`R/plot_4d_scatterbar.R`

`plot_4d_scatterbar.Rd`

The functions `plot_3d_scatterbar`

, `plot_3d_scatterbox`

, `plot_4d_scatterbar`

and `plot_4d_scatterbox`

are useful for plotting one-way or two-way ANOVA designs with randomised blocks or repeated measures. The blocks or subjects can be mapped to the `shapes`

argument in both functions (up to 25 levels can be mapped to `shapes`

; there will be an error if this number is exceeded). The 3d versions use the categorical variable (`xcol`

) for grouping (e.g. one-way ANOVA designs), and 4d versions take an additional grouping variable (e.g. two-way ANOVA designs) that is passed to either `boxes`

or `bars`

argument.

```
plot_4d_scatterbar(
data,
xcol,
ycol,
bars,
shapes,
facet,
ErrorType = "SD",
symsize = 3,
s_alpha = 0.8,
b_alpha = 1,
bwid = 0.7,
jitter = 0.1,
ewid = 0.2,
TextXAngle = 0,
LogYTrans,
LogYBreaks = waiver(),
LogYLabels = waiver(),
LogYLimits = NULL,
facet_scales = "fixed",
fontsize = 20,
group_wid = 0.8,
symthick,
bthick,
ColPal = c("okabe_ito", "all_grafify", "bright", "contrast", "dark", "fishy", "kelly",
"light", "muted", "pale", "r4", "safe", "vibrant"),
ColRev = FALSE,
ColSeq = TRUE,
...
)
```

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

- xcol
name of the column with the categorical factor to plot on X axis. If column is numeric, enter as

`factor(col)`

.- ycol
name of the column to plot on quantitative variable on the Y axis.

- bars
name of the column containing grouping within the factor plotted on X axis. Can be categorical or numeric X. If your table has numeric X and you want to plot as factor, enter

`xcol = factor(name of colum)`

.- shapes
name of the column that contains matched observations, e.g. subject IDs, experiment ID.

- facet
add another variable from the data table to create faceted graphs using

`ggplot2`

facet_wrap.- ErrorType
select the type of error bars to display. Default is "SD" (standard deviation). Other options are "SEM" (standard error of the mean) and "CI95" (95% confidence interval based on t distributions).

- symsize
size of symbols, default set to 3.

- s_alpha
fractional opacity of symbols, default set to 0.8 (i.e. 80% opacity). Set

`s_alpha = 0`

to not show scatter plot.- b_alpha
fractional opacity of boxes. Default is set to 0, which results in white boxes inside violins. Change to any value >0 up to 1 for different levels of transparency.

- bwid
width of boxes; default 0.7.

- jitter
extent of jitter (scatter) of symbols, default is 0.1. Increase to reduce symbol overlap, set to 0 for aligned symbols.

- ewid
width of error bars, default set to 0.2.

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

- LogYTrans
transform Y axis into "log10" or "log2"

- LogYBreaks
argument for

`ggplot2[scale_y_continuous]`

for Y axis breaks on log scales, default is`waiver()`

, or provide a vector of desired breaks.- LogYLabels
argument for

`ggplot2[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.

- 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.- group_wid
space between the factors along X-axis, i.e., dodge width. Default

`group_wid = 0.8`

(range 0-1), which can be set to 0 if you'd like the two plotted as`position = position_identity()`

.- symthick
size (in 'pt' units) of outline of symbol lines (

`stroke`

), default =`fontsize`

/22.- bthick
thickness (in 'pt' units) of bar and error bar lines; default =

`fontsize`

/22.- ColPal
grafify colour palette to apply, default "okabe_ito"; see

`graf_palettes`

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

- 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`

.- ...
any additional arguments to pass to

`ggplot2`

stat_summary or`ggplot2`

geom_point.

This function returns a `ggplot2`

object of class "gg" and "ggplot".

These functions rely on `ggplot`

with `geom_point`

and `geom_bar`

(through `stat_summary`

) or `geom_boxplot`

geometries.

Variables other than the quantitative variable (`ycol`

) will be automatically converted to categorical variables even if they are numeric in the data table.

Shapes are always plotted in black colour, and their opacity can be changed with the `s_alpha`

argument and overlap can be reduced with the `jitter`

argument. Other arguments are similar to other plot functions as briefly explained below.

Bars depict means using `stat_summary`

with `geom = "bar", fun = "mean"`

, and bar width is set to 0.7 (cannot be changed). Error bar width can be changed with the `ewid`

argument.

Boxplot geometry uses `geom_boxplot`

with `position = position_dodge(width = 0.9), width = 0.6`

. The thick line within the boxplot depicts the median, the box the IQR (interquartile range) and the whiskers show 1.5*IQR.

In 4d versions, the two grouping variables (i.e. `xcol`

and either `boxes`

or `bars`

) are passed to ggplot aesthetics through `group = interaction{ xcol, shapes}`

.

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`

(logical TRUE/FALSE) decides whether colours are picked by respecting the order in the palette or the most distant ones using `colorRampPalette`

.

All four functions can be expanded further, for example with `facet_grid`

or `facet_wrap`

.

```
#3d version for 1-way data with blocking
plot_3d_scatterbox(data = data_1w_death,
xcol = Genotype, ycol = Death, shapes = Experiment)
#compare above graph to
plot_scatterbox(data = data_1w_death, xcol = Genotype, ycol = Death)
#4d version for 2-way data with blocking
plot_4d_scatterbox(data = data_2w_Tdeath,
xcol = Genotype,
ycol = PI,
boxes = Time,
shapes = Experiment)
plot_4d_scatterbar(data = data_2w_Festing,
xcol = Strain,
ycol = GST,
bars = Treatment,
shapes = Block)
```