Package 'GGally'

Title: 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.
Authors: 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 <[email protected]>
License: GPL (>= 2.0)
Version: 2.2.1.9000
Built: 2024-11-11 05:16:31 UTC
Source: https://github.com/ggobi/ggally

Help Index

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

## S3 method for class 'gg'
e1 + e2

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

Arguments

e1

An object of class ggnostic or ggplot
e2

A component to add to e1
location
"all", TRUE

All row and col combinations

"none"

No row and column combinations

"upper"

Locations where the column value is higher than the row value

"lower"

Locations where the row value is higher than the column value

"diag"

Locations where the column value is equal to the row value

matrix or data.frame

matrix values will be converted into data.frames.

  • A data.frame with the exact column names c("row", "col")

  • A data.frame with the number of rows and columns matching the plot matrix object provided. Each cell will be tested for a "truthy" value to determine if the location should be kept.

rows

numeric vector of the rows to be used. Will be used with cols if location is NULL
cols

numeric vector of the cols to be used. Will be used with rows if location is NULL

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. [Experimental]

See Also

ggplot2::+.gg and ggplot2::theme()

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"
))

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

data

A glyphmap structure.
var_fill

Variable name to use to set the fill color
color

Set the color to draw in, default is "white"
size

Set the line size, default is 0.5
fill

fill value used if var_fill is NULL
...

other arguments passed onto ggplot2::geom_rect()

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

data

A glyphmap structure.
color

Set the color to draw in, default is "white"
size

Set the line size, default is 1.5
...

other arguments passed onto ggplot2::geom_line()

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/pisa/pisaproducts/database-cbapisa2012.htm

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

col

standard color name used to retrieve hex color value

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

model

model to be sent to broom::augment(), broom::glance(), and broom::tidy()
lmStars

boolean that determines if stars are added to labels

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)

Evaluate data column

Description

Evaluate data column

Usage

eval_data_col(data, aes_col)

Arguments

data

data set to evaluate the data with
aes_col

Single value from an ggplot2::aes(...) object

Value

Aes mapping with the x and y values switched

Examples

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

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

types

list of functions that follow the ggmatrix function standard: function(data, mapping, ...){ #make ggplot2 object }. One key should be a 'default' key for a default switch case.
mapping_val

mapping value to switch on. Defaults to the 'y' variable of the aesthetics list.

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

pm

ggmatrix object to select from
i

row from the top
j

column from the left
...

ignored

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])

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

data

data set using
mapping

aesthetics being used
...

other arguments passed to geom_autopoint(...)

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

data

data set using
mapping

aesthetics being used
...

other arguments are sent to geom_bar
rescale

boolean to decide whether or not to rescale the count output. Only applies to numeric data

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

...

other arguments ignored

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

data

data set using
mapping

aesthetics being used
...

other arguments being supplied to geom_boxplot

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

data

data set using
mapping

aesthetics being used
label_format

formatter function for displaying proportions, not taken into account if a label aesthetic is provided in mapping
...

other arguments passed to geom_text(...)
remove_background

should the panel.background be removed?
remove_percentage_axis

should percentage axis be removed? Removes the y-axis for ggally_colbar() and x-axis for ggally_rowbar()
reverse_fill_levels

should the levels of the fill variable be reversed?
geom_bar_args

other arguments passed to geom_bar(...)

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",
  use = "complete.obs",
  display_grid = FALSE,
  digits = 3,
  title_args = list(...),
  group_args = list(...),
  justify_labels = "right",
  align_percent = 0.5,
  title = "Corr",
  alignPercent = warning("deprecated. Use `align_percent`"),
  displayGrid = warning("deprecated. Use `display_grid`")
)

Arguments

data

data set using
mapping

aesthetics being used
...

other arguments being supplied to geom_text() for the title and groups
stars

logical value which determines if the significance stars should be displayed. Given the cor.test p-values, display

"***"

if the p-value is ⁠< 0.001⁠

"**"

if the p-value is ⁠< 0.01⁠

"*"

if the p-value is ⁠< 0.05⁠

"."

if the p-value is ⁠< 0.10⁠

""

otherwise
method

method supplied to cor function
use

use supplied to cor function
display_grid

if TRUE, display aligned panel grid lines. If FALSE (default), display a thin panel border.
digits

number of digits to be displayed after the decimal point. See formatC for how numbers are calculated.
title_args

arguments being supplied to the title's geom_text()
group_args

arguments being supplied to the split-by-color group's geom_text()
justify_labels

justify argument supplied when formatting the labels
align_percent

relative align position of the text. When justify_labels = 0.5, this should not be needed to be set.
title

title text to be displayed
alignPercent, displayGrid

deprecated. Please use their snake-case counterparts.

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

(Deprecated. 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

data

data set using
mapping

aesthetics being used
alignPercent

right align position of numbers. Default is 60 percent across the horizontal
method

method supplied to cor function
use

use supplied to cor function
corAlignPercent

deprecated. Use parameter alignPercent
corMethod

deprecated. Use parameter method
corUse

deprecated. Use parameter use
displayGrid

if TRUE, display aligned panel gridlines
...

other arguments being supplied to geom_text

Details

Estimate correlation from the given data.

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

data

data set using
mapping

aesthetics being used
...

other arguments passed to geom_tile(...)

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

data

data set using
mapping

aesthetics being used
...

other arguments passed to ggplot2::geom_point()
scale_max_size

max_size argument supplied to ggplot2::scale_size_area()
geom_text_args

other arguments passed to ggplot2::geom_text()

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

data

data set using
mapping

aesthetics being used
cells

Which statistic should be displayed in table cells?
fill

Which statistic should be used for filling table cells?
...

other arguments passed to geom_text(...)
geom_tile_args

other arguments passed to geom_tile(...)

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

data

data set using
mapping

aesthetics being used
...

parameters sent to either stat_density2d or geom_density2d

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

data

data set using
mapping

aesthetics being used.
...

other arguments sent to stat_density
rescale

boolean to decide whether or not to rescale the count output

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

data

data set using
mapping

aesthetics being used
...

other arguments being sent to stat_bin

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

data

dataset being plotted
mapping

aesthetics being used (x is the variable the plot will be made for)
label

title to be displayed in the middle. Defaults to mapping$x
labelSize

size of variable label
labelXPercent

percent of horizontal range
labelYPercent

percent of vertical range
labelHJust

hjust supplied to label
labelVJust

vjust supplied to label
gridLabelSize

size of grid labels
...

other arguments for geom_text

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

data

data set using
mapping

aesthetics being used
...

other arguments being supplied to geom_jitter

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))

Faceted bar plot

Description

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

Usage

ggally_facetbar(data, mapping, ...)

Arguments

data

data set using
mapping

aesthetics being used
...

other arguments are sent to geom_bar

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

data

data set using
mapping

aesthetics being used
...

other arguments being sent to stat_density

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

data

data set using
mapping

aesthetics being used
...

other arguments being sent to either geom_histogram or stat_density
den_strip

boolean to decide whether or not to plot a density strip(TRUE) or a facet density(FALSE) plot.

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

data

data set using
mapping

aesthetics being used
...

parameters sent to stat_bin()

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

data

ignored
mapping

ignored
size

size of the geom_text 'NA'
color

color of the geom_text 'NA'
...

other arguments sent to geom_text

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

data, mapping, ..., lineColor, lineType

parameters supplied to ggally_nostic_line
linePosition

4 / n is the general cutoff point for Cook's Distance

Details

A line is added at Fp,n−p(0.5)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

data, mapping, ...

supplied directly to ggally_nostic_line
linePosition, lineColor, lineSize, lineAlpha, lineType

parameters supplied to ggplot2::geom_line() for the cutoff line
avgLinePosition, avgLineColor, avgLineSize, avgLineAlpha, avgLineType

parameters supplied to ggplot2::geom_line() for the average line

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

data, mapping

supplied directly to ggplot2::ggplot()
...

parameters supplied to continuous_geom or combo_geom
linePosition, lineColor, lineSize, lineAlpha, lineType

parameters supplied to ggplot2::geom_line()
continuous_geom

ggplot2 geom that is executed after the line is (possibly) added and if the x data is continuous
combo_geom

ggplot2 geom that is executed after the line is (possibly) added and if the x data is discrete
mapColorToFill

boolean to determine if combo plots should cut the color mapping to the fill mapping

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

data, mapping, ...

parameters supplied to ggally_nostic_line
linePosition, lineColor, lineSize, lineAlpha, lineType

parameters supplied to ggplot2::geom_line()
lineConfColor, lineConfSize, lineConfAlpha, lineConfType

parameters supplied to the confidence interval lines
pVal

percentiles of a N(0, sigma) distribution to be drawn
sigma

sigma value for the pVal percentiles
se

boolean to determine if the confidence intervals should be displayed
method, formula

parameters supplied to ggplot2::geom_smooth(). Defaults to "auto" and "y ~ x"

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

data, mapping, ..., lineColor

parameters supplied to ggally_nostic_line
linePosition

base comparison for a perfect fit

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

data, mapping, ..., lineColor

parameters supplied to ggally_nostic_line
linePosition

line that is drawn in the background of the plot. Defaults to the overall model's sigma value.

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

data, mapping, ...

parameters supplied to ggally_nostic_resid
sigma

sigma value for the pVal percentiles. Set to 1 for standardized residuals

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

data

data set using
mapping

aesthetics being used
...

other arguments are sent to geom_point

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

data

data set using
mapping

aesthetics being used. Only x and y will used and both are required
...

passed to geom_tile(...)
floor

don't display cells smaller than this value
ceiling

max value to scale frequencies. If any frequency is larger than the ceiling, the fill color is displayed darker than other rectangles

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

data

data set using
mapping

aesthetics being used
method, se

parameters supplied to geom_smooth
formula, ...

other arguments to add to geom_smooth
shrink

boolean to determine if y range is reduced to range of points or points and error ribbon

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

data

data set using
mapping

aesthetics being used
text_fn

function that takes in x and y and returns a text string
title

title text to be displayed
na.rm

logical value which determines if NA values are removed. If TRUE, no warning message will be displayed.
display_grid

if TRUE, display aligned panel grid lines. If FALSE (default), display a thin panel border.
justify_labels

justify argument supplied when formatting the labels
justify_text

justify argument supplied when formatting the returned text_fn(x, y) values
sep

separation value to be placed between the labels and text
family

font family used when displaying all text. This value will be set in title_args or group_args if no family value exists. By using "mono", groups will align with each other.
title_args

arguments being supplied to the title's geom_text()
group_args

arguments being supplied to the split-by-color group's geom_text()
align_percent

relative align position of the text. When title_hjust = 0.5 and group_hjust = 0.5, this should not be needed to be set.
title_hjust, group_hjust

hjust sent to geom_text() for the title and group values respectively. Any hjust value supplied in title_args or group_args will take precedence.

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

data

data set using
mapping

aesthetics being used
text_fn

function that takes an x and weights and returns a text string
text_fn_vertical

function that takes an x and weights and returns a text string, used when x is discrete and y is continuous. If not provided, will use text_fn, replacing spaces by carriage returns.
...

other arguments passed to geom_text(...)
x

a numeric vector
weights

an optional numeric vectors of weights. If NULL, equal weights of 1 will be taken into account.

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

data

data set using
mapping

aesthetics being used
keep.zero.cells

If TRUE, display cells with no observation.
...

other arguments passed to geom_text(...)
geom_tile_args

other arguments passed to geom_tile(...)

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

label

text that you want to appear
mapping

aesthetics that don't relate to position (such as color)
xP

horizontal position percentage
yP

vertical position percentage
xrange

range of the data around it. Only nice to have if plotting in a matrix
yrange

range of the data around it. Only nice to have if plotting in a matrix
...

other arguments for geom_text

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

data

data set using
mapping

aesthetics being used
...

other arguments passed to ggplot2::geom_line()
include_zero

Should 0 be included on the y-axis?

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

data

dataset to be used, can have both categorical and numerical variables
outcome

name or position of the outcome variable (one variable only)
explanatory

names or positions of the explanatory variables (if NULL, will take all variables other than outcome)
mapping

additional aesthetic to be used, for example to indicate weights (see examples)
types

custom types of plots to use, see ggduo
...

additional arguments passed to ggduo (see examples)
rowbar_args

additional arguments passed to ggally_rowbar (see examples)

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

x

a model object to be tidied with broom::tidy() or a data frame (see Details)
mapping

default aesthetic mapping
conf.int

display confidence intervals as error bars?
conf.level

level of confidence intervals (passed to broom::tidy() if x is not a data frame)
exponentiate

if TRUE, x-axis will be logarithmic (also passed to broom::tidy() if x is not a data frame)
exclude_intercept

should the intercept be excluded from the plot?
vline

print a vertical line?
vline_intercept

xintercept for the vertical line. "auto" for x = 0 (or x = 1 if exponentiate is TRUE)
vline_color

color of the vertical line
vline_linetype

line type of the vertical line
vline_size

size of the vertical line
errorbar_color

color of the error bars
errorbar_height

height of the error bars
errorbar_linetype

line type of the error bars
errorbar_size

size of the error bars
sort

"none" (default) do not sort, "ascending" sort by increasing coefficient value, or "descending" sort by decreasing coefficient value
...

additional arguments sent to ggplot2::geom_point()

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

data

a data frame or matrix containing numeric (continuous) data. If any of the columns contain non-numeric data, they will be dropped with a warning.
method

a vector of two character strings. The first value gives the method for computing covariances in the presence of missing values, and must be (an abbreviation of) one of "everything", "all.obs", "complete.obs", "na.or.complete" or "pairwise.complete.obs". The second value gives the type of correlation coefficient to compute, and must be one of "pearson", "kendall" or "spearman". See cor for details. Defaults to c("pairwise", "pearson").
cor_matrix

the named correlation matrix to use for calculations. Defaults to the correlation matrix of data when data is supplied.
nbreaks

the number of breaks to apply to the correlation coefficients, which results in a categorical color scale. See 'Note'. Defaults to NULL (no breaks, continuous scaling).
digits

the number of digits to show in the breaks of the correlation coefficients: see cut for details. Defaults to 2.
name

a character string for the legend that shows the colors of the correlation coefficients. Defaults to "" (no legend name).
low

the lower color of the gradient for continuous scaling of the correlation coefficients. Defaults to "#3B9AB2" (blue).
mid

the midpoint color of the gradient for continuous scaling of the correlation coefficients. Defaults to "#EEEEEE" (very light grey).
high

the upper color of the gradient for continuous scaling of the correlation coefficients. Defaults to "#F21A00" (red).
midpoint

the midpoint value for continuous scaling of the correlation coefficients. Defaults to 0.
palette

if nbreaks is used, a ColorBrewer palette to use instead of the colors specified by low, mid and high. Defaults to NULL.
geom

the geom object to use. Accepts either "tile", "circle", "text" or "blank".
min_size

when geom has been set to "circle", the minimum size of the circles. Defaults to 2.
max_size

when geom has been set to "circle", the maximum size of the circles. Defaults to 6.
label

whether to add correlation coefficients to the plot. Defaults to FALSE.
label_alpha

whether to make the correlation coefficients increasingly transparent as they come close to 0. Also accepts any numeric value between 0 and 1, in which case the level of transparency is set to that fixed value. Defaults to FALSE (no transparency).
label_color

the color of the correlation coefficients. Defaults to "grey75".
label_round

the decimal rounding of the correlation coefficients. Defaults to 1.
label_size

the size of the correlation coefficients. Defaults to 4.
limits

bounding of color scaling for correlations, set limits = NULL or FALSE to remove
drop

if using nbreaks, whether to drop unused breaks from the color scale. Defaults to FALSE (recommended).
layout.exp

a multiplier to expand the horizontal axis to the left if variable names get clipped. Defaults to 0 (no expansion).
legend.position

where to put the legend of the correlation coefficients: see theme for details. Defaults to "bottom".
legend.size

the size of the legend title and labels, in points: see theme for details. Defaults to 9.
...

other arguments supplied to geom_text for the diagonal labels.

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

data

data set using. Can have both numerical and categorical data.
mapping

aesthetic mapping (besides x and y). See aes(). If mapping is numeric, columns will be set to the mapping value and mapping will be set to NULL.
columnsX, columnsY

which columns are used to make plots. Defaults to all columns.
title, xlab, ylab

title, x label, and y label for the graph
types

see Details
axisLabels

either "show" to display axisLabels or "none" for no axis labels
columnLabelsX, columnLabelsY

label names to be displayed. Defaults to names of columns being used.
labeller

labeller for facets. See labellers. Common values are "label_value" (default) and "label_parsed".
switch

switch parameter for facet_grid. See ggplot2::facet_grid. By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both"
showStrips

boolean to determine if each plot's strips should be displayed. NULL will default to the top and right side plots only. TRUE or FALSE will turn all strips on or off respectively.
legend

May be the two objects described below or the default NULL value. The legend position can be moved by using ggplot2's theme element pm + theme(legend.position = "bottom")

a numeric vector of length 2

provides the location of the plot to use the legend for the plot matrix's legend. Such as legend = c(3,5) which will use the legend from the plot in the third row and fifth column

a single numeric value

provides the location of a plot according to the display order. Such as legend = 3 in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position c(1,2)

a object from grab_legend()

a predetermined plot legend that will be displayed directly
cardinality_threshold

maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15
progress

NULL (default) for a progress bar in interactive sessions with more than 15 plots, TRUE for a progress bar, FALSE for no progress bar, or a function that accepts at least a plot matrix and returns a new progress::progress_bar. See ggmatrix_progress.
xProportions, yProportions

Value to change how much area is given for each plot. Either NULL (default), numeric value matching respective length, grid::unit object with matching respective length or "auto" for automatic relative proportions based on the number of levels for categorical variables.
legends

[Deprecated]

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

data

data.frame that contains all columns to be displayed. This data will be melted before being passed into the function fn
mapping

aesthetic mapping (besides x and y). See aes()
columnsX

columns to be displayed in the plot matrix
columnsY

rows to be displayed in the plot matrix
fn

function to be executed. Similar to ggpairs and ggduo, the function may either be a string identifier or a real function that wrap understands.
...

extra arguments passed directly to fn
columnLabelsX, columnLabelsY

column and row labels to display in the plot matrix
xlab, ylab, title

plot matrix labels
scales

parameter supplied to ggplot2::facet_grid. Default behavior is "free"

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

fn

this value is passed directly to an empty wrap call. Please see ?wrap for more details.

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

plots

list of plots to be put into matrix
nrow, ncol

number of rows and columns
xAxisLabels, yAxisLabels

strip titles for the x and y axis respectively. Set to NULL to not be displayed
title, xlab, ylab

title, x label, and y label for the graph. Set to NULL to not be displayed
byrow

boolean that determines whether the plots should be ordered by row or by column
showStrips

boolean to determine if each plot's strips should be displayed. NULL will default to the top and right side plots only. TRUE or FALSE will turn all strips on or off respectively.
showAxisPlotLabels, showXAxisPlotLabels, showYAxisPlotLabels

booleans that determine if the plots axis labels are printed on the X (bottom) or Y (left) part of the plot matrix. If showAxisPlotLabels is set, both showXAxisPlotLabels and showYAxisPlotLabels will be set to the given value.
labeller

labeller for facets. See labellers. Common values are "label_value" (default) and "label_parsed".
switch

switch parameter for facet_grid. See ggplot2::facet_grid. By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both"
xProportions, yProportions

Value to change how much area is given for each plot. Either NULL (default), numeric value matching respective length, or grid::unit object with matching respective length
progress

NULL (default) for a progress bar in interactive sessions with more than 15 plots, TRUE for a progress bar, FALSE for no progress bar, or a function that accepts at least a plot matrix and returns a new progress::progress_bar. See ggmatrix_progress.
data

data set using. This is the data to be used in place of 'ggally_data' if the plot is a string to be evaluated at print time
gg

ggplot2 theme objects to be applied to every plot
legend

May be the two objects described below or the default NULL value. The legend position can be moved by using ggplot2's theme element pm + theme(legend.position = "bottom")

a numeric vector of length 2

provides the location of the plot to use the legend for the plot matrix's legend. Such as legend = c(3,5) which will use the legend from the plot in the third row and fifth column

a single numeric value

provides the location of a plot according to the display order. Such as legend = 3 in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position c(1,2)

a object from grab_legend()

a predetermined plot legend that will be displayed directly

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

pm

ggmatrix object to be plotted
...

ignored
progress, progress_format

Please use the 'progress' parameter in your ggmatrix-like function. See ggmatrix_progress for a few examples. These parameters will soon be deprecated.

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

[Experimental]

Usage

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

Arguments

pm

ggmatrix plot object
location
"all", TRUE

All row and col combinations

"none"

No row and column combinations

"upper"

Locations where the column value is higher than the row value

"lower"

Locations where the row value is higher than the column value

"diag"

Locations where the column value is equal to the row value

matrix or data.frame

matrix values will be converted into data.frames.

  • A data.frame with the exact column names c("row", "col")

  • A data.frame with the number of rows and columns matching the plot matrix object provided. Each cell will be tested for a "truthy" value to determine if the location should be kept.

rows

numeric vector of the rows to be used. Will be used with cols if location is NULL
cols

numeric vector of the cols to be used. Will be used with rows if location is NULL

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

format, clear, show_after, ...

parameters supplied directly to progress::progress_bar$new()

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 = c("", ""),
  quantize.weights = FALSE,
  subset.threshold = 0,
  top8.nodes = FALSE,
  trim.labels = FALSE,
  ...
)

Arguments

net

an object of class network, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see edgeset.constructors and network for details. If the object is of class igraph and the intergraph package is installed, it will be used to convert the object: see asNetwork for details.
mode

a placement method from those provided in the sna package: see gplot.layout for details. Also accepts the names of two numeric vertex attributes of net, or a matrix of numeric coordinates, in which case the first two columns of the matrix are used. Defaults to the Fruchterman-Reingold force-directed algorithm.
layout.par

options to be passed to the placement method, as listed in gplot.layout. Defaults to NULL.
layout.exp

a multiplier to expand the horizontal axis if node labels get clipped: see expand_range for details. Defaults to 0 (no expansion).
size

size of the network nodes. If the nodes are weighted, their area is proportionally scaled up to the size set by size. Defaults to 9.
alpha

a level of transparency for nodes, vertices and arrows. Defaults to 1.
weight

the weighting method for the nodes, which might be a vertex attribute or a vector of size values. Also accepts "indegree", "outdegree", "degree" or "freeman" to size the nodes by their unweighted degree centrality ("degree" and "freeman" are equivalent): see degree for details. All node weights must be positive. Defaults to "none" (no weighting).
weight.legend

the name to assign to the legend created by weight. Defaults to NA (no name).
weight.method

see weight
weight.min

whether to subset the network to nodes with a minimum size, based on the values of weight. Defaults to NA (preserves all nodes).
weight.max

whether to subset the network to nodes with a maximum size, based on the values of weight. Defaults to NA (preserves all nodes).
weight.cut

whether to cut the size of the nodes into a certain number of quantiles. Accepts TRUE, which tries to cut the sizes into quartiles, or any positive numeric value, which tries to cut the sizes into that many quantiles. If the size of the nodes do not contain the specified number of distinct quantiles, the largest possible number is used. See quantile and cut for details. Defaults to FALSE (does nothing).
group

the groups of the nodes, either as a vector of values or as a vertex attribute. If set to mode on a bipartite network, the nodes will be grouped as "actor" if they belong to the primary mode and "event" if they belong to the secondary mode.
group.legend

the name to assign to the legend created by group.
node.group

see group
node.color

a vector of character strings to color the nodes with, holding as many colors as there are levels in node.group. Defaults to NULL, which will assign grayscale colors to each group.
node.alpha

transparency of the nodes. Inherits from alpha.
segment.alpha

the level of transparency of the edges. Defaults to alpha, which defaults to 1.
segment.color

the color of the edges, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to "grey50".
segment.label

the labels to plot at the middle of the edges, as a single value, a vector of values, or as an edge attribute. Defaults to NULL (no edge labels).
segment.size

the size of the edges, in points, as a single numeric value, a vector of values, or as an edge attribute. Defaults to 0.25.
arrow.size

the size of the arrows for directed network edges, in points. See arrow for details. Defaults to 0 (no arrows).
arrow.gap

a setting aimed at improving the display of edge arrows by plotting slightly shorter edges. Accepts any value between 0 and 1, where a value of 0.05 will generally achieve good results when the size of the nodes is reasonably small. Defaults to 0 (no shortening).
arrow.type

the type of the arrows for directed network edges. See arrow for details. Defaults to "closed".
label

whether to label the nodes. If set to TRUE, nodes are labeled with their vertex names. If set to a vector that contains as many elements as there are nodes in net, nodes are labeled with these. If set to any other vector of values, the nodes are labeled only when their vertex name matches one of these values. Defaults to FALSE (no labels).
label.nodes

see label
label.size

the size of the node labels, in points, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to size / 2 (half the maximum node size), which defaults to 6.
label.trim

whether to apply some trimming to the node labels. Accepts any function that can process a character vector, or a strictly positive numeric value, in which case the labels are trimmed to a fixed-length substring of that length: see substr for details. Defaults to FALSE (does nothing).
legend.size

the size of the legend symbols and text, in points. Defaults to 9.
legend.position

the location of the plot legend(s). Accepts all legend.position values supported by theme. Defaults to "right".
names

deprecated: see group.legend and size.legend
quantize.weights

deprecated: see weight.cut
subset.threshold

deprecated: see weight.min
top8.nodes

deprecated: this functionality was experimental and has been removed entirely from ggnet
trim.labels

deprecated: see label.trim
...

other arguments passed to the geom_text object that sets the node labels: see geom_text for details.

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

net

an object of class network, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see edgeset.constructors and network for details. If the object is of class igraph and the intergraph package is installed, it will be used to convert the object: see asNetwork for details.
mode

a placement method from those provided in the sna package: see gplot.layout for details. Also accepts the names of two numeric vertex attributes of net, or a matrix of numeric coordinates, in which case the first two columns of the matrix are used. Defaults to the Fruchterman-Reingold force-directed algorithm.
layout.par

options to be passed to the placement method, as listed in gplot.layout. Defaults to NULL.
layout.exp

a multiplier to expand the horizontal axis if node labels get clipped: see expand_range for details. Defaults to 0 (no expansion).
alpha

the level of transparency of the edges and nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts "mode" on bipartite networks (see 'Details'). Defaults to 1 (no transparency).
color

the color of the nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts "mode" on bipartite networks (see 'Details'). Defaults to grey75.
shape

the shape of the nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts "mode" on bipartite networks (see 'Details'). Defaults to 19 (solid circle).
size

the size of the nodes, in points, which might be a single value, a vertex attribute, or a vector of values. Also accepts "indegree", "outdegree", "degree" or "freeman" to size the nodes by their unweighted degree centrality ("degree" and "freeman" are equivalent): see degree for details. All node sizes must be strictly positive. Also accepts "mode" on bipartite networks (see 'Details'). Defaults to 9.
max_size

the maximum size of the node when size produces nodes of different sizes, in points. Defaults to 9.
na.rm

whether to subset the network to nodes that are not missing a given vertex attribute. If set to any vertex attribute of net, the nodes for which this attribute is NA will be removed. Defaults to NA (does nothing).
palette

the palette to color the nodes, when color is not a color value or a vector of color values. Accepts named vectors of color values, or if RColorBrewer is installed, any ColorBrewer palette name: see RColorBrewer::brewer.pal() and https://colorbrewer2.org/ for details. Defaults to NULL, which will create an array of grayscale color values if color is not a color value or a vector of color values.
alpha.palette

the palette to control the transparency levels of the nodes set by alpha when the levels are not numeric values. Defaults to NULL, which will create an array of alpha transparency values if alpha is not a numeric value or a vector of numeric values.
alpha.legend

the name to assign to the legend created by alpha when its levels are not numeric values. Defaults to NA (no name).
color.palette

see palette
color.legend

the name to assign to the legend created by palette. Defaults to NA (no name).
shape.palette

the palette to control the shapes of the nodes set by shape when the shapes are not numeric values. Defaults to NULL, which will create an array of shape values if shape is not a numeric value or a vector of numeric values.
shape.legend

the name to assign to the legend created by shape when its levels are not numeric values. Defaults to NA (no name).
size.palette

the palette to control the sizes of the nodes set by size when the sizes are not numeric values.
size.legend

the name to assign to the legend created by size. Defaults to NA (no name).
size.zero

whether to accept zero-sized nodes based on the value(s) of size. Defaults to FALSE, which ensures that zero-sized nodes are still shown in the plot and its size legend.
size.cut

whether to cut the size of the nodes into a certain number of quantiles. Accepts TRUE, which tries to cut the sizes into quartiles, or any positive numeric value, which tries to cut the sizes into that many quantiles. If the size of the nodes do not contain the specified number of distinct quantiles, the largest possible number is used. See quantile and cut for details. Defaults to FALSE (does nothing).
size.min

whether to subset the network to nodes with a minimum size, based on the values of size. Defaults to NA (preserves all nodes).
size.max

whether to subset the network to nodes with a maximum size, based on the values of size. Defaults to NA (preserves all nodes).
label

whether to label the nodes. If set to TRUE, nodes are labeled with their vertex names. If set to a vector that contains as many elements as there are nodes in net, nodes are labeled with these. If set to any other vector of values, the nodes are labeled only when their vertex name matches one of these values. Defaults to FALSE (no labels).
label.alpha

the level of transparency of the node labels, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to 1 (no transparency).
label.color

the color of the node labels, as a color value, a vector of color values, or as a vertex attribute containing color values. Defaults to "black".
label.size

the size of the node labels, in points, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to max_size / 2 (half the maximum node size), which defaults to 4.5.
label.trim

whether to apply some trimming to the node labels. Accepts any function that can process a character vector, or a strictly positive numeric value, in which case the labels are trimmed to a fixed-length substring of that length: see substr for details. Defaults to FALSE (does nothing).
node.alpha

see alpha
node.color

see color
node.label

see label
node.shape

see shape
node.size

see size
edge.alpha

the level of transparency of the edges. Defaults to the value of alpha, which defaults to 1.
edge.color

the color of the edges, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to "grey50".
edge.lty

the linetype of the edges, as a linetype value, a vector of linetype values, or as an edge attribute containing linetype values. Defaults to "solid".
edge.size

the size of the edges, in points, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. All edge sizes must be strictly positive. Defaults to 0.25.
edge.label

the labels to plot at the middle of the edges, as a single value, a vector of values, or as an edge attribute. Defaults to NULL (no edge labels).
edge.label.alpha

the level of transparency of the edge labels, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. Defaults to 1 (no transparency).
edge.label.color

the color of the edge labels, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to label.color, which defaults to "black".
edge.label.fill

the background color of the edge labels. Defaults to "white".
edge.label.size

the size of the edge labels, in points, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. All edge label sizes must be strictly positive. Defaults to max_size / 2 (half the maximum node size), which defaults to 4.5.
arrow.size

the size of the arrows for directed network edges, in points. See arrow for details. Defaults to 0 (no arrows).
arrow.gap

a setting aimed at improving the display of edge arrows by plotting slightly shorter edges. Accepts any value between 0 and 1, where a value of 0.05 will generally achieve good results when the size of the nodes is reasonably small. Defaults to 0 (no shortening).
arrow.type

the type of the arrows for directed network edges. See arrow for details. Defaults to "closed".
legend.size

the size of the legend symbols and text, in points. Defaults to 9.
legend.position

the location of the plot legend(s). Accepts all legend.position values supported by theme. Defaults to "right".
...

other arguments passed to the geom_text object that sets the node labels: see geom_text for details.

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

gg

an object of class ggplot.
net

an object of class network, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see edgeset.constructors and network for details. If the object is of class igraph and the intergraph package is installed, it will be used to convert the object: see asNetwork for details.
size

size of the network nodes. Defaults to 3. If the nodes are weighted, their area is proportionally scaled up to the size set by size.
alpha

a level of transparency for nodes, vertices and arrows. Defaults to 0.75.
weight

if present, the unquoted name of a vertex attribute in data. Otherwise nodes are unweighted.
node.group

NULL, the default, or the unquoted name of a vertex attribute that will be used to determine the color of each node.
node.color

If node.group is null, a character string specifying a color.
node.alpha

transparency of the nodes. Inherits from alpha.
ring.group

if not NULL, the default, the unquoted name of a vertex attribute that will be used to determine the color of each node border.
segment.alpha

transparency of the vertex links. Inherits from alpha
segment.color

color of the vertex links. Defaults to "grey".
great.circles

whether to draw edges as great circles using the geosphere package. Defaults to FALSE
segment.size

size of the vertex links, as a vector of values or as a single value. Defaults to 0.25.
arrow.size

size of the vertex arrows for directed network plotting, in centimeters. Defaults to 0.
label.nodes

label nodes with their vertex names attribute. If set to TRUE, all nodes are labelled. Also accepts a vector of character strings to match with vertex names.
label.size

size of the labels. Defaults to size / 2.
...

other arguments supplied to geom_text for the node labels. Arguments pertaining to the title or other items can be achieved through ggplot2 methods.

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

model

statistical model object such as output from stats::lm or stats::glm
...

arguments passed directly to ggduo
columnsX

columns to be displayed in the plot matrix. Defaults to the predictor columns of the model
columnsY

rows to be displayed in the plot matrix. Defaults to residuals, leave one out sigma value, diagonal of the hat matrix, and Cook's Distance. The possible values are the response variables in the model and the added columns provided by broom::augment(). See details for more information.
columnLabelsX, columnLabelsY

column and row labels to display in the plot matrix
xlab, ylab, title

plot matrix labels passed directly to ggmatrix
continuous, combo, discrete

list of functions for each y variable. See details for more information.
progress

NULL (default) for a progress bar in interactive sessions with more than 15 plots, TRUE for a progress bar, FALSE for no progress bar, or a function that accepts at least a plot matrix and returns a new progress::progress_bar. See ggmatrix_progress.
data

data defaults to a 'broomify'ed model object. This object will contain information about the X variables, Y variables, and multiple broom outputs. See broomify(model) for more information

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

data

data set using. Can have both numerical and categorical data.
mapping

aesthetic mapping (besides x and y). See aes(). If mapping is numeric, columns will be set to the mapping value and mapping will be set to NULL.
columns

which columns are used to make plots. Defaults to all columns.
title, xlab, ylab

title, x label, and y label for the graph
upper

see Details
lower

see Details
diag

see Details
params

[Deprecated] see wrap_fn_with_param_arg
...

[Deprecated] use mapping
axisLabels

either "show" to display axisLabels, "internal" for labels in the diagonal plots, or "none" for no axis labels
columnLabels

label names to be displayed. Defaults to names of columns being used.
labeller

labeller for facets. See labellers. Common values are "label_value" (default) and "label_parsed".
switch

switch parameter for facet_grid. See ggplot2::facet_grid. By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both"
showStrips

boolean to determine if each plot's strips should be displayed. NULL will default to the top and right side plots only. TRUE or FALSE will turn all strips on or off respectively.
legend

May be the two objects described below or the default NULL value. The legend position can be moved by using ggplot2's theme element pm + theme(legend.position = "bottom")

a numeric vector of length 2

provides the location of the plot to use the legend for the plot matrix's legend. Such as legend = c(3,5) which will use the legend from the plot in the third row and fifth column

a single numeric value

provides the location of a plot according to the display order. Such as legend = 3 in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position c(1,2)

a object from grab_legend()

a predetermined plot legend that will be displayed directly
cardinality_threshold

maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15
progress

NULL (default) for a progress bar in interactive sessions with more than 15 plots, TRUE for a progress bar, FALSE for no progress bar, or a function that accepts at least a plot matrix and returns a new progress::progress_bar. See ggmatrix_progress.
proportions

Value to change how much area is given for each plot. Either NULL (default), numeric value matching respective length, grid::unit object with matching respective length or "auto" for automatic relative proportions based on the number of levels for categorical variables.
legends

[Deprecated]

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

data

the dataset to plot
columns

a vector of variables (either names or indices) to be axes in the plot
groupColumn

a single variable to group (color) by
scale

method used to scale the variables (see Details)
scaleSummary

if scale=="center", summary statistic to univariately center each variable by
centerObsID

if scale=="centerObs", row number of case plot should univariately be centered on
missing

method used to handle missing values (see Details)
order

method used to order the axes (see Details)
showPoints

logical operator indicating whether points should be plotted or not
splineFactor

logical or numeric operator indicating whether spline interpolation should be used. Numeric values will multiplied by the number of columns, TRUE will default to cubic interpolation, AsIs to set the knot count directly and 0, FALSE, or non-numeric values will not use spline interpolation.
alphaLines

value of alpha scaler for the lines of the parcoord plot or a column name of the data
boxplot

logical operator indicating whether or not boxplots should underlay the distribution of each variable
shadeBox

color of underlying box which extends from the min to the max for each variable (no box is plotted if shadeBox == NULL)
mapping

aes string to pass to ggplot object
title

character string denoting the title of the plot

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

data

a data matrix. Should contain numerical (continuous) data.
columns

an option to choose the column to be used in the raw dataset. Defaults to 1:ncol(data).
color

an option to group the dataset by the factor variable and color them by different colors. Defaults to NULL, i.e. no coloring. If supplied, it will be converted to a factor.
alpha

an option to set the transparency in scatterplots for large data. Defaults to 1.
corMethod

method argument supplied to cor

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

s

an object of class survfit
CI

should a confidence interval be plotted? Defaults to TRUE for single stratum objects and FALSE for multiple strata objects.
plot.cens

mark the censored observations?
surv.col

colour of the survival estimate. Defaults to black for one stratum, and to the default ggplot2 colours for multiple strata. Length of vector with colour names should be either 1 or equal to the number of strata.
cens.col

colour of the points that mark censored observations.
lty.est

linetype of the survival curve(s). Vector length should be either 1 or equal to the number of strata.
lty.ci

linetype of the bounds that mark the 95% CI.
size.est

line width of the survival curve
size.ci

line width of the 95% CI
cens.size

point size of the censoring points
cens.shape

shape of the points that mark censored observations.
back.white

if TRUE the background will not be the default grey of ggplot2 but will be white with borders around the plot.
xlab

the label of the x-axis.
ylab

the label of the y-axis.
main

the plot label.
order.legend

boolean to determine if the legend display should be ordered by final survival time

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::hue_pal(
        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

data

dataset to be used, can have both categorical and numerical variables
columnsX, columnsY

names or positions of which columns are used to make plots. Defaults to all columns.
cells

Which statistic should be displayed in table cells?
fill

Which statistic should be used for filling table cells?
mapping

additional aesthetic to be used, for example to indicate weights (see examples)
...

additional arguments passed to ggduo (see examples)

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

...

supplied directly to ggduo
columnLabelsX

remove top strips for the X axis by default
xlab

defaults to "time"

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

data

A data frame containing variables named in x_major, x_minor, y_major and y_minor.
height, width

The height and width of each glyph. Defaults to 95% of the resolution of the data. Specify the width absolutely by supplying a numeric vector of length 1, or relative to the
polar

A logical of length 1, specifying whether the glyphs should be drawn in polar coordinates. Defaults to FALSE.
x_major, y_major

The name of the variable (as a string) for the major x and y axes. Together, the
x

glyphplot to be printed
...

ignored

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

data

A data frame containing variables named in x_major, x_minor, y_major and y_minor.
x_major, x_minor, y_major, y_minor

The name of the variable (as a string) for the major and minor x and y axes. Together, each unique
polar

A logical of length 1, specifying whether the glyphs should be drawn in polar coordinates. Defaults to FALSE.
height, width

The height and width of each glyph. Defaults to 95% of the resolution of the data. Specify the width absolutely by supplying a numeric vector of length 1, or relative to the
y_scale, x_scale

The scaling function to be applied to each set of minor values within a grid cell. Defaults to identity so that no scaling is performed.

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

p

ggplot2 plot object
x

legend object that has been grabbed from a ggplot2 object
...

ignored
plotNew

boolean to determine if the grid.newpage() command and a new blank rectangle should be printed

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).

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

data

data used in ggplot2 plot
mapping

ggplot2 aes() mapping
val

key to retrieve from mapping

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

data

a data matrix. Should contain numerical (continuous) data.
columns

an option to choose the column to be used in the raw dataset. Defaults to 1:ncol(data)
color

an option to choose a factor variable to be grouped with. Defaults to (NULL)

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

current

the current aesthetics

Aes name

Description

Aes name

Usage

mapping_string(aes_col)

Arguments

aes_col

Single value from ggplot2::aes(...)

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

mapping

output of ggplot2::aes(...)

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

model

model in question
data

equivalent to broom::augment(model)
lmStars

boolean that determines if stars are added to labels

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.

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

p

plot to be displayed

Print ggmatrix object

Description

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

Usage

## S3 method for class 'ggmatrix'
print(x, newpage = is.null(vp), vp = NULL, ...)

Arguments

x

plot to display
newpage

draw new (empty) page first?
vp

viewport to draw plot in
...

arguments passed onto ggmatrix_gtable

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)

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

pm

ggally object to be altered
value

ggplot object to be placed
i

row from the top
j

column from the left
...

ignored

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)

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

mapping

output of ggplot2::aes(...)
to

set of mapping keys to check

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

x

numeric vector
xlim

value used in range

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

scag

scagnostics object
vars

character vector of the variables to be ordered
measure

scagnostics measure to order according to

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

data

a data matrix. Should contain numerical (continuous) data.
columns

an option to choose the column to be used in the raw dataset. Defaults to 1:ncol(data)
color

an option to group the dataset by the factor variable and color them by different colors. Defaults to NULL
alpha

an option to set the transparency in scatterplots for large data. Defaults to 1.

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

classVar

class variable (vector from original dataset)
axisVars

variables to be plotted as axes (data frame)
specClass

character string matching to level of classVar; instead of looking for separation between any class and the rest, will only look for separation between this class and the rest

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

x

numeric vector

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.

Usage

## S3 method for class 'ggmatrix'
str(object, ..., raw = FALSE)

Arguments

object

ggmatrix object to be viewed
...

passed on to the default str method
raw

boolean to determine if the plots should be converted to text or kept as original objects

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

data

a data matrix. Should contain numerical (continuous) data.
columns

an option to choose the column to be used in the raw dataset. Defaults to 1:ncol(data)
color

an option to choose a factor variable to be grouped with. Defaults to (NULL)
corMethod

method argument supplied to cor

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

Modify a ggmatrix object by adding an ggplot2 object to all

Usage

v1_ggmatrix_theme()

Examples

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

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())

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

name

Vignette name to open. If no name is provided, the vignette index will be opened

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

funcVal

function that the params will be applied to. The function should follow the api of function(data, mapping, ...){}. funcVal is allowed to be a string of one of the ggally_NAME functions, such as "points" for ggally_points or "facetdensity" for ggally_facetdensity.
params

named vector or list of parameters to be applied to the funcVal
funcArgName

name of function to be displayed
...

named parameters to be supplied to wrap_fn_with_param_arg

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)