Creating summary statistics

We will now cover some basic measures of a central tendency, dispersion, and simple plots. The first question that we will address is How does R handle missing values in calculations? To see what happens, create a vector with a missing value (NA in the R language), then sum the values of the vector with sum():

> a <- c(1, 2, 3, NA)

> sum(a)
[1] NA

Unlike SAS, which would sum the non-missing values, R does not sum the non-missing values, but simply returns NA, indicating that at least one value is missing. Now, we could create a new vector with the missing value deleted but you can also include the syntax to exclude any missing values with na.rm = TRUE:

> sum(a, na.rm = TRUE)
[1] 6

Functions exist to identify measures of the central tendency and dispersion of a vector:

> data <- c(4, 3, 2, 5.5, 7.8, 9, 14, 20)

> mean(data)
[1] 8.1625

> median(data)
[1] 6.65

> sd(data)
[1] 6.142112

> max(data)
[1] 20

> min(data)
[1] 2

> range...