ggplot2

# Extending the class CoordJitter <- ggproto( "CoordJitter", CoordCartesian, # Fields amount = 0, # Methods is_linear = function() FALSE, transform = function(self, data, panel_params) data <- ggproto_parent(CoordCartesian, self)$transform(data, panel_params) data$x <- jitter(data$x, amount = self$amount) data$y <- jitter(data$y, amount = self$amount) data ) # Building a constructor coord_jitter <- function(amount = 0.005, xlim = NULL, ylim = NULL, expand = TRUE, clip = "on", reverse = "none") ggproto( NULL, CoordJitter, amount = amount, limits = list(x = xlim, y = ylim), reverse = reverse, expand = expand, clip = clip ) # Use new coord in plot set.seed(42) ggplot(mpg, aes(drv, displ)) + geom_boxplot() + coord_jitter()

coord_sf( xlim = NULL, ylim = NULL, expand = TRUE, crs = NULL, default_crs = NULL, datum = sf::st_crs(4326), label_graticule = waiver(), label_axes = waiver(), lims_method = "cross", ndiscr = 100, default = FALSE, clip = "on", reverse = "none" ) geom_sf( mapping = aes(), data = NULL, stat = "sf", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) geom_sf_label( mapping = aes(), data = NULL, stat = "sf_coordinates", position = "nudge", ..., parse = FALSE, label.padding = unit(0.25, "lines"), label.r = unit(0.15, "lines"), label.size = deprecated(), border.colour = NULL, border.color = NULL, text.colour = NULL, text.color = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, fun.geometry = NULL ) geom_sf_text( mapping = aes(), data = NULL, stat = "sf_coordinates", position = "nudge", ..., parse = FALSE, check_overlap = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, fun.geometry = NULL ) stat_sf( mapping = NULL, data = NULL, geom = "rect", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )

if (requireNamespace("sf", quietly = TRUE)) nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE) ggplot(nc) + geom_sf(aes(fill = AREA)) # If not supplied, coord_sf() will take the CRS from the first layer # and automatically transform all other layers to use that CRS. This # ensures that all data will correctly line up nc_3857 <- sf::st_transform(nc, 3857) ggplot() + geom_sf(data = nc) + geom_sf(data = nc_3857, colour = "red", fill = NA) # Unfortunately if you plot other types of feature you'll need to use # show.legend to tell ggplot2 what type of legend to use nc_3857$mid <- sf::st_centroid(nc_3857$geometry) ggplot(nc_3857) + geom_sf(colour = "white") + geom_sf(aes(geometry = mid, size = AREA), show.legend = "point") # You can also use layers with x and y aesthetics. To have these interpreted # as longitude/latitude you need to set the default CRS in coord_sf() ggplot(nc_3857) + geom_sf() + annotate("point", x = -80, y = 35, colour = "red", size = 4) + coord_sf(default_crs = sf::st_crs(4326)) # To add labels, use geom_sf_label(). ggplot(nc_3857[1:3, ]) + geom_sf(aes(fill = AREA)) + geom_sf_label(aes(label = NAME)) # Thanks to the power of sf, a geom_sf nicely handles varying projections # setting the aspect ratio correctly. if (requireNamespace('maps', quietly = TRUE)) library(maps) world1 <- sf::st_as_sf(map('world', plot = FALSE, fill = TRUE)) ggplot() + geom_sf(data = world1) world2 <- sf::st_transform( world1, "+proj=laea +y_0=0 +lon_0=155 +lat_0=-90 +ellps=WGS84 +no_defs" ) ggplot() + geom_sf(data = world2)

# Please see extension vignette NULL

# Extending the class GeomSimplePoint <- ggproto( "GeomSimplePoint", Geom, # Fields required_aes = c("x", "y"), draw_key = draw_key_point, # Methods draw_panel = function(data, panel_params, coord) data <- coord$transform(data, panel_params) grid::pointsGrob(data$x, data$y) ) # Building a constructor geom_simple_point <- function(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) layer( mapping = mapping, data = data, geom = GeomSimplePoint, stat = stat, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) # Use new geom in plot ggplot(mpg, aes(displ, hwy)) + geom_simple_point()

# Extending the class GuideDescribe <- ggproto( "GuideDescribe", Guide, # Fields elements = list(text = "legend.text", margin = "legend.margin"), hashables = rlang::exprs(key$.label), # Methods build_title = function(...) zeroGrob(), # Turn off title build_labels = function(key, elements, params) labels <- key$.label n <- length(labels) labels <- paste0(paste0(labels[-n], collapse = ", "), ", and ", labels[n]) labels <- paste0("A guide showing ", labels, " categories") element_grob(elements$text, label = labels, margin_x = TRUE, margin_y = TRUE) , measure_grobs = function(grobs, params, elements) # Measuring in centimetres is the convention width <- grid::convertWidth(grid::grobWidth(grobs$labels), "cm", valueOnly = TRUE) height <- grid::convertHeight(grid::grobHeight(grobs$labels), "cm", valueOnly = TRUE) list(width = unit(width, "cm"), height = unit(height, "cm")) , assemble_drawing = function(self, grobs, layout, sizes, params, elements) gt <- gtable::as.gtable(grobs$labels, width = sizes$width, height = sizes$height) gt <- gtable::gtable_add_padding(gt, elements$margin) gt ) # Building a constructor guide_describe <- function(position = NULL) new_guide(position = position, super = GuideDescribe) # Use new guide plot ggplot(mpg, aes(displ, hwy, colour = class)) + geom_point() + guides(colour = guide_describe("bottom"))

# None: Layer is not intended to be extended

# Some dummy layout components facet <- facet_null() coord <- coord_cartesian() # Use in custom `ggplot_build()` methods layout <- ggproto(NULL, Layout, facet = facet, coord = coord)

# Extending the class PositionRank <- ggproto( "PositionRank", Position, # Fields required_aes = c("x", "y"), # Methods setup_params = function(self, data) list(width = self$width), compute_panel = function(data, params, scales) width <- params$width if (is.null(width)) width <- resolution(data$x, zero = FALSE, TRUE) * 0.4 rank <- stats::ave(data$y, data$group, FUN = rank) rank <- scales::rescale(rank, to = c(-width, width) / 2) data$x <- data$x + rank data ) # Building a constructor position_rank <- function(width = NULL) ggproto(NULL, PositionRank, width = width) # Use new position in plot ggplot(mpg, aes(drv, displ)) + geom_point(position = position_rank(width = 0.5))

# TODO: find easy to digest example NULL

# Extending the class StatKmeans <- ggproto( "StatKmeans", Stat, # Fields required_aes = c("x", "y"), # You can relate computed variables to aesthetics using `after_stat()` # in defaults default_aes = aes(colour = after_stat(cluster)), # Methods compute_panel = function(data, scales, k = 2L) km <- kmeans(cbind(scale(data$x), scale(data$y)), centers = k) data$cluster <- factor(km$cluster) data ) # Building a constructor stat_kmeans <- function(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., k = 2L, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) layer( mapping = mapping, data = data, geom = geom, stat = StatKmeans, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, k = k, ...) ) # Use new stat in plot ggplot(mpg, aes(displ, hwy)) + stat_kmeans(k = 3)

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

add_gg(e1, e2) e1 %+% e2

base <- ggplot(mpg, aes(displ, hwy)) + geom_point() base + geom_smooth() # To override the data, you must use %+% base %+% subset(mpg, fl == "p") # Alternatively, you can add multiple components with a list. # This can be useful to return from a function. base + list(subset(mpg, fl == "p"), geom_smooth())

add_theme(t1, t2, t2name, call = caller_env())

aes(x, y, ...)

aes(x = mpg, y = wt) aes(mpg, wt) # You can also map aesthetics to functions of variables aes(x = mpg ^ 2, y = wt / cyl) # Or to constants aes(x = 1, colour = "smooth") # Aesthetic names are automatically standardised aes(col = x) aes(fg = x) aes(color = x) aes(colour = x) # aes() is passed to either ggplot() or specific layer. Aesthetics supplied # to ggplot() are used as defaults for every layer. ggplot(mpg, aes(displ, hwy)) + geom_point() ggplot(mpg) + geom_point(aes(displ, hwy)) # Tidy evaluation ---------------------------------------------------- # aes() automatically quotes all its arguments, so you need to use tidy # evaluation to create wrappers around ggplot2 pipelines. The # simplest case occurs when your wrapper takes dots: scatter_by <- function(data, ...) ggplot(data) + geom_point(aes(...)) scatter_by(mtcars, disp, drat) # If your wrapper has a more specific interface with named arguments, # you need the "embrace operator": scatter_by <- function(data, x, y) ggplot(data) + geom_point(aes( x , y )) scatter_by(mtcars, disp, drat) # Note that users of your wrapper can use their own functions in the # quoted expressions and all will resolve as it should! cut3 <- function(x) cut_number(x, 3) scatter_by(mtcars, cut3(disp), drat)

aes_(x, y, ...) aes_string(x, y, ...) aes_q(x, y, ...)

aes_all(vars)

aes_all(names(mtcars)) aes_all(c("x", "y", "col", "pch"))

aes_auto(data = NULL, ...)

# Bar chart example p <- ggplot(mtcars, aes(factor(cyl))) # Default plotting p + geom_bar() # To change the interior colouring use fill aesthetic p + geom_bar(fill = "red") # Compare with the colour aesthetic which changes just the bar outline p + geom_bar(colour = "red") # Combining both, you can see the changes more clearly p + geom_bar(fill = "white", colour = "red") # Both colour and fill can take an rgb specification. p + geom_bar(fill = "#00abff") # Use NA for a completely transparent colour. p + geom_bar(fill = NA, colour = "#00abff") # Colouring scales differ depending on whether a discrete or # continuous variable is being mapped. For example, when mapping # fill to a factor variable, a discrete colour scale is used. ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) + geom_bar() # When mapping fill to continuous variable a continuous colour # scale is used. ggplot(faithfuld, aes(waiting, eruptions)) + geom_raster(aes(fill = density)) # Some geoms only use the colour aesthetic but not the fill # aesthetic (e.g. geom_point() or geom_line()). p <- ggplot(economics, aes(x = date, y = unemploy)) p + geom_line() p + geom_line(colour = "green") p + geom_point() p + geom_point(colour = "red") # For large datasets with overplotting the alpha # aesthetic will make the points more transparent. set.seed(1) df <- data.frame(x = rnorm(5000), y = rnorm(5000)) p <- ggplot(df, aes(x,y)) p + geom_point() p + geom_point(alpha = 0.5) p + geom_point(alpha = 1/10) # Alpha can also be used to add shading. p <- ggplot(economics, aes(x = date, y = unemploy)) + geom_line() p yrng <- range(economics$unemploy) p <- p + geom_rect( aes(NULL, NULL, xmin = start, xmax = end, fill = party), ymin = yrng[1], ymax = yrng[2], data = presidential ) p p + scale_fill_manual(values = alpha(c("blue", "red"), .3))

# These functions can be used inside the `aes()` function # used as the `mapping` argument in layers, for example: # geom_density(mapping = aes(y = after_stat(scaled))) after_stat(x) after_scale(x) from_theme(x) stage(start = NULL, after_stat = NULL, after_scale = NULL)

# Default histogram display ggplot(mpg, aes(displ)) + geom_histogram(aes(y = after_stat(count))) # Scale tallest bin to 1 ggplot(mpg, aes(displ)) + geom_histogram(aes(y = after_stat(count / max(count)))) # Use a transparent version of colour for fill ggplot(mpg, aes(class, hwy)) + geom_boxplot(aes(colour = class, fill = after_scale(alpha(colour, 0.4)))) # Use stage to modify the scaled fill ggplot(mpg, aes(class, hwy)) + geom_boxplot(aes(fill = stage(class, after_scale = alpha(fill, 0.4)))) # Making a proportional stacked density plot ggplot(mpg, aes(cty)) + geom_density( aes( colour = factor(cyl), fill = after_scale(alpha(colour, 0.3)), y = after_stat(count / sum(n[!duplicated(group)])) ), position = "stack", bw = 1 ) + geom_density(bw = 1) # Imitating a ridgeline plot ggplot(mpg, aes(cty, colour = factor(cyl))) + geom_ribbon( stat = "density", outline.type = "upper", aes( fill = after_scale(alpha(colour, 0.3)), ymin = after_stat(group), ymax = after_stat(group + ndensity) ) ) # Labelling a bar plot ggplot(mpg, aes(class)) + geom_bar() + geom_text( aes( y = after_stat(count + 2), label = after_stat(count) ), stat = "count" ) # Labelling the upper hinge of a boxplot, # inspired by June Choe ggplot(mpg, aes(displ, class)) + geom_boxplot(outlier.shape = NA) + geom_text( aes( label = after_stat(xmax), x = stage(displ, after_stat = xmax) ), stat = "boxplot", hjust = -0.5 )

p <- ggplot(mtcars, aes(wt, mpg)) # A basic scatter plot p + geom_point(size = 4) # Using the colour aesthetic p + geom_point(aes(colour = factor(cyl)), size = 4) # Using the shape aesthetic p + geom_point(aes(shape = factor(cyl)), size = 4) # Using fill p <- ggplot(mtcars, aes(factor(cyl))) p + geom_bar() p + geom_bar(aes(fill = factor(cyl))) p + geom_bar(aes(fill = factor(vs))) # Using linetypes ggplot(economics_long, aes(date, value01)) + geom_line(aes(linetype = variable)) # Multiple groups with one aesthetic p <- ggplot(nlme::Oxboys, aes(age, height)) # The default is not sufficient here. A single line tries to connect all # the observations. p + geom_line() # To fix this, use the group aesthetic to map a different line for each # subject. p + geom_line(aes(group = Subject)) # Different groups on different layers p <- p + geom_line(aes(group = Subject)) # Using the group aesthetic with both geom_line() and geom_smooth() # groups the data the same way for both layers p + geom_smooth(aes(group = Subject), method = "lm", se = FALSE) # Changing the group aesthetic for the smoother layer # fits a single line of best fit across all boys p + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE) # Overriding the default grouping # Sometimes the plot has a discrete scale but you want to draw lines # that connect across groups. This is the strategy used in interaction # plots, profile plots, and parallel coordinate plots, among others. # For example, we draw boxplots of height at each measurement occasion. p <- ggplot(nlme::Oxboys, aes(Occasion, height)) + geom_boxplot() p # There is no need to specify the group aesthetic here; the default grouping # works because occasion is a discrete variable. To overlay individual # trajectories, we again need to override the default grouping for that layer # with aes(group = Subject) p + geom_line(aes(group = Subject), colour = "blue")

df <- data.frame(x = 1:10 , y = 1:10) p <- ggplot(df, aes(x, y)) p + geom_line(linetype = 2) p + 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. p + geom_line(linetype = "3313") # Mapping line type from a grouping variable ggplot(economics_long, aes(date, value01)) + geom_line(aes(linetype = variable)) # Linewidth examples ggplot(economics, aes(date, unemploy)) + geom_line(linewidth = 2, lineend = "round") ggplot(economics, aes(date, unemploy)) + geom_line(aes(linewidth = uempmed), lineend = "round") # Size examples 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 p + geom_point() p + geom_point(shape = 5) p + geom_point(shape = "k", size = 3) p + geom_point(shape = ".") p + geom_point(shape = NA) 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) p <- ggplot(df2, aes(x, y)) p + 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) p + geom_point(aes(shape = z), size = 4, colour = "Red") + scale_shape_identity() p + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") + scale_shape_identity()

# Generate data: means and standard errors of means for prices # for each type of cut dmod <- lm(price ~ cut, data = diamonds) cut <- unique(diamonds$cut) cuts_df <- data.frame( cut, predict(dmod, data.frame(cut), se = TRUE)[c("fit", "se.fit")] ) ggplot(cuts_df) + aes( x = cut, y = fit, ymin = fit - se.fit, ymax = fit + se.fit, colour = cut ) + geom_pointrange() # Using annotate p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point() p p + annotate( "rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25, fill = "dark grey", alpha = .5 ) # Geom_segment examples p + geom_segment( aes(x = 2, y = 15, xend = 2, yend = 25), arrow = arrow(length = unit(0.5, "cm")) ) p + geom_segment( aes(x = 2, y = 15, xend = 3, yend = 15), arrow = arrow(length = unit(0.5, "cm")) ) p + geom_segment( aes(x = 5, y = 30, xend = 3.5, yend = 25), arrow = arrow(length = unit(0.5, "cm")) ) # You can also use geom_segment() to recreate plot(type = "h") # from base R: set.seed(1) counts <- as.data.frame(table(x = rpois(100, 5))) counts$x <- as.numeric(as.character(counts$x)) with(counts, plot(x, Freq, type = "h", lwd = 10)) ggplot(counts, aes(x = x, y = Freq)) + geom_segment(aes(yend = 0, xend = x), size = 10)

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

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 = 2.5, linewidth = 1.5) p + annotate("text", x = 2:3, y = 20:21, label = c("my label", "label 2")) p + annotate("text", x = 4, y = 25, label = "italic(R) ^ 2 == 0.75", parse = TRUE) p + annotate("text", x = 4, y = 25, label = "paste(italic(R) ^ 2, \" = .75\")", parse = TRUE)

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

if (require("maps")) data(us.cities) capitals <- subset(us.cities, capital == 2) ggplot(capitals, aes(long, lat)) + annotation_borders("state") + geom_point(aes(size = pop)) + scale_size_area() + coord_quickmap() if (require("maps")) # Same map, with some world context ggplot(capitals, aes(long, lat)) + annotation_borders("world", xlim = c(-130, -60), ylim = c(20, 50)) + geom_point(aes(size = pop)) + scale_size_area() + coord_quickmap()

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

# 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)

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

# 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", \(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x)) ) + scale_y_log10( breaks = scales::trans_breaks("log10", \(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 a + annotation_logticks(sides = "lr", outside = TRUE) + coord_cartesian(clip = "off") # Ticks outside plot # 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::label_math(10^.x)) + scale_y_continuous(name = "brain", labels = scales::label_math(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_transform(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") )

annotation_map(map, ...)

if (requireNamespace("maps", quietly = TRUE)) # location of cities in North Carolina df <- data.frame( name = c("Charlotte", "Raleigh", "Greensboro"), lat = c(35.227, 35.772, 36.073), long = c(-80.843, -78.639, -79.792) ) p <- ggplot(df, aes(x = long, y = lat)) + annotation_map( map_data("state"), fill = "antiquewhite", colour = "darkgrey" ) + geom_point(color = "blue") + geom_text( aes(label = name), hjust = 1.105, vjust = 1.05, color = "blue" ) # use without coord_sf() is possible but not recommended p + xlim(-84, -76) + ylim(34, 37.2) if (requireNamespace("sf", quietly = TRUE)) # use with coord_sf() for appropriate projection p + coord_sf( crs = sf::st_crs(3347), default_crs = sf::st_crs(4326), # data is provided as long-lat xlim = c(-84, -76), ylim = c(34, 37.2) ) # you can mix annotation_map() and geom_sf() nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE) p + geom_sf( data = nc, inherit.aes = FALSE, fill = NA, color = "black", linewidth = 0.1 ) + coord_sf(crs = sf::st_crs(3347), default_crs = sf::st_crs(4326))

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

# 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()

as.listggproto(x, inherit = TRUE, ...)

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

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 faceting variable, be sure to dispatch # your labeller to the right variable with labeller() p + facet_grid(cyl ~ am, labeller = labeller(am = to_string))

autolayer(object, ...)

autoplot(object, ...)

benchplot(x)

benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point()) benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point() + facet_grid(. ~ cyl)) # With tidy eval: p <- expr(ggplot(mtcars, aes(mpg, wt)) + geom_point()) benchplot(!!p)

has_flipped_aes( data, params = list(), main_is_orthogonal = NA, range_is_orthogonal = NA, group_has_equal = FALSE, ambiguous = FALSE, main_is_continuous = FALSE, main_is_optional = FALSE, default = FALSE ) flip_data(data, flip = NULL) flipped_names(flip = FALSE)

binned_scale( aesthetics, scale_name = deprecated(), palette, name = waiver(), breaks = waiver(), labels = waiver(), limits = NULL, rescaler = rescale, oob = squish, expand = waiver(), na.value = NA_real_, n.breaks = NULL, nice.breaks = TRUE, right = TRUE, transform = "identity", trans = deprecated(), show.limits = FALSE, guide = "bins", position = "left", fallback.palette = NULL, call = caller_call(), super = ScaleBinned )

calc_element( element, theme, verbose = FALSE, skip_blank = FALSE, call = caller_env() )

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

check_device( feature, action = "warn", op = NULL, maybe = FALSE, call = caller_env() )

# Typically you'd run `check_device()` inside a function that might produce # advanced graphics. # The check is designed for use in control flow statements in the test mode if (check_device("patterns", action = "test")) print("Yay") else print("Nay") # Automatically throw a warning when unavailable if (check_device("compositing", action = "warn")) print("Yay") else print("Nay") # Possibly throw an error try(check_device("glyphs", action = "abort"))

class_ggplot( data = waiver(), ..., layers = list(), scales = NULL, guides = NULL, mapping = aes(), theme = NULL, coordinates = coord_cartesian(default = TRUE), facet = facet_null(), layout = NULL, labels = labs(), meta = list(), plot_env = parent.frame() )

class_ggplot_built(..., data = NULL, layout = NULL, plot = NULL)

class_labels(labels = list(), ...)

class_mapping(x = list(), ..., env = globalenv())

class_theme(elements = list(), ..., complete = FALSE, validate = TRUE)

combine_vars(data, env = emptyenv(), vars = NULL, drop = TRUE)

complete_theme(theme = NULL, default = theme_get())

my_theme <- theme(line = element_line(colour = "red")) complete_theme(my_theme)

continuous_scale( aesthetics, scale_name = deprecated(), palette, name = waiver(), breaks = waiver(), minor_breaks = waiver(), n.breaks = NULL, labels = waiver(), limits = NULL, rescaler = rescale, oob = censor, expand = waiver(), na.value = NA, transform = "identity", trans = deprecated(), guide = "legend", position = "left", fallback.palette = NULL, call = caller_call(), super = ScaleContinuous )

coord_cartesian( xlim = NULL, ylim = NULL, expand = TRUE, default = FALSE, clip = "on", reverse = "none", ratio = NULL )

# 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) # Similarly, we can use expand = FALSE to turn off expansion with the # default limits p + coord_cartesian(expand = FALSE) # Using a fixed ratio: 1 y-axis unit is 100 x-axis units # Plot window can be resized and aspect ratio will be maintained p + coord_cartesian(ratio = 100) # You can see the same thing with this 2d histogram d <- ggplot(diamonds, aes(carat, price)) + stat_bin_2d(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))

coord_fixed(ratio = 1, ...)

# 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) p + coord_fixed(xlim = c(15, 30)) # Resize the plot to see that the specified aspect ratio is maintained

coord_flip(xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")

# The preferred method of creating horizontal instead of vertical boxplots ggplot(diamonds, aes(price, cut)) + geom_boxplot() # Using `coord_flip()` to make the same plot ggplot(diamonds, aes(cut, price)) + geom_boxplot() + coord_flip() # With swapped aesthetics, the y-scale controls the left axis ggplot(diamonds, aes(y = carat)) + geom_histogram() + scale_y_reverse() # In `coord_flip()`, the x-scale controls the left axis ggplot(diamonds, aes(carat)) + geom_histogram() + coord_flip() + scale_x_reverse() # In line and area plots, swapped aesthetics require an explicit orientation df <- data.frame(a = 1:5, b = (1:5) ^ 2) ggplot(df, aes(b, a)) + geom_area(orientation = "y") # The same plot with `coord_flip()` ggplot(df, aes(a, b)) + geom_area() + coord_flip()

coord_map( projection = "mercator", ..., parameters = NULL, orientation = NULL, xlim = NULL, ylim = NULL, clip = "on" ) coord_quickmap(xlim = NULL, ylim = NULL, expand = TRUE, clip = "on")

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 if (require("maps")) # With correct mercator projection nzmap + coord_map() if (require("maps")) # With the aspect ratio approximation nzmap + coord_quickmap() if (require("maps")) # Other projections nzmap + coord_map("azequalarea", orientation = c(-36.92, 174.6, 0)) if (require("maps")) states <- map_data("state") usamap <- ggplot(states, aes(long, lat, group = group)) + geom_polygon(fill = "white", colour = "black") # Use cartesian coordinates usamap if (require("maps")) # With mercator projection usamap + coord_map() if (require("maps")) # See ?mapproject for coordinate systems and their parameters usamap + coord_map("gilbert") if (require("maps")) # 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") if (require("maps")) usamap + coord_map("conic", lat0 = 30) if (require("maps")) usamap + coord_map("bonne", lat0 = 50) if (require("maps")) # 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") if (require("maps")) # Looking up up at South Pole worldmap + coord_map("ortho", orientation = c(-90, 0, 0)) if (require("maps")) # Centered on New York (currently has issues with closing polygons) worldmap + coord_map("ortho", orientation = c(41, -74, 0))

coord_munch(coord, data, range, segment_length = 0.01, is_closed = FALSE)

coord_polar(theta = "x", start = 0, direction = 1, clip = "on") coord_radial( theta = "x", start = 0, end = NULL, thetalim = NULL, rlim = NULL, expand = TRUE, direction = deprecated(), clip = "off", r.axis.inside = NULL, rotate.angle = FALSE, inner.radius = 0, reverse = "none", r_axis_inside = deprecated(), rotate_angle = deprecated() )

# 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_radial(theta = "y", expand = FALSE) # A coxcomb plot = bar chart + polar coordinates cxc <- ggplot(mtcars, aes(x = factor(cyl))) + geom_bar(width = 1, colour = "black") cxc + coord_radial(expand = FALSE) # A new type of plot? cxc + coord_radial(theta = "y", expand = FALSE) # The bullseye chart pie + coord_radial(expand = FALSE) # 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_col(width = 1) + scale_fill_manual(values = c("red", "yellow")) + coord_radial("y", start = pi / 3, expand = FALSE) + 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_radial(expand = FALSE) # Race track plot doh + geom_bar(width = 0.9, position = "fill") + coord_radial(theta = "y", expand = FALSE) # A partial polar plot ggplot(mtcars, aes(disp, mpg)) + geom_point() + coord_radial(start = -0.4 * pi, end = 0.4 * pi, inner.radius = 0.3) # Similar with coord_cartesian(), you can set limits. ggplot(mtcars, aes(disp, mpg)) + geom_point() + coord_radial( start = -0.4 * pi, end = 0.4 * pi, inner.radius = 0.3, thetalim = c(200, 300), rlim = c(15, 30), )

coord_transform( x = "identity", y = "identity", xlim = NULL, ylim = NULL, limx = deprecated(), limy = deprecated(), clip = "on", expand = TRUE, reverse = "none" ) coord_trans(...)

# See ?geom_boxplot for other 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_transform(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_transform(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_transform(x = scales::transform_exp(10), y = scales::transform_exp(10)) # cf. ggplot(diamonds, aes(carat, price)) + geom_point() + geom_smooth(method = "lm") # Also works with discrete scales set.seed(1) df <- data.frame(a = abs(rnorm(26)),letters) plot <- ggplot(df,aes(a,letters)) + geom_point() plot + coord_transform(x = "log10") plot + coord_transform(x = "sqrt")

cut_interval(x, n = NULL, length = NULL, ...) cut_number(x, n = NULL, ...) cut_width(x, width, center = NULL, boundary = NULL, closed = "right", ...)

table(cut_interval(1:100, 10)) table(cut_interval(1:100, 11)) set.seed(1) 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)) table(cut_width(runif(1000), 0.1, labels = FALSE))

datetime_scale( aesthetics, transform, trans = deprecated(), palette, breaks = pretty_breaks(), minor_breaks = waiver(), labels = waiver(), date_breaks = waiver(), date_labels = waiver(), date_minor_breaks = waiver(), timezone = NULL, guide = "legend", call = caller_call(), ... )

diamonds

discrete_scale( aesthetics, scale_name = deprecated(), palette, name = waiver(), breaks = waiver(), minor_breaks = waiver(), labels = waiver(), limits = NULL, expand = waiver(), na.translate = TRUE, na.value = NA, drop = TRUE, guide = "legend", position = "left", fallback.palette = NULL, call = caller_call(), super = ScaleDiscrete )

draw_key_point(data, params, size) draw_key_abline(data, params, size) draw_key_rect(data, params, size) draw_key_polygon(data, params, size) draw_key_blank(data, params, size) draw_key_boxplot(data, params, size) draw_key_crossbar(data, params, size) draw_key_path(data, params, size) draw_key_vpath(data, params, size) draw_key_dotplot(data, params, size) draw_key_linerange(data, params, size) draw_key_pointrange(data, params, size) draw_key_smooth(data, params, size) draw_key_text(data, params, size) draw_key_label(data, params, size) draw_key_vline(data, params, size) draw_key_timeseries(data, params, size)

p <- ggplot(economics, aes(date, psavert, color = "savings rate")) # key glyphs can be specified by their name p + geom_line(key_glyph = "timeseries") # key glyphs can be specified via their drawing function p + geom_line(key_glyph = draw_key_rect)

economics economics_long

element_grob(element, ...)

element_render(theme, element, ..., name = NULL)

expand_limits(...)

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)))

expansion(mult = 0, add = 0) expand_scale(mult = 0, add = 0)

# No space below the bars but 10% above them ggplot(mtcars) + geom_bar(aes(x = factor(cyl))) + scale_y_continuous(expand = expansion(mult = c(0, .1))) # Add 2 units of space on the left and right of the data ggplot(subset(diamonds, carat > 2), aes(cut, clarity)) + geom_jitter() + scale_x_discrete(expand = expansion(add = 2)) # Reproduce the default range expansion used # when the 'expand' argument is not specified ggplot(subset(diamonds, carat > 2), aes(cut, price)) + geom_jitter() + scale_x_discrete(expand = expansion(add = .6)) + scale_y_continuous(expand = expansion(mult = .05))

facet_grid( rows = NULL, cols = NULL, scales = "fixed", space = "fixed", shrink = TRUE, labeller = "label_value", as.table = TRUE, switch = NULL, drop = TRUE, margins = FALSE, axes = "margins", axis.labels = "all", facets = deprecated() )

p <- ggplot(mpg, aes(displ, cty)) + geom_point() # Use vars() to supply variables from the dataset: p + facet_grid(rows = vars(drv)) p + facet_grid(cols = vars(cyl)) p + facet_grid(vars(drv), vars(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 # faceting variables, the data will be repeated across the missing # combinations: df <- data.frame(displ = mean(mpg$displ), cty = mean(mpg$cty)) p + facet_grid(cols = vars(cyl)) + geom_point(data = df, colour = "red", size = 2) # When scales are constant, duplicated axes can be shown with # or without labels ggplot(mpg, aes(cty, hwy)) + geom_point() + facet_grid(year ~ drv, axes = "all", axis.labels = "all_x") # 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(vars(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)) # Margins ---------------------------------------------------------- # Margins can be specified logically (all yes or all no) or for specific # variables as (character) variable names mg <- ggplot(mtcars, aes(x = mpg, y = wt)) + geom_point() 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")

facet_null(shrink = TRUE)

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

facet_wrap( facets, nrow = NULL, ncol = NULL, scales = "fixed", space = "fixed", shrink = TRUE, labeller = "label_value", as.table = TRUE, switch = deprecated(), drop = TRUE, dir = "h", strip.position = "top", axes = "margins", axis.labels = "all" )

p <- ggplot(mpg, aes(displ, hwy)) + geom_point() # Use vars() to supply faceting variables: p + facet_wrap(vars(class)) # Control the number of rows and columns with nrow and ncol p + facet_wrap(vars(class), nrow = 4) # You can facet by multiple variables ggplot(mpg, aes(displ, hwy)) + geom_point() + facet_wrap(vars(cyl, drv)) # Use the `labeller` option to control how labels are printed: ggplot(mpg, aes(displ, hwy)) + geom_point() + facet_wrap(vars(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(vars(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(vars(class), scales = "free") # When scales are constant, duplicated axes can be shown with # or without labels ggplot(mpg, aes(displ, hwy)) + geom_point() + facet_wrap(vars(class), axes = "all", axis.labels = "all_y") # To repeat the same data in every panel, simply construct a data frame # that does not contain the faceting variable. ggplot(mpg, aes(displ, hwy)) + geom_point(data = transform(mpg, class = NULL), colour = "grey85") + geom_point() + facet_wrap(vars(class)) # Use `strip.position` to display the facet labels at the side of your # choice. Setting it to `bottom` makes it act as a subtitle for the 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(vars(variable), scales = "free_y", nrow = 2, strip.position = "top") + theme(strip.background = element_blank(), strip.placement = "outside") # The two letters determine the starting position, so 'tr' starts # in the top-right. # The first letter determines direction, so 'tr' fills top-to-bottom. # `dir = "tr"` is equivalent to `dir = "v", as.table = FALSE` ggplot(mpg, aes(displ, hwy)) + geom_point() + facet_wrap(vars(class), dir = "tr")

faithfuld

fill_alpha(fill, alpha)

# Typical colour input fill_alpha("red", 0.5) if (utils::packageVersion("grid") > "4.2") # Pattern input fill_alpha(list(grid::linearGradient()), 0.5)

find_panel(table) panel_cols(table) panel_rows(table)

fortify(model, data, ...)

fortifyglht(model, data, ...) fortifyconfint.glht(model, data, ...) fortifysummary.glht(model, data, ...) fortifycld(model, data, ...)

if (require("multcomp") && require("broom")) withAutoprint(\ # examplesIf amod <- aov(breaks ~ wool + tension, data = warpbreaks) wht <- multcomp::glht(amod, linfct = multcomp::mcp(tension = "Tukey")) tidy(wht) # recommended fortify(wht) ggplot(tidy(wht), aes(contrast, estimate)) + geom_point() ci <- confint(wht) tidy(ci) # recommended fortify(ci) ggplot(tidy(confint(wht)), aes(contrast, estimate, ymin = conf.low, ymax = conf.high)) + geom_pointrange() smry <- summary(wht) tidy(smry) # recommended fortify(smry) ggplot(mapping = aes(contrast, estimate)) + geom_linerange(aes(ymin = conf.low, ymax = conf.high), data = tidy(ci)) + geom_point(aes(size = adj.p.value), data = tidy(smry)) + scale_size(transform = "reverse") cld <- multcomp::cld(wht) tidy(cld) # recommended fortify(cld) \) # examplesIf

fortifylm(model, data = model$model, ...)

if (require("broom")) withAutoprint(\ # examplesIf mod <- lm(mpg ~ wt, data = mtcars) # Show augmented model head(augment(mod)) head(fortify(mod)) # Using augment to convert model to ready-to-plot data ggplot(augment(mod), aes(.fitted, .resid)) + geom_point() + geom_hline(yintercept = 0) + geom_smooth(se = FALSE) # Colouring by original data not included in the model ggplot(augment(mod, mtcars), aes(.fitted, .std.resid, colour = factor(cyl))) + geom_point() \) # examplesIf

fortifymap(model, data, ...)

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

fortifySpatialPolygonsDataFrame(model, data, region = NULL, ...) fortifySpatialPolygons(model, data, ...) fortifyPolygons(model, data, ...) fortifyPolygon(model, data, ...) fortifySpatialLinesDataFrame(model, data, ...) fortifyLines(model, data, ...) fortifyLine(model, data, ...)

geom_abline( mapping = NULL, data = NULL, stat = "identity", ..., slope, intercept, na.rm = FALSE, show.legend = NA, inherit.aes = FALSE ) geom_hline( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., yintercept, na.rm = FALSE, show.legend = NA, inherit.aes = FALSE ) geom_vline( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., xintercept, na.rm = FALSE, show.legend = NA, inherit.aes = FALSE )

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)

geom_bar( mapping = NULL, data = NULL, stat = "count", position = "stack", ..., just = 0.5, lineend = "butt", linejoin = "mitre", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) geom_col( mapping = NULL, data = NULL, stat = "identity", position = "stack", ..., just = 0.5, lineend = "butt", linejoin = "mitre", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_count( mapping = NULL, data = NULL, geom = "bar", position = "stack", ..., orientation = NA, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )

# 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)) # Map class to y instead to flip the orientation ggplot(mpg) + geom_bar(aes(y = class)) # Bar charts are automatically stacked when multiple bars are placed # at the same location. The order of the fill is designed to match # the legend g + geom_bar(aes(fill = drv)) # If you need to flip the order (because you've flipped the orientation) # call position_stack() explicitly: ggplot(mpg, aes(y = class)) + geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) + theme(legend.position = "top") # To show (e.g.) means, you need geom_col() df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2)) ggplot(df, aes(trt, outcome)) + geom_col() # But geom_point() displays 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) # Use `just` to control how columns are aligned with axis breaks: df <- data.frame(x = as.Date(c("2020-01-01", "2020-02-01")), y = 1:2) # Columns centered on the first day of the month ggplot(df, aes(x, y)) + geom_col(just = 0.5) # Columns begin on the first day of the month ggplot(df, aes(x, y)) + geom_col(just = 1)

geom_bin_2d( mapping = NULL, data = NULL, stat = "bin2d", position = "identity", ..., lineend = "butt", linejoin = "mitre", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_bin_2d( mapping = NULL, data = NULL, geom = "tile", position = "identity", ..., binwidth = NULL, bins = 30, breaks = NULL, drop = TRUE, boundary = NULL, closed = NULL, center = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )

d <- ggplot(diamonds, aes(x, y)) + xlim(4, 10) + ylim(4, 10) d + geom_bin_2d() # You can control the size of the bins by specifying the number of # bins in each direction: d + geom_bin_2d(bins = 10) d + geom_bin_2d(bins = list(x = 30, y = 10)) # Or by specifying the width of the bins d + geom_bin_2d(binwidth = c(0.1, 0.1))

geom_blank( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., show.legend = NA, inherit.aes = TRUE )

ggplot(mtcars, aes(wt, mpg)) # Nothing to see here!

geom_boxplot( mapping = NULL, data = NULL, stat = "boxplot", position = "dodge2", ..., outliers = TRUE, outlier.colour = NULL, outlier.color = NULL, outlier.fill = NULL, outlier.shape = NULL, outlier.size = NULL, outlier.stroke = 0.5, outlier.alpha = NULL, whisker.colour = NULL, whisker.color = NULL, whisker.linetype = NULL, whisker.linewidth = NULL, staple.colour = NULL, staple.color = NULL, staple.linetype = NULL, staple.linewidth = NULL, median.colour = NULL, median.color = NULL, median.linetype = NULL, median.linewidth = NULL, box.colour = NULL, box.color = NULL, box.linetype = NULL, box.linewidth = NULL, notch = FALSE, notchwidth = 0.5, staplewidth = 0, varwidth = FALSE, na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE ) stat_boxplot( mapping = NULL, data = NULL, geom = "boxplot", position = "dodge2", ..., orientation = NA, coef = 1.5, quantile.type = 7, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )

p <- ggplot(mpg, aes(class, hwy)) p + geom_boxplot() # Orientation follows the discrete axis ggplot(mpg, aes(hwy, class)) + geom_boxplot() p + geom_boxplot(notch = TRUE) p + geom_boxplot(varwidth = TRUE) p + geom_boxplot(fill = "white", colour = "#3366FF") # By default, outlier points match the colour of the box. Use # outlier.colour to override p + geom_boxplot(outlier.colour = "red", outlier.shape = 1) # Remove outliers when overlaying boxplot with original data points p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2) # Boxplots are automatically dodged when any aesthetic is a factor p + geom_boxplot(aes(colour = drv)) # You can also use boxplots with continuous x, as long as you supply # a grouping variable. cut_width is particularly useful ggplot(diamonds, aes(carat, price)) + geom_boxplot() ggplot(diamonds, aes(carat, price)) + geom_boxplot(aes(group = cut_width(carat, 0.25))) # Adjust the transparency of outliers using outlier.alpha ggplot(diamonds, aes(carat, price)) + geom_boxplot(aes(group = cut_width(carat, 0.25)), outlier.alpha = 0.1) # It's possible to draw a boxplot with your own computations if you # use stat = "identity": set.seed(1) y <- rnorm(100) df <- data.frame( x = 1, y0 = min(y), y25 = quantile(y, 0.25), y50 = median(y), y75 = quantile(y, 0.75), y100 = max(y) ) ggplot(df, aes(x)) + geom_boxplot( aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100), stat = "identity" )