Type:	Package
Title:	Extension to 'ggplot2'
Version:	2.3.0
Description:	The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
URL:	https://ggobi.github.io/ggally/, https://github.com/ggobi/ggally
BugReports:	https://github.com/ggobi/ggally/issues
Depends:	ggplot2 (≥ 3.5.2), R (≥ 4.3)
Imports:	dplyr (≥ 1.1.0), ggstats (≥ 0.9.0), grDevices, grid, gtable (≥ 0.2.0), lifecycle, magrittr, progress, RColorBrewer, rlang, S7 (≥ 0.2.0), scales (≥ 1.3.0), tidyr (≥ 1.3.0), utils
Suggests:	broom (≥ 0.7.0), broom.helpers (≥ 1.3.0), chemometrics, crosstalk, emmeans, geosphere (≥ 1.5-1), ggforce, Hmisc, igraph (≥ 1.0.1), intergraph (≥ 2.0-2), knitr, labelled, mapproj, maps (≥ 3.1.0), network (≥ 1.17.1), nnet, rmarkdown, scagnostics, sna (≥ 2.3-2), spelling, survival, testthat (≥ 3.0.0), vdiffr
RdMacros:	lifecycle
Config/Needs/website:	tidyverse/tidytemplate
Config/testthat/edition:	3
Config/usethis/last-upkeep:	2025-06-13
Encoding:	UTF-8
Language:	en-US
LazyData:	true
LazyLoad:	yes
RoxygenNote:	7.3.2
SystemRequirements:	openssl
Collate:	'GGally-package.R' 'data-australia-pisa-2012.R' 'data-baseball.R' 'data-flea.R' 'data-happy.R' 'data-nasa.R' 'data-pigs.R' 'data-psychademic.R' 'data-tips.R' 'data-twitter_spambots.R' 'deprecated.R' 'find-combo.R' 'gg-plots.R' 'ggally_colbar.R' 'ggally_cross.R' 'ggaly_trends.R' 'ggbivariate.R' 'ggcoef.R' 'ggcorr.R' 'ggfacet.R' 'gglyph.R' 'ggpairs_getput.R' 'ggmatrix.R' 'ggmatrix_gtable.R' 'ggmatrix_gtable_helpers.R' 'ggmatrix_legend.R' 'ggmatrix_make_plot.R' 'ggmatrix_print.R' 'ggmatrix_progress.R' 'ggnet.R' 'ggnet2.R' 'ggnetworkmap.R' 'ggnostic.R' 'ggpairs.R' 'ggpairs_add.R' 'ggpairs_internal_plots.R' 'ggparcoord.R' 'ggsave.R' 'ggscatmat.R' 'ggsurv.R' 'ggtable.R' 'reexports.R' 'utils-pipe.R' 'utils.R' 'vig_ggally.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2025-07-17 16:24:06 UTC; barret
Author:	Barret Schloerke [aut, cre], Di Cook [aut, ths], Joseph Larmarange [aut], Francois Briatte [aut], Moritz Marbach [aut], Edwin Thoen [aut], Amos Elberg [aut], Ott Toomet [ctb], Jason Crowley [aut], Heike Hofmann [ths], Hadley Wickham [ths]
Maintainer:	Barret Schloerke <schloerke@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-18 08:40:18 UTC

GGally: Extension to 'ggplot2'

Description

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Author(s)

Maintainer: Barret Schloerke schloerke@gmail.com (ORCID)

Authors:

• Di Cook dicook@monash.edu (ORCID) [thesis advisor]

• Joseph Larmarange joseph@larmarange.net (ORCID)

• Francois Briatte f.briatte@gmail.com

• Moritz Marbach mmarbach@mail.uni-mannheim.de

• Edwin Thoen edwinthoen@gmail.com

• Amos Elberg amos.elberg@gmail.com

• Jason Crowley crowley.jason.s@gmail.com

Other contributors:

• Ott Toomet otoomet@gmail.com [contributor]

• Heike Hofmann hhofmann4@unl.edu (ORCID) [thesis advisor]

• Hadley Wickham h.wickham@gmail.com (ORCID) [thesis advisor]

See Also

Useful links:

• https://ggobi.github.io/ggally/

• https://github.com/ggobi/ggally

• Report bugs at https://github.com/ggobi/ggally/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Add new aes

Description

Add new aesthetics to a previous aes.

Usage

add_and_overwrite_aes(current, new)

Value

aes_ output

Author(s)

Barret Schloerke

Examples

data(diamonds, package = "ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ]
pm <- ggpairs(diamonds.samp,
  columns = 5:7,
  mapping = ggplot2::aes(color = color),
  upper = list(continuous = "cor", mapping = ggplot2::aes(color = clarity)),
  lower = list(continuous = "cor", mapping = ggplot2::aes(color = cut)),
  title = "Diamonds Sample"
)
str(pm)

Add reference boxes around each cell of the glyphmap.

Description

Add reference boxes around each cell of the glyphmap.

Usage

add_ref_boxes(
  data,
  var_fill = NULL,
  color = "white",
  size = 0.5,
  fill = NA,
  ...
)

Arguments

Add reference lines for each cell of the glyphmap.

Description

Add reference lines for each cell of the glyphmap.

Usage

add_ref_lines(data, color = "white", size = 1.5, ...)

Arguments

Modify a ggmatrix object by adding an ggplot2 object to all plots

Description

This operator allows you to add ggplot2 objects to a ggmatrix object.

Usage

add_to_ggmatrix(e1, e2, location = NULL, rows = NULL, cols = NULL)

Arguments

Details

If the first object is an object of class ggmatrix, you can add the following types of objects, and it will return a modified ggplot2 object.

• theme: update plot theme

• scale: replace current scale

• coord: override current coordinate system

The + operator completely replaces elements with elements from e2.

add_to_ggmatrix gives you more control to modify only some subplots. This function may be replaced and/or removed in the future.

See Also

ggmatrix_location

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive
data(tips)

pm <- ggpairs(tips[, 2:4], ggplot2::aes(color = sex))
## change to black and white theme
pm + ggplot2::theme_bw()
## change to linedraw theme
p_(pm + ggplot2::theme_linedraw())
## change to custom theme
p_(pm + ggplot2::theme(panel.background = ggplot2::element_rect(fill = "lightblue")))
## add a list of information
extra <- list(ggplot2::theme_bw(), ggplot2::labs(caption = "My caption!"))
p_(pm + extra)

## modify scale
p_(pm + scale_fill_brewer(type = "qual"))
## only first row
p_(add_to_ggmatrix(pm, scale_fill_brewer(type = "qual"), rows = 1:2))
## only second col
p_(add_to_ggmatrix(pm, scale_fill_brewer(type = "qual"), cols = 2:3))
## only to upper triangle of plot matrix
p_(add_to_ggmatrix(
  pm,
  scale_fill_brewer(type = "qual"),
  location = "upper"
))

Programme for International Student Assessment (PISA) 2012 Data for Australia

Description

About PISA

Usage

data(australia_PISA2012)

Format

A data frame with 8247 rows and 32 variables

Details

The Programme for International Student Assessment (PISA) is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. To date, students representing more than 70 economies have participated in the assessment.

While 65 economies took part in the 2012 study, this data set only contains information from the country of Australia.

• gender : Factor w/ 2 levels "female","male": 1 1 2 2 2 1 1 1 2 1 ...

• age : Factor w/ 4 levels "4","5","6","7": 2 2 2 4 3 1 2 2 2 2 ...

• homework : num 5 5 9 3 2 3 4 3 5 1 ...

• desk : num 1 0 1 1 1 1 1 1 1 1 ...

• room : num 1 1 1 1 1 1 1 1 1 1 ...

• study : num 1 1 1 1 1 1 1 1 1 1 ...

• computer : num 1 1 1 1 1 1 1 1 1 1 ...

• software : num 1 1 1 1 1 1 1 1 1 1 ...

• internet : num 1 1 1 1 1 1 1 1 1 1 ...

• literature : num 0 0 1 0 1 1 1 1 1 0 ...

• poetry : num 0 0 1 0 1 1 0 1 1 1 ...

• art : num 1 0 1 0 1 1 0 1 1 1 ...

• textbook : num 1 1 1 1 1 0 1 1 1 1 ...

• dictionary : num 1 1 1 1 1 1 1 1 1 1 ...

• dishwasher : num 1 1 1 1 0 1 1 1 1 1 ...

• PV1MATH : num 562 565 602 520 613 ...

• PV2MATH : num 569 557 594 507 567 ...

• PV3MATH : num 555 553 552 501 585 ...

• PV4MATH : num 579 538 526 521 596 ...

• PV5MATH : num 548 573 619 547 603 ...

• PV1READ : num 582 617 650 554 605 ...

• PV2READ : num 571 572 608 560 557 ...

• PV3READ : num 602 560 594 517 627 ...

• PV4READ : num 572 564 575 564 597 ...

• PV5READ : num 585 565 620 572 598 ...

• PV1SCIE : num 583 627 668 574 639 ...

• PV2SCIE : num 579 600 665 612 635 ...

• PV3SCIE : num 593 574 620 571 666 ...

• PV4SCIE : num 567 582 592 598 700 ...

• PV5SCIE : num 587 625 656 662 670 ...

• SENWGT_STU : num 0.133 0.133 0.141 0.141 0.141 ...

• possessions: num 10 8 12 9 11 11 10 12 12 11 ...

Source

https://www.oecd.org/en/data/datasets/pisa-2012-cba-database.html

Yearly batting records for all major league baseball players

Description

This data frame contains batting statistics for a subset of players collected from http://www.baseball-databank.org/. There are a total of 21,699 records, covering 1,228 players from 1871 to 2007. Only players with more 15 seasons of play are included.

Usage

baseball

Format

A 21699 x 22 data frame

Variables

Variables:

• id, unique player id

• year, year of data

• stint

• team, team played for

• lg, league

• g, number of games

• ab, number of times at bat

• r, number of runs

• h, hits, times reached base because of a batted, fair ball without error by the defense

• X2b, hits on which the batter reached second base safely

• X3b, hits on which the batter reached third base safely

• hr, number of home runs

• rbi, runs batted in

• sb, stolen bases

• cs, caught stealing

• bb, base on balls (walk)

• so, strike outs

• ibb, intentional base on balls

• hbp, hits by pitch

• sh, sacrifice hits

• sf, sacrifice flies

• gidp, ground into double play

References

http://www.baseball-databank.org/

RColorBrewer Set1 colors

Description

RColorBrewer Set1 colors

Usage

brew_colors(col)

Arguments

Broomify a model

Description

broom::augment a model and add broom::glance and broom::tidy output as attributes. X and Y variables are also added.

Usage

broomify(model, lmStars = TRUE)

Arguments

Value

broom::augmented data frame with the broom::glance data.frame and broom::tidy data.frame as 'broom_glance' and 'broom_tidy' attributes respectively. var_x and var_y variables are also added as attributes

Examples

data(mtcars)
model <- stats::lm(mpg ~ wt + qsec + am, data = mtcars)
broomified_model <- broomify(model)
str(broomified_model)

Get vector of variable types from data frame

Description

Get vector of variable types from data frame

Usage

column_is_character(df)

column_is_factor(df)

Arguments

Value

character vector with variable types, with names corresponding to the variable names from df

Author(s)

Jason Crowley

Evaluate data column

Description

Evaluate data column

Usage

eval_data_col(data, aes_col)

Arguments

Value

Aes mapping with the x and y values switched

Examples

mapping <- ggplot2::aes(Petal.Length)
eval_data_col(iris, mapping$x)

Find plot types

Description

Retrieves the type of plot for the specific columns

Usage

find_plot_type(col1Name, col2Name, type1, type2, isAllNa, allowDiag)

Arguments

Author(s)

Barret Schloerke

Historical data used for classification examples.

Description

This data contains physical measurements on three species of flea beetles.

Usage

data(flea)

Format

A data frame with 74 rows and 7 variables

Details

• species Ch. concinna, Ch. heptapotamica, Ch. heikertingeri

• tars1 width of the first joint of the first tarsus in microns

• tars2 width of the second joint of the first tarsus in microns

• head the maximal width of the head between the external edges of the eyes in 0.01 mm

• aede1 the maximal width of the aedeagus in the fore-part in microns

• aede2 the front angle of the aedeagus (1 unit = 7.5 degrees)

• aede3 the aedeagus width from the side in microns

References

Lubischew, A. A. (1962), On the Use of Discriminant Functions in Taxonomy, Biometrics 18:455-477.

Function switch

Description

Function that allows you to call different functions based upon an aesthetic variable value.

Usage

fn_switch(types, mapping_val = "y")

Arguments

Examples

ggnostic_continuous_fn <- fn_switch(list(
  default = ggally_points,
  .fitted = ggally_points,
  .se.fit = ggally_nostic_se_fit,
  .resid = ggally_nostic_resid,
  .hat = ggally_nostic_hat,
  .sigma = ggally_nostic_sigma,
  .cooksd = ggally_nostic_cooksd,
  .std.resid = ggally_nostic_std_resid
))

ggnostic_combo_fn <- fn_switch(list(
  default = ggally_box_no_facet,
  fitted = ggally_box_no_facet,
  .se.fit = ggally_nostic_se_fit,
  .resid = ggally_nostic_resid,
  .hat = ggally_nostic_hat,
  .sigma = ggally_nostic_sigma,
  .cooksd = ggally_nostic_cooksd,
  .std.resid = ggally_nostic_std_resid
))

Subset a ggmatrix object

Description

Retrieves the ggplot object at the desired location.

Usage

getPlot(pm, i, j)

## S3 method for class 'ggmatrix'
pm[i, j, ...]

Arguments

Author(s)

Barret Schloerke

See Also

putPlot

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
plotMatrix2 <- ggpairs(tips[, 3:2], upper = list(combo = "denstrip"))
p_(plotMatrix2[1, 2])

Get x axis labels

Description

Retrieves x axis labels from the plot object directly.

Usage

get_x_axis_labels(p, xRange)

Arguments

Scatterplot for continuous and categorical variables

Description

Make scatterplots compatible with both continuous and categorical variables using geom_autopoint from package ggforce.

Usage

ggally_autopoint(data, mapping, ...)

ggally_autopointDiag(data, mapping, ...)

Arguments

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_autopoint(tips, mapping = aes(x = tip, y = total_bill)))
p_(ggally_autopoint(tips, mapping = aes(x = tip, y = sex)))
p_(ggally_autopoint(tips, mapping = aes(x = smoker, y = sex)))
p_(ggally_autopoint(tips, mapping = aes(x = smoker, y = sex, color = day)))
p_(ggally_autopoint(tips, mapping = aes(x = smoker, y = sex), size = 8))
p_(ggally_autopoint(tips, mapping = aes(x = smoker, y = sex), alpha = .9))

p_(ggpairs(
  tips,
  mapping = aes(colour = sex),
  upper = list(discrete = "autopoint", combo = "autopoint", continuous = "autopoint"),
  diag = list(discrete = "autopointDiag", continuous = "autopointDiag")
))

Bar plot

Description

Displays a bar plot for the diagonal of a ggpairs plot matrix.

Usage

ggally_barDiag(data, mapping, ..., rescale = FALSE)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_barDiag(tips, mapping = ggplot2::aes(x = day)))
p_(ggally_barDiag(tips, mapping = ggplot2::aes(x = tip), binwidth = 0.25))

Blank plot

Description

Draws nothing.

Usage

ggally_blank(...)

ggally_blankDiag(...)

Arguments

Details

Makes a "blank" ggplot object that will only draw white space

Author(s)

Barret Schloerke

See Also

ggplot2::element_blank()

Box plot

Description

Make a box plot with a given data set. ggally_box_no_facet will be a single panel plot, while ggally_box will be a faceted plot

Usage

ggally_box(data, mapping, ...)

ggally_box_no_facet(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_box(tips, mapping = ggplot2::aes(x = total_bill, y = sex)))
p_(ggally_box(
  tips,
  mapping        = ggplot2::aes(sex, total_bill, color = sex),
  outlier.colour = "red",
  outlier.shape  = 13,
  outlier.size   = 8
))

Column and row bar plots

Description

Plot column or row percentage using bar plots.

Usage

ggally_colbar(
  data,
  mapping,
  label_format = scales::label_percent(accuracy = 0.1),
  ...,
  remove_background = FALSE,
  remove_percentage_axis = FALSE,
  reverse_fill_levels = FALSE,
  geom_bar_args = NULL
)

ggally_rowbar(
  data,
  mapping,
  label_format = scales::label_percent(accuracy = 0.1),
  ...,
  remove_background = FALSE,
  remove_percentage_axis = FALSE,
  reverse_fill_levels = TRUE,
  geom_bar_args = NULL
)

Arguments

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_colbar(tips, mapping = aes(x = smoker, y = sex)))
p_(ggally_rowbar(tips, mapping = aes(x = smoker, y = sex)))

# change labels' size
p_(ggally_colbar(tips, mapping = aes(x = smoker, y = sex), size = 8))

# change labels' colour and use bold
p_(ggally_colbar(tips,
  mapping = aes(x = smoker, y = sex),
  colour = "white", fontface = "bold"
))

# display number of observations instead of proportions
p_(ggally_colbar(tips, mapping = aes(x = smoker, y = sex, label = after_stat(count))))

# custom bar width
p_(ggally_colbar(tips, mapping = aes(x = smoker, y = sex), geom_bar_args = list(width = .5)))

# change format of labels
p_(ggally_colbar(tips,
  mapping = aes(x = smoker, y = sex),
  label_format = scales::label_percent(accuracy = .01, decimal.mark = ",")
))

p_(ggduo(
  data = as.data.frame(Titanic),
  mapping = aes(weight = Freq),
  columnsX = "Survived",
  columnsY = c("Sex", "Class", "Age"),
  types = list(discrete = "rowbar"),
  legend = 1
))

Correlation value plot

Description

Estimate correlation from the given data. If a color variable is supplied, the correlation will also be calculated per group.

Usage

ggally_cor(
  data,
  mapping,
  ...,
  stars = TRUE,
  method = "pearson",
  display_grid = FALSE,
  digits = 3,
  title_args = list(...),
  group_args = list(...),
  justify_labels = "right",
  align_percent = 0.5,
  title = "Corr",
  na.rm = NA,
  use = deprecated(),
  alignPercent = deprecated(),
  displayGrid = deprecated()
)

Arguments

Author(s)

Barret Schloerke

See Also

ggally_statistic, ggally_cor_v1_5

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_cor(tips, mapping = ggplot2::aes(total_bill, tip)))
# display with grid
p_(ggally_cor(
  tips,
  mapping = ggplot2::aes(total_bill, tip),
  display_grid = TRUE
))
# change text attributes
p_(ggally_cor(
  tips,
  mapping = ggplot2::aes(x = total_bill, y = tip),
  size = 15,
  colour = I("red"),
  title = "Correlation"
))
# split by a variable
p_(ggally_cor(
  tips,
  mapping = ggplot2::aes(total_bill, tip, color = sex),
  size = 5
))

Correlation value plot

Description

Estimate correlation from the given data.

This function is deprecated and will be removed in future releases.

See ggally_cor.

Usage

ggally_cor_v1_5(
  data,
  mapping,
  alignPercent = 0.6,
  method = "pearson",
  use = "complete.obs",
  corAlignPercent = NULL,
  corMethod = NULL,
  corUse = NULL,
  displayGrid = TRUE,
  ...
)

Arguments

Author(s)

Barret Schloerke

See Also

ggally_cor

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_cor_v1_5(tips, mapping = ggplot2::aes(total_bill, tip)))

# display with no grid
p_(ggally_cor_v1_5(
  tips,
  mapping = ggplot2::aes(total_bill, tip),
  displayGrid = FALSE
))

# change text attributes
p_(ggally_cor_v1_5(
  tips,
  mapping = ggplot2::aes(x = total_bill, y = tip),
  size = 15,
  colour = I("red")
))

# split by a variable
p_(ggally_cor_v1_5(
  tips,
  mapping = ggplot2::aes(total_bill, tip, color = sex),
  size = 5
))

Display counts of observations

Description

Plot the number of observations by using rectangles with proportional areas.

Usage

ggally_count(data, mapping, ...)

ggally_countDiag(data, mapping, ...)

Arguments

Details

You can adjust the size of rectangles with the x.width argument.

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_count(tips, mapping = ggplot2::aes(x = smoker, y = sex)))
p_(ggally_count(tips, mapping = ggplot2::aes(x = smoker, y = sex, fill = day)))

p_(ggally_count(
  as.data.frame(Titanic),
  mapping = ggplot2::aes(x = Class, y = Survived, weight = Freq)
))
p_(ggally_count(
  as.data.frame(Titanic),
  mapping = ggplot2::aes(x = Class, y = Survived, weight = Freq),
  x.width = 0.5
))
# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

p_(ggally_countDiag(tips, mapping = ggplot2::aes(x = smoker)))
p_(ggally_countDiag(tips, mapping = ggplot2::aes(x = smoker, fill = sex)))

Plots the number of observations

Description

Plot the number of observations by using square points with proportional areas. Could be filled according to chi-squared statistics computed by stat_cross(). Labels could also be added (see examples).

Usage

ggally_cross(data, mapping, ..., scale_max_size = 20, geom_text_args = NULL)

Arguments

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_cross(tips, mapping = aes(x = smoker, y = sex)))
p_(ggally_cross(tips, mapping = aes(x = day, y = time)))

# Custom max size
p_(ggally_cross(tips, mapping = aes(x = smoker, y = sex)) +
  scale_size_area(max_size = 40))

# Custom fill
p_(ggally_cross(tips, mapping = aes(x = smoker, y = sex), fill = "red"))

# Custom shape
p_(ggally_cross(tips, mapping = aes(x = smoker, y = sex), shape = 21))

# Fill squares according to standardized residuals
d <- as.data.frame(Titanic)
p_(ggally_cross(
  d,
  mapping = aes(x = Class, y = Survived, weight = Freq, fill = after_stat(std.resid))
) +
  scale_fill_steps2(breaks = c(-3, -2, 2, 3), show.limits = TRUE))

# Add labels
p_(ggally_cross(
  tips,
  mapping = aes(
    x = smoker, y = sex, colour = smoker,
    label = scales::percent(after_stat(prop))
  )
))

# Customize labels' appearance and same size for all squares
p_(ggally_cross(
  tips,
  mapping = aes(
    x = smoker, y = sex,
    size = NULL, # do not map size to a variable
    label = scales::percent(after_stat(prop))
  ),
  size = 40, # fix value for points size
  fill = "darkblue",
  geom_text_args = list(colour = "white", fontface = "bold", size = 6)
))

Display a cross-tabulated table

Description

ggally_crosstable is a variation of ggally_table with few modifications: (i) table cells are drawn; (ii) x and y axis are not expanded (and therefore are not aligned with other ggally_* plots); (iii) content and fill of cells can be easily controlled with dedicated arguments.

Usage

ggally_crosstable(
  data,
  mapping,
  cells = c("observed", "prop", "row.prop", "col.prop", "expected", "resid", "std.resid"),
  fill = c("none", "std.resid", "resid"),
  ...,
  geom_tile_args = list(colour = "grey50")
)

Arguments

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)

# differences with ggally_table()
p_(ggally_table(tips, mapping = aes(x = day, y = time)))
p_(ggally_crosstable(tips, mapping = aes(x = day, y = time)))

# display column proportions
p_(ggally_crosstable(tips, mapping = aes(x = day, y = sex), cells = "col.prop"))

# display row proportions
p_(ggally_crosstable(tips, mapping = aes(x = day, y = sex), cells = "row.prop"))

# change size of text
p_(ggally_crosstable(tips, mapping = aes(x = day, y = sex), size = 8))

# fill cells with standardized residuals
p_(ggally_crosstable(tips, mapping = aes(x = day, y = sex), fill = "std.resid"))

# change scale for fill
p_(ggally_crosstable(tips, mapping = aes(x = day, y = sex), fill = "std.resid") +
  scale_fill_steps2(breaks = c(-2, 0, 2), show.limits = TRUE))

Bivariate density plot

Description

Make a 2D density plot from a given data.

Usage

ggally_density(data, mapping, ...)

Arguments

Details

The aesthetic "fill" determines whether or not stat_density2d (filled) or geom_density2d (lines) is used.

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_density(tips, mapping = ggplot2::aes(x = total_bill, y = tip)))
p_(ggally_density(
  tips,
  mapping = ggplot2::aes(total_bill, tip, fill = after_stat(level))
))
p_(ggally_density(
  tips,
  mapping = ggplot2::aes(total_bill, tip, fill = after_stat(level))
) + ggplot2::scale_fill_gradient(breaks = c(0.05, 0.1, 0.15, 0.2)))

Univariate density plot

Description

Displays a density plot for the diagonal of a ggpairs plot matrix.

Usage

ggally_densityDiag(data, mapping, ..., rescale = FALSE)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill)))
p_(ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill, color = day)))

Tile plot with facets

Description

Displays a Tile Plot as densely as possible.

Usage

ggally_denstrip(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_denstrip(tips, mapping = ggplot2::aes(x = total_bill, y = sex)))
p_(ggally_denstrip(
  tips,
  mapping = ggplot2::aes(sex, tip), binwidth = 0.2
) + ggplot2::scale_fill_gradient(low = "grey80", high = "black"))

Internal axis labels for ggpairs

Description

This function is used when axisLabels == "internal".

Usage

ggally_diagAxis(
  data,
  mapping,
  label = mapping$x,
  labelSize = 5,
  labelXPercent = 0.5,
  labelYPercent = 0.55,
  labelHJust = 0.5,
  labelVJust = 0.5,
  gridLabelSize = 4,
  ...
)

Arguments

Author(s)

Jason Crowley and Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_diagAxis(tips, ggplot2::aes(x = tip)))
p_(ggally_diagAxis(tips, ggplot2::aes(x = sex)))

Grouped dot plot

Description

Add jittering with the box plot. ggally_dot_no_facet will be a single panel plot, while ggally_dot will be a faceted plot

Usage

ggally_dot(data, mapping, ...)

ggally_dot_no_facet(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_dot(tips, mapping = ggplot2::aes(x = total_bill, y = sex)))
p_(ggally_dot(
  tips,
  mapping = ggplot2::aes(sex, total_bill, color = sex)
))
p_(ggally_dot(
  tips,
  mapping = ggplot2::aes(sex, total_bill, color = sex, shape = sex)
) + ggplot2::scale_shape(solid = FALSE))

Box and dot plot

Description

Place box plots or dot plots on the graph

Usage

ggally_dot_and_box(data, mapping, ..., boxPlot = TRUE)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_dot_and_box(
  tips,
  mapping = ggplot2::aes(x = total_bill, y = sex, color = sex),
  boxPlot = TRUE
))
p_(ggally_dot_and_box(
  tips,
  mapping = ggplot2::aes(x = total_bill, y = sex, color = sex),
  boxPlot = FALSE
))

Faceted bar plot

Description

X variables are plotted using geom_bar and are faceted by the Y variable.

Usage

ggally_facetbar(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_facetbar(tips, ggplot2::aes(x = sex, y = smoker, fill = time)))
p_(ggally_facetbar(tips, ggplot2::aes(x = smoker, y = sex, fill = time)))

Faceted density plot

Description

Make density plots by displaying subsets of the data in different panels.

Usage

ggally_facetdensity(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_facetdensity(tips, mapping = ggplot2::aes(x = total_bill, y = sex)))
p_(ggally_facetdensity(
  tips,
  mapping = ggplot2::aes(sex, total_bill, color = sex)
))

Density or tiles plot with facets

Description

Make tile plot or density plot as compact as possible.

Usage

ggally_facetdensitystrip(data, mapping, ..., den_strip = FALSE)

Arguments

Author(s)

Barret Schloerke

Examples

example(ggally_facetdensity)
example(ggally_denstrip)

Faceted histogram

Description

Display subsetted histograms of the data in different panels.

Usage

ggally_facethist(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_facethist(tips, mapping = ggplot2::aes(x = tip, y = sex)))
p_(ggally_facethist(tips, mapping = ggplot2::aes(x = tip, y = sex), binwidth = 0.1))

NA plot

Description

Draws a large NA in the middle of the plotting area. This plot is useful when all X or Y data is NA

Usage

ggally_na(data = NULL, mapping = NULL, size = 10, color = "grey20", ...)

ggally_naDiag(...)

Arguments

Author(s)

Barret Schloerke

ggnostic Cook's distance

Description

A function to display stats::cooks.distance().

Usage

ggally_nostic_cooksd(
  data,
  mapping,
  ...,
  linePosition = pf(0.5, length(attr(data, "var_x")), nrow(data) - length(attr(data,
    "var_x"))),
  lineColor = brew_colors("grey"),
  lineType = 2
)

Arguments

Details

A line is added at F_{p,n-p}(0.5) to display the general cutoff point for Cook's Distance.

Reference: Michael H. Kutner, Christopher J. Nachtsheim, John Neter, and William Li. Applied linear statistical models. The McGraw-Hill / Irwin series operations and decision sciences. McGraw-Hill Irwin, 2005, p. 403

Value

ggplot2 plot object

See Also

stats::cooks.distance()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_cooksd(dt, ggplot2::aes(wt, .cooksd)))

ggnostic leverage points

Description

A function to display stats::influence's hat information against a given explanatory variable.

Usage

ggally_nostic_hat(
  data,
  mapping,
  ...,
  linePosition = 2 * sum(eval_data_col(data, mapping$y))/nrow(data),
  lineColor = brew_colors("grey"),
  lineSize = 0.5,
  lineAlpha = 1,
  lineType = 2,
  avgLinePosition = sum(eval_data_col(data, mapping$y))/nrow(data),
  avgLineColor = brew_colors("grey"),
  avgLineSize = lineSize,
  avgLineAlpha = lineAlpha,
  avgLineType = 1
)

Arguments

Details

As stated in stats::influence() documentation:

hat: a vector containing the diagonal of the 'hat' matrix.

The diagonal elements of the 'hat' matrix describe the influence each response value has on the fitted value for that same observation.

A suggested "cutoff" line is added to the plot at a height of 2 * p / n and an expected line at a height of p / n. If either linePosition or avgLinePosition is NULL, the respective line will not be drawn.

Value

ggplot2 plot object

See Also

stats::influence()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_hat(dt, ggplot2::aes(wt, .hat)))

ggnostic background line with geom

Description

If a non-null linePosition value is given, a line will be drawn before the given continuous_geom or combo_geom is added to the plot.

Usage

ggally_nostic_line(
  data,
  mapping,
  ...,
  linePosition = NULL,
  lineColor = "red",
  lineSize = 0.5,
  lineAlpha = 1,
  lineType = 1,
  continuous_geom = ggplot2::geom_point,
  combo_geom = ggplot2::geom_boxplot,
  mapColorToFill = TRUE
)

Arguments

Details

Functions with a color in their name have different default color behavior.

Value

ggplot2 plot object

ggnostic residuals

Description

If non-null pVal and sigma values are given, confidence interval lines will be added to the plot at the specified pVal percentiles of a N(0, sigma) distribution.

Usage

ggally_nostic_resid(
  data,
  mapping,
  ...,
  linePosition = 0,
  lineColor = brew_colors("grey"),
  lineSize = 0.5,
  lineAlpha = 1,
  lineType = 1,
  lineConfColor = brew_colors("grey"),
  lineConfSize = lineSize,
  lineConfAlpha = lineAlpha,
  lineConfType = 2,
  pVal = c(0.025, 0.975),
  sigma = attr(data, "broom_glance")$sigma,
  se = TRUE,
  method = "auto",
  formula = y ~ x
)

Arguments

Value

ggplot2 plot object

See Also

stats::residuals

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_resid(dt, ggplot2::aes(wt, .resid)))

ggnostic fitted value's standard error

Description

A function to display stats::predict's standard errors

Usage

ggally_nostic_se_fit(
  data,
  mapping,
  ...,
  lineColor = brew_colors("grey"),
  linePosition = NULL
)

Arguments

Details

As stated in stats::predict documentation:

If the logical 'se.fit' is 'TRUE', standard errors of the predictions are calculated. If the numeric argument 'scale' is set (with optional ”df'), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit.

Since the se.fit is TRUE and scale is unset by default, the standard errors are extracted from the model fit.

A base line of 0 is added to give reference to a perfect fit.

Value

ggplot2 plot object

See Also

stats::influence()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_se_fit(dt, ggplot2::aes(wt, .se.fit)))

ggnostic leave one out model sigma

Description

A function to display stats::influence()'s sigma value.

Usage

ggally_nostic_sigma(
  data,
  mapping,
  ...,
  lineColor = brew_colors("grey"),
  linePosition = attr(data, "broom_glance")$sigma
)

Arguments

Details

As stated in stats::influence() documentation:

sigma: a vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression. (The approximations needed for GLMs can result in this being 'NaN'.)

A line is added to display the overall model's sigma value. This gives a baseline for comparison

Value

ggplot2 plot object

See Also

stats::influence()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_sigma(dt, ggplot2::aes(wt, .sigma)))

ggnostic standardized residuals

Description

If non-null pVal and sigma values are given, confidence interval lines will be added to the plot at the specified pVal locations of a N(0, 1) distribution.

Usage

ggally_nostic_std_resid(data, mapping, ..., sigma = 1)

Arguments

Value

ggplot2 plot object

See Also

stats::rstandard()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars))
p_(ggally_nostic_std_resid(dt, ggplot2::aes(wt, .std.resid)))

Scatter plot

Description

Make a scatter plot with a given data set.

Usage

ggally_points(data, mapping, ...)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(mtcars)
p_(ggally_points(mtcars, mapping = ggplot2::aes(disp, hp)))
p_(ggally_points(mtcars, mapping = ggplot2::aes(disp, hp)))
p_(ggally_points(
  mtcars,
  mapping = ggplot2::aes(
    x     = disp,
    y     = hp,
    color = as.factor(cyl),
    size  = gear
  )
))

Mosaic plot

Description

Plots the mosaic plot by using fluctuation.

Usage

ggally_ratio(
  data,
  mapping = ggplot2::aes(!!!stats::setNames(lapply(colnames(data)[1:2], as.name), c("x",
    "y"))),
  ...,
  floor = 0,
  ceiling = NULL
)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_ratio(tips, ggplot2::aes(sex, day)))
p_(ggally_ratio(tips, ggplot2::aes(sex, day)) + ggplot2::coord_equal())
# only plot tiles greater or equal to 20 and scale to a max of 50
p_(ggally_ratio(
  tips, ggplot2::aes(sex, day),
  floor = 20, ceiling = 50
) + ggplot2::theme(aspect.ratio = 4 / 2))

Scatter plot with a smoothed line

Description

Add a smoothed condition mean with a given scatter plot.

Usage

ggally_smooth(
  data,
  mapping,
  ...,
  method = "lm",
  formula = y ~ x,
  se = TRUE,
  shrink = TRUE
)

ggally_smooth_loess(data, mapping, ...)

ggally_smooth_lm(data, mapping, ...)

Arguments

Details

Y limits are reduced to match original Y range with the goal of keeping the Y axis the same across plots.

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_smooth(tips, mapping = ggplot2::aes(x = total_bill, y = tip)))
p_(ggally_smooth(tips, mapping = ggplot2::aes(total_bill, tip, color = sex)))

Generalized text display

Description

Generalized text display

Usage

ggally_statistic(
  data,
  mapping,
  text_fn,
  title,
  na.rm = NA,
  display_grid = FALSE,
  justify_labels = "right",
  justify_text = "left",
  sep = ": ",
  family = "mono",
  title_args = list(),
  group_args = list(),
  align_percent = 0.5,
  title_hjust = 0.5,
  group_hjust = 0.5
)

Arguments

See Also

ggally_cor

Summarize a continuous variable by each value of a discrete variable

Description

Display summary statistics of a continuous variable for each value of a discrete variable.

Usage

ggally_summarise_by(
  data,
  mapping,
  text_fn = weighted_median_iqr,
  text_fn_vertical = NULL,
  ...
)

weighted_median_iqr(x, weights = NULL)

weighted_mean_sd(x, weights = NULL)

Arguments

Details

weighted_median_iqr computes weighted median and interquartile range.

weighted_mean_sd computes weighted mean and standard deviation.

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

if (require(Hmisc)) {
  data(tips)
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day)))
  p_(ggally_summarise_by(tips, mapping = aes(x = day, y = total_bill)))

  # colour is kept only if equal to the discrete variable
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day, color = day)))
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day, color = sex)))
  p_(ggally_summarise_by(tips, mapping = aes(x = day, y = total_bill, color = day)))

  # custom text size
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day), size = 6))

  # change statistic to display
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day), text_fn = weighted_mean_sd))

  # custom stat function
  weighted_sum <- function(x, weights = NULL) {
    if (is.null(weights)) weights <- 1
    paste0("Total : ", round(sum(x * weights, na.rm = TRUE), digits = 1))
  }
  p_(ggally_summarise_by(tips, mapping = aes(x = total_bill, y = day), text_fn = weighted_sum))
}

Display a table of the number of observations

Description

Plot the number of observations as a table. Other statistics computed by stat_cross could be used (see examples).

Usage

ggally_table(
  data,
  mapping,
  keep.zero.cells = FALSE,
  ...,
  geom_tile_args = NULL
)

ggally_tableDiag(
  data,
  mapping,
  keep.zero.cells = FALSE,
  ...,
  geom_tile_args = NULL
)

Arguments

Note

The colour aesthetic is taken into account only if equal to x or y.

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggally_table(tips, mapping = aes(x = smoker, y = sex)))
p_(ggally_table(tips, mapping = aes(x = day, y = time)))
p_(ggally_table(tips, mapping = aes(x = smoker, y = sex, colour = smoker)))

# colour is kept only if equal to x or y
p_(ggally_table(tips, mapping = aes(x = smoker, y = sex, colour = day)))

# diagonal version
p_(ggally_tableDiag(tips, mapping = aes(x = smoker)))

# custom label size and color
p_(ggally_table(tips, mapping = aes(x = smoker, y = sex), size = 16, color = "red"))

# display column proportions
p_(ggally_table(
  tips,
  mapping = aes(x = day, y = sex, label = scales::percent(after_stat(col.prop)))
))

# draw table cells
p_(ggally_table(
  tips,
  mapping = aes(x = smoker, y = sex),
  geom_tile_args = list(colour = "black", fill = "white")
))

# Use standardized residuals to fill table cells
p_(ggally_table(
  as.data.frame(Titanic),
  mapping = aes(
    x = Class, y = Survived, weight = Freq,
    fill = after_stat(std.resid),
    label = scales::percent(after_stat(col.prop), accuracy = .1)
  ),
  geom_tile_args = list(colour = "black")
) +
  scale_fill_steps2(breaks = c(-3, -2, 2, 3), show.limits = TRUE))

Text plot

Description

Plot text for a plot.

Usage

ggally_text(
  label,
  mapping = ggplot2::aes(color = I("black")),
  xP = 0.5,
  yP = 0.5,
  xrange = c(0, 1),
  yrange = c(0, 1),
  ...
)

Arguments

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

p_(ggally_text("Example 1"))
p_(ggally_text("Example\nTwo", mapping = ggplot2::aes(size = 15), color = I("red")))

Trends line plot

Description

Plot trends using line plots. For continuous y variables, plot the evolution of the mean. For binary y variables, plot the evolution of the proportion.

Usage

ggally_trends(data, mapping, ..., include_zero = FALSE)

Arguments

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
tips_f <- tips
tips_f$day <- factor(tips$day, c("Thur", "Fri", "Sat", "Sun"))

# Numeric variable
p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill)))
p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill, colour = time)))

# Binary variable
p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker)))
p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker, colour = sex)))

# Discrete variable with 3 or more categories
p_(ggally_trends(tips_f, mapping = aes(x = smoker, y = day)))
p_(ggally_trends(tips_f, mapping = aes(x = smoker, y = day, color = sex)))

# Include zero on Y axis
p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill), include_zero = TRUE))
p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker), include_zero = TRUE))

# Change line size
p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker, colour = sex), size = 3))

# Define weights with the appropriate aesthetic
d <- as.data.frame(Titanic)
p_(ggally_trends(
  d,
  mapping = aes(x = Class, y = Survived, weight = Freq, color = Sex),
  include_zero = TRUE
))

Display an outcome using several potential explanatory variables

Description

ggbivariate is a variant of ggduo for plotting an outcome variable with several potential explanatory variables.

Usage

ggbivariate(
  data,
  outcome,
  explanatory = NULL,
  mapping = NULL,
  types = NULL,
  ...,
  rowbar_args = NULL
)

Arguments

Author(s)

Joseph Larmarange

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggbivariate(tips, "smoker", c("day", "time", "sex", "tip")))

# Personalize plot title and legend title
p_(ggbivariate(
  tips, "smoker", c("day", "time", "sex", "tip"),
  title = "Custom title"
) +
  labs(fill = "Smoker ?"))

# Customize fill colour scale
p_(ggbivariate(tips, "smoker", c("day", "time", "sex", "tip")) +
  scale_fill_brewer(type = "qual"))

# Customize labels
p_(ggbivariate(
  tips, "smoker", c("day", "time", "sex", "tip"),
  rowbar_args = list(
    colour = "white",
    size = 4,
    fontface = "bold",
    label_format = scales::label_percent(accurary = 1)
  )
))

# Choose the sub-plot from which get legend
p_(ggbivariate(tips, "smoker"))
p_(ggbivariate(tips, "smoker", legend = 3))

# Use mapping to indicate weights
d <- as.data.frame(Titanic)
p_(ggbivariate(d, "Survived", mapping = aes(weight = Freq)))

# outcome can be numerical
p_(ggbivariate(tips, outcome = "tip", title = "tip"))

Model coefficients with broom and ggplot2

Description

Plot the coefficients of a model with broom and ggplot2. For an updated and improved version, see ggcoef_model().

Usage

ggcoef(
  x,
  mapping = aes(!!as.name("estimate"), !!as.name("term")),
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  exclude_intercept = FALSE,
  vline = TRUE,
  vline_intercept = "auto",
  vline_color = "gray50",
  vline_linetype = "dotted",
  vline_size = 1,
  errorbar_color = "gray25",
  errorbar_height = 0,
  errorbar_linetype = "solid",
  errorbar_size = 0.5,
  sort = c("none", "ascending", "descending"),
  ...
)

Arguments

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

library(broom)
reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris)
p_(ggcoef(reg))

d <- as.data.frame(Titanic)
reg2 <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq)
ggcoef(reg2, exponentiate = TRUE)
ggcoef(
  reg2,
  exponentiate = TRUE, exclude_intercept = TRUE,
  errorbar_height = .2, color = "blue", sort = "ascending"
)

Correlation matrix

Description

Function for making a correlation matrix plot, using ggplot2. The function is directly inspired by Tian Zheng and Yu-Sung Su's corrplot function in the 'arm' package. Please visit https://github.com/briatte/ggcorr for the latest version of ggcorr, and see the vignette at https://briatte.github.io/ggcorr/ for many examples of how to use it.

Usage

ggcorr(
  data,
  method = c("pairwise", "pearson"),
  cor_matrix = NULL,
  nbreaks = NULL,
  digits = 2,
  name = "",
  low = "#3B9AB2",
  mid = "#EEEEEE",
  high = "#F21A00",
  midpoint = 0,
  palette = NULL,
  geom = "tile",
  min_size = 2,
  max_size = 6,
  label = FALSE,
  label_alpha = FALSE,
  label_color = "black",
  label_round = 1,
  label_size = 4,
  limits = c(-1, 1),
  drop = is.null(limits) || identical(limits, FALSE),
  layout.exp = 0,
  legend.position = "right",
  legend.size = 9,
  ...
)

Arguments

Note

Recommended values for the nbreaks argument are 3 to 11, as values above 11 are visually difficult to separate and are not supported by diverging ColorBrewer palettes.

Author(s)

Francois Briatte, with contributions from Amos B. Elberg and Barret Schloerke

See Also

cor and corrplot in the arm package.

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

# Basketball statistics provided by Nathan Yau at Flowing Data.
dt <- read.csv("http://datasets.flowingdata.com/ppg2008.csv")

# Default output.
p_(ggcorr(dt[, -1]))

# Labeled output, with coefficient transparency.
p_(ggcorr(dt[, -1],
  label = TRUE,
  label_alpha = TRUE
))

# Custom options.
p_(ggcorr(
  dt[, -1],
  name = expression(rho),
  geom = "circle",
  max_size = 10,
  min_size = 2,
  size = 3,
  hjust = 0.75,
  nbreaks = 6,
  angle = -45,
  palette = "PuOr" # colorblind safe, photocopy-able
))

# Supply your own correlation matrix
p_(ggcorr(
  data = NULL,
  cor_matrix = cor(dt[, -1], use = "pairwise")
))

ggplot2 generalized pairs plot for two columns sets of data

Description

Make a matrix of plots with a given data set with two different column sets

Usage

ggduo(
  data,
  mapping = NULL,
  columnsX = 1:ncol(data),
  columnsY = 1:ncol(data),
  title = NULL,
  types = list(continuous = "smooth_loess", comboVertical = "box_no_facet",
    comboHorizontal = "facethist", discrete = "count"),
  axisLabels = c("show", "none"),
  columnLabelsX = colnames(data[columnsX]),
  columnLabelsY = colnames(data[columnsY]),
  labeller = "label_value",
  switch = NULL,
  xlab = NULL,
  ylab = NULL,
  showStrips = NULL,
  legend = NULL,
  cardinality_threshold = 15,
  progress = NULL,
  xProportions = NULL,
  yProportions = NULL,
  legends = deprecated()
)

Arguments

Details

types is a list that may contain the variables 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, If a string is supplied, it must be a character string representing the tail end of a ggally_NAME function. The list of current valid ggally_NAME functions is visible in a dedicated vignette.

continuous

This option is used for continuous X and Y data.

comboHorizontal

This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.

comboVertical

This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.

discrete

This option is used for categorical X and Y data.

na

This option is used when all X data is NA, all Y data is NA, or either all X or Y data is NA.

If 'blank' is ever chosen as an option, then ggduo will produce an empty plot.

If a function is supplied as an option, it should implement the function api of function(data, mapping, ...){#make ggplot2 plot}. If a specific function needs its parameters set, wrap(fn, param1 = val1, param2 = val2) the function with its parameters.

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(baseball)

# Keep players from 1990-1995 with at least one at bat
# Add how many singles a player hit
# (must do in two steps as X1b is used in calculations)
dt <- transform(
  subset(baseball, year >= 1990 & year <= 1995 & ab > 0),
  X1b = h - X2b - X3b - hr
)
# Add
#  the player's batting average,
#  the player's slugging percentage,
#  and the player's on base percentage
# Make factor a year, as each season is discrete
dt <- transform(
  dt,
  batting_avg = h / ab,
  slug = (X1b + 2 * X2b + 3 * X3b + 4 * hr) / ab,
  on_base = (h + bb + hbp) / (ab + bb + hbp),
  year = as.factor(year)
)


pm <- ggduo(
  dt,
  c("year", "g", "ab", "lg"),
  c("batting_avg", "slug", "on_base"),
  mapping = ggplot2::aes(color = lg)
)
# Prints, but
#   there is severe over plotting in the continuous plots
#   the labels could be better
#   want to add more hitting information
p_(pm)

# address overplotting issues and add a title
pm <- ggduo(
  dt,
  c("year", "g", "ab", "lg"),
  c("batting_avg", "slug", "on_base"),
  columnLabelsX = c("year", "player game count", "player at bat count", "league"),
  columnLabelsY = c("batting avg", "slug %", "on base %"),
  title = "Baseball Hitting Stats from 1990-1995",
  mapping = ggplot2::aes(color = lg),
  types = list(
    # change the shape and add some transparency to the points
    continuous = wrap("smooth_loess", alpha = 0.50, shape = "+")
  ),
  showStrips = FALSE
)

p_(pm)

# Use "auto" to adapt width of the sub-plots
pm <- ggduo(
  dt,
  c("year", "g", "ab", "lg"),
  c("batting_avg", "slug", "on_base"),
  mapping = ggplot2::aes(color = lg),
  xProportions = "auto"
)

p_(pm)

# Custom widths & heights of the sub-plots
pm <- ggduo(
  dt,
  c("year", "g", "ab", "lg"),
  c("batting_avg", "slug", "on_base"),
  mapping = ggplot2::aes(color = lg),
  xProportions = c(6, 4, 3, 2),
  yProportions = c(1, 2, 1)
)

p_(pm)

# Example derived from:
## R Data Analysis Examples | Canonical Correlation Analysis.  UCLA: Institute for Digital
##   Research and Education.
##   from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis
##   (accessed May 22, 2017).
# "Example 1. A researcher has collected data on three psychological variables, four
#  academic variables (standardized test scores) and gender for 600 college freshman.
#  She is interested in how the set of psychological variables relates to the academic
#  variables and gender. In particular, the researcher is interested in how many
#  dimensions (canonical variables) are necessary to understand the association between
#  the two sets of variables."
data(psychademic)
summary(psychademic)

(psych_variables <- attr(psychademic, "psychology"))
(academic_variables <- attr(psychademic, "academic"))

## Within correlation
p_(ggpairs(psychademic, columns = psych_variables))
p_(ggpairs(psychademic, columns = academic_variables))

## Between correlation
loess_with_cor <- function(data, mapping, ..., method = "pearson") {
  x <- eval_data_col(data, mapping$x)
  y <- eval_data_col(data, mapping$y)
  cor <- cor(x, y, method = method)
  ggally_smooth_loess(data, mapping, ...) +
    ggplot2::geom_label(
      data = data.frame(
        x = min(x, na.rm = TRUE),
        y = max(y, na.rm = TRUE),
        lab = round(cor, digits = 3)
      ),
      mapping = ggplot2::aes(x = x, y = y, label = lab),
      hjust = 0, vjust = 1,
      size = 5, fontface = "bold",
      inherit.aes = FALSE # do not inherit anything from the ...
    )
}
pm <- ggduo(
  psychademic,
  rev(psych_variables), academic_variables,
  types = list(continuous = loess_with_cor),
  showStrips = FALSE
)
suppressWarnings(p_(pm)) # ignore warnings from loess

# add color according to sex
pm <- ggduo(
  psychademic,
  mapping = ggplot2::aes(color = sex),
  rev(psych_variables), academic_variables,
  types = list(continuous = loess_with_cor),
  showStrips = FALSE,
  legend = c(5, 2)
)
suppressWarnings(p_(pm))


# add color according to sex
pm <- ggduo(
  psychademic,
  mapping = ggplot2::aes(color = motivation),
  rev(psych_variables), academic_variables,
  types = list(continuous = loess_with_cor),
  showStrips = FALSE,
  legend = c(5, 2)
) +
  ggplot2::theme(legend.position = "bottom")
suppressWarnings(p_(pm))
#  dt,
#  c("year", "g", "ab", "lg", "lg"),
#  c("batting_avg", "slug", "on_base", "hit_type"),
#  columnLabelsX = c("year", "player game count", "player at bat count", "league", ""),
#  columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"),
#  title = "Baseball Hitting Stats from 1990-1995 (player strike in 1994)",
#  mapping = aes(color = year),
#  types = list(
#    continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"),
#    comboHorizontal = wrap(display_hit_type_combo, binwidth = 15),
#    discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15)
#  ),
#  showStrips = FALSE
## make the 5th column blank, except for the legend
#  australia_PISA2012,
#  c("gender", "age", "homework", "possessions"),
#  c("PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH"),
#  types = list(
#    continuous = "points",
#    combo = "box",
#    discrete = "ratio"
#  )
#  australia_PISA2012,
#  c("gender", "age", "homework", "possessions"),
#  c("PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH"),
#  mapping = ggplot2::aes(color = gender),
#  types = list(
#    continuous = wrap("smooth", alpha = 0.25, method = "loess"),
#    combo = "box",
#    discrete = "ratio"
#  )

Single ggplot2 plot matrix with facet_grid

Description

Single ggplot2 plot matrix with facet_grid

Usage

ggfacet(
  data,
  mapping = NULL,
  columnsX = 1:ncol(data),
  columnsY = 1:ncol(data),
  fn = ggally_points,
  ...,
  columnLabelsX = names(data[columnsX]),
  columnLabelsY = names(data[columnsY]),
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  scales = "free"
)

Arguments

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive
if (requireNamespace("chemometrics", quietly = TRUE)) {
  data(NIR, package = "chemometrics")
  NIR_sub <- data.frame(NIR$yGlcEtOH, NIR$xNIR[, 1:3])
  str(NIR_sub)
  x_cols <- c("X1115.0", "X1120.0", "X1125.0")
  y_cols <- c("Glucose", "Ethanol")

  # using ggduo directly
  p <- ggduo(NIR_sub, x_cols, y_cols, types = list(continuous = "points"))
  p_(p)

  # using ggfacet
  p <- ggfacet(NIR_sub, x_cols, y_cols)
  p_(p)

  # add a smoother
  p <- ggfacet(NIR_sub, x_cols, y_cols, fn = "smooth_loess")
  p_(p)
  # same output
  p <- ggfacet(NIR_sub, x_cols, y_cols, fn = ggally_smooth_loess)
  p_(p)

  # Change scales to be the same in for every row and for every column
  p <- ggfacet(NIR_sub, x_cols, y_cols, scales = "fixed")
  p_(p)
}

Plot only legend of plot function

Description

Plot only legend of plot function

Usage

gglegend(fn)

Arguments

Value

a function that when called with arguments will produce the legend of the plotting function supplied.

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

# display regular plot
p_(ggally_points(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)))

# Make a function that will only print the legend
points_legend <- gglegend(ggally_points)
p_(points_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)))

# produce the sample legend plot, but supply a string that 'wrap' understands
same_points_legend <- gglegend("points")
identical(
  attr(attr(points_legend, "fn"), "original_fn"),
  attr(attr(same_points_legend, "fn"), "original_fn")
)

# Complicated examples
custom_legend <- wrap(gglegend("points"), size = 6)
p_(custom_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)))

# Use within ggpairs
pm <- ggpairs(
  iris, 1:2,
  mapping = ggplot2::aes(color = Species),
  upper = list(continuous = gglegend("points"))
)
p_(pm)

# Place a legend in a specific location
pm <- ggpairs(iris, 1:2, mapping = ggplot2::aes(color = Species))
# Make the legend
pm[1, 2] <- points_legend(iris, ggplot2::aes(Sepal.Width, Sepal.Length, color = Species))
p_(pm)

ggplot2 plot matrix

Description

Make a generic matrix of ggplot2 plots.

Usage

ggmatrix(
  plots,
  nrow,
  ncol,
  xAxisLabels = NULL,
  yAxisLabels = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  byrow = TRUE,
  showStrips = NULL,
  showAxisPlotLabels = TRUE,
  showXAxisPlotLabels = TRUE,
  showYAxisPlotLabels = TRUE,
  labeller = NULL,
  switch = NULL,
  xProportions = NULL,
  yProportions = NULL,
  progress = NULL,
  data = NULL,
  gg = NULL,
  legend = NULL
)

Arguments

Memory usage

Now that the print.ggmatrix method uses a large gtable object, rather than print each plot independently, memory usage may be of concern. From small tests, memory usage flutters around object.size(data) * 0.3 * length(plots). So, for a 80Mb random noise dataset with 100 plots, about 2.4 Gb of memory needed to print. For the 3.46 Mb diamonds dataset with 100 plots, about 100 Mb of memory was needed to print. The benefits of using the ggplot2 format greatly outweigh the price of about 20% increase in memory usage from the prior ad-hoc print method.

Author(s)

Barret Schloerke

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

plotList <- list()
for (i in 1:6) {
  plotList[[i]] <- ggally_text(paste("Plot #", i, sep = ""))
}
pm <- ggmatrix(
  plotList,
  2, 3,
  c("A", "B", "C"),
  c("D", "E"),
  byrow = TRUE
)
p_(pm)

pm <- ggmatrix(
  plotList,
  2, 3,
  xAxisLabels = c("A", "B", "C"),
  yAxisLabels = NULL,
  byrow = FALSE,
  showXAxisPlotLabels = FALSE
)
p_(pm)

ggmatrix gtable object

Description

Specialized method to print the ggmatrix object.

Usage

ggmatrix_gtable(
  pm,
  ...,
  progress = NULL,
  progress_format = formals(ggmatrix_progress)$format
)

Arguments

Author(s)

Barret Schloerke

Examples

data(tips)
pm <- ggpairs(tips, c(1, 3, 2), mapping = ggplot2::aes(color = sex))
ggmatrix_gtable(pm)

ggmatrix plot locations

Description

Usage

ggmatrix_location(pm, location = NULL, rows = NULL, cols = NULL)

Arguments

Details

Convert many types of location values to a consistent data.frame of row and col values.

Value

Data frame with columns c("row", "col") containing locations for the plot matrix

Examples

pm <- ggpairs(tips, 1:3)

# All locations
ggmatrix_location(pm, location = "all")
ggmatrix_location(pm, location = TRUE)

# No locations
ggmatrix_location(pm, location = "none")

# "upper" triangle locations
ggmatrix_location(pm, location = "upper")

# "lower" triangle locations
ggmatrix_location(pm, location = "lower")

# "diag" locations
ggmatrix_location(pm, location = "diag")

# specific rows
ggmatrix_location(pm, rows = 2)

# specific columns
ggmatrix_location(pm, cols = 2)

# row and column combinations
ggmatrix_location(pm, rows = c(1, 2), cols = c(1, 3))

# matrix locations
mat <- matrix(TRUE, ncol = 3, nrow = 3)
mat[1, 1] <- FALSE
locs <- ggmatrix_location(pm, location = mat)
## does not contain the 1, 1 cell
locs

# Use the output of a prior ggmatrix_location
ggmatrix_location(pm, location = locs)

ggmatrix default progress bar

Description

ggmatrix default progress bar

Usage

ggmatrix_progress(
  format = " plot: [:plot_i, :plot_j] [:bar]:percent est::eta ",
  clear = TRUE,
  show_after = 0,
  ...
)

Arguments

Value

function that accepts a plot matrix as the first argument and ... for future expansion. Internally, the plot matrix is used to determine the total number of plots for the progress bar.

Examples

p_ <- GGally::print_if_interactive

pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress())
p_(pm)

# does not clear after finishing
pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress(clear = FALSE))
p_(pm)

Network plot

Description

Function for plotting network objects using ggplot2, now replaced by the ggnet2 function, which provides additional control over plotting parameters. Please visit https://github.com/briatte/ggnet for the latest version of ggnet2, and https://briatte.github.io/ggnet/ for a vignette that contains many examples and explanations.

Usage

ggnet(
  net,
  mode = "fruchtermanreingold",
  layout.par = NULL,
  layout.exp = 0,
  size = 9,
  alpha = 1,
  weight = "none",
  weight.legend = NA,
  weight.method = weight,
  weight.min = NA,
  weight.max = NA,
  weight.cut = FALSE,
  group = NULL,
  group.legend = NA,
  node.group = group,
  node.color = NULL,
  node.alpha = alpha,
  segment.alpha = alpha,
  segment.color = "grey50",
  segment.label = NULL,
  segment.size = 0.25,
  arrow.size = 0,
  arrow.gap = 0,
  arrow.type = "closed",
  label = FALSE,
  label.nodes = label,
  label.size = size/2,
  label.trim = FALSE,
  legend.size = 9,
  legend.position = "right",
  names = deprecated(),
  quantize.weights = deprecated(),
  subset.threshold = deprecated(),
  top8.nodes = deprecated(),
  trim.labels = deprecated(),
  ...
)

Arguments

Details

The degree centrality measures that can be produced through the weight argument will take the directedness of the network into account, but will be unweighted. To compute weighted network measures, see the tnet package by Tore Opsahl (help("tnet", package = "tnet")).

Author(s)

Moritz Marbach and Francois Briatte, with help from Heike Hofmann, Pedro Jordano and Ming-Yu Liu

See Also

ggnet2 in this package, gplot in the sna package, and plot.network in the network package

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

library(network)

# random adjacency matrix
x           <- 10
ndyads      <- x * (x - 1)
density     <- x / ndyads
m           <- matrix(0, nrow = x, ncol = x)
dimnames(m) <- list(letters[1:x], letters[1:x])
m[row(m) != col(m)] <- runif(ndyads) < density
m

# random undirected network
n <- network::network(m, directed = FALSE)
n

ggnet(n, label = TRUE, alpha = 1, color = "white", segment.color = "black")

# random groups
g <- sample(letters[1:3], 10, replace = TRUE)
g

# color palette
p <- c("a" = "steelblue", "b" = "forestgreen", "c" = "tomato")

p_(ggnet(n, node.group = g, node.color = p, label = TRUE, color = "white"))

# edge arrows on a directed network
p_(ggnet(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10))

Network plot

Description

Function for plotting network objects using ggplot2, with additional control over graphical parameters that are not supported by the ggnet function. Please visit https://github.com/briatte/ggnet for the latest version of ggnet2, and https://briatte.github.io/ggnet/ for a vignette that contains many examples and explanations.

Usage

ggnet2(
  net,
  mode = "fruchtermanreingold",
  layout.par = NULL,
  layout.exp = 0,
  alpha = 1,
  color = "grey75",
  shape = 19,
  size = 9,
  max_size = 9,
  na.rm = NA,
  palette = NULL,
  alpha.palette = NULL,
  alpha.legend = NA,
  color.palette = palette,
  color.legend = NA,
  shape.palette = NULL,
  shape.legend = NA,
  size.palette = NULL,
  size.legend = NA,
  size.zero = FALSE,
  size.cut = FALSE,
  size.min = NA,
  size.max = NA,
  label = FALSE,
  label.alpha = 1,
  label.color = "black",
  label.size = max_size/2,
  label.trim = FALSE,
  node.alpha = alpha,
  node.color = color,
  node.label = label,
  node.shape = shape,
  node.size = size,
  edge.alpha = 1,
  edge.color = "grey50",
  edge.lty = "solid",
  edge.size = 0.25,
  edge.label = NULL,
  edge.label.alpha = 1,
  edge.label.color = label.color,
  edge.label.fill = "white",
  edge.label.size = max_size/2,
  arrow.size = 0,
  arrow.gap = 0,
  arrow.type = "closed",
  legend.size = 9,
  legend.position = "right",
  ...
)

Arguments

Details

The degree centrality measures that can be produced through the size argument will take the directedness of the network into account, but will be unweighted. To compute weighted network measures, see the tnet package by Tore Opsahl (help("tnet", package = "tnet")).

The nodes of bipartite networks can be mapped to their mode by passing the "mode" argument to any of alpha, color, shape and size, in which case the nodes of the primary mode will be mapped as "actor", and the nodes of the secondary mode will be mapped as "event".

Author(s)

Moritz Marbach and Francois Briatte, with help from Heike Hofmann, Pedro Jordano and Ming-Yu Liu

See Also

ggnet in this package, gplot in the sna package, and plot.network in the network package

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

library(network)

# random adjacency matrix
x           <- 10
ndyads      <- x * (x - 1)
density     <- x / ndyads
m           <- matrix(0, nrow = x, ncol = x)
dimnames(m) <- list(letters[1:x], letters[1:x])
m[row(m) != col(m)] <- runif(ndyads) < density
m

# random undirected network
n <- network::network(m, directed = FALSE)
n

p_(ggnet2(n, label = TRUE))
p_(ggnet2(n, label = TRUE, shape = 15))
p_(ggnet2(n, label = TRUE, shape = 15, color = "black", label.color = "white"))

# add vertex attribute
x = network.vertex.names(n)
x = ifelse(x %in% c("a", "e", "i"), "vowel", "consonant")
n %v% "phono" = x

p_(ggnet2(n, color = "phono"))
p_(ggnet2(n, color = "phono", palette = c("vowel" = "gold", "consonant" = "grey")))
p_(ggnet2(n, shape = "phono", color = "phono"))

if (require(RColorBrewer)) {

  # random groups
  n %v% "group" <- sample(LETTERS[1:3], 10, replace = TRUE)

  p_(ggnet2(n, color = "group", palette = "Set2"))

}

# random weights
n %e% "weight" <- sample(1:3, network.edgecount(n), replace = TRUE)
p_(ggnet2(n, edge.size = "weight", edge.label = "weight"))

# edge arrows on a directed network
p_(ggnet2(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10))

# Padgett's Florentine wedding data
data(flo, package = "network")
flo

p_(ggnet2(flo, label = TRUE))
p_(ggnet2(flo, label = TRUE, label.trim = 4, vjust = -1, size = 3, color = 1))
p_(ggnet2(flo, label = TRUE, size = 12, color = "white"))

Network plot map overlay

Description

Plots a network with ggplot2 suitable for overlay on a ggmap plot or ggplot2

Usage

ggnetworkmap(
  gg,
  net,
  size = 3,
  alpha = 0.75,
  weight,
  node.group,
  node.color = NULL,
  node.alpha = NULL,
  ring.group,
  segment.alpha = NULL,
  segment.color = "grey",
  great.circles = FALSE,
  segment.size = 0.25,
  arrow.size = 0,
  label.nodes = FALSE,
  label.size = size/2,
  ...
)

Arguments

Details

This is a descendant of the original ggnet function. ggnet added the innovation of plotting the network geographically. However, ggnet needed to be the first object in the ggplot chain. ggnetworkmap does not. If passed a ggplot object as its first argument, such as output from ggmap, ggnetworkmap will plot on top of that chart, looking for vertex attributes lon and lat as coordinates. Otherwise, ggnetworkmap will generate coordinates using the Fruchterman-Reingold algorithm.

This is a function for plotting graphs generated by network or igraph in a more flexible and elegant manner than permitted by ggnet. The function does not need to be the first plot in the ggplot chain, so the graph can be plotted on top of a map or other chart. Segments can be straight lines, or plotted as great circles. Note that the great circles feature can produce odd results with arrows and with vertices beyond the plot edges; this is a ggplot2 limitation and cannot yet be fixed. Nodes can have two color schemes, which are then plotted as the center and ring around the node. The color schemes are selected by adding scale_fill_ or scale_color_ just like any other ggplot2 plot. If there are no rings, scale_color sets the color of the nodes. If there are rings, scale_color sets the color of the rings, and scale_fill sets the color of the centers. Note that additional arguments in the ... are passed to geom_text for plotting labels.

Author(s)

Amos Elberg. Original by Moritz Marbach, Francois Briatte

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

invisible(lapply(c("ggplot2", "maps", "network", "sna"), base::library, character.only = TRUE))

## Example showing great circles on a simple map of the USA
## http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/

airports <- read.csv("http://datasets.flowingdata.com/tuts/maparcs/airports.csv", header = TRUE)
rownames(airports) <- airports$iata

# select some random flights
set.seed(123)
flights <- data.frame(
  origin = sample(airports[200:400, ]$iata, 200, replace = TRUE),
  destination = sample(airports[200:400, ]$iata, 200, replace = TRUE)
)

# convert to network
flights <- network(flights, directed = TRUE)

# add geographic coordinates
flights %v% "lat" <- airports[network.vertex.names(flights), "lat"]
flights %v% "lon" <- airports[network.vertex.names(flights), "long"]

# drop isolated airports
delete.vertices(flights, which(degree(flights) < 2))

# compute degree centrality
flights %v% "degree" <- degree(flights, gmode = "digraph")

# add random groups
flights %v% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE)

# create a map of the USA
usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) +
  geom_polygon(aes(group = group),
    color = "grey65",
    fill = "#f9f9f9", linewidth = 0.2
  )

# overlay network data to map
p <- ggnetworkmap(
  usa, flights,
  size = 4, great.circles = TRUE,
  node.group = mygroup, segment.color = "steelblue",
  ring.group = degree, weight = degree
)
p_(p)

## Exploring a community of spambots found on Twitter
## Data by Amos Elberg: see ?twitter_spambots for details

data(twitter_spambots)

# create a world map
world <- fortify(map("world", plot = FALSE, fill = TRUE))
world <- ggplot(world, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group),
    color = "grey65",
    fill = "#f9f9f9", linewidth = 0.2
  )

# view global structure
p <- ggnetworkmap(world, twitter_spambots)
p_(p)

# domestic distribution
p <- ggnetworkmap(net = twitter_spambots)
p_(p)

# topology
p <- ggnetworkmap(net = twitter_spambots, arrow.size = 0.5)
p_(p)

# compute indegree and outdegree centrality
twitter_spambots %v% "indegree" <- degree(twitter_spambots, cmode = "indegree")
twitter_spambots %v% "outdegree" <- degree(twitter_spambots, cmode = "outdegree")

p <- ggnetworkmap(
  net = twitter_spambots,
  arrow.size = 0.5,
  node.group = indegree,
  ring.group = outdegree, size = 4
) +
  scale_fill_continuous("Indegree", high = "red", low = "yellow") +
  labs(color = "Outdegree")
p_(p)

# show some vertex attributes associated with each account
p <- ggnetworkmap(
  net = twitter_spambots,
  arrow.size = 0.5,
  node.group = followers,
  ring.group = friends,
  size = 4,
  weight = indegree,
  label.nodes = TRUE, vjust = -1.5
) +
  scale_fill_continuous("Followers", high = "red", low = "yellow") +
  labs(color = "Friends") +
  scale_color_continuous(low = "lightgreen", high = "darkgreen")
p_(p)

Plot matrix of statistical model diagnostics

Description

Plot matrix of statistical model diagnostics

Usage

ggnostic(
  model,
  ...,
  columnsX = attr(data, "var_x"),
  columnsY = c(".resid", ".sigma", ".hat", ".cooksd"),
  columnLabelsX = attr(data, "var_x_label"),
  columnLabelsY = gsub("\\.", " ", gsub("^\\.", "", columnsY)),
  xlab = "explanatory variables",
  ylab = "diagnostics",
  title = paste(deparse(model$call, width.cutoff = 500L), collapse = "\n"),
  continuous = list(default = ggally_points, .fitted = ggally_points, .se.fit =
    ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma
    = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid =
    ggally_nostic_std_resid),
  combo = list(default = ggally_box_no_facet, .fitted = ggally_box_no_facet, .se.fit =
    ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma
    = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid =
    ggally_nostic_std_resid),
  discrete = list(default = ggally_ratio, .fitted = ggally_ratio, .se.fit = ggally_ratio,
    .resid = ggally_ratio, .hat = ggally_ratio, .sigma = ggally_ratio, .cooksd =
    ggally_ratio, .std.resid = ggally_ratio),
  progress = NULL,
  data = broomify(model)
)

Arguments

columnsY

broom::augment() collects data from the supplied model and returns a data.frame with the following columns (taken directly from broom documentation). These columns are the only allowed values in the columnsY parameter to ggnostic.

.resid

Residuals

.hat

Diagonal of the hat matrix

.sigma

Estimate of residual standard deviation when corresponding observation is dropped from model

.cooksd

Cooks distance, stats::cooks.distance()

.fitted

Fitted values of model

.se.fit

Standard errors of fitted values

.std.resid

Standardized residuals

response variable name

The response variable in the model may be added. Such as "mpg" in the model lm(mpg ~ ., data = mtcars)

continuous, combo, discrete types

Similar to ggduo and ggpairs, functions may be supplied to display the different column types. However, since the Y rows are fixed, each row has it's own corresponding function in each of the plot types: continuous, combo, and discrete. Each plot type list can have keys that correspond to the broom::augment() output: ".fitted", ".resid", ".std.resid", ".sigma", ".se.fit", ".hat", ".cooksd". An extra key, "default", is used to plot the response variables of the model if they are included. Having a function for each diagnostic allows for very fine control over the diagnostics plot matrix. The functions for each type list are wrapped into a switch function that calls the function corresponding to the y variable being plotted. These switch functions are then passed directly to the types parameter in ggduo.

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive
data(mtcars)

# use mtcars dataset and alter the 'am' column to display actual name values
mtc <- mtcars
mtc$am <- c("0" = "automatic", "1" = "manual")[as.character(mtc$am)]

# step the complete model down to a smaller model
mod <- stats::step(stats::lm(mpg ~ ., data = mtc), trace = FALSE)

# display using defaults
pm <- ggnostic(mod)
p_(pm)

# color by am value
pm <- ggnostic(mod, mapping = ggplot2::aes(color = am))
p_(pm)

# turn resid smooth error ribbon off
pm <- ggnostic(mod, continuous = list(.resid = wrap("nostic_resid", se = FALSE)))
p_(pm)


## plot residuals vs fitted in a ggpairs plot matrix
dt <- broomify(mod)
pm <- ggpairs(
  dt, c(".fitted", ".resid"),
  columnLabels = c("fitted", "residuals"),
  lower = list(continuous = ggally_nostic_resid)
)
p_(pm)

ggplot2 generalized pairs plot

Description

Make a matrix of plots with a given data set

Usage

ggpairs(
  data,
  mapping = NULL,
  columns = 1:ncol(data),
  title = NULL,
  upper = list(continuous = "cor", combo = "box_no_facet", discrete = "count", na = "na"),
  lower = list(continuous = "points", combo = "facethist", discrete = "facetbar", na =
    "na"),
  diag = list(continuous = "densityDiag", discrete = "barDiag", na = "naDiag"),
  params = deprecated(),
  ...,
  xlab = NULL,
  ylab = NULL,
  axisLabels = c("show", "internal", "none"),
  columnLabels = colnames(data[columns]),
  labeller = "label_value",
  switch = NULL,
  showStrips = NULL,
  legend = NULL,
  cardinality_threshold = 15,
  progress = NULL,
  proportions = NULL,
  legends = deprecated()
)

Arguments

Details

upper and lower are lists that may contain the variables 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, it must be a character string representing the tail end of a ggally_NAME function. The list of current valid ggally_NAME functions is visible in a dedicated vignette.

continuous

This option is used for continuous X and Y data.

combo

This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.

discrete

This option is used for categorical X and Y data.

na

This option is used when all X data is NA, all Y data is NA, or either all X or Y data is NA.

diag is a list that may only contain the variables 'continuous', 'discrete', and 'na'. Each element of the diag list is a string implementing the following options:

continuous

exactly one of ('densityDiag', 'barDiag', 'blankDiag'). This option is used for continuous X data.

discrete

exactly one of ('barDiag', 'blankDiag'). This option is used for categorical X and Y data.

na

exactly one of ('naDiag', 'blankDiag'). This option is used when all X data is NA.

If 'blank' is ever chosen as an option, then ggpairs will produce an empty plot.

If a function is supplied as an option to upper, lower, or diag, it should implement the function api of function(data, mapping, ...){#make ggplot2 plot}. If a specific function needs its parameters set, wrap(fn, param1 = val1, param2 = val2) the function with its parameters.

Value

ggmatrix object that if called, will print

Author(s)

Barret Schloerke, Jason Crowley, Di Cook, Heike Hofmann, Hadley Wickham

References

John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. The Generalized Pairs Plot. Journal of Computational and Graphical Statistics, vol. 22, no. 1, pp. 79-91, 2012.

See Also

wrap v1_ggmatrix_theme

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive


## Quick example, with and without colour
data(flea)
ggpairs(flea, columns = 2:4)
pm <- ggpairs(flea, columns = 2:4, ggplot2::aes(colour = species))
p_(pm)
# Note: colour should be categorical, else you will need to reset
# the upper triangle to use points instead of trying to compute corr

data(tips)
pm <- ggpairs(tips[, 1:3])
p_(pm)
pm <- ggpairs(tips, 1:3, columnLabels = c("Total Bill", "Tip", "Sex"))
p_(pm)
pm <- ggpairs(tips, upper = "blank")
p_(pm)

## Plot Types
# Change default plot behavior
pm <- ggpairs(
  tips[, c(1, 3, 4, 2)],
  upper = list(continuous = "density", combo = "box_no_facet"),
  lower = list(continuous = "points", combo = "dot_no_facet")
)
p_(pm)
# Supply Raw Functions (may be user defined functions!)
pm <- ggpairs(
  tips[, c(1, 3, 4, 2)],
  upper = list(continuous = ggally_density, combo = ggally_box_no_facet),
  lower = list(continuous = ggally_points, combo = ggally_dot_no_facet)
)
p_(pm)

# Use sample of the diamonds data
data(diamonds, package = "ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ]

# Different aesthetics for different plot sections and plot types
pm <- ggpairs(
  diamonds.samp[, 1:5],
  mapping = ggplot2::aes(color = cut),
  upper = list(continuous = wrap("density", alpha = 0.5), combo = "box_no_facet"),
  lower = list(continuous = wrap("points", alpha = 0.3), combo = wrap("dot_no_facet", alpha = 0.4)),
  title = "Diamonds"
)
p_(pm)

## Axis Label Variations
# Only Variable Labels on the diagonal (no axis labels)
pm <- ggpairs(tips[, 1:3], axisLabels = "internal")
p_(pm)
# Only Variable Labels on the outside (no axis labels)
pm <- ggpairs(tips[, 1:3], axisLabels = "none")
p_(pm)

## Facet Label Variations
#  Default:
df_x <- rnorm(100)
df_y <- df_x + rnorm(100, 0, 0.1)
df <- data.frame(x = df_x, y = df_y, c = sqrt(df_x^2 + df_y^2))
pm <- ggpairs(
  df,
  columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)")
)
p_(pm)
#  Parsed labels:
pm <- ggpairs(
  df,
  columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)"),
  labeller = "label_parsed"
)
p_(pm)

## Plot Insertion Example
custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example")
# ggplot example taken from example(geom_text)
plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x = wt, y = mpg, label = rownames(mtcars)))
plot <- plot +
  ggplot2::geom_text(ggplot2::aes(colour = factor(cyl)), size = 3) +
  ggplot2::scale_colour_discrete(l = 40)
custom_car[1, 2] <- plot
personal_plot <- ggally_text(
  "ggpairs allows you\nto put in your\nown plot.\nLike that one.\n <---"
)
custom_car[1, 3] <- personal_plot
p_(custom_car)

## Remove binwidth warning from ggplot2
# displays warning about picking a better binwidth
pm <- ggpairs(tips, 2:3)
p_(pm)
# no warning displayed
pm <- ggpairs(tips, 2:3, lower = list(combo = wrap("facethist", binwidth = 0.5)))
p_(pm)
# no warning displayed with user supplied function
pm <- ggpairs(tips, 2:3, lower = list(combo = wrap(ggally_facethist, binwidth = 0.5)))
p_(pm)

## Remove panel grid lines from correlation plots
pm <- ggpairs(
  flea,
  columns = 2:4,
  upper = list(continuous = wrap(ggally_cor, displayGrid = FALSE))
)
p_(pm)

## Custom with/height of subplots
pm <- ggpairs(tips, columns = c(2, 3, 5))
p_(pm)

pm <- ggpairs(tips, columns = c(2, 3, 5), proportions = "auto")
p_(pm)

pm <- ggpairs(tips, columns = c(2, 3, 5), proportions = c(1, 3, 2))
p_(pm)

Parallel coordinate plot

Description

A function for plotting static parallel coordinate plots, utilizing the ggplot2 graphics package.

Usage

ggparcoord(
  data,
  columns = 1:ncol(data),
  groupColumn = NULL,
  scale = "std",
  scaleSummary = "mean",
  centerObsID = 1,
  missing = "exclude",
  order = columns,
  showPoints = FALSE,
  splineFactor = FALSE,
  alphaLines = 1,
  boxplot = FALSE,
  shadeBox = NULL,
  mapping = NULL,
  title = ""
)

Arguments

Details

scale is a character string that denotes how to scale the variables in the parallel coordinate plot. Options:

std

: univariately, subtract mean and divide by standard deviation

robust

: univariately, subtract median and divide by median absolute deviation

uniminmax

: univariately, scale so the minimum of the variable is zero, and the maximum is one

globalminmax

: no scaling is done; the range of the graphs is defined by the global minimum and the global maximum

center

: use uniminmax to standardize vertical height, then center each variable at a value specified by the scaleSummary param

centerObs

: use uniminmax to standardize vertical height, then center each variable at the value of the observation specified by the centerObsID param

missing is a character string that denotes how to handle missing missing values. Options:

exclude

: remove all cases with missing values

mean

: set missing values to the mean of the variable

median

: set missing values to the median of the variable

min10

: set missing values to 10% below the minimum of the variable

random

: set missing values to value of randomly chosen observation on that variable

order is either a vector of indices or a character string that denotes how to order the axes (variables) of the parallel coordinate plot. Options:

(default)

: order by the vector denoted by columns

(given vector)

: order by the vector specified

anyClass

: order variables by their separation between any one class and the rest (as opposed to their overall variation between classes). This is accomplished by calculating the F-statistic for each class vs. the rest, for each axis variable. The axis variables are then ordered (decreasing) by their maximum of k F-statistics, where k is the number of classes.

allClass

: order variables by their overall F statistic (decreasing) from an ANOVA with groupColumn as the explanatory variable (note: it is required to specify a groupColumn with this ordering method). Basically, this method orders the variables by their variation between classes (most to least).

skewness

: order variables by their sample skewness (most skewed to least skewed)

Outlying

: order by the scagnostic measure, Outlying, as calculated by the package scagnostics. Other scagnostic measures available to order by are Skewed, Clumpy, Sparse, Striated, Convex, Skinny, Stringy, and Monotonic. Note: To use these methods of ordering, you must have the scagnostics package loaded.

Value

ggplot object that if called, will print

Author(s)

Jason Crowley, Barret Schloerke, Dianne Cook, Heike Hofmann, Hadley Wickham

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

# use sample of the diamonds data for illustrative purposes
data(diamonds, package = "ggplot2")
diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ]

# basic parallel coordinate plot, using default settings
p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10))
p_(p)

# this time, color by diamond cut
p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2)
p_(p)

# underlay univariate boxplots, add title, use uniminmax scaling
p <- ggparcoord(
  data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2,
  scale = "uniminmax", boxplot = TRUE, title = "Parallel Coord. Plot of Diamonds Data"
)
p_(p)

# utilize ggplot2 aes to switch to thicker lines
p <- ggparcoord(
  data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2,
  title = "Parallel Coord. Plot of Diamonds Data", mapping = ggplot2::aes(linewidth = 1)
) +
  ggplot2::scale_linewidth_identity()
p_(p)

# basic parallel coord plot of the msleep data, using 'random' imputation and
# coloring by diet (can also use variable names in the columns and groupColumn
# arguments)
data(msleep, package = "ggplot2")
p <- ggparcoord(
  data = msleep, columns = 6:11, groupColumn = "vore", missing =
    "random", scale = "uniminmax"
)
p_(p)

# center each variable by its median, using the default missing value handler,
# 'exclude'
p <- ggparcoord(
  data = msleep, columns = 6:11, groupColumn = "vore", scale =
    "center", scaleSummary = "median"
)
p_(p)

# with the iris data, order the axes by overall class (Species) separation using
# the anyClass option
p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass")
p_(p)

# add points to the plot, add a title, and use an alpha scalar to make the lines
# transparent
p <- ggparcoord(
  data = iris, columns = 1:4, groupColumn = 5, order = "anyClass",
  showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data",
  alphaLines = 0.3
)
p_(p)

# color according to a column
iris2 <- iris
iris2$alphaLevel <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0)[iris2$Species]
p <- ggparcoord(
  data = iris2, columns = 1:4, groupColumn = 5, order = "anyClass",
  showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data",
  alphaLines = "alphaLevel"
)
p_(p)

## Use splines on values, rather than lines (all produce the same result)
columns <- c(1, 5:10)
p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = TRUE)
p_(p)
p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = 3)
p_(p)

Traditional scatterplot matrix for purely quantitative variables

Description

This function makes a scatterplot matrix for quantitative variables with density plots on the diagonal and correlation printed in the upper triangle.

Usage

ggscatmat(
  data,
  columns = 1:ncol(data),
  color = NULL,
  alpha = 1,
  corMethod = "pearson"
)

Arguments

Author(s)

Mengjia Ni, Di Cook

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(flea)

p_(ggscatmat(flea, columns = 2:4))
p_(ggscatmat(flea, columns = 2:4, color = "species"))

Survival curves

Description

This function produces Kaplan-Meier plots using ggplot2. As a first argument it needs a survfit object, created by the survival package. Default settings differ for single stratum and multiple strata objects.

Usage

ggsurv(
  s,
  CI = "def",
  plot.cens = TRUE,
  surv.col = "gg.def",
  cens.col = "gg.def",
  lty.est = 1,
  lty.ci = 2,
  size.est = 0.5,
  size.ci = size.est,
  cens.size = 2,
  cens.shape = 3,
  back.white = FALSE,
  xlab = "Time",
  ylab = "Survival",
  main = "",
  order.legend = TRUE
)

Arguments

Value

An object of class ggplot

Author(s)

Edwin Thoen

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

if (require(survival) && require(scales)) {
  lung <- survival::lung
  sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung)
  p_(ggsurv(sf.lung))

  # Multiple strata examples
  sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung)
  pl.sex <- ggsurv(sf.sex)
  p_(pl.sex)

  # Adjusting the legend of the ggsurv fit
  p_(pl.sex +
    ggplot2::guides(linetype = "none") +
    ggplot2::scale_colour_discrete(
      name   = "Sex",
      breaks = c(1, 2),
      labels = c("Male", "Female")
    ))

  # Multiple factors
  lung2 <- dplyr::mutate(lung, older = as.factor(age > 60))
  sf.sex2 <- survival::survfit(Surv(time, status) ~ sex + older, data = lung2)
  pl.sex2 <- ggsurv(sf.sex2)
  p_(pl.sex2)

  # Change legend title
  p_(pl.sex2 + labs(color = "New Title", linetype = "New Title"))

  # We can still adjust the plot after fitting
  kidney <- survival::kidney
  sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney)
  pl.kid <- ggsurv(sf.kid, plot.cens = FALSE)
  p_(pl.kid)

  # Zoom in to first 80 days
  p_(pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1)))

  # Add the diseases names to the plot and remove legend
  p_(pl.kid +
    ggplot2::annotate(
      "text",
      label = c("PKD", "Other", "GN", "AN"),
      x = c(90, 125, 5, 60),
      y = c(0.8, 0.65, 0.55, 0.30),
      size = 5,
      colour = scales::pal_hue(
        h         = c(0, 360) + 15,
        c         = 100,
        l         = 65,
        h.start   = 0,
        direction = 1
      )(4)
    ) +
    ggplot2::guides(color = "none", linetype = "none"))
}

Cross-tabulated tables of discrete variables

Description

ggtable is a variant of ggduo for quick cross-tabulated tables of discrete variables.

Usage

ggtable(
  data,
  columnsX = 1:ncol(data),
  columnsY = 1:ncol(data),
  cells = c("observed", "prop", "row.prop", "col.prop", "expected", "resid", "std.resid"),
  fill = c("none", "std.resid", "resid"),
  mapping = NULL,
  ...
)

Arguments

Author(s)

Joseph Larmarange

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(tips)
p_(ggtable(tips, "smoker", c("day", "time", "sex")))

# displaying row proportions
p_(ggtable(tips, "smoker", c("day", "time", "sex"), cells = "row.prop"))

# filling cells with standardized residuals
p_(ggtable(tips, "smoker", c("day", "time", "sex"), fill = "std.resid", legend = 1))

# if continuous variables are provided, just displaying some summary statistics
p_(ggtable(tips, c("smoker", "total_bill"), c("day", "time", "sex", "tip")))

# specifying weights
d <- as.data.frame(Titanic)
p_(ggtable(
  d,
  "Survived",
  c("Class", "Sex", "Age"),
  mapping = aes(weight = Freq),
  cells = "row.prop",
  fill = "std.resid"
))

Multiple time series

Description

GGally implementation of ts.plot. Wraps around the ggduo function and removes the column strips

Usage

ggts(..., columnLabelsX = NULL, xlab = "time")

Arguments

Value

ggmatrix object

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

p_(ggts(pigs, "time", c("gilts", "profit", "s_per_herdsz", "production", "herdsz")))

Glyph plot class

Description

Glyph plot class

Usage

glyphplot(data, width, height, polar, x_major, y_major)

is.glyphplot(x)

## S3 method for class 'glyphplot'
x[...]

## S3 method for class 'glyphplot'
print(x, ...)

Arguments

Author(s)

Di Cook, Heike Hofmann, Hadley Wickham

Create glyphplot data

Description

Create the data needed to generate a glyph plot.

Usage

glyphs(
  data,
  x_major,
  x_minor,
  y_major,
  y_minor,
  polar = FALSE,
  height = ggplot2::rel(0.95),
  width = ggplot2::rel(0.95),
  y_scale = identity,
  x_scale = identity
)

Arguments

Author(s)

Di Cook, Heike Hofmann, Hadley Wickham

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(nasa)
nasaLate <- nasa[
  nasa$date >= as.POSIXct("1998-01-01") &
    nasa$lat >= 20 &
    nasa$lat <= 40 &
    nasa$long >= -80 &
    nasa$long <= -60,
]
temp.gly <- glyphs(nasaLate, "long", "day", "lat", "surftemp", height = 2.5)
p_(ggplot2::ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) +
  add_ref_lines(temp.gly, color = "grey90") +
  add_ref_boxes(temp.gly, color = "grey90") +
  ggplot2::geom_path() +
  ggplot2::theme_bw() +
  ggplot2::labs(x = "", y = ""))

Grab the legend and print it as a plot

Description

Grab the legend and print it as a plot

Usage

grab_legend(p)

## S3 method for class 'legend_guide_box'
print(x, ..., plotNew = FALSE)

Arguments

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

library(ggplot2)
histPlot <-
  ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_histogram(binwidth = 1 / 4)
(right <- histPlot)
(bottom <- histPlot + theme(legend.position = "bottom"))
(top <- histPlot + theme(legend.position = "top"))
(left <- histPlot + theme(legend.position = "left"))

p_(grab_legend(right))
p_(grab_legend(bottom))
p_(grab_legend(top))
p_(grab_legend(left))

Data related to happiness from the General Social Survey, 1972-2006.

Description

This data extract is taken from Hadley Wickham's productplots package. The original description follows, with minor edits.

Usage

data(happy)

Format

A data frame with 51020 rows and 10 variables

Details

The data is a small sample of variables related to happiness from the General Social Survey (GSS). The GSS is a yearly cross-sectional survey of Americans, run from 1972. We combine data for 25 years to yield 51,020 observations, and of the over 5,000 variables, we select nine related to happiness:

• age. age in years: 18–89.

• degree. highest education: lt high school, high school, junior college, bachelor, graduate.

• finrela. relative financial status: far above, above average, average, below average, far below.

• happy. happiness: very happy, pretty happy, not too happy.

• health. health: excellent, good, fair, poor.

• marital. marital status: married, never married, divorced, widowed, separated.

• sex. sex: female, male.

• wtsall. probability weight. 0.43–6.43.

References

Smith, Tom W., Peter V. Marsden, Michael Hout, Jibum Kim. General Social Surveys, 1972-2006. [machine-readable data file]. Principal Investigator, Tom W. Smith; Co-Principal Investigators, Peter V. Marsden and Michael Hout, NORC ed. Chicago: National Opinion Research Center, producer, 2005; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut, distributor. 1 data file (57,061 logical records) and 1 codebook (3,422 pp).

Is Blank Plot? Find out if the plot equals a blank plot

Description

Is Blank Plot? Find out if the plot equals a blank plot

Usage

is_blank_plot(p)

Examples

GGally:::is_blank_plot(ggally_blank())
GGally:::is_blank_plot(ggally_points(mtcars, ggplot2::aes(disp, hp)))

Check if object is a date

Description

Check if object is a date

Usage

is_date(x)

Arguments

Check if an object is a ggmatrix

Description

Check if an object is a ggmatrix

Usage

is_ggmatrix(x)

Arguments

Value

Logical value indicating if the object is a ggmatrix

Examples

is_ggmatrix(ggpairs(mtcars))
is_ggmatrix(ggplot2::ggplot())

Check if plot is horizontal

Description

Check if plot is horizontal

Usage

is_horizontal(data, mapping, val = "y")

is_character_column(data, mapping, val = "y")

Arguments

Value

Boolean determining if the data is a character-like data

Examples

is_horizontal(iris, ggplot2::aes(Sepal.Length, Species)) # TRUE
is_horizontal(iris, ggplot2::aes(Sepal.Length, Species), "x") # FALSE
is_horizontal(iris, ggplot2::aes(Sepal.Length, Sepal.Width)) # FALSE

lowertriangle - rearrange dataset as the preparation of ggscatmat function

Description

function for making the melted dataset used to plot the lowertriangle scatterplots.

Usage

lowertriangle(data, columns = 1:ncol(data), color = NULL)

Arguments

Author(s)

Mengjia Ni, Di Cook

Examples

data(flea)
head(lowertriangle(flea, columns = 2:4))
head(lowertriangle(flea))
head(lowertriangle(flea, color = "species"))

Aesthetic mapping color fill

Description

Replace the fill with the color and make color NULL.

Usage

mapping_color_to_fill(current)

Arguments

Aes name

Description

Aes name

Usage

mapping_string(aes_col)

Arguments

Value

character string

Examples

mapping <- ggplot2::aes(Petal.Length)
mapping_string(mapping$x)

Swap x and y mapping

Description

Swap x and y mapping

Usage

mapping_swap_x_y(mapping)

Arguments

Value

Aes mapping with the x and y values switched

Examples

mapping <- ggplot2::aes(Petal.Length, Sepal.Width)
mapping
mapping_swap_x_y(mapping)

Model term names

Description

Retrieve either the response variable names, the beta variable names, or beta variable names. If the model is an object of class 'lm', by default, the beta variable names will include anova significance stars.

Usage

model_response_variables(model, data = broom::augment(model))

model_beta_variables(model, data = broom::augment(model))

model_beta_label(model, data = broom::augment(model), lmStars = TRUE)

Arguments

Value

character vector of names

Data from the Data Expo JSM 2006.

Description

This data was provided by NASA for the competition.

Usage

data(nasa)

Format

A data frame with 41472 rows and 17 variables

Details

The data shows 6 years of monthly measurements of a 24x24 spatial grid from Central America:

• time integer specifying temporal order of measurements

• x, y, lat, long spatial location of measurements.

• cloudhigh, cloudlow, cloudmid, ozone, pressure, surftemp, temperature are the various satellite measurements.

• date, day, month, year specifying the time of measurements.

• id unique ide for each spatial position.

References

Murrell, P. (2010) The 2006 Data Expo of the American Statistical Association. Computational Statistics, 25:551-554.

United Kingdom Pig Production

Description

This data contains about the United Kingdom Pig Production from the book 'Data' by Andrews and Herzberg. The original data can be on Statlib: http://lib.stat.cmu.edu/datasets/Andrews/T62.1

Usage

data(pigs)

Format

A data frame with 48 rows and 8 variables

Details

The time variable has been added from a combination of year and quarter

• time year + (quarter - 1) / 4

• year year of production

• quarter quarter of the year of production

• gilts number of sows giving birth for the first time

• profit ratio of price to an index of feed price

• s_per_herdsz ratio of the number of breeding pigs slaughtered to the total breeding herd size

• production number of pigs slaughtered that were reared for meat

• herdsz breeding herd size

References

Andrews, David F., and Agnes M. Herzberg. Data: a collection of problems from many fields for the student and research worker. Springer Science & Business Media, 2012.

Plot Types

Description

Retrieves the type of plot that should be used for all combinations

Usage

plot_types(data, columnsX, columnsY, allowDiag = TRUE)

Arguments

Author(s)

Barret Schloerke

Get plotting data type

Description

Get plotting data type

Usage

plotting_data_type(x)

Arguments

Print ggmatrix object

Description

Print method taken from ggplot2:::print.ggplot and altered for a ggmatrix object

Arguments

Author(s)

Barret Schloerke

Examples

data(tips)
pMat <- ggpairs(tips, c(1, 3, 2), mapping = ggplot2::aes(color = sex))
pMat # calls print(pMat), which calls print.ggmatrix(pMat)

Print if not CRAN

Description

Small function to print a plot if the R session is interactive or in a CI build

Usage

print_if_interactive(p)

Arguments

UCLA canonical correlation analysis data

Description

This data contains 600 observations on eight variables

Usage

data(psychademic)

Format

A data frame with 600 rows and 8 variables

Details

• locus_of_control - psychological

• self_concept - psychological

• motivation - psychological. Converted to four character groups

• read - academic

• write - academic

• math - academic

• science - academic

• female - academic. Dropped from original source

• sex - academic. Added as a character version of female column

References

R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital Research and Education. from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis (accessed May 22, 2017).

Insert a plot into a ggmatrix object

Description

Function to place your own plot in the layout.

Usage

putPlot(pm, value, i, j)

## S3 replacement method for class 'ggmatrix'
pm[i, j, ...] <- value

Arguments

Author(s)

Barret Schloerke

See Also

getPlot

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example")
# ggplot example taken from example(geom_text)
plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x = wt, y = mpg, label = rownames(mtcars)))
plot <- plot +
  ggplot2::geom_text(ggplot2::aes(colour = factor(cyl)), size = 3) +
  ggplot2::scale_colour_discrete(l = 40)
custom_car[1, 2] <- plot
personal_plot <- ggally_text(
  "ggpairs allows you\nto put in your\nown plot.\nLike that one.\n <---"
)
custom_car[1, 3] <- personal_plot
# custom_car

# remove plots after creating a plot matrix
custom_car[2, 1] <- NULL
custom_car[3, 1] <- "blank" # the same as storing null
custom_car[3, 2] <- NULL
p_(custom_car)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggstats

geom_stripped_cols, geom_stripped_rows, ggcoef_compare, ggcoef_model, ggcoef_multinom, ggcoef_plot, signif_stars, stat_cross, stat_prop, stat_weighted_mean, StatCross, StatProp, StatWeightedMean

Remove colour mapping unless found in select mapping keys

Description

Remove colour mapping unless found in select mapping keys

Usage

remove_color_unless_equal(mapping, to = c("x", "y"))

Arguments

Value

Aes mapping with colour mapping kept only if found in selected mapping keys.

Examples

mapping <- aes(x = sex, y = age, colour = sex)

mapping <- aes(x = sex, y = age, colour = region)
remove_color_unless_equal(mapping)

Rescaling functions

Description

Rescaling functions

Usage

range01(x)

max1(x)

mean0(x)

min0(x)

rescale01(x, xlim = NULL)

rescale11(x, xlim = NULL)

Arguments

Find order of variables

Description

Find order of variables based on a specified scagnostic measure by maximizing the index values of that measure along the path.

Usage

scag_order(scag, vars, measure)

Arguments

Value

character vector of variable ordered according to the given scagnostic measure

Author(s)

Barret Schloerke

Plots the lowertriangle and density plots of the scatter plot matrix.

Description

Function for making scatterplots in the lower triangle and diagonal density plots.

Usage

scatmat(data, columns = 1:ncol(data), color = NULL, alpha = 1)

Arguments

Author(s)

Mengjia Ni, Di Cook

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

data(flea)

p_(scatmat(flea, columns = 2:4))
p_(scatmat(flea, columns = 2:4, color = "species"))

Order axis variables

Description

Order axis variables by separation between one class and the rest (most separation to least).

Usage

singleClassOrder(classVar, axisVars, specClass = NULL)

Arguments

Value

character vector of names of axisVars ordered such that the first variable has the most separation between one of the classes and the rest, and the last variable has the least (as measured by F-statistics from an ANOVA)

Author(s)

Jason Crowley

Sample skewness

Description

Calculate the sample skewness of a vector while ignoring missing values.

Usage

skewness(x)

Arguments

Value

sample skewness of x

Author(s)

Jason Crowley

ggmatrix structure

Description

View the condensed version of the ggmatrix object. The attribute "class" is ALWAYS altered to "_class" to avoid recursion.

Arguments

Tipping data

Description

One waiter recorded information about each tip he received over a period of a few months working in one restaurant. He collected several variables:

Usage

tips

Format

A data frame with 244 rows and 7 variables

Details

• tip in dollars,

• bill in dollars,

• sex of the bill payer,

• whether there were smokers in the party,

• day of the week,

• time of day,

• size of the party.

In all he recorded 244 tips. The data was reported in a collection of case studies for business statistics (Bryant & Smith 1995).

References

Bryant, P. G. and Smith, M (1995) Practical Data Analysis: Case Studies in Business Statistics. Homewood, IL: Richard D. Irwin Publishing:

Twitter spambots

Description

A network of spambots found on Twitter as part of a data mining project.

Usage

data(twitter_spambots)

Format

An object of class network with 120 edges and 94 vertices.

Details

Each node of the network is identified by the Twitter screen name of the account and further carries five vertex attributes:

• location user's location, as provided by the user

• lat latitude, based on the user's location

• lon longitude, based on the user's location

• followers number of Twitter accounts that follow this account

• friends number of Twitter accounts followed by the account

Author(s)

Amos Elberg

Rearrange dataset as the preparation of ggscatmat function

Description

Function for making the dataset used to plot the uppertriangle plots.

Usage

uppertriangle(
  data,
  columns = 1:ncol(data),
  color = NULL,
  corMethod = "pearson"
)

Arguments

Author(s)

Mengjia Ni, Di Cook

Examples

data(flea)
head(uppertriangle(flea, columns = 2:4))
head(uppertriangle(flea))
head(uppertriangle(flea, color = "species"))

Modify a ggmatrix object by adding an ggplot2 object to all

Description

This function allows cleaner axis labels for your plots, but is deprecated. You can achieve the same effect by specifying strip's background and placement properties (see Examples).

Usage

v1_ggmatrix_theme()

Examples

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

# Cleaner axis labels with v1_ggmatrix_theme
p_(ggpairs(iris, 1:2) + v1_ggmatrix_theme())

# Move the column names to the left and bottom
p_(ggpairs(iris, 1:2, switch = "both") + v1_ggmatrix_theme())

# Manually specifying axis labels properties
p_(
  ggpairs(iris, 1:2) +
  theme(
    strip.background = element_rect(fill = "white"),
    strip.placement = "outside"
  )
)

# This way you have even more control over how the final plot looks.
# For example, if you want to set the background color to yellow:
p_(
  ggpairs(iris, 1:2) +
  theme(
    strip.background = element_rect(fill = "yellow"),
    strip.placement = "outside"
  )
)

View GGally vignettes

Description

This function will open the directly to the vignette requested. If no name is provided, the index of all GGally vignettes will be opened.

Usage

vig_ggally(name)

Arguments

Details

This method allows for vignettes to be hosted remotely, reducing GGally's package size, and installation time.

Examples

# View `ggnostic` vignette
vig_ggally("ggnostic")

# View all vignettes by GGally
vig_ggally()

Wrap a function with different parameter values

Description

Wraps a function with the supplied parameters to force different default behavior. This is useful for functions that are supplied to ggpairs. It allows you to change the behavior of one function, rather than creating multiple functions with different parameter settings.

Usage

wrap_fn_with_param_arg(
  funcVal,
  params = NULL,
  funcArgName = deparse(substitute(funcVal))
)

wrapp(funcVal, params = NULL, funcArgName = deparse(substitute(funcVal)))

wrap(funcVal, ..., funcArgName = deparse(substitute(funcVal)))

wrap_fn_with_params(funcVal, ..., funcArgName = deparse(substitute(funcVal)))

Arguments

Details

wrap is identical to wrap_fn_with_params. These function take the new parameters as arguments.

wrapp is identical to wrap_fn_with_param_arg. These functions take the new parameters as a single list.

The params and fn attributes are there for debugging purposes. If either attribute is altered, the function must be re-wrapped to have the changes take effect.

Value

a function(data, mapping, ...){} that will wrap the original function with the parameters applied as arguments

Examples

# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

# example function that prints 'val'
fn <- function(data, mapping, val = 2) {
  print(val)
}
fn(data = NULL, mapping = NULL) # 2

# wrap function to change default value 'val' to 5 instead of 2
wrapped_fn1 <- wrap(fn, val = 5)
wrapped_fn1(data = NULL, mapping = NULL) # 5
# you may still supply regular values
wrapped_fn1(data = NULL, mapping = NULL, val = 3) # 3

# wrap function to change 'val' to 5 using the arg list
wrapped_fn2 <- wrap_fn_with_param_arg(fn, params = list(val = 5))
wrapped_fn2(data = NULL, mapping = NULL) # 5

# change parameter settings in ggpairs for a particular function
## Goal output:
regularPlot <- ggally_points(
  iris,
  ggplot2::aes(Sepal.Length, Sepal.Width),
  size = 5, color = "red"
)
p_(regularPlot)

# Wrap ggally_points to have parameter values size = 5 and color = 'red'
w_ggally_points <- wrap(ggally_points, size = 5, color = "red")
wrappedPlot <- w_ggally_points(
  iris,
  ggplot2::aes(Sepal.Length, Sepal.Width)
)
p_(wrappedPlot)

# Double check the aes parameters are the same for the geom_point layer
identical(regularPlot$layers[[1]]$aes_params, wrappedPlot$layers[[1]]$aes_params)

# Use a wrapped function in ggpairs
pm <- ggpairs(iris, 1:3, lower = list(continuous = wrap(ggally_points, size = 5, color = "red")))
p_(pm)
pm <- ggpairs(iris, 1:3, lower = list(continuous = w_ggally_points))
p_(pm)