plot_befafter_shapes are for plotting matched data joined by lines. These functions take X and Y variables along with a data column with matching information (e.g. matched subjects or experiments etc.) and plot symbols matched by colour or shape.
plot_befafter_box( data, xcol, ycol, match, facet, PlotShapes = FALSE, symsize = 3, s_alpha = 0.8, b_alpha = 1, bwid = 0.4, jitter = 0.1, TextXAngle = 0, LogYTrans, LogYBreaks = waiver(), LogYLabels = waiver(), LogYLimits = NULL, facet_scales = "fixed", fontsize = 20, symthick, bthick, ColPal = c("okabe_ito", "all_grafify", "bright", "contrast", "dark", "fishy", "kelly", "light", "muted", "pale", "r4", "safe", "vibrant"), ColSeq = TRUE, ColRev = FALSE, SingleColour = "NULL", ... )
a data table object, e.g. data.frame or tibble.
name of the column containing the categorical variable to be plotted on the X axis.
name of the column containing the quantitative Y values.
name of the column with the grouping variable to pass on to
add another variable from the data table to create faceted graphs using
logical TRUE or FALSE (default = FALSE) if the shape of the symbol is to be mapped to the
match variable. Note that only 25 shapes allowed.
size of symbols, default set to 3.
fractional opacity of symbols, default set to 0.8 (i.e., 80% opacity).
fractional opacity of boxes, default set to 1.
width of boxplots; default 0.4.
extent of jitter (scatter) of symbols, default is 0.1. Increase to reduce symbol overlap, set to 0 for aligned symbols.
orientation of text on X-axis; default 0 degrees. Change to 45 or 90 to remove overlapping text.
transform Y axis into "log10" or "log2"
ggplot2[scale_y_continuous] for Y axis breaks on log scales, default is
waiver(), or provide a vector of desired breaks.
ggplot2[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.
whether or not to fix scales on X & Y axes for all facet facet graphs. Can be
free_x (for Y and X axis one at a time, respectively).
base_size of fonts in
theme_classic, default set to size 20.
size (in 'pt' units) of outline of symbol lines (
stroke), default =
thickness (in 'pt' units) of lines and boxes lines; default =
grafify colour palette to apply, 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
whether to reverse order of colour within the selected palette, default F (FALSE); can be set to T (TRUE).
a colour hexcode (starting with #), a number between 1-154, or names of colours from
grafify or base R palettes 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
This function returns a
ggplot2 object of class "gg" and "ggplot".
plot_befafter_box to also get a boxplot with matched data. In this function, the categorical variable along X axis is mapped to the fill-colour aesthetic.
The default is a plot without matching shapes. Change the
PlotShapes argument to
TRUE for plot similar to
plot_befafter_shapes. Note that with
PlotShapes = TRUE the colour of symbols will always be black and the X-axis variable is mapped to the fill colour of boxplots.
Note that only 25 shapes are available, and there will be errors with
plot_befafter_shapes when there are fewer than 25 matched observations; instead use default (
PlotShapes = FALSE).
Add another variable to make faceted graphs with the
Colours can be changed using
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
To plot a graph with a single colour along the X axis variable, use the
#plot without legends if necessary plot_befafter_box(data = data_t_pdiff, xcol = Condition, ycol = Mass, match = Subject) #with PlotShapes = TRUE plot_befafter_box(data = data_t_pdiff, xcol = Condition, ycol = Mass, match = Subject, PlotShapes = TRUE) #2way ANOVA design with randomised blocks plot_befafter_box(data = data_2w_Tdeath, xcol = Time2, ycol = PI, match = Experiment, facet = Genotype)