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Summary Statistics

Source: R/summaryStats.R
summaryStats.Rd

Produces a table of summary statistics for the data. If the argument group is missing, calculates a matrix of summary statistics for the data in x. If group is present, the elements of group are interpreted as group labels and the summary statistics are displayed for each group separately.

Usage

summaryStats(x, ...)

# Default S3 method
summaryStats(
  x,
  group = rep("Data", length(x)),
  data.order = TRUE,
  digits = 2,
  ...
)

# S3 method for class 'formula'
summaryStats(x, data = NULL, data.order = TRUE, digits = 2, ...)

# S3 method for class 'matrix'
summaryStats(x, data.order = TRUE, digits = 2, ...)

Arguments

x

either a single vector of values, a formula of the form data ~ group, or a matrix.

...

Optional arguments that are passed to the summary statistic functions. For example na.rm = TRUE will help if there are missing values in the (response) variable.

group

a vector of group labels.

data.order

if TRUE, the group order is the order in which the groups are first encountered in group. If FALSE, the order is alphabetical.

digits

the number of decimal places to display.

data

an optional data frame containing the variables in the model.

Value

A teaching summary is printed as a side effect. The returned value is invisible so that classroom use can focus on the printed summary while programmatic use can still save the result.

If x is a single variable and no grouping is supplied, an invisible list is returned with the following named items:

min

Minimum value.

max

Maximum value.

mean

Mean value.

var

Variance – the average of the squares of the deviations of the data values from the sample mean.

sd

Standard deviation – the square root of the variance.

n

Number of data values – size of the dataset.

nMissing

If there are missing values, and na.rm has been set to TRUE, the number of missing values.

iqr

Midspread (IQR) – the range spanned by the central half of the data; the interquartile range.

skewness

Skewness statistic – indicates how skewed the data set is. Positive values indicate right-skew data. Negative values indicate left-skew data.

lq

Lower quartile.

median

Median – the middle value when the batch is ordered.

uq

Upper quartile.

If grouping is provided, either by using the group argument, by using a formula, or by passing a matrix whose columns represent groups, the function invisibly returns a data.frame with one row for each group and columns containing the summary statistics.

Methods (by class)

  • summaryStats(default): Summary Statistics

  • summaryStats(formula): Summary Statistics

  • summaryStats(matrix): Summary Statistics

Examples


## STATS20x data:
data(course.df)

## Single variable summary
with(course.df, summaryStats(Exam))
#> Minimum value:           11 
#> Maximum value:           93 
#> Mean value:              52.88 
#> Median:                  51.5 
#> Upper quartile:          68.5 
#> Lower quartile:          40 
#> Variance:                348.87 
#> Standard deviation:      18.68 
#> Midspread (IQR):         28.5 
#> Skewness:                0.16 
#> Number of data values:   146 

## Using a formula
summaryStats(Exam ~ Stage1, course.df)
#>   Sample Size     Mean Median  Std Dev Midspread
#> A          49 68.75510     72 15.47381     20.00
#> B          46 51.52174     52 14.75238     19.75
#> C          51 38.84314     41 11.67625     16.50

## Using a matrix
courseMatrix = cbind(course.df$Exam, course.df$Assign, course.df$Test)
summaryStats(courseMatrix)
#>   Sample Size     Mean Median   Std Dev Midspread
#> 1         146 52.87671   51.5 18.677994      28.5
#> 2         146 13.82740   14.8  4.405115       5.5
#> 3         146 11.56712   11.8  3.779292       5.4

## Saving and extracting the information
sumStats = summaryStats(Exam ~ Degree, course.df)
#>       Sample Size     Mean Median  Std Dev Midspread
#> BA             17 52.41176   60.0 24.57402     43.00
#> BCom           49 58.81633   60.0 16.23868     24.00
#> BSc            64 45.82812   42.5 15.80090     17.25
#> Other          16 63.37500   66.0 19.76824     35.75
sumStats
#>       Sample Size     Mean Median  Std Dev Midspread
#> BA             17 52.41176   60.0 24.57402     43.00
#> BCom           49 58.81633   60.0 16.23868     24.00
#> BSc            64 45.82812   42.5 15.80090     17.25
#> Other          16 63.37500   66.0 19.76824     35.75

## Just the BAs
sumStats['BA', ]
#>    Sample Size     Mean Median  Std Dev Midspread
#> BA          17 52.41176     60 24.57402        43

## Just the means
sumStats$mean
#> [1] 52.41176 58.81633 45.82812 63.37500