Create a histogram plot.

gghistogram(data, x, y = "..count..", combine = FALSE, merge = FALSE,
  color = "black", fill = NA, palette = NULL, size = NULL,
  linetype = "solid", alpha = 0.5, bins = NULL, binwidth = NULL,
  title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL,
  panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "mean",
  "median"), add.params = list(linetype = "dashed"), rug = FALSE,
  add_density = FALSE, label = NULL, font.label = list(size = 11, color =
  "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE,
  ggtheme = theme_pubr(), ...)

Arguments

data
a data frame
x
variable to be drawn.
y
one of "..density.." or "..count..".
combine
logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.
merge
logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.
color, fill
histogram line color and fill color.
palette
the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
size
Numeric value (e.g.: size = 1). change the size of points and outlines.
linetype
line type. See show_line_types.
alpha
numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.
bins
Number of bins. Defaults to 30.
binwidth
numeric value specifying bin width. use value between 0 and 1 when you have a strong dense dotplot. For example binwidth = 0.2. Read more about binwidth.
title
plot main title.
xlab
character vector specifying x axis labels. Use xlab = FALSE to hide xlab.
ylab
character vector specifying y axis labels. Use ylab = FALSE to hide ylab.
facet.by
character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.
panel.labs
a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).
short.panel.labs
logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.
add
allowed values are one of "mean" or "median" (for adding mean or median line, respectively).
add.params
parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").
rug
logical value. If TRUE, add marginal rug.
add_density
logical value. If TRUE, add density curves.
label
the name of the column containing point labels. Can be also a character vector with length = nrow(data).
font.label
a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").
label.select
can be of two formats:
  • a character vector specifying some labels to show.
  • a list containing one or the combination of the following components:
    • top.up and top.down: to display the labels of the top up/down points. For example, label.select = list(top.up = 10, top.down = 4).
    • criteria: to filter, for example, by x and y variabes values, use this: label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` %in% c('A', 'B')").
repel
a logical value, whether to use ggrepel to avoid overplotting text labels or not.
label.rectangle
logical value. If TRUE, add rectangle underneath the text, making it easier to read.
ggtheme
function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....
...
other arguments to be passed to geom_histogram and ggpar.

Details

The plot can be easily customized using the function ggpar(). Read ?ggpar for changing:

  • main title and axis labels: main, xlab, ylab
  • axis limits: xlim, ylim (e.g.: ylim = c(0, 30))
  • axis scales: xscale, yscale (e.g.: yscale = "log2")
  • color palettes: palette = "Dark2" or palette = c("gray", "blue", "red")
  • legend title, labels and position: legend = "right"
  • plot orientation : orientation = c("vertical", "horizontal", "reverse")

See also

ggdensity and ggpar

Examples

# Create some data format set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4)
#> sex weight #> 1 F 53.79293 #> 2 F 55.27743 #> 3 F 56.08444 #> 4 F 52.65430
# Basic density plot # Add mean line and marginal rug gghistogram(wdata, x = "weight", fill = "lightgray", add = "mean", rug = TRUE)
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
# Change outline colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", palette = c("#00AFBB", "#E7B800"))
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
# Change outline and fill colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800"))
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
# Combine histogram and density plots gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, fill = "sex", palette = c("#00AFBB", "#E7B800"), add_density = TRUE)
#> Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.