Type: Package
Title: Generate Descriptive Statistics
Version: 0.6.0
Description: Generate descriptive statistics such as measures of location, dispersion, frequency tables, cross tables, group summaries and multiple one/two way tables.
Depends: R(≥ 3.3.0)
Imports: dplyr, ggplot2, magrittr, rlang, scales, stats, tidyr, utils
Suggests: covr, gridExtra, knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr, xplorerr
License: MIT + file LICENSE
URL: https://descriptr.rsquaredacademy.com/, https://github.com/rsquaredacademy/descriptr
BugReports: https://github.com/rsquaredacademy/descriptr/issues
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.2.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2024-11-08 08:51:03 UTC; HP
Author: Aravind Hebbali ORCID iD [aut, cre]
Maintainer: Aravind Hebbali <hebbali.aravind@gmail.com>
Repository: CRAN
Date/Publication: 2024-11-08 09:20:01 UTC

descriptr package

Description

Generate descriptive statistics and explore statistical distributions

Author(s)

Maintainer: Aravind Hebbali hebbali.aravind@gmail.com (ORCID)

See Also

Useful links:

Multiple One & Two Way Tables

Description

ds_auto_freq_table creates multiple one way tables by creating a frequency table for each categorical variable in a data frame. ds_auto_cross_table creates multiple two way tables by creating a cross table for each unique pair of categorical variables in a data frame.

Usage

ds_auto_freq_table(data, ...)

ds_auto_cross_table(data, ...)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

Details

ds_auto_freq_table is a extension of the ds_freq_table function. It creates a frequency table for each categorical variable in the dataframe. ds_auto_cross_table is a extension of the ds_cross_table function. It creates a two way table for each unique pair of categorical variables in the dataframe.

Deprecated Functions

ds_oway_tables() and ds_tway_tables() have been deprecated. Instead use ds_auto_freq_table() and ds_auto_cross_table().

See Also

link{ds_freq_table} link{ds_cross_table}

Examples

# frequency table for all columns
ds_auto_freq_table(mtcarz)

# frequency table for multiple columns
ds_auto_freq_table(mtcarz, cyl, gear)

# cross table for all columns
ds_auto_cross_table(mtcarz)

# cross table for multiple columns
ds_auto_cross_table(mtcarz, cyl, gear, am)

Tabulation

Description

Generate summary statistics for all continuous variables in data.

Usage

ds_auto_group_summary(data, ...)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

Examples

# summary statistics of mpg & disp for each level of cyl & gear
ds_auto_group_summary(mtcarz, cyl, gear, mpg, disp)

Descriptive statistics and frquency tables

Description

Generate summary statistics & frequency table for all continuous variables in data.

Usage

ds_auto_summary_stats(data, ...)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

Examples

# all columns
ds_auto_summary_stats(mtcarz)

# multiple columns
ds_auto_summary_stats(mtcarz, disp, hp)

Two way table

Description

Creates two way tables of categorical variables. The tables created can be visualized as bar plots and mosaic plots.

Usage

ds_cross_table(data, var_1, var_2)

## S3 method for class 'ds_cross_table'
plot(x, stacked = FALSE, proportional = FALSE, print_plot = TRUE, ...)

ds_twoway_table(data, var_1, var_2)

Arguments

data

A data.frame or a tibble.

var_1

First categorical variable.

var_2

Second categorical variable.

x

An object of class cross_table.

stacked

If FALSE, the columns of height are portrayed as stacked bars, and if TRUE the columns are portrayed as juxtaposed bars.

proportional

If TRUE, the height of the bars is proportional.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

...

Further arguments to be passed to or from methods.

Examples

# cross table
k <- ds_cross_table(mtcarz, cyl, gear)
k

# bar plot
plot(k)

# stacked bar plot
plot(k, stacked = TRUE)

# proportional bar plot
plot(k, proportional = TRUE)

# returns tibble
ds_twoway_table(mtcarz, cyl, gear)

Corrected Sum of Squares

Description

Compute the corrected sum of squares

Usage

ds_css(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

Examples

# vector
ds_css(mtcars$mpg)

# data.frame
ds_css(mtcars, mpg)

Coefficient of Variation

Description

Compute the coefficient of variation

Usage

ds_cvar(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

Examples

# vector
ds_cvar(mtcars$mpg)

# data.frame
ds_cvar(mtcars, mpg)

Extreme observations

Description

Returns the most extreme observations.

Usage

ds_extreme_obs(data, col, decimals = 2)

Arguments

data

A numeric vector or data.frame or tibble.

col

Column in data.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Examples

# data.frame
ds_extreme_obs(mtcarz, mpg)

# vector
ds_extreme_obs(mtcarz$mpg)

# decimal places
ds_extreme_obs(mtcarz$mpg, decimals = 3)

Frequency table

Description

Frequency table for categorical and continuous data and returns the frequency, cumulative frequency, frequency percent and cumulative frequency percent. plot.ds_freq_table() creates bar plot for the categorical data and histogram for continuous data.

Usage

ds_freq_table(data, col, bins = 5)

## S3 method for class 'ds_freq_table'
plot(x, print_plot = TRUE, ...)

Arguments

data

A data.frame or a tibble.

col

Column in data.

bins

Number of intervals into which the data must be split.

x

An object of class ds_freq_table.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

...

Further arguments to be passed to or from methods.

See Also

ds_cross_table

Examples

# categorical data
ds_freq_table(mtcarz, cyl)

# barplot
k <- ds_freq_table(mtcarz, cyl)
plot(k)

# continuous data
ds_freq_table(mtcarz, mpg)

# barplot
k <- ds_freq_table(mtcarz, mpg)
plot(k)

Geometric Mean

Description

Computes the geometric mean

Usage

ds_gmean(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

See Also

ds_hmean mean

Examples

# vector
ds_gmean(mtcars$mpg)

# data.frame
ds_gmean(mtcars, mpg)

Groupwise descriptive statistics

Description

Descriptive statistics of a continuous variable for the different levels of a categorical variable. boxplot.group_summary() creates boxplots of the continuous variable for the different levels of the categorical variable.

Usage

ds_group_summary(data, group_by, cols)

## S3 method for class 'ds_group_summary'
plot(x, print_plot = TRUE, ...)

Arguments

data

A data.frame or a tibble.

group_by

Column in data.

cols

Column in data.

x

An object of the class ds_group_summary.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

...

Further arguments to be passed to or from methods.

Value

ds_group_summary() returns an object of class "ds_group_summary". An object of class "ds_group_summary" is a list containing the following components:

stats

A data frame containing descriptive statistics for the different levels of the factor variable.

tidy_stats

A tibble containing descriptive statistics for the different levels of the factor variable.

plotdata

Data for boxplot method.

See Also

ds_summary_stats

Examples

# ds_group summary
ds_group_summary(mtcarz, cyl, mpg)

# boxplot
k <- ds_group_summary(mtcarz, cyl, mpg)
plot(k)

# tibble
k$tidy_stats

Category wise descriptive statistics

Description

Descriptive statistics of a continuous variable for the combination of levels of two or more categorical variables.

Usage

ds_group_summary_interact(data, col, ...)

Arguments

data

A data.frame or a tibble.

col

Column in data; continuous variable.

...

Columns in data; categorical variables.

See Also

ds_group_summary

Examples

ds_group_summary_interact(mtcarz, mpg, cyl, gear)

Harmonic Mean

Description

Computes the harmonic mean

Usage

ds_hmean(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

See Also

ds_gmean mean

Examples

# vector
ds_hmean(mtcars$mpg)

# data.frame
ds_hmean(mtcars, mpg)

Kurtosis

Description

Compute the kurtosis of a probability distribution.

Usage

ds_kurtosis(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

References

Sheskin, D.J. (2000) Handbook of Parametric and Nonparametric Statistical Procedures, Second Edition. Boca Raton, Florida: Chapman & Hall/CRC.

See Also

ds_skewness

Examples

# vector
ds_kurtosis(mtcars$mpg)

# data.frame
ds_kurtosis(mtcars, mpg)

Launch Shiny App

Description

Launches shiny app

Usage

ds_launch_shiny_app()

Deprecated Function

launch_descriptr() has been deprecated. Instead use ds_launch_shiny_app().

Examples

## Not run: 
ds_launch_shiny_app()

## End(Not run)

Mean Absolute Deviation

Description

Compute the mean absolute deviation about the mean

Usage

ds_mdev(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

Details

The ds_mdev function computes the mean absolute deviation about the mean. It is different from mad in stats package as the statistic used to compute the deviations is not median but mean. Any NA values are stripped from x before computation takes place

See Also

mad

Examples

# vector
ds_mdev(mtcars$mpg)

# data.frame
ds_mdev(mtcars, mpg)

Measures of location

Description

Returns the measures of location such as mean, median & mode.

Usage

ds_measures_location(data, ..., trim = 0.05, decimals = 2)

Arguments

data

A data.frame or tibble or numeric vector.

...

Column(s) in data or numeric vectors.

trim

The fraction of values to be trimmed before computing the mean.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Examples

# single column
ds_measures_location(mtcarz, mpg)

# multiple columns
ds_measures_location(mtcarz, mpg, disp)

# all columns
ds_measures_location(mtcarz)

# vector
ds_measures_location(mtcarz$mpg)

# vectors of different length
disp <- mtcarz$disp[1:10]
ds_measures_location(mtcarz$mpg, disp)

# decimal places
ds_measures_location(mtcarz, disp, hp, decimals = 3)

Measures of symmetry

Description

Returns the measures of symmetry such as skewness and kurtosis.

Usage

ds_measures_symmetry(data, ..., decimals = 2)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Examples

# single column
ds_measures_symmetry(mtcarz, mpg)

# multiple columns
ds_measures_symmetry(mtcarz, mpg, disp)

# all columns
ds_measures_symmetry(mtcarz)

# vector
ds_measures_symmetry(mtcarz$mpg)

# vectors of different length
disp <- mtcarz$disp[1:10]
ds_measures_symmetry(mtcarz$mpg, disp)

# decimal places
ds_measures_symmetry(mtcarz, disp, hp, decimals = 3)

Measures of variation

Description

Returns the measures of location such as range, variance and standard deviation.

Usage

ds_measures_variation(data, ..., decimals = 2)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Examples

# single column
ds_measures_variation(mtcarz, mpg)

# multiple columns
ds_measures_variation(mtcarz, mpg, disp)

# all columns
ds_measures_variation(mtcarz)

# vector
ds_measures_variation(mtcarz$mpg)

# vectors of different length
disp <- mtcarz$disp[1:10]
ds_measures_variation(mtcarz$mpg, disp)

# decimal places
ds_measures_variation(mtcarz, disp, hp, decimals = 3)

Mode

Description

Compute the sample mode

Usage

ds_mode(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

Details

Any NA values are stripped from x before computation takes place.

Value

Mode of x

See Also

mean median

Examples

# vector
ds_mode(mtcars$mpg)

# data.frame
ds_mode(mtcars, mpg)

Percentiles

Description

Returns the percentiles

Usage

ds_percentiles(data, ..., decimals = 2)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Examples

# single column
ds_percentiles(mtcarz, mpg)

# multiple columns
ds_percentiles(mtcarz, mpg, disp)

# all columns
ds_percentiles(mtcarz)

# vector
ds_percentiles(mtcarz$mpg)

# vectors of different length
disp <- mtcarz$disp[1:10]
ds_percentiles(mtcarz$mpg, disp)

# decimal places
ds_percentiles(mtcarz, disp, hp, decimals = 3)

Generate bar plots

Description

Creates bar plots if the data has categorical variables.

Usage

ds_plot_bar(data, ..., fill = "blue", print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

fill

Color of the bars.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# plot single variable
ds_plot_bar(mtcarz, cyl)

# plot multiple variables
ds_plot_bar(mtcarz, cyl, gear)

# plot all variables
ds_plot_bar(mtcarz)

Generate grouped bar plots

Description

Creates grouped bar plots if the data has categorical variables.

Usage

ds_plot_bar_grouped(data, ..., print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# subset data
mt <- dplyr::select(mtcarz, cyl, gear, am)

# grouped bar plot
ds_plot_bar_grouped(mtcarz, cyl, gear)

# plot all variables
ds_plot_bar_grouped(mt)

Generate stacked bar plots

Description

Creates stacked bar plots if the data has categorical variables.

Usage

ds_plot_bar_stacked(data, ..., print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# subset data
mt <- dplyr::select(mtcarz, cyl, gear, am)

# stacked bar plot
ds_plot_bar_stacked(mtcarz, cyl, gear)

# plot all variables
ds_plot_bar_stacked(mt)

Compare distributions

Description

Creates box plots if the data has both categorical & continuous variables.

Usage

ds_plot_box_group(data, ..., print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# subset data
mt <- dplyr::select(mtcarz, cyl, disp, mpg)

# plot select variables
ds_plot_box_group(mtcarz, cyl, gear, mpg)

# plot all variables
ds_plot_box_group(mt)

Generate box plots

Description

Creates box plots if the data has continuous variables.

Usage

ds_plot_box_single(data, ..., print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# plot single variable
ds_plot_box_single(mtcarz, mpg)

# plot multiple variables
ds_plot_box_single(mtcarz, mpg, disp, hp)

# plot all variables
ds_plot_box_single(mtcarz)

Generate density plots

Description

Creates density plots if the data has continuous variables.

Usage

ds_plot_density(data, ..., color = "blue", print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

color

Color of the plot.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# plot single variable
ds_plot_density(mtcarz, mpg)

# plot multiple variables
ds_plot_density(mtcarz, mpg, disp, hp)

# plot all variables
ds_plot_density(mtcarz)

Generate histograms

Description

Creates histograms if the data has continuous variables.

Usage

ds_plot_histogram(data, ..., bins = 5, fill = "blue", print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

bins

Number of bins in the histogram.

fill

Color of the histogram.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# plot single variable
ds_plot_histogram(mtcarz, mpg)

# plot multiple variables
ds_plot_histogram(mtcarz, mpg, disp, hp)

# plot all variables
ds_plot_histogram(mtcarz)

Generate scatter plots

Description

Creates scatter plots if the data has continuous variables.

Usage

ds_plot_scatter(data, ..., print_plot = TRUE)

Arguments

data

A data.frame or tibble.

...

Column(s) in data.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Examples

# plot select variables
ds_plot_scatter(mtcarz, mpg, disp)

# plot all variables
ds_plot_scatter(mtcarz)

Range

Description

Compute the range of a numeric vector

Usage

ds_range(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

Value

Range of x

See Also

range

Examples

# vector
ds_range(mtcars$mpg)

# data.frame
ds_range(mtcars, mpg)

Index Values

Description

Returns index of values.

Usage

ds_rindex(data, values)

Arguments

data

a numeric vector

values

a numeric vector containing the values whose index is returned

Value

Index of the values in data. In case, data does not contain index, NULL is returned.

Examples

# returns index of 21
ds_rindex(mtcars$mpg, 21)

# returns NULL
ds_rindex(mtcars$mpg, 22)

Screen data

Description

Screen data and return details such as variable names, class, levels and missing values. plot.ds_screener() creates bar plots to visualize of missing observations for each variable in a data set.

Usage

ds_screener(data)

## S3 method for class 'ds_screener'
plot(x, ...)

Arguments

data

A tibble or a data.frame.

x

An object of class ds_screener.

...

Further arguments to be passed to or from methods.

Value

ds_screener() returns an object of class "ds_screener". An object of class "ds_screener" is a list containing the following components:

Rows

Number of rows in the data frame.

Columns

Number of columns in the data frame.

Variables

Names of the variables in the data frame.

Types

Class of the variables in the data frame.

Count

Length of the variables in the data frame.

nlevels

Number of levels of a factor variable.

levels

Levels of factor variables in the data frame.

Missing

Number of missing observations in each variable.

MissingPer

Percent of missing observations in each variable.

MissingTotal

Total number of missing observations in the data frame.

MissingTotPer

Total percent of missing observations in the data frame.

MissingRows

Total number of rows with missing observations in the data frame.

MissingCols

Total number of columns with missing observations in the data frame.

Examples

# screen data
ds_screener(mtcarz)
ds_screener(airquality)

# plot
x <- ds_screener(airquality)
plot(x)

Skewness

Description

Compute the skewness of a probability distribution.

Usage

ds_skewness(data, x = NULL)

Arguments

data

A numeric vector or data.frame.

x

Column in data.

References

Sheskin, D.J. (2000) Handbook of Parametric and Nonparametric Statistical Procedures, Second Edition. Boca Raton, Florida: Chapman & Hall/CRC.

See Also

kurtosis

Examples

# vector
ds_skewness(mtcars$mpg)

# data.frame
ds_skewness(mtcars, mpg)

Standard error of mean

Description

Returns the standard error of mean.

Usage

ds_std_error(x)

Arguments

x

A numeric vector.

Examples

ds_std_error(mtcars$mpg)

Descriptive statistics

Description

Range of descriptive statistics for continuous data.

Usage

ds_summary_stats(data, ...)

Arguments

data

An object of type numeric or data.frame.

...

Column(s) in data.

See Also

summary ds_freq_table ds_cross_table

Examples

# numeric data
ds_summary_stats(mtcarz$mpg)

# single variable
ds_summary_stats(mtcarz, mpg)

# multiple variables
ds_summary_stats(mtcarz, mpg, disp, hp)

# all variables
ds_summary_stats(mtcarz)

Tail Observations

Description

Returns the n highest/lowest observations from a numeric vector.

Usage

ds_tailobs(data, n, type = c("low", "high"), decimals = 2)

Arguments

data

a numeric vector

n

number of observations to be returned

type

if low, the n lowest observations are returned, else the highest n observations are returned.

decimals

An option to specify the exact number of decimal places to use. The default number of decimal places is 2.

Details

Any NA values are stripped from data before computation takes place.

Value

n highest/lowest observations from data

See Also

top_n

Examples

# 5 lowest observations
ds_tailobs(mtcarz$mpg, 5)

# 5 highest observations
ds_tailobs(mtcarz$mpg, 5, type = "high")

# specify decimal places to display
ds_tailobs(mtcarz$mpg, 5, decimals = 3)

Tidy descriptive statistics

Description

Descriptive statistics for multiple variables.

Usage

ds_tidy_stats(data, ...)

Arguments

data

A tibble or a data.frame.

...

Columns in x.

Value

A tibble.

Deprecated Functions

ds_multi_stats() have been deprecated. Instead use ds_tidy_stats().

Examples

# all columns
ds_tidy_stats(mtcarz)

# multiple columns
ds_tidy_stats(mtcarz, mpg, disp, hp)

High School and Beyond Data Set

Description

A dataset containing demographic information and standardized test scores of high school students.

Usage

hsb

Format

A data frame with 200 rows and 10 variables:

id

id of the student

female

gender of the student

race

ethnic background of the student

ses

socio-economic status of the student

schtyp

school type

prog

program type

read

scores from test of reading

write

scores from test of writing

math

scores from test of math

science

scores from test of science

socst

scores from test of social studies

Source

https://nces.ed.gov/surveys/hsb/

mtcarz

Description

Copy of mtcars data set with modified variable types

Usage

mtcarz

Format

An object of class data.frame with 32 rows and 11 columns.