Plot data distribution as histograms.

Source: R/plot_histogram.R

This function takes a data table, a quantitative variable (ycol) and a grouping variable (group), if available, and plots a histogram graph using geom_histogram). Alternatives are plot_density, or plot_qqline.

plot_histogram(
  data,
  ycol,
  group,
  facet,
  PlotType = c("Counts", "Normalised counts"),
  BinSize = 30,
  c_alpha = 0.8,
  TextXAngle = 0,
  facet_scales = "fixed",
  fontsize = 20,
  linethick,
  alpha,
  LogYTrans,
  LogYBreaks = waiver(),
  LogYLabels = waiver(),
  LogYLimits = NULL,
  ColPal = c("okabe_ito", "all_grafify", "bright", "contrast", "dark", "fishy", "kelly",
    "light", "muted", "pale", "r4", "safe", "vibrant"),
  ColSeq = TRUE,
  ColRev = FALSE,
  SingleColour = NULL,
  ...
)

Arguments

data

a data table e.g. data.frame or tibble.

ycol

name of the column (without quotes) with the quantitative variable whose histogram distribution is to be plotted.

group

name of the column containing a categorical grouping variable.

facet

add another variable (without quotes) from the data table to create faceted graphs using facet_wrap.

PlotType

the default (Counts) plot will plot counts in the bins, which can be changed to Normalised counts.

BinSize

number of distinct bins to use on X-axis, default set to 30.

c_alpha

fractional opacity of colour filled within histograms, default set to 0.8 (i.e. 80% opacity).

TextXAngle

orientation of text on X-axis; default 0 degrees. Change to 45 or 90 to remove overlapping text.

facet_scales

whether or not to fix scales on X & Y axes for all facet facet graphs. Can be fixed (default), free, free_y or free_x (for Y and X axis one at a time, respectively).

fontsize

parameter of base_size of fonts in theme_classic, default set to size 20.

linethick

thickness of symbol border, default set to fontsize/22.

alpha

deprecated old argument for c_alpha; retained for backward compatibility.

LogYTrans

transform Y axis into "log10" or "log2" (in quotes).

LogYBreaks

argument for scale_y_continuous for Y axis breaks on log scales, default is waiver(), or provide a vector of desired breaks.

LogYLabels

argument for scale_y_continuous for Y axis labels on log scales, default is waiver(), or provide a vector of desired labels.

LogYLimits

a vector of length two specifying the range (minimum and maximum) of the Y axis.

ColPal

grafify colour palette to apply (in quotes), default "okabe_ito"; see graf_palettes for available palettes.

ColSeq

logical TRUE or FALSE. Default TRUE for sequential colours from chosen palette. Set to FALSE for distant colours, which will be applied using scale_fill_grafify2.

ColRev

whether to reverse order of colour within the selected palette, default F (FALSE); can be set to T (TRUE).

SingleColour

a colour hexcode (starting with #, e.g., "#E69F00"), a number between 1-154, or names of colours from grafify palettes or base R to fill along X-axis aesthetic. Accepts any colour other than "black"; use grey_lin11, which is almost black.

...

any additional arguments to pass to geom_histogram.

Value

This function returns a ggplot2 object of class "gg" and "ggplot".

Details

Note that the function requires the quantitative Y variable first, and groups them based on a categorical variable passed on via the group argument. The grouping variable is mapped to the fill aesthetics in geom_histogram.

ColPal & ColRev options are applied to both fill and colour scales. Colours available can be seen quickly with plot_grafify_palette. Colours can be changed using ColPal, ColRev or ColSeq arguments. ColPal can be one of the following: "okabe_ito", "dark", "light", "bright", "pale", "vibrant, "muted" or "contrast". ColRev (logical TRUE/FALSE) decides whether colours are chosen from first-to-last or last-to-first from within the chosen palette. ColSeq decides whether colours are picked by respecting the order in the palette or the most distant ones using colorRampPalette.

Examples

#Basic usage
plot_histogram(data = data_t_pratio, 
ycol = Cytokine, group = Genotype, 
BinSize = 10)

#with log transformation
plot_histogram(data = data_t_pratio, 
ycol = log(Cytokine), group = Genotype, 
BinSize = 10)

#Normalised counts
plot_histogram(data = data_t_pratio, 
ycol = log(Cytokine), group = Genotype, 
PlotType = "Normalised counts", 
BinSize = 10)