sjPlot (version 1.8.1)

sjp.gpt: Plot grouped proportional tables

Description

Plot grouped proportional crosstables, where the proportion of each level of x for the highest category in y is plotted, for each subgroup of groups.

Usage

sjp.gpt(x, y, groups, geom.colors = "Set1", geom.size = 4,
  shape.fill.color = "#f0f0f0", shapes = c(15, 16, 17, 18, 21, 22, 23, 24,
  25, 7, 8, 9, 10, 12), title = NULL, axisLabels = NULL,
  axisTitle.x = NULL, axisTitle.y = NULL, legendTitle = NULL,
  legendLabels = NULL, breakTitleAt = 50, breakLabelsAt = 15,
  breakLegendTitleAt = 20, breakLegendLabelsAt = 20, axisLimits = NULL,
  gridBreaksAt = NULL, showTotal = TRUE, annotateTotal = TRUE,
  showP = TRUE, showN = TRUE, hideLegend = FALSE, printPlot = TRUE)

Arguments

x

categorical variable, where the proportion of each categorie in the highest category of y will be printed along the x-axis.

y

categorical or numeric variable. If not a binary variable, y will be recoded into a binary variable, dichtomized at the highest category and all remaining categories.

groups

grouping variable, which will define the y-axis

geom.colors

User defined color palette for geoms. If specified, must either be vector with color values of same length as groups defined in varGroup, or a specific color brewer palette code (see 'Note').

geom.size

size resp. width of the geoms (bar width or point size, depending on type argument). Note that bar and bin widths mostly need smaller values than dot sizes (i.e. if type = "dots"). By default, geom.size = NULL, whic

shape.fill.color

optional color vector, fill-color for non-filled shapes

shapes

numeric vector with shape styles, used to map the different categories of x.

title

plot title as string. Example: title = "my title". Use NULL to automatically detect variable names that will be used as title (see set_label) for details). If title =

axisLabels

character vector with groups labels for the y-axis.

axisTitle.x

title for the x-axis. By default, "" is used, i.e. no title is printed. If axisTitle.x = NULL, the variable name will be automatically detected and used as title (see set_label

axisTitle.y

title for the-y axis. By default, "" is used, i.e. no title is printed. If axisTitle.y = NULL, variable name will be automatically detected and used as title (see set_label

legendTitle

title of the plot legend, as string.

legendLabels

a character vector with labels for the guide/legend.

breakTitleAt

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

breakLabelsAt

determines how many chars of the category labels are displayed in one line and when a line break is inserted.

breakLegendTitleAt

determines how many chars of the legend's title are displayed in one line and when a line break is inserted.

breakLegendLabelsAt

determines how many chars of the legend labels are displayed in one line and when a line break is inserted.

axisLimits

numeric vector of length 2, defining the range of the plot axis.

gridBreaksAt

set breaks for the axis, i.e. at every gridBreaksAt'th position a major grid is being printed.

showTotal

logical, if TRUE, a total summary line for all aggregated groups is added.

annotateTotal

logical, if TRUE and showTotal = TRUE, the total-row in the figure will be highlighted with a slightly shaded background.

showP

logical, if TRUE, axis labels for groups will also contain the results of the chisq.test of x and y for each groups.

showN

logical, if TRUE, axis labels for groups will also contain the total number of cases in each groups.

hideLegend

logical, indicates whether legend (guide) should be shown or not.

printPlot

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).

Examples

library(sjmisc)
data(efc)

# the proportion of dependency levels in female
# elderly, for each family carer's relationship
# to elderly
sjp.gpt(efc$e42dep, efc$e16sex, efc$e15relat)

# proportion of educational levels in highest
# dependency category of elderly, for different
# care levels
sjp.gpt(efc$c172code, efc$e42dep, efc$n4pstu)