# rcode03

## Describing data: R code for Chapter 3 examples

Click on a function argument for a short description of its meaning. The variable names are plucked from the examples further below.

Sample mean, assuming that there are no missing () entries:
`mean()`

Standard deviation, assuming that there are no missing (NA) entries:
`sd()`

Calculate a statistic by group, assuming that there are no missing (NA) entries:
`tapply(, , )`

Obtain a subset of data from a data frame:
`subset(, )`

Other new methods:
Variance and coefficient of variation.
Median and interquartile range.
Round to a preferred number of decimals.
Cumulative frequency distribution.
Table of descriptive statistics by group.
Mean and standard deviation from a frequency table.

### Example 3.1. Gliding snakes

Sample mean, standard deviation, variance and coefficient of variation of undulation rate of 8 gliding paradise tree snakes, in Hz.

```snakeData <- read.csv(url("http://whitlockschluter.zoology.ubc.ca/wp-content/data/chapter03/chap03e1GlidingSnakes.csv"))
```

Draw a histogram of the data.

```hist(snakeData\$undulationRateHz, right = FALSE)
```

Commands for a fancier histogram are shown .

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

```mean(snakeData\$undulationRate)
sd(snakeData\$undulationRate)
var(snakeData\$undulationRate)
```

Calculate the coefficient of variation.

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

### Table 3.1-2. Numbers of convictions

Mean and standard deviation from a frequency table. The data are from Chapter 2.

Read and inspect the data. It is a frequency table of the number of convictions.

```convictionsFreq <- read.csv(url("http://whitlockschluter.zoology.ubc.ca/wp-content/data/chapter03/chap03t1_2ConvictionsFreq.csv"))
```

Calculate the mean and standard deviation from the frequency table. First, use the `rep` command to "repeat" each value in the table, the number of times according to its frequency. Here, we store the result in `convictions`.

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

Then, calculate mean and standard deviation on the result.

```mean(convictions)
sd(convictions)
```

### Example 3.2. Spider running speed

Median, interquartile range, and box plot of running speed (cm/s) of male Tidarren spiders. We also include below the cumulative frequency distribution of running speed before amputation.

Read and inspect the 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("http://whitlockschluter.zoology.ubc.ca/wp-content/data/chapter03/chap03e2SpiderAmputation.csv"))
```

Box plot of the data. 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"))
boxplot(speed ~ treatment, data = spiderData)
```

Instructions for a fancier box plot, made with additional options, is shown .

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
```

Calculate the sample median of before-amputation running speed.

```median(speedBefore\$speed)
```

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)
```

Determine the interquartile range of before-amputation running speed. Type 5 reproduces the method we use in the book to calculate quartiles.

```IQR(speedBefore\$speed, type = 5)
```

Draw a cumulative frequency distribution of running speed before amputation (Figure 3.4-1).

```plot( ecdf(speedBefore\$speed), verticals = TRUE,
ylab = "Cumulative relative frequency",
xlab = "Running speed before amputation (cm/s)")
```

Instructions for a slightly more polished cumulative frequency distribution are .

### Example 3.3. Stickleback lateral plates

Draw multiple histograms and produce a table of descriptive statistics by group for plate numbers of three stickleback genotypes. We also include a table of frequencies and proportions of stickleback genotypes.

```sticklebackData <- read.csv(url("http://whitlockschluter.zoology.ubc.ca/wp-content/data/chapter03/chap03e3SticklebackPlates.csv"))
```

Draw multiple histograms of number of plates, one histogram per genotype. Begin by setting the preferred order of the three genotypes in the figure. Here, we use the `lattice` package to draw the histograms, so it must be loaded first.

```sticklebackData\$genotype <- factor(sticklebackData\$genotype,
levels = c("MM","Mm","mm"))
library(lattice)
histogram(~ plates | genotype, data = sticklebackData, breaks = seq(0,70,by=2),
layout = c(1, 3), col = "firebrick")
```

Commands to draw multiple histograms in base R, without using the `lattice` package, are . This method is more tedious, but allows for easier addition of options.

Make a table of descriptive statistics by group using `tapply`. The commands below assume that the variables contain no missing (NA) elements. We need to calculate all the statistics, one at a time. Save them so that we can put them all together in a table afterward.

To begin, get the sample sizes by group (genotype):

```n <- tapply(sticklebackData\$plates, INDEX = sticklebackData\$genotype, FUN = length)
n
```

Next, calculate the mean number of plates by group. Let's round the results to 1 decimal place to make it easier to read them when we place into a table. To accomplish this, set `digits = 1` in the `round` function.

```meanPlates <- tapply(sticklebackData\$plates, INDEX = sticklebackData\$genotype,
FUN = mean)
meanPlates <- round(meanPlates, digits = 1)
meanPlates
```

Repeat for medians.

```medianPlates <- tapply(sticklebackData\$plates, INDEX = sticklebackData\$genotype,
FUN = median)
medianPlates
```

Repeat for standard deviations, including rounding.

```sdPlates <- tapply(sticklebackData\$plates, INDEX = sticklebackData\$genotype, FUN = sd)
sdPlates <- round(sdPlates, 1)
sdPlates
```

Finally, the interquartile range by group.

```iqrPlates <- tapply(sticklebackData\$plates, INDEX = sticklebackData\$genotype,
FUN = IQR, type = 5)
iqrPlates
```

Make the table by assembling all the results in a new data frame (data frames can be used as tables for display purposes).

```sticklebackTable <- data.frame(genotype = names(n), n = n,
mean = meanPlates, median = medianPlates,
sd = sdPlates, iqrange = iqrPlates)
sticklebackTable
```

We can make a table of frequencies and proportions of the stickleback genotypes (Table 3.5-1). Below, we first generate a frequency table of genotypes (the `dnn` argument names the variable in the table).

```sticklebackFreq <- table(sticklebackData\$genotype, dnn = "genotype")
sticklebackFreq
```

We then convert the table to a data frame so that we can include the proportions in the table too.

```sticklebackFreq <- data.frame(sticklebackFreq)
sticklebackFreq
```

Finally, we calculate the proportions and put them into the data frame. To convert frequencies to proportions, divide the frequencies by the sum of the frequencies.

```sticklebackFreq\$proportion <- sticklebackFreq\$Freq / sum(sticklebackFreq\$Freq)
sticklebackFreq
```

The table would look even nicer if you round the proportions before including them in the table (give this a try).

When there are missing values, some functions (including `mean`, `sd` and `median`) need to be told to remove them before calculation. This is accomplished by inserting an extra argument `na.rm=TRUE` when using the functions.
A numeric variable.
A numeric variable.
A numeric variable.
A categorical variable indicating the groups to which individuals belong.
The name of the function to calculate the sample statistic by group (here, the median).
The name of the data frame.
A logical expression (statement) indicating which variable in the data frame to base the subset upon, and which subset to take. Note the double equal sign "==" in the expression, indicating "is equal to". This example grabs all rows of the data frame for which the variable `treatment` is "before"

```hist(snakeData\$undulationRateHz, right = FALSE, las = 1, col = "firebrick",
breaks = seq(0.8,2.2,by=0.2), xlab = "Undulation rate (Hz)",
ylab = "Frequency", main = "")
```

```par(bty = "l")
boxplot(speed ~ treatment, data = spiderData, ylim = c(0,max(spiderData\$speed)),
col = "goldenrod1", boxwex = 0.5, whisklty = 1, las = 1,
xlab = "Amputation treatment", ylab = "Running speed (cm/s)")
```

```par(bty = "l")
plot( ecdf(speedBefore\$speed), verticals = TRUE,
las = 1, main = "", do.points = FALSE,
ylab = "Cumulative relative frequency",
xlab = "Running speed before amputation (cm/s)" )
```

```oldpar = par(no.readonly = TRUE) # make backup of default graph settings
par(mfrow = c(3,1), las = 1, oma = c(4, 6, 2, 6), mar = c(2, 5, 4, 2)) # adjust margins
hist(sticklebackData\$plates[sticklebackData\$genotype == "MM"], right = FALSE,
breaks = seq(0,70,by=2), main = "MM", col = "firebrick",
las = 1, ylab = "Frequency")
hist(sticklebackData\$plates[sticklebackData\$genotype == "Mm"], right = FALSE,
breaks = seq(0,70,by=2), main = "MM", col = "firebrick",
las = 1, ylab = "Frequency")
hist(sticklebackData\$plates[sticklebackData\$genotype == "mm"], right = FALSE,
breaks = seq(0,70,by=2), main = "MM", col = "firebrick",
las = 1, ylab = "Frequency")
mtext("Number of lateral plates", side = 1, outer = TRUE, padj = 1.5)
par(oldpar) # revert to default graph settings
```