IQR(speedBefore$speed, type = 5)

## [1] 0.705

Download and inspect the data.

sticklebackData <- read.csv(url("https://whitlockschluter3e.zoology.ubc.ca/Data/chapter03/chap03e3SticklebackPlates.csv"), stringsAsFactors = FALSE)
head(sticklebackData)

##     id plates genotype
## 1  4-1     11       mm
## 2  4-2     63       Mm
## 3  4-4     22       Mm
## 4  4-5     10       Mm
## 5 4-10     14       mm
## 6 4-12     11       mm

Order genotypes

Begin by setting the preferred order of the three genotypes in the figure.

sticklebackData$genotype <- factor(sticklebackData$genotype, 
      levels = c("MM","Mm","mm"))

Multiple histograms

One histogram per genotype, stacked vertically, all using the same x-axis scale.

ggplot(sticklebackData, aes(x = plates)) + 
    geom_histogram(fill = "firebrick", col = "black", 
        boundary = 0, closed = "left", binwidth = 2) +
    labs(x = "Number of lateral plates", y = "Frequency") + 
    facet_wrap( ~ genotype, ncol = 1, scales = "free_y") +
    theme_classic()

Statistics by group

We’ll use the summarize() command from the dplyr package. The commands below assume that the variables contain no missing (NA) elements. The n() function calculates the number of cases. The function round(x, digits = 1) rounds the contents of x to the desired number of decimal places (here, 1).

sticklebackTable <- summarize(group_by(sticklebackData, genotype), 
    n = n(),
    Mean = round( mean(plates), digits = 1 ),
    Median = median(plates),
    Std.dev = round( sd(plates), digits = 1),
    Interq.range = IQR(plates, type = 5))

data.frame(sticklebackTable)

##   genotype   n Mean Median Std.dev Interq.range
## 1       MM  82 62.8     63     3.4            2
## 2       Mm 174 50.4     59    15.1           21
## 3       mm  88 11.7     11     3.6            3

Cumulative frequency distribution

ggplot(speedBefore, aes(x = speed)) + 
    stat_ecdf(color = "firebrick", size = 1.5) +
    labs(x = "Running speed before amputation (cm/s)", 
        y = "Cumulative relative frequency") + 
    theme_classic()

Make table

sticklebackTable <- summarize(group_by(sticklebackData, genotype), 
    n = n())
sticklebackTable$Proportion <- sticklebackTable$n/sum(sticklebackTable$n)
data.frame(sticklebackTable)

##   genotype   n Proportion
## 1       MM  82  0.2383721
## 2       Mm 174  0.5058140
## 3       mm  88  0.2558140

The table would look even better if you rounded the proportions (give this a try).