Descriptive Statistics in R

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)Obsługiwane przez

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)Obsługiwane przez

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 scoresObsługiwane przez

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.varObsługiwane przez

Using the psych package

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

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,...)Obsługiwane przez

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 vsObsługiwane przez

See also : aggregating data.

To Practice

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