This function takes a data table, ycol of quantitative variable and a categorical grouping variable (group), if available, and plots a density graph using geom_density). Alternatives are plot_histogram, or plot_qqline.
plot_density(
data,
ycol,
group,
facet,
PlotType = c("Density", "Counts", "Normalised counts"),
c_alpha = 0.2,
TextXAngle = 0,
facet_scales = "fixed",
fontsize = 20,
linethick,
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,
...
)This function returns a ggplot2 object of class "gg" and "ggplot".
a data table e.g. data.frame or tibble.
name of the column (without quotes) with the quantitative variable whose density distribution is to be plotted.
name of the column containing a categorical grouping variable (optional Since v5.0.0).
add another variable (without quotes) from the data table to create faceted graphs using facet_wrap.
the default (Density) plot will be the probability density curve, which can be changed to Counts or Normalised counts.
fractional opacity of filled colours under the curve, default set to 0.2 (i.e. 20% opacity).
orientation of text on X-axis; default 0 degrees. Change to 45 or 90 to remove overlapping text.
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).
parameter of base_size of fonts in theme_classic, default set to size 20.
thickness of symbol border, default set to fontsize/22.
transform Y axis into "log10" or "log2" (in quotes).
argument for scale_y_continuous for Y axis breaks on log scales, default is waiver(), or provide a vector of desired breaks.
argument for scale_y_continuous for Y axis labels on log scales, default is waiver(), or provide a vector of desired labels.
a vector of length two specifying the range (minimum and maximum) of the Y axis.
grafify colour palette to apply (in quotes), default "okabe_ito"; see graf_palettes for available palettes.
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.
whether to reverse order of colour within the selected palette, default F (FALSE); can be set to T (TRUE).
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_density.
Note that the function requires the quantitative Y variable first, and groups them based on an X variable. The group variable is mapped to the fill and colour aesthetics in geom_density.
Colours can be changed using ColPal, ColRev or ColSeq arguments. Colours available can be seen quickly with plot_grafify_palette.
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.
plot_density(data = data_t_pratio,
ycol = log(Cytokine), group = Genotype)
#with faceting
plot_density(data = data_cholesterol,
ycol = Cholesterol, group = Treatment,
facet = Treatment, fontsize = 12)
#Counts
plot_density(data = data_cholesterol,
ycol = Cholesterol, group = Treatment,
PlotType = "Counts",
facet = Treatment, fontsize = 12)
Run the code above in your browser using DataLab