Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. Furthermore, fitted lines can be added for each group as well as for the overall plot.
plot_scatter( data, x, y, grp, title = "", legend.title = NULL, legend.labels = NULL, dot.labels = NULL, axis.titles = NULL, dot.size = 1.5, label.size = 3, colors = "metro", fit.line = NULL, fit.grps = NULL, show.rug = FALSE, show.legend = TRUE, show.ci = FALSE, wrap.title = 50, wrap.legend.title = 20, wrap.legend.labels = 20, jitter = 0.05, emph.dots = FALSE, grid = FALSE )
A data frame, or a grouped data frame.
Name of the variable for the x-axis.
Name of the variable for the y-axis.
Optional, name of the grouping-variable. If not missing, the scatter plot will be grouped. See 'Examples'.
Character vector, used as plot title. By default,
response_labels is called to retrieve the label of
the dependent variable, which will be used as title. Use
title = ""
to remove title.
Character vector, used as legend title for plots that have a legend.
character vector with labels for the guide/legend.
Character vector with names for each coordinate pair given
y, so text labels are added to the plot.
Must be of same length as
dot.labels has a different length, data points will be trimmed
dot.labels = NULL (default),
no labels are printed.
character vector of length one or two, defining the title(s) for the x-axis and y-axis.
Numeric, size of the dots that indicate the point estimates.
Size of text labels if argument
dot.labels is used.
May be a character vector of color values in hex-format, valid
color value names (see
demo("colors")) or a name of a pre-defined
color palette. Following options are valid for the
If not specified, a default color brewer palette will be used, which is suitable for the plot style.
"gs", a greyscale will be used.
"bw", and plot-type is a line-plot, the plot is black/white and uses different line types to distinguish groups (see this package-vignette).
colors is any valid color brewer palette name, the related palette will be used. Use
RColorBrewer::display.brewer.all() to view all available palette names.
There are some pre-defined color palettes in this package, see
sjPlot-themes for details.
Else specify own color values or names as vector (e.g.
colors = "#00ff00" or
colors = c("firebrick", "blue")).
Specifies the method to add a fitted line accross
the data points. Possible values are for instance
NULL, no line is plotted.
fit.line adds a fitted line for the complete data, while
adds a fitted line for each subgroup of
TRUE, a marginal rug plot is displayed
in the graph.
For Marginal Effects plots, shows or hides the legend.
TRUE), adds notches to the box plot, which are
used to compare groups; if the notches of two boxes do not overlap,
medians are considered to be significantly different.
Numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.
numeric, determines how many chars of the legend's title are displayed in one line and when a line break is inserted.
numeric, determines how many chars of the legend labels are displayed in one line and when a line break is inserted.
Numeric, between 0 and 1. If
show.data = TRUE, you can
add a small amount of random variation to the location of each data point.
jitter then indicates the width, i.e. how much of a bin's width
will be occupied by the jittered values.
TRUE, overlapping points at same coordinates
will be becomme larger, so point size indicates amount of overlapping.
TRUE, multiple plots are plotted as grid
A ggplot-object. For grouped data frames, a list of ggplot-objects for each group in the data.
# load sample date library(sjmisc) library(sjlabelled) data(efc) # simple scatter plot plot_scatter(efc, e16sex, neg_c_7) # simple scatter plot, increased jittering plot_scatter(efc, e16sex, neg_c_7, jitter = .4) # grouped scatter plot plot_scatter(efc, c160age, e17age, e42dep) # grouped scatter plot with marginal rug plot # and add fitted line for complete data plot_scatter( efc, c12hour, c160age, c172code, show.rug = TRUE, fit.line = "lm" ) #> `geom_smooth()` using formula 'y ~ x' # grouped scatter plot with marginal rug plot # and add fitted line for each group plot_scatter( efc, c12hour, c160age, c172code, show.rug = TRUE, fit.grps = "loess", grid = TRUE ) #> `geom_smooth()` using formula 'y ~ x'