Summary stats

We will now cover some basic measures of central tendency, dispersion, and simple plots. The first question that we will address is how R handles the 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 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 the measures of 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(data)
[1]  2 20

> quantile(data)
   0%   25%...