R: Histogram plot

gghistogram {ggpubr} | R Documentation

## Histogram plot

### Description

Create a histogram plot.

### Usage

    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. http://r4ds.had.co.nz/eda.html).
`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)

    # Basic density plot
    # Add mean line and marginal rug
    gghistogram(wdata, x = "weight", fill = "lightgray",
       add = "mean", rug = TRUE)

    # Change outline colors by groups ("sex")
    # Use custom color palette
    gghistogram(wdata, x = "weight",
       add = "mean", rug = TRUE,
       color = "sex", palette = c("#00AFBB", "#E7B800"))

    # 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"))

    # Combine histogram and density plots
    gghistogram(wdata, x = "weight",
       add = "mean", rug = TRUE,
       fill = "sex", palette = c("#00AFBB", "#E7B800"),
       add_density = TRUE)

[Package ggpubr version 0.1.6 Index]