stat_cor | R Documentation |
Add correlation coefficients with p-values to a scatter plot. Can be also used to add 'R2'.
stat_cor( mapping = NULL, data = NULL, method = "pearson", alternative = "two.sided", cor.coef.name = c("R", "rho", "tau"), label.sep = ", ", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, output.type = "expression", digits = 2, r.digits = digits, p.digits = digits, r.accuracy = NULL, p.accuracy = NULL, geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
method |
a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman". |
alternative |
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter. |
cor.coef.name |
character. Can be one of |
label.sep |
a character string to separate the terms. Default is ", ", to separate the correlation coefficient and the p.value. |
label.x.npc, label.y.npc |
can be
If too short they will be recycled. |
label.x, label.y |
|
output.type |
character One of "expression", "latex", "tex" or "text". |
digits, r.digits, p.digits |
integer indicating the number of decimal places (round) or significant digits (signif) to be used for the correlation coefficient and the p-value, respectively.. |
r.accuracy |
a real value specifying the number of decimal places of
precision for the correlation coefficient. Default is NULL. Use (e.g.) 0.01
to show 2 decimal places of precision. If specified, then |
p.accuracy |
a real value specifying the number of decimal places of
precision for the p-value. Default is NULL. Use (e.g.) 0.0001 to show 4
decimal places of precision. If specified, then |
geom |
The geometric object to use to display the data, either as a
|
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
na.rm |
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
other arguments to pass to |
correlation coefficient
correlation coefficient squared
formatted label for the correlation coefficient
formatted label for the squared correlation coefficient
label for the p-value
default labeldisplayed by stat_cor()
ggscatter
# Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # Scatter plot with correlation coefficient #::::::::::::::::::::::::::::::::::::::::::::::::: sp <- ggscatter(df, x = "wt", y = "mpg", add = "reg.line", # Add regressin line add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line conf.int = TRUE # Add confidence interval ) # Add correlation coefficient sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) # Specify the number of decimal places of precision for p and r # Using 3 decimal places for the p-value and # 2 decimal places for the correlation coefficient (r) sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) # Show only the r.label but not the p.label sp + stat_cor(aes(label = ..r.label..), label.x = 3) # Use R2 instead of R ggscatter(df, x = "wt", y = "mpg", add = "reg.line") + stat_cor( aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), label.x = 3 ) # Color by groups and facet #:::::::::::::::::::::::::::::::::::::::::::::::::::: sp <- ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", add = "reg.line", conf.int = TRUE) sp + stat_cor(aes(color = cyl), label.x = 3)
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