Package 'animint2'

Description

This operator allows you to add objects to a ggplot or theme object.

Usage

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

e1 %+% e2

e1 %+replace% e2

Arguments

Details

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

• data.frame: replace current data.frame (must use %+%)

• uneval: replace current aesthetics

• layer: add new layer

• theme: update plot theme

• scale: replace current scale

• coord: override current coordinate system

• facet: override current coordinate faceting

If the first object is an object of class theme, you can add another theme object. This will return a modified theme object.

For theme objects, the + operator and the %+replace% can be used to modify elements in themes.

The + operator updates the elements of e1 that differ from elements specified (not NULL) in e2. Thus this operator can be used to incrementally add or modify attributes of a ggplot theme.

In contrast, the %+replace% operator replaces the entire element; any element of a theme not specified in e2 will not be present in the resulting theme (i.e. NULL). Thus this operator can be used to overwrite an entire theme.

See Also

theme

Examples

### Adding objects to a ggplot object
p <- ggplot(mtcars, aes(wt, mpg, colour = disp)) +
  geom_point()

p
p + coord_cartesian(ylim = c(0, 40))
p + scale_colour_continuous(breaks = c(100, 300))
p + guides(colour = "colourbar")

# Use a different data frame
m <- mtcars[1:10, ]
p %+% m

### Adding objects to a theme object
# Compare these results of adding theme objects to other theme objects
add_el <- theme_grey() + theme(text = element_text(family = "Times"))
rep_el <- theme_grey() %+replace% theme(text = element_text(family = "Times"))

add_el$text
rep_el$text

Description

Modify properties of an element in a theme object

Usage

add_theme(t1, t2, t2name)

Arguments

Description

Add a showSelected aesthetic if legend is specified

Usage

addShowSelectedForLegend(meta, legend, L)

Arguments

Value

L : Layer with additional mapping to new aesthetic

Description

Add the showSelected/clickSelects params to the aesthetics mapping

Usage

addSSandCSasAesthetics(aesthetics, extra_params)

Arguments

Details

Used before calling ggplot_build in parsePlot and while checking animint extensions to raise error

Value

Modified aesthetics list with showSelected/clickSelects params added

Description

Generate aesthetic mappings that describe how variables in the data are mapped to visual properties (aesthetics) of geoms. This function also standardise aesthetic names by performs partial name matching, converting color to colour, and old style R names to ggplot names (eg. pch to shape, cex to size)

Usage

aes(x, y, ...)

Arguments

See Also

See aes_q/aes_string for standard evaluation versions of aes.

See aes_colour_fill_alpha, aes_linetype_size_shape and aes_position for more specific examples with different aesthetics.

Examples

aes(x = mpg, y = wt)
aes(mpg, wt)

# You can also map aesthetics to functions of variables
aes(x = mpg ^ 2, y = wt / cyl)

# Aesthetic names are automatically standardised
aes(col = x)
aes(fg = x)
aes(color = x)
aes(colour = x)

# aes is almost always used with ggplot() or a layer
ggplot(mpg, aes(displ, hwy)) + geom_point()
ggplot(mpg) + geom_point(aes(displ, hwy))

# Aesthetics supplied to ggplot() are used as defaults for every layer
# you can override them, or supply different aesthetics for each layer

Description

Aesthetic mappings describe how variables in the data are mapped to visual properties (aesthetics) of geoms. aes uses non-standard evaluation to capture the variable names. aes_ and aes_string require you to explicitly quote the inputs either with "" for aes_string(), or with quote or ~ for aes_(). (aes_q is an alias to aes_)

Usage

aes_(x, y, ...)

aes_string(x, y, ...)

aes_q(x, y, ...)

Arguments

Details

It's better to use aes_q(), because there's no easy way to create the equivalent to aes(colour = "my colour") or aes{x = `X$1`} with aes_string().

aes_string and aes_ are particularly useful when writing functions that create plots because you can use strings or quoted names/calls to define the aesthetic mappings, rather than having to use substitute to generate a call to aes().

See Also

aes

Examples

# Three ways of generating the same aesthetics
aes(mpg, wt, col = cyl)
aes_(quote(mpg), quote(wt), col = quote(cyl))
aes_(~mpg, ~wt, col = ~cyl)
aes_string("mpg", "wt", col = "cyl")

# You can't easily mimic these calls with aes_string
aes(`$100`, colour = "smooth")
aes_(~ `$100`, colour = "smooth")
# Ok, you can, but it requires a _lot_ of quotes
aes_string("`$100`", colour = '"smooth"')

# Convert strings to names with as.name
var <- "cyl"
aes(col = x)
aes_(col = as.name(var))

Description

This page demonstrates the usage of a sub-group of aesthetics; colour, fill and alpha.

Examples

# Bar chart example
c <- ggplot(mtcars, aes(factor(cyl)))
# Default plotting
c + geom_bar()
# To change the interior colouring use fill aesthetic
c + geom_bar(fill = "red")
# Compare with the colour aesthetic which changes just the bar outline
c + geom_bar(colour = "red")
# Combining both, you can see the changes more clearly
c + geom_bar(fill = "white", colour = "red")

# The aesthetic fill also takes different colouring scales
# setting fill equal to a factor variable uses a discrete colour scale
k <- ggplot(mtcars, aes(factor(cyl), fill = factor(vs)))
k + geom_bar()

# Fill aesthetic can also be used with a continuous variable
m <- ggplot(faithfuld, aes(waiting, eruptions))
m + geom_raster()
m + geom_raster(aes(fill = density))

# Some geoms don't use both aesthetics (i.e. geom_point or geom_line)
b <- ggplot(economics, aes(x = date, y = unemploy))
b + geom_line()
b + geom_line(colour = "green")
b + geom_point()
b + geom_point(colour = "red")

# For large datasets with overplotting the alpha
# aesthetic will make the points more transparent
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
h  <- ggplot(df, aes(x,y))
h + geom_point()
h + geom_point(alpha = 0.5)
h + geom_point(alpha = 1/10)

# Alpha can also be used to add shading
j <- b + geom_line()
j
yrng <- range(economics$unemploy)
j <- j + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party),
ymin = yrng[1], ymax = yrng[2], data = presidential)
j
j + scale_fill_manual(values = alpha(c("blue", "red"), .3))

Description

This page demonstrates the usage of a sub-group of aesthetics; linetype, size and shape.

Examples

# Line types should be specified with either an integer, a name, or with a string of
# an even number (up to eight) of hexadecimal digits which give the lengths in
# consecutive positions in the string.
# 0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash

# Data
df <- data.frame(x = 1:10 , y = 1:10)
f <- ggplot(df, aes(x, y))
f + geom_line(linetype = 2)
f + geom_line(linetype = "dotdash")

# An example with hex strings, the string "33" specifies three units on followed
# by three off and "3313" specifies three units on followed by three off followed
# by one on and finally three off.
f + geom_line(linetype = "3313")

# Mapping line type from a variable
ggplot(economics_long, aes(date, value01)) +
  geom_line(aes(linetype = variable))

# Size examples
# Should be specified with a numerical value (in millimetres),
# or from a variable source
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point(size = 4)
p + geom_point(aes(size = qsec))
p + geom_point(size = 2.5) +
  geom_hline(yintercept = 25, size = 3.5)

# Shape examples
# Shape takes four types of values: an integer in [0, 25],
# a single character-- which uses that character as the plotting symbol,
# a . to draw the smallest rectangle that is visible (i.e., about one pixel)
# an NA to draw nothing
p + geom_point()
p + geom_point(shape = 5)
p + geom_point(shape = "k", size = 3)
p + geom_point(shape = ".")
p + geom_point(shape = NA)

# Shape can also be mapped from a variable
p + geom_point(aes(shape = factor(cyl)))

# A look at all 25 symbols
df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
s <- ggplot(df2, aes(x, y))
s + geom_point(aes(shape = z), size = 4) +
  scale_shape_identity()
# While all symbols have a foreground colour, symbols 19-25 also take a
# background colour (fill)
s + geom_point(aes(shape = z), size = 4, colour = "Red") +
  scale_shape_identity()
s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
  scale_shape_identity()

Description

This page demonstrates the usage of a sub-group of aesthetics; x, y, xmin, xmax, ymin, ymax, xend, and yend.

Examples

# Generate data: means and standard errors of means for prices
# for each type of cut
dmod <- lm(price ~ cut, data = diamonds)
cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut =
unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit,
ymax = fit + se.fit, colour = cut))
se + geom_pointrange()

# Using annotate
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
p + annotate("rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25,
  fill = "dark grey", alpha = .5)

# Geom_segment examples
p + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25),
  arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15),
  arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
  arrow = arrow(length = unit(0.5, "cm")))

# You can also use geom_segment to recreate plot(type = "h") :
counts <- as.data.frame(table(x = rpois(100, 5)))
counts$x <- as.numeric(as.character(counts$x))
with(counts, plot(x, Freq, type = "h", lwd = 10))

ggplot(counts, aes(x, Freq)) +
  geom_segment(aes(yend = 0, xend = x), size = 10)

Description

Create an animated, interactive data visualization. The easiest way to get started is by reading the Animint2 Manual, https://rcdata.nau.edu/genomic-ml/animint2-manual/Ch02-ggplot2.html

Usage

animint(...)

Arguments

Details

This function creates a list with the items in ... and attaches the animint class. It also provides default names for un-named ggplots. The list should contain ggplots and options. Each geom can be made interactive by using the showSelected and clickSelects parameters; each should be a character vector of selection variable names. For example geom_line(clickSelects="country") means that clicking the line changes the value of the "country" selection variable; geom_point(showSelected="year") means to only show the subset of data for the currently selected year.

Value

list of class animint

Author(s)

Toby Dylan Hocking

Examples

if(require('data.table'))setDTthreads(1)#for CRAN.
library(animint2)
data(WorldBank, package="animint2")
years <- unique(WorldBank[, "year", drop=FALSE])
y1960 <- subset(WorldBank, year==1960)
animint(
  title="Linked scatterplot and time series", #web page title.
  time=list(variable="year",ms=3000), #variable and time delay used for animation.
  duration=list(year=1000), #smooth transition duration in milliseconds.
  selector.types=list(country="multiple"), #single/multiple selection for each variable.
  first=list( #selected values to show when viz is first rendered.
    country=c("Canada", "Japan"),
    year=1970),
  ## ggplots are rendered together for an interactive data viz.
  ts=ggplot()+
    theme_animint(width=500)+
    make_tallrect(WorldBank, "year")+
    geom_text(aes(
      year, life.expectancy, label=country),
      showSelected="country",
      clickSelects="country",
      hjust=1,
      data=y1960)+
    scale_x_continuous(limits=c(1950, NA))+
    geom_line(aes(
      year, life.expectancy, group=country, color=region),
      clickSelects="country",
      data=WorldBank,
      size=4,
      alpha=0.55),
  scatter=ggplot()+
    geom_point(aes(
      fertility.rate, life.expectancy,
      key=country, colour=region, size=population),
      showSelected="year",
      clickSelects="country",
      data=WorldBank)+
    geom_text(aes(
      fertility.rate, life.expectancy,
      key=country,
      label=country),
      showSelected=c("country", "year"),
      data=WorldBank)+
    geom_text(aes(
      5, 80, key=1, label=paste("year =", year)),
      showSelected="year",
      data=years)+
    scale_size_animint(pixel.range=c(2,20), breaks=10^(4:9)))

Description

This function converts an animint plot.list into a directory of files which can be used to render the interactive data visualization in a web browser.

Usage

animint2dir(
  plot.list,
  out.dir = NULL,
  json.file = "plot.json",
  open.browser = interactive(),
  css.file = ""
)

Arguments

Value

invisible list of ggplots in list format.

Examples

if(require('data.table'))setDTthreads(1)#for CRAN.

## Make a Gapminder plot (aka Google motion chart), which is actually
## just a scatterplot with size and color that moves over time.
library(animint2)
data(WorldBank)
gapminder <- list(
  title="Linked scatterplot and time series",
  ts=ggplot()+
    make_tallrect(WorldBank, "year")+
    geom_line(aes(year, life.expectancy, group=country, color=region),
              clickSelects="country",
              data=WorldBank, size=4, alpha=3/5),
  time=list(variable="year",ms=3000),
  duration=list(year=1000),
  scatter=ggplot()+
    geom_point(aes(fertility.rate, life.expectancy,
                   key=country, colour=region, size=population),
               showSelected="year",
               clickSelects="country",
               data=WorldBank)+
    geom_text(aes(fertility.rate, life.expectancy, label=country),
              showSelected=c("country", "year"),
              data=WorldBank)+
    make_text(WorldBank, 5, 80, "year")+
    scale_size_animint(pixel.range=c(2,20), breaks=10^(4:9)))
animint2dir(gapminder)

data(worldPop)
## Linked bar and line plots of world population by subcontinent,
## inspired by polychartjs.
popPlots <- list(
  bars=ggplot()+
    geom_bar(aes(x=subcontinent, y=population),
             clickSelects="subcontinent",
             showSelected="year",
             data=worldPop, stat="identity", position="identity")+
    ## This make_text creates a geom_text that shows the current
    ## selected value of the year variable.
    make_text(worldPop, 1, 3e6, "year")+
    coord_flip(),
  lines=ggplot()+
    ## This make_tallrect tiles the background of the lineplot with
    ## rects that can be clicked to select the year variable.
    make_tallrect(worldPop, "year")+
    ## This geom_point does not have aes(clickSelects) so its alpha
    ## transparency behaves normally: all points have alpha=1/4.
    geom_point(aes(year, population, colour=type),
               data=worldPop, size=4, alpha=1/4)+
    ## This geom_line DOES have aes(clickSelects) so only the
    ## selected line has the specified alpha=3/4. The other
    ## unselected lines have 0.5 less (alpha=1/4).
    geom_line(aes(year, population, group=subcontinent),
              clickSelects="subcontinent",
              data=worldPop, size=4, alpha=3/4))
animint2dir(popPlots)
## Make it animated by specifying year as the variable to animate and
## an interval of 2000 milliseconds between animation frames.
popAnim <- c(popPlots, list(time=list(variable="year",ms=2000)))
animint2dir(popAnim)
## Make the animation smooth by specifying a duration of 1000 ms for
## geoms with aes(showSelected=year).
popSmooth <- c(popAnim, list(duration=list(year=1000)))
animint2dir(popSmooth)

Description

This function takes a named list of ggplots, generates interactive animations, and pushes the generated files to a specified GitHub repository. You can choose to keep the repository private or public. Before using this function set your appropriate git 'user.username' and 'user.email'

Usage

animint2pages(
  plot.list,
  github_repo,
  owner = NULL,
  commit_message = "Commit from animint2pages",
  private = FALSE,
  required_opts = c("title", "source"),
  ...
)

Arguments

Value

The function returns the initialized GitHub repository object.

Examples

## Not run: 
library(animint2)
mtcars$Cyl <- factor(mtcars$cyl)
viz <- animint(
  ggplot(mtcars, aes(x = mpg, y = disp, color=Cyl)) +
    geom_point(),
  ggplot(mtcars, aes(x = hp, y = wt, color=Cyl)) +
    geom_point(),
  title="Motor Trend Cars data viz",
  source="https://github.com/animint/animint2/blob/master/R/z_pages.R"
)
animint2pages(viz, "animint2pages-example-mtcars")

## End(Not run)

Description

Shiny ui output function

Usage

animintOutput(outputId)

Arguments

See Also

http://shiny.rstudio.com/articles/building-outputs.html

Description

This function adds geoms to a plot. Unlike typical a geom function, the properties of the geoms are not mapped from variables of a data frame, but are instead passed in as vectors. This is useful for adding small annotations (such as text labels) or if you have your data in vectors, and for some reason don't want to put them in a data frame.

Usage

annotate(
  geom,
  x = NULL,
  y = NULL,
  xmin = NULL,
  xmax = NULL,
  ymin = NULL,
  ymax = NULL,
  xend = NULL,
  yend = NULL,
  ...,
  na.rm = FALSE
)

Arguments

Details

Note that all position aesthetics are scaled (i.e. they will expand the limits of the plot so they are visible), but all other aesthetics are set. This means that layers created with this function will never affect the legend.

Examples

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p + annotate("text", x = 4, y = 25, label = "Some text")
p + annotate("text", x = 2:5, y = 25, label = "Some text")
p + annotate("rect", xmin = 3, xmax = 4.2, ymin = 12, ymax = 21,
  alpha = .2)
p + annotate("segment", x = 2.5, xend = 4, y = 15, yend = 25,
  colour = "blue")
p + annotate("pointrange", x = 3.5, y = 20, ymin = 12, ymax = 28,
  colour = "red", size = 1.5)

p + annotate("text", x = 2:3, y = 20:21, label = c("my label", "label 2"))

Description

This is a special geom intended for use as static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the grob, and the grob will not be modified by any ggplot settings or mappings).

Usage

annotation_custom(grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf)

Arguments

Details

Most useful for adding tables, inset plots, and other grid-based decorations.

Note

annotation_custom expects the grob to fill the entire viewport defined by xmin, xmax, ymin, ymax. Grobs with a different (absolute) size will be center-justified in that region. Inf values can be used to fill the full plot panel (see examples).

Examples

# Dummy plot
df <- data.frame(x = 1:10, y = 1:10)
base <- ggplot(df, aes(x, y)) +
  geom_blank() +
  theme_bw()

# Full panel annotation
base + annotation_custom(
  grob = grid::roundrectGrob(),
  xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf
)

# Inset plot
df2 <- data.frame(x = 1 , y = 1)
g <- ggplotGrob(ggplot(df2, aes(x, y)) +
  geom_point() +
  theme(plot.background = element_rect(colour = "black")))
base +
  annotation_custom(grob = g, xmin = 1, xmax = 10, ymin = 8, ymax = 10)

Description

This annotation adds log tick marks with diminishing spacing. These tick marks probably make sense only for base 10.

Usage

annotation_logticks(
  base = 10,
  sides = "bl",
  scaled = TRUE,
  short = unit(0.1, "cm"),
  mid = unit(0.2, "cm"),
  long = unit(0.3, "cm"),
  colour = "black",
  size = 0.5,
  linetype = 1,
  alpha = 1,
  color = NULL,
  ...
)

Arguments

See Also

scale_y_continuous, scale_y_log10 for log scale transformations.

coord_trans for log coordinate transformations.

Examples

# Make a log-log plot (without log ticks)
a <- ggplot(msleep, aes(bodywt, brainwt)) +
 geom_point(na.rm = TRUE) +
 scale_x_log10(
   breaks = scales::trans_breaks("log10", function(x) 10^x),
   labels = scales::trans_format("log10", scales::math_format(10^.x))
 ) +
 scale_y_log10(
   breaks = scales::trans_breaks("log10", function(x) 10^x),
   labels = scales::trans_format("log10", scales::math_format(10^.x))
 ) +
 theme_bw()

a + annotation_logticks()                # Default: log ticks on bottom and left
a + annotation_logticks(sides = "lr")    # Log ticks for y, on left and right
a + annotation_logticks(sides = "trbl")  # All four sides

# Hide the minor grid lines because they don't align with the ticks
a + annotation_logticks(sides = "trbl") + theme(panel.grid.minor = element_blank())

# Another way to get the same results as 'a' above: log-transform the data before
# plotting it. Also hide the minor grid lines.
b <- ggplot(msleep, aes(log10(bodywt), log10(brainwt))) +
 geom_point(na.rm = TRUE) +
 scale_x_continuous(name = "body", labels = scales::math_format(10^.x)) +
 scale_y_continuous(name = "brain", labels = scales::math_format(10^.x)) +
 theme_bw() + theme(panel.grid.minor = element_blank())

b + annotation_logticks()

# Using a coordinate transform requires scaled = FALSE
t <- ggplot(msleep, aes(bodywt, brainwt)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10") +
  theme_bw()
t + annotation_logticks(scaled = FALSE)

# Change the length of the ticks
a + annotation_logticks(
  short = unit(.5,"mm"),
  mid = unit(3,"mm"),
  long = unit(4,"mm")
)

Description

Annotation: maps.

Usage

annotation_map(map, ...)

Arguments

Examples

if (require("maps")) {
usamap <- map_data("state")

seal.sub <- subset(seals, long > -130 & lat < 45 & lat > 40)
ggplot(seal.sub, aes(x = long, y = lat)) +
  annotation_map(usamap, fill = "NA", colour = "grey50") +
  geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat))

seal2 <- transform(seal.sub,
  latr = cut(lat, 2),
  longr = cut(long, 2))

ggplot(seal2,  aes(x = long, y = lat)) +
  annotation_map(usamap, fill = "NA", colour = "grey50") +
  geom_segment(aes(xend = long + delta_long, yend = lat + delta_lat)) +
  facet_grid(latr ~ longr, scales = "free", space = "free")
}

Description

This is a special version of geom_raster optimised for static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the raster, and the raster must already have its own colours).

Usage

annotation_raster(raster, xmin, xmax, ymin, ymax, interpolate = FALSE)

Arguments

Details

Most useful for adding bitmap images.

Examples

# Generate data
rainbow <- matrix(hcl(seq(0, 360, length.out = 50 * 50), 80, 70), nrow = 50)
ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  annotation_raster(rainbow, 15, 20, 3, 4)
# To fill up whole plot
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow, -Inf, Inf, -Inf, Inf) +
  geom_point()

rainbow2 <- matrix(hcl(seq(0, 360, length.out = 10), 80, 70), nrow = 1)
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow2, -Inf, Inf, -Inf, Inf) +
  geom_point()
rainbow2 <- matrix(hcl(seq(0, 360, length.out = 10), 80, 70), nrow = 1)
ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster(rainbow2, -Inf, Inf, -Inf, Inf, interpolate = TRUE) +
  geom_point()

Description

This transforms objects to labeller functions. Used internally by labeller().

Usage

as_labeller(x, default = label_value, multi_line = TRUE)

Arguments

See Also

labeller(), labellers

Examples

p <- ggplot(mtcars, aes(disp, drat)) + geom_point()
p + facet_wrap(~am)

# Rename labels on the fly with a lookup character vector
to_string <- as_labeller(c(`0` = "Zero", `1` = "One"))
p + facet_wrap(~am, labeller = to_string)

# Quickly transform a function operating on character vectors to a
# labeller function:
appender <- function(string, suffix = "-foo") paste0(string, suffix)
p + facet_wrap(~am, labeller = as_labeller(appender))

# If you have more than one facetting variable, be sure to dispatch
# your labeller to the right variable with labeller()
p + facet_grid(cyl ~ am, labeller = labeller(am = to_string))

Description

This will not include the object's super member.

Usage

## S3 method for class 'gganimintproto'
as.list(x, inherit = TRUE, ...)

Arguments

Description

autoplot uses ggplot2 to draw a particular plot for an object of a particular class in a single command. This defines the S3 generic that other classes and packages can extend.

Usage

autoplot(object, ...)

Arguments

Value

a ggplot object

See Also

ggplot and fortify

Description

Create a layer of map borders.

Usage

borders(
  database = "world",
  regions = ".",
  fill = NA,
  colour = "grey50",
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

Examples

if (require("maps")) {

ia <- map_data("county", "iowa")
mid_range <- function(x) mean(range(x))
seats <- plyr::ddply(ia, "subregion", plyr::colwise(mid_range, c("lat", "long")))
ggplot(ia, aes(long, lat)) +
  geom_polygon(aes(group = group), fill = NA, colour = "grey60") +
  geom_text(aes(label = subregion), data = seats, size = 2, angle = 45)

data(us.cities)
capitals <- subset(us.cities, capital == 2)
ggplot(capitals, aes(long, lat)) +
  borders("state") +
  geom_point(aes(size = pop)) +
  scale_size_area() +
  coord_quickmap()

# Same map, with some world context
ggplot(capitals, aes(long, lat)) +
  borders("world", xlim = c(-130, -60), ylim = c(20, 50)) +
  geom_point(aes(size = pop)) +
  scale_size_area() +
  coord_quickmap()
}

Description

Two noisy signals were sampled from a latent signal with known breakpoints, which were used to measure the error of estimated models with 1,...,20 segments.

Usage

data(breakpoints)

Format

A list of 5 data.frames: error contains the breakpointError of the estimated models, signals contains the noisy signals, breaks contains the breakpoints in the estimated signals, segments contains the estimated segments, and imprecision contains the normalized imprecision curves which were used to evaluate the error.

Source

The breakpointError package was used to measure the model error, see etc/breakpoints.R.

Description

Calculate the element properties, by inheriting properties from its parents

Usage

calc_element(element, theme, verbose = FALSE)

Arguments

Examples

t <- theme_grey()
calc_element('text', t)

# Compare the "raw" element definition to the element with calculated inheritance
t$axis.text.x
calc_element('axis.text.x', t, verbose = TRUE)

# This reports that axis.text.x inherits from axis.text,
# which inherits from text. You can view each of them with:
t$axis.text.x
t$axis.text
t$text

Description

A model for detecting graphical model structure changes was applied to some simulated data for which we know the real changes.

Usage

data(change)

Format

List of 4 data.frames: coefs contains the coefficients for every variable at every point in the regularization path, truth contains information about which variables are real changes, likelihood contains the train and test likelihood, and position contains positions for laying out the graphical model nodes on the 2D plane.

Source

Song Liu, personal communication.

References

Liu S, Quinn JA, Gutmann MU, Sugiyama M. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD 2013.

Description

Check animation variable for errors

Usage

checkAnimationTimeVar(timeVarList)

Arguments

Value

NULL :Stops with an error for invalid input

Description

Check extra_params argument for duplicates, non-named list

Usage

checkExtraParams(extra_params, aes_mapping)

Arguments

Value

Modified extra_params list

Description

Check if showSelected and clickSelects have been used as aesthetics as in old syntax. If yes, raise error

Usage

checkForSSandCSasAesthetics(aesthetics, plot_name)

Arguments

Value

NULL Throws error if used as aesthetics

Description

Performs error checking on the plot for animint extensions

Usage

checkPlotForAnimintExtensions(p, plot_name)

Arguments

Value

NULL :Stops with an error for invalid input

Description

Check that plot.list is a list and every element is named

Usage

checkPlotList(plot.list)

Arguments

Value

Throws an error for invalid values

Description

Issue warnings for non interactive plots where there is only one showSelected value

Usage

checkSingleShowSelectedValue(selectors)

Arguments

Value

NULL

Description

ChromHMM was run for one iPS sample for 100 iterations and its convergence metrics and parameter values were saved at each iteration.

Usage

data("ChromHMMiterations")

Format

Named list of 3 data.frames: metrics, transition, emission.

Source

https://github.com/tdhock/ChromHMM-viz/blob/master/iterations.R

Description

Monthly observations of atmospheric variables 1995-2000, between 113.75W-56.25W, 21.25S-36.25N with 2.5 degree grid spacing. Variables: pressure, temperature, ozone and low, medium, high cloud cover. Temperatures are given in degrees celsius (original data had Kelvin).

Usage

data(climate)

Format

A data frame with 41472 observations on the following 16 variables.

time

a numeric vector

y

a numeric vector

x

a numeric vector

lat

a numeric vector

long

a numeric vector

date

a POSIXct

cloudhigh

a numeric vector

cloudlow

a numeric vector

cloudmid

a numeric vector

ozone

a numeric vector

pressure

a numeric vector

surftemp

a numeric vector

temperature

a numeric vector

id

a character vector

month

a numeric vector

year

a numeric vector

Source

NASA Goddard Institute for Space Studies (GISS) subset of the monthly climatology of the International Satellite Cloud Climatology Project (ISCCP)

Description

Filter out columns that do not need to be copied

Usage

colsNotToCopy(g, s.aes)

Arguments

Value

Character vector of columns not to be copied

Description

50, ..., 800 labeled pairs were generated according to the squared L1, L2, and Linf norms, with input features in [-3, 3]. Labels are -1 when there is no significant difference between the ranks of Xt and Xtp, and 1 when the rank of Xtp is significantly higher than the rank of Xt. SVMrank ignores the pairs labeled -1, but SVMcompare uses them to learn a more accurate predictive model, as shown by the test error and level curves of the learned functions.

Usage

data(compare)

Format

List of 4 data.frames: error contains the test error of the learned models, bayes contains the Bayes classification error of the latent ranking function applied to the test data, rank contains the ranking functions evaluated on a grid of input points, train.pairs contains the data that were used to train the algorithms.

References

Hocking TD, Spanurattana S, Sugiyama M. Support vector comparison machines (2013).

Description

The Cartesian coordinate system is the most familiar, and common, type of coordinate system. Setting limits on the coordinate system will zoom the plot (like you're looking at it with a magnifying glass), and will not change the underlying data like setting limits on a scale will.

Usage

coord_cartesian(xlim = NULL, ylim = NULL, expand = TRUE)

Arguments

Examples

# There are two ways of zooming the plot display: with scales or
# with coordinate systems.  They work in two rather different ways.

p <- ggplot(mtcars, aes(disp, wt)) +
  geom_point() +
  geom_smooth()
p

# Setting the limits on a scale converts all values outside the range to NA.
p + scale_x_continuous(limits = c(325, 500))

# Setting the limits on the coordinate system performs a visual zoom.
# The data is unchanged, and we just view a small portion of the original
# plot. Note how smooth continues past the points visible on this plot.
p + coord_cartesian(xlim = c(325, 500))

# By default, the same expansion factor is applied as when setting scale
# limits. You can set the limits precisely by setting expand = FALSE
p + coord_cartesian(xlim = c(325, 500), expand = FALSE)

# Simiarly, we can use expand = FALSE to turn off expansion with the
# default limits
p + coord_cartesian(expand = FALSE)

# You can see the same thing with this 2d histogram
d <- ggplot(diamonds, aes(carat, price)) +
  stat_bin2d(bins = 25, colour = "white")
d

# When zooming the scale, the we get 25 new bins that are the same
# size on the plot, but represent smaller regions of the data space
d + scale_x_continuous(limits = c(0, 1))

# When zooming the coordinate system, we see a subset of original 50 bins,
# displayed bigger
d + coord_cartesian(xlim = c(0, 1))

Description

A fixed scale coordinate system forces a specified ratio between the physical representation of data units on the axes. The ratio represents the number of units on the y-axis equivalent to one unit on the x-axis. The default, ratio = 1, ensures that one unit on the x-axis is the same length as one unit on the y-axis. Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa. This is similar to eqscplot, but it works for all types of graphics.

Usage

coord_fixed(ratio = 1, xlim = NULL, ylim = NULL, expand = TRUE)

Arguments

Examples

# ensures that the ranges of axes are equal to the specified ratio by
# adjusting the plot aspect ratio

p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + coord_fixed(ratio = 1)
p + coord_fixed(ratio = 5)
p + coord_fixed(ratio = 1/5)

# Resize the plot to see that the specified aspect ratio is maintained

Description

Flipped cartesian coordinates so that horizontal becomes vertical, and vertical, horizontal. This is primarily useful for converting geoms and statistics which display y conditional on x, to x conditional on y.

Usage

coord_flip(xlim = NULL, ylim = NULL, expand = TRUE)

Arguments

Examples

h <- ggplot(diamonds, aes(carat)) +
  geom_histogram()
h
h + coord_flip()
h + coord_flip() + scale_x_reverse()

# You can also use it to flip line and area plots:
df <- data.frame(x = 1:5, y = (1:5) ^ 2)
ggplot(df, aes(x, y)) +
  geom_area()
last_plot() + coord_flip()

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_map does. These projections account for the fact that the actual length (in km) of one degree of longitude varies between the equator and the pole. Near the equator, the ratio between the lengths of one degree of latitude and one degree of longitude is approximately 1. Near the pole, it is tends towards infinity because the length of one degree of longitude tends towards 0. For regions that span only a few degrees and are not too close to the poles, setting the aspect ratio of the plot to the appropriate lat/lon ratio approximates the usual mercator projection. This is what coord_quickmap does. With coord_map all elements of the graphic have to be projected which is not the case here. So coord_quickmap has the advantage of being much faster, in particular for complex plots such as those using with geom_tile, at the expense of correctness in the projection. This coordinate system provides the full range of map projections available in the mapproj package.

Usage

coord_map(
  projection = "mercator",
  ...,
  orientation = NULL,
  xlim = NULL,
  ylim = NULL
)

coord_quickmap(xlim = NULL, ylim = NULL, expand = TRUE)

Arguments

Examples

if (require("maps")) {
nz <- map_data("nz")
# Prepare a map of NZ
nzmap <- ggplot(nz, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

# Plot it in cartesian coordinates
nzmap
# With correct mercator projection
nzmap + coord_map()
# With the aspect ratio approximation
nzmap + coord_quickmap()

# Other projections
nzmap + coord_map("cylindrical")
nzmap + coord_map("azequalarea", orientation = c(-36.92,174.6,0))

states <- map_data("state")
usamap <- ggplot(states, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

# Use cartesian coordinates
usamap
# With mercator projection
usamap + coord_map()
usamap + coord_quickmap()
# See ?mapproject for coordinate systems and their parameters
usamap + coord_map("gilbert")
usamap + coord_map("lagrange")

# For most projections, you'll need to set the orientation yourself
# as the automatic selection done by mapproject is not available to
# ggplot
usamap + coord_map("orthographic")
usamap + coord_map("stereographic")
usamap + coord_map("conic", lat0 = 30)
usamap + coord_map("bonne", lat0 = 50)

# World map, using geom_path instead of geom_polygon
world <- map_data("world")
worldmap <- ggplot(world, aes(x = long, y = lat, group = group)) +
  geom_path() +
  scale_y_continuous(breaks = (-2:2) * 30) +
  scale_x_continuous(breaks = (-4:4) * 45)

# Orthographic projection with default orientation (looking down at North pole)
worldmap + coord_map("ortho")
# Looking up up at South Pole
worldmap + coord_map("ortho", orientation = c(-90, 0, 0))
# Centered on New York (currently has issues with closing polygons)
worldmap + coord_map("ortho", orientation = c(41, -74, 0))
}

Description

The polar coordinate system is most commonly used for pie charts, which are a stacked bar chart in polar coordinates.

Usage

coord_polar(theta = "x", start = 0, direction = 1)

Arguments

Examples

# NOTE: Use these plots with caution - polar coordinates has
# major perceptual problems.  The main point of these examples is
# to demonstrate how these common plots can be described in the
# grammar.  Use with EXTREME caution.

#' # A pie chart = stacked bar chart + polar coordinates
pie <- ggplot(mtcars, aes(x = factor(1), fill = factor(cyl))) +
 geom_bar(width = 1)
pie + coord_polar(theta = "y")



# A coxcomb plot = bar chart + polar coordinates
cxc <- ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar(width = 1, colour = "black")
cxc + coord_polar()
# A new type of plot?
cxc + coord_polar(theta = "y")

# The bullseye chart
pie + coord_polar()

# Hadley's favourite pie chart
df <- data.frame(
  variable = c("does not resemble", "resembles"),
  value = c(20, 80)
)
ggplot(df, aes(x = "", y = value, fill = variable)) +
  geom_bar(width = 1, stat = "identity") +
  scale_fill_manual(values = c("red", "yellow")) +
  coord_polar("y", start = pi / 3) +
  labs(title = "Pac man")

# Windrose + doughnut plot
if (require("ggplot2movies")) {
movies$rrating <- cut_interval(movies$rating, length = 1)
movies$budgetq <- cut_number(movies$budget, 4)

doh <- ggplot(movies, aes(x = rrating, fill = budgetq))

# Wind rose
doh + geom_bar(width = 1) + coord_polar()
# Race track plot
doh + geom_bar(width = 0.9, position = "fill") + coord_polar(theta = "y")
}

Description

coord_trans is different to scale transformations in that it occurs after statistical transformation and will affect the visual appearance of geoms - there is no guarantee that straight lines will continue to be straight.

Usage

coord_trans(
  x = "identity",
  y = "identity",
  limx = NULL,
  limy = NULL,
  xtrans,
  ytrans
)

Arguments

Details

All current transformations only work with continuous values - see trans_new for list of transformations, and instructions on how to create your own.

Examples

# Three ways of doing transformation in ggplot:
#  * by transforming the data
ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_point()
#  * by transforming the scales
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10()
#  * by transforming the coordinate system:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  coord_trans(x = "log10", y = "log10")

# The difference between transforming the scales and
# transforming the coordinate system is that scale
# transformation occurs BEFORE statistics, and coordinate
# transformation afterwards.  Coordinate transformation also
# changes the shape of geoms:

d <- subset(diamonds, carat > 0.5)

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10()

ggplot(d, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  coord_trans(x = "log10", y = "log10")

# Here I used a subset of diamonds so that the smoothed line didn't
# drop below zero, which obviously causes problems on the log-transformed
# scale

# With a combination of scale and coordinate transformation, it's
# possible to do back-transformations:
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10() +
  scale_y_log10() +
  coord_trans(x = scales::exp_trans(10), y = scales::exp_trans(10))

# cf.
ggplot(diamonds, aes(carat, price)) +
  geom_point() +
  geom_smooth(method = "lm")

# Also works with discrete scales
df <- data.frame(a = abs(rnorm(26)),letters)
plot <- ggplot(df,aes(a,letters)) + geom_point()

plot + coord_trans(x = "log10")
plot + coord_trans(x = "sqrt")

Description

cut_interval makes n groups with equal range, cut_number makes n groups with (approximately) equal numbers of observations; cut_width makes groups of width width.

Usage

cut_interval(x, n = NULL, length = NULL, ...)

cut_number(x, n = NULL, ...)

cut_width(
  x,
  width,
  center = NULL,
  boundary = NULL,
  closed = c("right", "left")
)

Arguments

Author(s)

Randall Prium contributed most of the implementation of cut_width.

See Also

cut_number

Examples

table(cut_interval(1:100, 10))
table(cut_interval(1:100, 11))

table(cut_number(runif(1000), 10))

table(cut_width(runif(1000), 0.1))
table(cut_width(runif(1000), 0.1, boundary = 0))
table(cut_width(runif(1000), 0.1, center = 0))

Description

A dataset containing the prices and other attributes of almost 54,000 diamonds. The variables are as follows:

Usage

diamonds

Format

A data frame with 53940 rows and 10 variables:

• price: price in US dollars ($326–$18,823)

• carat: weight of the diamond (0.2–5.01)

• cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)

• color: diamond colour, from J (worst) to D (best)

• clarity: a measurement of how clear the diamond is (I1 (worst), SI1, SI2, VS1, VS2, VVS1, VVS2, IF (best))

• x: length in mm (0–10.74)

• y: width in mm (0–58.9)

• z: depth in mm (0–31.8)

• depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)

• table: width of top of diamond relative to widest point (43–95)

Description

This dataset was produced from US economic time series data available from https://fred.stlouisfed.org/. economics is in "wide" format, economics_long is in "long" format.

Usage

economics

economics_long

Format

A data frame with 478 rows and 6 variables

• date. Month of data collection

• psavert, personal savings rate,

• pce, personal consumption expenditures, in billions of dollars,

• unemploy, number of unemployed in thousands,

• uempmed, median duration of unemployment, in week,

• pop, total population, in thousands,

An object of class data.frame with 2870 rows and 4 columns.

Description

Theme element: blank. This theme element draws nothing, and assigns no space

Usage

element_blank()

Description

Theme element: line.

Usage

element_line(
  colour = NULL,
  size = NULL,
  linetype = NULL,
  lineend = NULL,
  color = NULL
)

Arguments

Description

Most often used for backgrounds and borders.

Usage

element_rect(
  fill = NULL,
  colour = NULL,
  size = NULL,
  linetype = NULL,
  color = NULL
)

Arguments

Description

Theme element: text.

Usage

element_text(
  family = NULL,
  face = NULL,
  colour = NULL,
  size = NULL,
  hjust = NULL,
  vjust = NULL,
  angle = NULL,
  lineheight = NULL,
  color = NULL,
  margin = NULL,
  debug = NULL
)

Arguments

Description

panels or all plots. This function is a thin wrapper around geom_blank that makes it easy to add such values.

Usage

expand_limits(...)

Arguments

Examples

p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + expand_limits(x = 0)
p + expand_limits(y = c(1, 9))
p + expand_limits(x = 0, y = 0)

ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(colour = cyl)) +
  expand_limits(colour = seq(2, 10, by = 2))
ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(colour = factor(cyl))) +
  expand_limits(colour = factor(seq(2, 10, by = 2)))

Description

Lay out panels in a grid.

Usage

facet_grid(
  facets,
  margins = FALSE,
  scales = "fixed",
  space = "fixed",
  shrink = TRUE,
  labeller = "label_value",
  as.table = TRUE,
  switch = NULL,
  drop = TRUE
)

Arguments

Examples

p <- ggplot(mpg, aes(displ, cty)) + geom_point()

p + facet_grid(. ~ cyl)
p + facet_grid(drv ~ .)
p + facet_grid(drv ~ cyl)

# To change plot order of facet grid,
# change the order of variable levels with factor()

# If you combine a facetted dataset with a dataset that lacks those
# facetting variables, the data will be repeated across the missing
# combinations:
df <- data.frame(displ = mean(mpg$displ), cty = mean(mpg$cty))
p +
  facet_grid(. ~ cyl) +
  geom_point(data = df, colour = "red", size = 2)

# Free scales -------------------------------------------------------
# You can also choose whether the scales should be constant
# across all panels (the default), or whether they should be allowed
# to vary
mt <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
  geom_point()

mt + facet_grid(. ~ cyl, scales = "free")

# If scales and space are free, then the mapping between position
# and values in the data will be the same across all panels. This
# is particularly useful for categorical axes
ggplot(mpg, aes(drv, model)) +
  geom_point() +
  facet_grid(manufacturer ~ ., scales = "free", space = "free") +
  theme(strip.text.y = element_text(angle = 0))

# Facet labels ------------------------------------------------------
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
p

# label_both() displays both variable name and value
p + facet_grid(vs ~ cyl, labeller = label_both)

# label_parsed() parses text into mathematical expressions, see ?plotmath
mtcars$cyl2 <- factor(mtcars$cyl, labels = c("alpha", "beta", "sqrt(x, y)"))
ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  facet_grid(. ~ cyl2, labeller = label_parsed)

# label_bquote() makes it easy to construct math expressions
p + facet_grid(. ~ vs, labeller = label_bquote(cols = alpha ^ .(vs)))

# The facet strips can be displayed near the axes with switch
data <- transform(mtcars,
  am = factor(am, levels = 0:1, c("Automatic", "Manual")),
  gear = factor(gear, levels = 3:5, labels = c("Three", "Four", "Five"))
)
p <- ggplot(data, aes(mpg, disp)) + geom_point()
p + facet_grid(am ~ gear, switch = "both")
# It looks better without boxes around the strips
p + facet_grid(am ~ gear, switch = "both") +
  theme(strip.background = element_blank())

# Margins ----------------------------------------------------------

# Margins can be specified by logically (all yes or all no) or by specific
# variables as (character) variable names
mg <- ggplot(mtcars, aes(x = mpg, y = wt)) + geom_point()
mg + facet_grid(vs + am ~ gear)
mg + facet_grid(vs + am ~ gear, margins = TRUE)
mg + facet_grid(vs + am ~ gear, margins = "am")
# when margins are made over "vs", since the facets for "am" vary
# within the values of "vs", the marginal facet for "vs" is also
# a margin over "am".
mg + facet_grid(vs + am ~ gear, margins = "vs")
mg + facet_grid(vs + am ~ gear, margins = "gear")
mg + facet_grid(vs + am ~ gear, margins = c("gear", "am"))

Description

Facet specification: a single panel.

Usage

facet_null(shrink = TRUE)

Arguments

Examples

# facet_null is the default facetting specification if you
# don't override it with facet_grid or facet_wrap
ggplot(mtcars, aes(mpg, wt)) + geom_point()

Description

Most displays are roughly rectangular, so if you have a categorical variable with many levels, it doesn't make sense to try and display them all in one row (or one column). To solve this dilemma, facet_wrap wraps a 1d sequence of panels into 2d, making best use of screen real estate.

Usage

facet_wrap(
  facets,
  nrow = NULL,
  ncol = NULL,
  scales = "fixed",
  shrink = TRUE,
  labeller = "label_value",
  as.table = TRUE,
  switch = NULL,
  drop = TRUE,
  dir = "h"
)

Arguments

Examples

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class)

# Control the number of rows and columns with nrow and ncol
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class, nrow = 4)


# You can facet by multiple variables
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~ cyl + drv)
# Or use a character vector:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(c("cyl", "drv"))

# Use the `labeller` option to control how labels are printed:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(c("cyl", "drv"), labeller = "label_both")

# To change the order in which the panels appear, change the levels
# of the underlying factor.
mpg$class2 <- reorder(mpg$class, mpg$displ)
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class2)

# By default, the same scales are used for all panels. You can allow
# scales to vary across the panels with the `scales` argument.
# Free scales make it easier to see patterns within each panel, but
# harder to compare across panels.
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  facet_wrap(~class, scales = "free")

# To repeat the same data in every panel, simply construct a data frame
# that does not contain the facetting variable.
ggplot(mpg, aes(displ, hwy)) +
  geom_point(data = transform(mpg, class = NULL), colour = "grey85") +
  geom_point() +
  facet_wrap(~class)

# Use `switch` to display the facet labels near an axis, acting as
# a subtitle for this axis. This is typically used with free scales
# and a theme without boxes around strip labels.
ggplot(economics_long, aes(date, value)) +
  geom_line() +
  facet_wrap(~variable, scales = "free_y", nrow = 2, switch = "x") +
  theme(strip.background = element_blank())

Description

A 2d density estimate of the waiting and eruptions variables data faithful.

Usage

faithfuld

Format

A data frame with 5,625 observations and 3 variables.

Description

Data about Flu outbreaks.

Usage

data("FluView")

Format

The format is a named list of two data.frames.

Description

Format a gganimintproto object

Usage

## S3 method for class 'gganimintproto'
format(x, ..., flat = TRUE)

Arguments

Description

Rather than using this function, I now recomend using the broom package, which implements a much wider range of methods. fortify may be deprecated in the future.

Usage

fortify(model, data, ...)

Arguments

See Also

fortify.lm

Description

If you have missing values in your model data, you may need to refit the model with na.action = na.exclude.

Usage

## S3 method for class 'lm'
fortify(model, data = model$model, ...)

Arguments

Value

The original data with extra columns:

Examples

mod <- lm(mpg ~ wt, data = mtcars)
head(fortify(mod))
head(fortify(mod, mtcars))

plot(mod, which = 1)

ggplot(mod, aes(.fitted, .resid)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)

ggplot(mod, aes(.fitted, .stdresid)) +
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)

ggplot(fortify(mod, mtcars), aes(.fitted, .stdresid)) +
  geom_point(aes(colour = factor(cyl)))

ggplot(fortify(mod, mtcars), aes(mpg, .stdresid)) +
  geom_point(aes(colour = factor(cyl)))

plot(mod, which = 2)
ggplot(mod) +
  stat_qq(aes(sample = .stdresid)) +
  geom_abline()

plot(mod, which = 3)
ggplot(mod, aes(.fitted, sqrt(abs(.stdresid)))) +
  geom_point() +
  geom_smooth(se = FALSE)

plot(mod, which = 4)
ggplot(mod, aes(seq_along(.cooksd), .cooksd)) +
  geom_bar(stat = "identity")

plot(mod, which = 5)
ggplot(mod, aes(.hat, .stdresid)) +
  geom_vline(size = 2, colour = "white", xintercept = 0) +
  geom_hline(size = 2, colour = "white", yintercept = 0) +
  geom_point() + geom_smooth(se = FALSE)

ggplot(mod, aes(.hat, .stdresid)) +
  geom_point(aes(size = .cooksd)) +
  geom_smooth(se = FALSE, size = 0.5)

plot(mod, which = 6)
ggplot(mod, aes(.hat, .cooksd)) +
  geom_vline(xintercept = 0, colour = NA) +
  geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") +
  geom_smooth(se = FALSE) +
  geom_point()

ggplot(mod, aes(.hat, .cooksd)) +
  geom_point(aes(size = .cooksd / .hat)) +
  scale_size_area()

Description

This function turns a map into a data frame that can more easily be plotted with ggplot2.

Usage

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

Arguments

See Also

map_data and borders

Examples

if (require("maps")) {
ca <- map("county", "ca", plot = FALSE, fill = TRUE)
head(fortify(ca))
ggplot(ca, aes(long, lat)) +
  geom_polygon(aes(group = group))

tx <- map("county", "texas", plot = FALSE, fill = TRUE)
head(fortify(tx))
ggplot(tx, aes(long, lat)) +
  geom_polygon(aes(group = group), colour = "white")
}

Description

To figure out the correct variable name for region, inspect as.data.frame(model).

Usage

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

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

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

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

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

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

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

Arguments

Description

Output of an algorithm for optimal change-point detection.

Usage

data("FunctionalPruning")

Format

a named list of 4 data.frames

Source

https://github.com/tdhock/PeakSegFPOP-paper/blob/master/figure-constrained-PDPA-normal-real.R

Description

Allele frequencies for 100 loci and 12 populations were simulated over 100 generations.

Usage

data(generation.loci)

Format

A data frame with 120000 observations on the following 4 variables.

locus

a numeric vector

population

a numeric vector

generation

a numeric vector

frequency

a numeric vector

color

factor: blue, red, or neutral

type

factor: balancing, none, or positive

Source

Data generated using nicholsonppp::sim.drift.selection, see code in etc/generation.loci.R.

Description

These paired geoms and stats add straight lines to a plot, either horizontal, vertical or specified by slope and intercept. These are useful for annotating plots.

Usage

geom_abline(
  mapping = NULL,
  data = NULL,
  ...,
  slope,
  intercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_hline(
  mapping = NULL,
  data = NULL,
  ...,
  yintercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_vline(
  mapping = NULL,
  data = NULL,
  ...,
  xintercept,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

Details

These geoms act slightly different to other geoms. You can supply the parameters in two ways: either as arguments to the layer function, or via aesthetics. If you use arguments, e.g. geom_abline(intercept = 0, slope = 1), then behind the scenes the geom makes a new data frame containing just the data you've supplied. That means that the lines will be the same in all facets; if you want them to vary across facets, construct the data frame yourself and use aesthetics.

Unlike most other geoms, these geoms do not inherit aesthetics from the plot default, because they do not understand x and y aesthetics which are commonly set in the plot. They also do not affect the x and y scales.

Aesthetics

These geoms are drawn using with geom_line so support the same aesthetics: alpha, colour, linetype and size. They also each have aesthetics that control the position of the line:

• geom_vline: xintercept

• geom_hline: yintercept

• geom_abline: slope and intercept

See Also

See geom_segment for a more general approach to adding straight line segments to a plot.

Examples

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()

# Fixed values
p + geom_vline(xintercept = 5)
p + geom_vline(xintercept = 1:5)
p + geom_hline(yintercept = 20)

p + geom_abline() # Can't see it - outside the range of the data
p + geom_abline(intercept = 20)

# Calculate slope and intercept of line of best fit
coef(lm(mpg ~ wt, data = mtcars))
p + geom_abline(intercept = 37, slope = -5)
# But this is easier to do with geom_smooth:
p + geom_smooth(method = "lm", se = FALSE)

# To show different lines in different facets, use aesthetics
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  facet_wrap(~ cyl)

mean_wt <- data.frame(cyl = c(4, 6, 8), wt = c(2.28, 3.11, 4.00))
p + geom_hline(aes(yintercept = wt), mean_wt)

# You can also control other aesthetics
ggplot(mtcars, aes(mpg, wt, colour = wt)) +
  geom_point() +
  geom_hline(aes(yintercept = wt, colour = wt), mean_wt) +
  facet_wrap(~ cyl)

Description

There are two types of bar charts, determined by what is mapped to bar height. By default, geom_bar uses stat="count" which makes the height of the bar proportion to the number of cases in each group (or if the weight aethetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use stat="identity" and map a variable to the y aesthetic.

Usage

geom_bar(
  mapping = NULL,
  data = NULL,
  stat = "count",
  position = "stack",
  ...,
  width = NULL,
  binwidth = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_count(
  mapping = NULL,
  data = NULL,
  geom = "bar",
  position = "stack",
  ...,
  width = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

A bar chart maps the height of the bar to a variable, and so the base of the bar must always be shown to produce a valid visual comparison. This is why it doesn't make sense to use a log-scaled y axis with a bar chart.

By default, multiple x's occurring in the same place will be stacked atop one another by position_stack. If you want them to be dodged side-to-side, see position_dodge. Finally, position_fill shows relative proportions at each x by stacking the bars and then stretching or squashing to the same height.

Aesthetics

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

• x

• alpha

• colour

• fill

• linetype

• size

Computed variables

count

number of points in bin

prop

groupwise proportion

See Also

geom_histogram for continuous data, position_dodge for creating side-by-side barcharts.

stat_bin, which bins data in ranges and counts the cases in each range. It differs from stat_count, which counts the number of cases at each x position (without binning into ranges). stat_bin requires continuous x data, whereas stat_count can be used for both discrete and continuous x data.

Examples

# geom_bar is designed to make it easy to create bar charts that show
# counts (or sums of weights)
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()
# Total engine displacement of each class
g + geom_bar(aes(weight = displ))

# To show (e.g.) means, you need stat = "identity"
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(trt, outcome)) +
  geom_bar(stat = "identity")
# But geom_point() display exactly the same information and doesn't
# require the y-axis to touch zero.
ggplot(df, aes(trt, outcome)) +
  geom_point()

# You can also use geom_bar() with continuous data, in which case
# it will show counts at unique locations
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))
ggplot(df, aes(x)) + geom_bar()
# cf. a histogram of the same data
ggplot(df, aes(x)) + geom_histogram(binwidth = 0.5)


# Bar charts are automatically stacked when multiple bars are placed
# at the same location
g + geom_bar(aes(fill = drv))

# You can instead dodge, or fill them
g + geom_bar(aes(fill = drv), position = "dodge")
g + geom_bar(aes(fill = drv), position = "fill")

# To change plot order of bars, change levels in underlying factor
reorder_size <- function(x) {
  factor(x, levels = names(sort(table(x))))
}
ggplot(mpg, aes(reorder_size(class))) + geom_bar()

Description

Add heatmap of 2d bin counts.

Usage

geom_bin2d(
  mapping = NULL,
  data = NULL,
  stat = "bin2d",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_bin_2d(
  mapping = NULL,
  data = NULL,
  geom = "tile",
  position = "identity",
  ...,
  bins = 30,
  binwidth = NULL,
  drop = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments