Extra Geoms, Stats, Coords, Scales & Fonts for 'ggplot2'

Description

A package containing additional geoms, coords, stats, scales & fonts for ggplot2 2.0+

Author(s)

Bob Rudis (@hrbrmstr)

Geom Proto

Description

Geom Proto

Geom Proto

Geom Proto

Geom Proto

Geom Proto

References

https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs

Geom Cartogram

Description

Geom Cartogram

Absolute grob

Description

This grob has fixed dimensions and position.

Usage

absoluteGrob(grob, width = NULL, height = NULL, xmin = NULL,
  ymin = NULL, vp = NULL)

Details

It's still experimental

Text annotations in plot coordinate system

Description

Annotates the plot with text. Compared to annotate("text",...), the placement of the annotations is specified in plot coordinates (from 0 to 1) instead of data coordinates.

Usage

annotate_textp(label, x, y, facets = NULL, hjust = 0, vjust = 0,
  color = "black", alpha = NA, family = theme_get()$text$family,
  size = theme_get()$text$size, fontface = 1, lineheight = 1,
  box_just = ifelse(c(x, y) < 0.5, 0, 1), margin = unit(size/2, "pt"))

Arguments

Examples

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p <- p + geom_smooth(method = "lm", se = FALSE)
p + annotate_textp(x = 0.9, y = 0.35, label="A relative linear\nrelationship", hjust=1, color="red")

Bytes formatter: convert to byte measurement and display symbol.

Description

Bytes formatter: convert to byte measurement and display symbol.

Usage

byte_format(symbol = "auto", units = "binary")

Kb(x)

Mb(x)

Gb(x)

bytes(x, symbol = "auto", units = c("binary", "si"))

Arguments

Value

a function with three parameters, x, a numeric vector that returns a character vector, symbol the byte symbol (e.g. "Kb") desired and the measurement units (traditional binary or si for ISI metric units).

References

Units of Information (Wikipedia) : http://en.wikipedia.org/wiki/Units_of_information

Examples

byte_format()(sample(3000000000, 10))
bytes(sample(3000000000, 10))
Kb(sample(3000000000, 10))
Mb(sample(3000000000, 10))
Gb(sample(3000000000, 10))

Similar to coord_map but uses the PROJ.4 library/package for projection transformation

Description

The representation of a portion of the earth, which is approximately spherical, onto a flat 2D plane requires a projection. This is what coord_proj does, using the proj4::project() function from the proj4 package.

Usage

coord_proj(proj = NULL, inverse = FALSE, degrees = TRUE,
  ellps.default = "sphere", xlim = NULL, ylim = NULL)

Arguments

Details

A sample of the output from coord_proj() using the Winkel-Tripel projection:

Note

It is recommended that you use geom_cartogram with this coordinate system

When inverse is FALSE coord_proj makes a fairly large assumption that the coordinates being transformed are within -180:180 (longitude) and -90:90 (latitude). As such, it truncates all longitude & latitude input to fit within these ranges. More updates to this new coord_ are planned.

Examples

## Not run: 
# World in Winkel-Tripel

# U.S.A. Albers-style
usa <- world[world$region == "USA",]
usa <- usa[!(usa$subregion %in% c("Alaska", "Hawaii")),]

gg <- ggplot()
gg <- gg + geom_cartogram(data=usa, map=usa,
                    aes(x=long, y=lat, map_id=region))
gg <- gg + coord_proj(
             paste0("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96",
                    " +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"))
gg

# Showcase Greenland (properly)
greenland <- world[world$region == "Greenland",]

gg <- ggplot()
gg <- gg + geom_cartogram(data=greenland, map=greenland,
                    aes(x=long, y=lat, map_id=region))
gg <- gg + coord_proj(
             paste0("+proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0",
                    " +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"))
gg

## End(Not run)

Fortify contingency tables

Description

Fortify contingency tables

Usage

## S3 method for class 'table'
fortify(model, data, ...)

Arguments

Display a smooth density estimate.

Description

A kernel density estimate, useful for displaying the distribution of variables with underlying smoothness.

Usage

geom_bkde(mapping = NULL, data = NULL, stat = "bkde",
  position = "identity", bandwidth = NULL, range.x = NULL,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)

stat_bkde(mapping = NULL, data = NULL, geom = "area",
  position = "stack", kernel = "normal", canonical = FALSE,
  bandwidth = NULL, gridsize = 410, range.x = NULL, truncate = TRUE,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)

Arguments

Details

A sample of the output from geom_bkde():

Aesthetics

geom_bkde understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• color

• fill

• linetype

• size

Computed variables

density

density estimate

count

density * number of points - useful for stacked density plots

scaled

density estimate, scaled to maximum of 1

See Also

See geom_histogram, geom_freqpoly for other methods of displaying continuous distribution. See geom_violin for a compact density display.

Examples

data(geyser, package="MASS")

ggplot(geyser, aes(x=duration)) +
  stat_bkde(alpha=1/2)

ggplot(geyser, aes(x=duration)) +
  geom_bkde(alpha=1/2)

ggplot(geyser, aes(x=duration)) +
 stat_bkde(bandwidth=0.25)

ggplot(geyser, aes(x=duration)) +
  geom_bkde(bandwidth=0.25)

Contours from a 2d density estimate.

Description

Contours from a 2d density estimate.

Perform a 2D kernel density estimation using bkde2D and display the results with contours. This can be useful for dealing with overplotting

Usage

geom_bkde2d(mapping = NULL, data = NULL, stat = "bkde2d",
  position = "identity", bandwidth = NULL, range.x = NULL,
  lineend = "butt", contour = TRUE, linejoin = "round", linemitre = 1,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)

stat_bkde2d(mapping = NULL, data = NULL, geom = "density2d",
  position = "identity", contour = TRUE, bandwidth = NULL,
  grid_size = c(51, 51), range.x = NULL, truncate = TRUE, na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, ...)

Arguments

Details

A sample of the output from geom_bkde2d():

Computed variables

Same as stat_contour

See Also

geom_contour for contour drawing geom, stat_sum for another way of dealing with overplotting

Examples

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
       geom_point() +
       xlim(0.5, 6) +
       ylim(40, 110)

m + geom_bkde2d(bandwidth=c(0.5, 4))

m + stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon")

# If you map an aesthetic to a categorical variable, you will get a
# set of contours for each value of that variable
set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y)) +
       geom_bkde2d(bandwidth=c(0.5, 0.5), aes(colour = cut))
d

# If we turn contouring off, we can use use geoms like tiles:
d + stat_bkde2d(bandwidth=c(0.5, 0.5), geom = "raster",
                aes(fill = ..density..), contour = FALSE)

# Or points:
d + stat_bkde2d(bandwidth=c(0.5, 0.5), geom = "point",
                aes(size = ..density..),  contour = FALSE)

Map polygons layer enabling the display of show statistical information

Description

This replicates the old behaviour of geom_map(), enabling specifying of x and y aesthetics.

Usage

geom_cartogram(mapping = NULL, data = NULL, stat = "identity", ..., map,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Arguments

Aesthetics

geom_cartogram understands the following aesthetics (required aesthetics are in bold):

• map_id

• alpha

• colour

• fill

• group

• linetype

• size

• x

• y

Examples

## Not run: 
# When using geom_polygon, you will typically need two data frames:
# one contains the coordinates of each polygon (positions),  and the
# other the values associated with each polygon (values).  An id
# variable links the two together

ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))

values <- data.frame(
  id = ids,
  value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)

positions <- data.frame(
  id = rep(ids, each = 4),
  x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
  0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
  y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
  2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)

ggplot() +
  geom_cartogram(aes(x, y, map_id = id), map = positions, data=positions)

ggplot() +
  geom_cartogram(aes(x, y, map_id = id), map = positions, data=positions) +
  geom_cartogram(data=values, map=positions, aes(fill = value, map_id=id))

ggplot() +
  geom_cartogram(aes(x, y, map_id = id), map = positions, data=positions) +
  geom_cartogram(data=values, map=positions, aes(fill = value, map_id=id)) +
  ylim(0, 3)

# Better example
crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
crimesm <- reshape2::melt(crimes, id = 1)

if (require(maps)) {

  states_map <- map_data("state")

  ggplot() +
    geom_cartogram(aes(long, lat, map_id = region), map = states_map, data=states_map) +
    geom_cartogram(aes(fill = Murder, map_id = state), map=states_map, data=crimes)

  last_plot() + coord_map("polyconic")

  ggplot() +
    geom_cartogram(aes(long, lat, map_id=region), map = states_map, data=states_map) +
    geom_cartogram(aes(fill = value, map_id=state), map = states_map, data=crimesm) +
    coord_map("polyconic") +
    facet_wrap( ~ variable)
}

## End(Not run)

Dumbell charts

Description

The dumbbell geom is used to create dumbbell charts.

Usage

geom_dumbbell(mapping = NULL, data = NULL, ..., colour_x = NULL,
  size_x = NULL, colour_xend = NULL, size_xend = NULL,
  dot_guide = FALSE, dot_guide_size = NULL, dot_guide_colour = NULL,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)

Arguments

Details

Dumbbell dot plots — dot plots with two or more series of data — are an alternative to the clustered bar chart or slope graph.

Aesthetics

geom_segment understands the following aesthetics (required aesthetics are in bold):

• x

• y

• xend

• yend

• alpha

• colour

• group

• linetype

• size

Examples

library(ggplot2)

df <- data.frame(trt=LETTERS[1:5], l=c(20, 40, 10, 30, 50), r=c(70, 50, 30, 60, 80))

ggplot(df, aes(y=trt, x=l, xend=r)) +
  geom_dumbbell(size=3, color="#e3e2e1",
                colour_x = "#5b8124", colour_xend = "#bad744",
                dot_guide=TRUE, dot_guide_size=0.25) +
  labs(x=NULL, y=NULL, title="ggplot2 geom_dumbbell with dot guide") +
  theme_minimal() +
  theme(panel.grid.major.x=element_line(size=0.05))

Automatically enclose points in a polygon

Description

Automatically enclose points in a polygon

Usage

geom_encircle(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

Arguments

Details

A sample of the output from geom_encircle():

Value

adds a circle around the specified points

Author(s)

Ben Bolker

Examples

d <- data.frame(x=c(1,1,2),y=c(1,2,2)*100)

gg <- ggplot(d,aes(x,y))
gg <- gg + scale_x_continuous(expand=c(0.5,1))
gg <- gg + scale_y_continuous(expand=c(0.5,1))

gg + geom_encircle(s_shape=1, expand=0) + geom_point()

gg + geom_encircle(s_shape=1, expand=0.1, colour="red") + geom_point()

gg + geom_encircle(s_shape=0.5, expand=0.1, colour="purple") + geom_point()

gg + geom_encircle(data=subset(d, x==1), colour="blue", spread=0.02) +
  geom_point()

gg +geom_encircle(data=subset(d, x==2), colour="cyan", spread=0.04) +
  geom_point()

gg <- ggplot(mpg, aes(displ, hwy))
gg + geom_encircle(data=subset(mpg, hwy>40)) + geom_point()
gg + geom_encircle(aes(group=manufacturer)) + geom_point()
gg + geom_encircle(aes(group=manufacturer,fill=manufacturer),alpha=0.4)+
       geom_point()
gg + geom_encircle(aes(group=manufacturer,colour=manufacturer))+
       geom_point()

ss <- subset(mpg,hwy>31 & displ<2)

gg + geom_encircle(data=ss, colour="blue", s_shape=0.9, expand=0.07) +
  geom_point() + geom_point(data=ss, colour="blue")

Lollipop charts

Description

The lollipop geom is used to create lollipop charts.

Usage

geom_lollipop(mapping = NULL, data = NULL, ..., horizontal = FALSE,
  point.colour = NULL, point.size = NULL, na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE)

Arguments

Details

Lollipop charts are the creation of Andy Cotgreave going back to 2011. They are a combination of a thin segment, starting at with a dot at the top and are a suitable alternative to or replacement for bar charts.

Use the horizontal parameter to abate the need for coord_flip() (see the Arguments section for details).

A sample of the output from geom_lollipop():

Aesthetics

geom_point understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• fill

• group

• shape

• size

• stroke

Examples

df <- data.frame(trt=LETTERS[1:10],
                 value=seq(100, 10, by=-10))

ggplot(df, aes(trt, value)) + geom_lollipop()

ggplot(df, aes(value, trt)) + geom_lollipop(horizontal=TRUE)

Use ProPublica's StateFace font in ggplot2 plots

Description

The label parameter can be either a 2-letter state abbreviation or a full state name. geom_stateface() will take care of the translation to StateFace font glyph characters.

Usage

geom_stateface(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., parse = FALSE, nudge_x = 0, nudge_y = 0,
  check_overlap = FALSE, na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE)

Arguments

Details

The package will also take care of loading the StateFace font for PDF and other devices, but to use it with the on-screen ggplot2 device, you'll need to install the font on your system.

ggalt ships with a copy of the StateFace TTF font. You can run show_stateface() to get the filesystem location and then load the font manually from there.

A sample of the output from geom_stateface():

See Also

Other StateFace operations: load_stateface, show_stateface

Examples

## Not run: 
library(ggplot2)
library(ggalt)

# Run show_stateface() to see the location of the TTF StateFace font
# You need to install it for it to work

set.seed(1492)
dat <- data.frame(state=state.abb,
                  x=sample(100, 50),
                  y=sample(100, 50),
                  col=sample(c("#b2182b", "#2166ac"), 50, replace=TRUE),
                  sz=sample(6:15, 50, replace=TRUE),
                  stringsAsFactors=FALSE)
gg <- ggplot(dat, aes(x=x, y=y))
gg <- gg + geom_stateface(aes(label=state, color=col, size=sz))
gg <- gg + scale_color_identity()
gg <- gg + scale_size_identity()
gg

## End(Not run)

Connect control points/observations with an X-spline

Description

Draw an X-spline, a curve drawn relative to control points/observations. Patterned after geom_line in that it orders the points by x first before computing the splines.

Usage

geom_xspline(mapping = NULL, data = NULL, stat = "xspline",
  position = "identity", na.rm = TRUE, show.legend = NA,
  inherit.aes = TRUE, spline_shape = -0.25, open = TRUE,
  rep_ends = TRUE, ...)

stat_xspline(mapping = NULL, data = NULL, geom = "line",
  position = "identity", na.rm = TRUE, show.legend = NA,
  inherit.aes = TRUE, spline_shape = -0.25, open = TRUE,
  rep_ends = TRUE, ...)

Arguments

Details

A sample of the output from geom_xspline():

An X-spline is a line drawn relative to control points. For each control point, the line may pass through (interpolate) the control point or it may only approach (approximate) the control point; the behaviour is determined by a shape parameter for each control point.

If the shape parameter is greater than zero, the spline approximates the control points (and is very similar to a cubic B-spline when the shape is 1). If the shape parameter is less than zero, the spline interpolates the control points (and is very similar to a Catmull-Rom spline when the shape is -1). If the shape parameter is 0, the spline forms a sharp corner at that control point.

For open X-splines, the start and end control points must have a shape of 0 (and non-zero values are silently converted to zero).

For open X-splines, by default the start and end control points are replicated before the curve is drawn. A curve is drawn between (interpolating or approximating) the second and third of each set of four control points, so this default behaviour ensures that the resulting curve starts at the first control point you have specified and ends at the last control point. The default behaviour can be turned off via the repEnds argument.

Aesthetics

geom_xspline understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• color

• linetype

• size

Computed variables

• x

• y

References

Blanc, C. and Schlick, C. (1995), "X-splines : A Spline Model Designed for the End User", in Proceedings of SIGGRAPH 95, pp. 377-386. http://dept-info.labri.fr/~schlick/DOC/sig1.html

See Also

geom_line: Connect observations (x order); geom_path: Connect observations; geom_polygon: Filled paths (polygons); geom_segment: Line segments; xspline; grid.xspline

Other xspline implementations: geom_xspline2

Examples

set.seed(1492)
dat <- data.frame(x=c(1:10, 1:10, 1:10),
                  y=c(sample(15:30, 10), 2*sample(15:30, 10),
                      3*sample(15:30, 10)),
                  group=factor(c(rep(1, 10), rep(2, 10), rep(3, 10)))
)

ggplot(dat, aes(x, y, group=group, color=group)) +
  geom_point() +
  geom_line()

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point() +
  geom_line() +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(spline_shape=-0.4, size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(spline_shape=0.4, size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(spline_shape=1, size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(spline_shape=0, size=0.5)

ggplot(dat, aes(x, y, group=group, color=factor(group))) +
  geom_point(color="black") +
  geom_smooth(se=FALSE, linetype="dashed", size=0.5) +
  geom_xspline(spline_shape=-1, size=0.5)

Alternative implemenation for connecting control points/observations with an X-spline

Description

Alternative implemenation for connecting control points/observations with an X-spline

Usage

geom_xspline2(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

Arguments

Value

creates a spline curve

Author(s)

Ben Bolker

See Also

Other xspline implementations: geom_xspline

Load stateface font

Description

Makes the ProPublica StateFace font available to PDF, PostScript, et. al. devices.

Usage

load_stateface()

See Also

Other StateFace operations: geom_stateface, show_stateface

Plotly helpers

Description

Helper functions to make it easier to automatically create plotly charts

Usage

to_basic.GeomXspline(data, prestats_data, layout, params, p, ...)

to_basic.GeomBkde2d(data, prestats_data, layout, params, p, ...)

to_basic.GeomStateface(data, prestats_data, layout, params, p, ...)

Arguments

Show location of StateFace font

Description

Displays the path to the StateFace font. For the font to work in the on-screen plot device for ggplot2, you need to install the font on your system

Usage

show_stateface()

See Also

Other StateFace operations: geom_stateface, load_stateface

Compute and display a univariate averaged shifted histogram (polynomial kernel)

Description

See bin1 & ash1 for more information.

Usage

stat_ash(mapping = NULL, data = NULL, geom = "area", position = "stack",
  ab = NULL, nbin = 50, m = 5, kopt = c(2, 2), na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE, ...)

Arguments

Details

A sample of the output from stat_ash():

Aesthetics

geom_ash understands the following aesthetics (required aesthetics are in bold):

• x

• alpha

• color

• fill

• linetype

• size

Computed variables

density

ash density estimate

References

David Scott (1992), "Multivariate Density Estimation," John Wiley, (chapter 5 in particular).

B. W. Silverman (1986), "Density Estimation for Statistics and Data Analysis," Chapman & Hall.

Examples

# compare
library(gridExtra)
set.seed(1492)
dat <- data.frame(x=rnorm(100))
grid.arrange(ggplot(dat, aes(x)) + stat_ash(),
             ggplot(dat, aes(x)) + stat_bkde(),
             ggplot(dat, aes(x)) + stat_density(),
             nrow=3)

cols <- RColorBrewer::brewer.pal(3, "Dark2")
ggplot(dat, aes(x)) +
  stat_ash(alpha=1/2, fill=cols[3]) +
  stat_bkde(alpha=1/2, fill=cols[2]) +
  stat_density(alpha=1/2, fill=cols[1]) +
  geom_rug() +
  labs(x=NULL, y="density/estimate") +
  scale_x_continuous(expand=c(0,0)) +
  theme_bw() +
  theme(panel.grid=element_blank()) +
  theme(panel.border=element_blank())

Step ribbon statistic

Description

Provides stairstep values for ribbon plots

Usage

stat_stepribbon(mapping = NULL, data = NULL, geom = "ribbon",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, direction = "hv", ...)

Arguments

References

https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/9cFWHaH1CPs

Examples

x <- 1:10
df <- data.frame(x=x, y=x+10, ymin=x+7, ymax=x+12)

gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
                       stat="stepribbon", fill="#b2b2b2")
gg <- gg + geom_step(color="#2b2b2b")
gg

gg <- ggplot(df, aes(x, y))
gg <- gg + geom_ribbon(aes(ymin=ymin, ymax=ymax),
                       stat="stepribbon", fill="#b2b2b2",
                       direction="hv")
gg <- gg + geom_step(color="#2b2b2b")
gg