sjp.corr: Plot correlation matrix

Description

Plot correlation matrix as ellipses or tiles.

Usage

sjp.corr(data, title = NULL, axis.labels = NULL, type = c("circle", "tile"), sort.corr = TRUE, decimals = 3, na.deletion = c("listwise", "pairwise"), corr.method = c("spearman", "pearson", "kendall"), geom.colors = "RdBu", geom.size = 15, wrap.title = 50, wrap.labels = 20, show.legend = FALSE, legend.title = NULL, show.values = TRUE, show.p = TRUE, p.numeric = FALSE, prnt.plot = TRUE)

Arguments

data

matrix with correlation coefficients as returned by the cor-function, or a data.frame of variables where correlations between columns should be computed.

title

character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed.

axis.labels

character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.

type

indicates whether the geoms of correlation values should be plotted as "circle" (default) or as "tile".

sort.corr

logical, if TRUE (default), the axis labels are sorted according to the correlation strength. If FALSE, axis labels appear in order of how variables were included in the cor-computation or data frame.

decimals

indicates how many decimal values after comma are printed when the values labels are shown. Default is 3. Only applies when show.values = TRUE.

na.deletion

indicates how missing values are treated. May be either "listwise" (default) or "pairwise". May be abbreviated.

corr.method

indicates the correlation computation method. May be one of "spearman" (default), "pearson" or "kendall". May be abbreviated.

geom.colors

user defined color for geoms. See 'Details' in sjp.grpfrq.

geom.size

size resp. width of the geoms (bar width, line thickness or point size, depending on plot type and function). Note that bar and bin widths mostly need smaller values than dot sizes.

wrap.title

numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.

wrap.labels

numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.

show.legend

logical, if TRUE, and depending on plot type and function, a legend is added to the plot.

legend.title

character vector, used as title for the plot legend.

show.values

logical, whether values should be plotted or not.

show.p

logical, adds significance levels to values, or value and variable labels.

p.numeric

logical, if TRUE, the p-values are printed as numbers. If FALSE (default), asterisks are used.

prnt.plot

logical, if TRUE (default), plots the results as graph. Use FALSE if you don't want to plot any graphs. In either case, the ggplot-object will be returned as value.

Value

(Insisibily) returns the ggplot-object with the complete plot (plot) as well as the data frame that was used for setting up the ggplot-object (df) and the original correlation matrix (corr.matrix).

Details

Required argument is either a data.frame or a matrix with correlation coefficients as returned by the cor-function. In case of ellipses, the ellipses size indicates the strength of the correlation. Furthermore, blue and red colors indicate positive or negative correlations, where stronger correlations are darker.

Examples

Run this code

# create data frame with 5 random variables
mydf <- data.frame(cbind(runif(10), runif(10), runif(10), 
                         runif(10), runif(10)))

# plot correlation matrix using circles
sjp.corr(mydf)

# plot correlation matrix using square tiles without diagram background
sjp.corr(mydf, type = "tile")

# -------------------------------
# Data from the EUROFAMCARE sample dataset
# -------------------------------
library(sjmisc)
data(efc)

# retrieve variable and value labels
varlabs <- get_label(efc)

# create data frame
vars.index <- c(1, 4, 15, 19, 20, 21, 22, 24, 25)
mydf <- data.frame(efc[, vars.index])
colnames(mydf) <- varlabs[vars.index]

# show legend
sjp.corr(mydf, type = "tile", show.legend = TRUE)

# -------------------------------
# auto-detection of labels
# -------------------------------
# blank theme
set_theme("blank", axis.angle.x = 90)
sjp.corr(efc[, vars.index])