nice_normality {rempsyc} | R Documentation |
Easy normality check per group
Description
Easily make nice per-group density and QQ plots
through a wrapper around the ggplot2
and qqplotr
packages.
Usage
nice_normality(
data,
variable,
group = NULL,
colours,
groups.labels,
grid = TRUE,
shapiro = FALSE,
title = NULL,
histogram = FALSE,
breaks.auto = FALSE,
...
)
Arguments
data |
The data frame. |
variable |
The dependent variable to be plotted. |
group |
The group by which to plot the variable. |
colours |
Desired colours for the plot, if desired. |
groups.labels |
How to label the groups. |
grid |
Logical, whether to keep the default background grid or not. APA style suggests not using a grid in the background, though in this case some may find it useful to more easily estimate the slopes of the different groups. |
shapiro |
Logical, whether to include the p-value from the Shapiro-Wilk test on the plot. |
title |
An optional title, if desired. |
histogram |
Logical, whether to add an histogram on top of the density plot. |
breaks.auto |
If histogram = TRUE, then option to set bins/breaks
automatically, mimicking the default behaviour of
base R |
... |
Further arguments from |
Value
A plot of classes patchwork and ggplot, containing two plots,
resulting from nice_density
and nice_qq
.
See Also
Other functions useful in assumption testing:
nice_assumptions
, nice_density
,
nice_qq
, nice_var
,
nice_varplot
. Tutorial:
https://rempsyc.remi-theriault.com/articles/assumptions
Examples
# Make the basic plot
nice_normality(
data = iris,
variable = "Sepal.Length",
group = "Species"
)
# Further customization
nice_normality(
data = iris,
variable = "Sepal.Length",
group = "Species",
colours = c(
"#00BA38",
"#619CFF",
"#F8766D"
),
groups.labels = c(
"(a) Setosa",
"(b) Versicolor",
"(c) Virginica"
),
grid = FALSE,
shapiro = TRUE
)