# Descriptive Statistics

R provides a wide range of functions for obtaining summary statistics. One method of obtaining descriptive statistics is to use the **sapply( )** function with a specified summary statistic.

```
# get means for variables in data frame mydata
# excluding missing values
sapply(mydata, mean, na.rm=TRUE)
```

Possible functions used in sapply include **mean, sd, var, min, max, median, range, and quantile**.

There are also numerous R functions designed to provide a range of descriptive statistics at once. For example

```
# mean,median,25th and 75th quartiles,min,max
summary(mydata)
# Tukey min,lower-hinge, median,upper-hinge,max
fivenum(x)
```

Using the Hmisc package

```
library(Hmisc)
describe(mydata)
# n, nmiss, unique, mean, 5,10,25,50,75,90,95th percentiles
# 5 lowest and 5 highest scores
```

Using the **pastecs** package

```
library(pastecs)
stat.desc(mydata)
# nbr.val, nbr.null, nbr.na, min max, range, sum,
#
median, mean, SE.mean, CI.mean, var, std.dev, coef.var
```

Using the psych package

```
library(psych)
describe(mydata)
# item name ,item number, nvalid,
mean, sd,
#
median, mad, min, max, skew, kurtosis, se
```

## Summary Statistics by Group

A simple way of generating summary statistics by grouping variable is available in the psych package.

```
library(psych)
describe.by(mydata, group,...)
```

The doBy package provides much of the functionality of SAS PROC SUMMARY. It defines the desired table using a model formula and a function. Here is a simple example.

```
library(doBy)
summaryBy(mpg + wt ~ cyl + vs, data = mtcars,
FUN = function(x) {
c(m = mean(x), s = sd(x))
} )
# produces mpg.m wt.m mpg.s wt.s for each
# combination of the levels of cyl and vs
```

**See also** : aggregating data.

## To Practice

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