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plot_gpt.R
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plot_gpt.R
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utils::globalVariables("n")
#' @title Plot grouped proportional tables
#' @name plot_gpt
#'
#' @description Plot grouped proportional crosstables, where the proportion of
#' each level of \code{x} for the highest category in \code{y}
#' is plotted, for each subgroup of \code{grp}.
#'
#' @param x Categorical variable, where the proportion of each category in
#' \code{x} for the highest category of \code{y} will be printed
#' along the x-axis.
#' @param y Categorical or numeric variable. If not a binary variable, \code{y}
#' will be recoded into a binary variable, dichtomized at the highest
#' category and all remaining categories.
#' @param grp Grouping variable, which will define the y-axis
#' @param shape.fill.color Optional color vector, fill-color for non-filled shapes
#' @param shapes Numeric vector with shape styles, used to map the different
#' categories of \code{x}.
#' @param show.total Logical, if \code{TRUE}, a total summary line for all aggregated
#' \code{grp} is added.
#' @param annotate.total Logical, if \code{TRUE} and \code{show.total = TRUE},
#' the total-row in the figure will be highlighted with a slightly
#' shaded background.
#' @param axis.lim Numeric vector of length 2, defining the range of the plot axis.
#' Depending on plot type, may effect either x- or y-axis, or both.
#' For multiple plot outputs (e.g., from \code{type = "eff"} or
#' \code{type = "slope"} in \code{\link{plot_model}}), \code{axis.lim} may
#' also be a list of vectors of length 2, defining axis limits for each
#' plot (only if non-faceted).
#' @param show.p Logical, adds significance levels to values, or value and
#' variable labels.
#'
#' @return A ggplot-object.
#'
#' @inheritParams plot_scatter
#' @inheritParams plot_grpfrq
#' @inheritParams plot_xtab
#'
#' @details The p-values are based on \code{\link[stats]{chisq.test}} of \code{x}
#' and \code{y} for each \code{grp}.
#'
#' @examples
#' if (requireNamespace("haven")) {
#' data(efc)
#'
#' # the proportion of dependency levels in female
#' # elderly, for each family carer's relationship
#' # to elderly
#' plot_gpt(efc, e42dep, e16sex, e15relat)
#'
#' # proportion of educational levels in highest
#' # dependency category of elderly, for different
#' # care levels
#' plot_gpt(efc, c172code, e42dep, n4pstu)
#' }
#' @import ggplot2
#' @export
plot_gpt <- function(
data,
x,
y,
grp,
colors = "metro",
geom.size = 2.5,
shape.fill.color = "#f0f0f0",
shapes = c(15, 16, 17, 18, 21, 22, 23, 24, 25, 7, 8, 9, 10, 12),
title = NULL,
axis.labels = NULL,
axis.titles = NULL,
legend.title = NULL,
legend.labels = NULL,
wrap.title = 50,
wrap.labels = 15,
wrap.legend.title = 20,
wrap.legend.labels = 20,
axis.lim = NULL,
grid.breaks = NULL,
show.total = TRUE,
annotate.total = TRUE,
show.p = TRUE,
show.n = TRUE)
{
# get data
name.x <- deparse(substitute(x))
name.y <- deparse(substitute(y))
name.grp <- deparse(substitute(grp))
pl <- NULL
if (inherits(data, "grouped_df")) {
# get grouped data
grps <- get_grouped_data(data)
# now plot everything
for (i in seq_len(nrow(grps))) {
# copy back labels to grouped data frame
tmp <- sjlabelled::copy_labels(grps$data[[i]], data)
# prepare argument list, including title
tmp.title <- get_grouped_plottitle(data, grps, i, sep = "\n")
# copy data
x <- tmp[[name.x]]
y <- tmp[[name.y]]
grp <- tmp[[name.grp]]
# plot
plots <- gpt_helper(
x, y, grp, colors, geom.size, shape.fill.color, shapes, title = tmp.title,
axis.labels, axis.titles, legend.title, legend.labels, wrap.title,
wrap.labels, wrap.legend.title, wrap.legend.labels, axis.lim,
grid.breaks, show.total, annotate.total, show.p, show.n, name.x, name.y, name.grp
)
# add plots, check for NULL results
pl <- c(pl, list(plots))
}
} else {
# copy data
x <- data[[name.x]]
y <- data[[name.y]]
grp <- data[[name.grp]]
# plot
pl <- gpt_helper(
x, y, grp, colors, geom.size, shape.fill.color, shapes, title,
axis.labels, axis.titles, legend.title, legend.labels, wrap.title,
wrap.labels, wrap.legend.title, wrap.legend.labels, axis.lim,
grid.breaks, show.total, annotate.total, show.p, show.n, name.x, name.y, name.grp
)
}
pl
}
gpt_helper <- function(
x, y, grp, colors, geom.size, shape.fill.color, shapes, title,
axis.labels, axis.titles, legend.title, legend.labels, wrap.title,
wrap.labels, wrap.legend.title, wrap.legend.labels, axis.lim,
grid.breaks, show.total, annotate.total, show.p, show.n, name.x, name.y, name.grp
) {
# any missing names?
if (is.null(name.x) || name.x == "NULL") name.x <- ""
if (is.null(name.y) || name.y == "NULL") name.y <- ""
if (is.null(name.grp) || name.grp == "NULL") name.grp <- ""
# copy titles
if (is.null(axis.titles)) {
axisTitle.x <- NULL
axisTitle.y <- NULL
} else {
axisTitle.x <- axis.titles[1]
if (length(axis.titles) > 1)
axisTitle.y <- axis.titles[2]
else
axisTitle.y <- NULL
}
# try to automatically set labels if not passed as argument
x <- suppressMessages(sjmisc::to_factor(x))
ylabels <-
sjlabelled::get_labels(
y,
attr.only = F,
values = NULL,
non.labelled = T
)
# get only value label for hightest category
ylabels <- ylabels[length(ylabels)]
if (is.null(axis.labels)) {
axis.labels <-
sjlabelled::get_labels(
grp,
attr.only = F,
values = NULL,
non.labelled = T
)
}
if (is.null(axisTitle.y)) {
axisTitle.y <-
paste0(
"Proportion of ",
sjlabelled::get_label(x, def.value = name.x),
" in ",
sjlabelled::get_label(y, def.value = name.y),
" (",
ylabels,
")"
)
}
if (is.null(legend.title)) {
legend.title <- sjlabelled::get_label(x, def.value = name.x)
}
if (is.null(legend.labels)) {
legend.labels <-
sjlabelled::get_labels(
x,
attr.only = F,
values = NULL,
non.labelled = T
)
}
# set labels that are still missing, but which need values
if (is.null(axis.labels)) axis.labels <- as.character(seq_len(length(grp)))
# wrap titles and labels
if (!is.null(legend.labels)) legend.labels <- sjmisc::word_wrap(legend.labels, wrap.legend.labels)
if (!is.null(legend.title)) legend.title <- sjmisc::word_wrap(legend.title, wrap.legend.title)
if (!is.null(title)) title <- sjmisc::word_wrap(title, wrap.title)
if (!is.null(axisTitle.x)) axisTitle.x <- sjmisc::word_wrap(axisTitle.x, wrap.title)
if (!is.null(axisTitle.y)) axisTitle.y <- sjmisc::word_wrap(axisTitle.y, wrap.title)
if (!is.null(axis.labels)) axis.labels <- sjmisc::word_wrap(axis.labels, wrap.labels)
# final data frame for plot
newdf <- data.frame()
group.p <- character()
group.n <- character()
# create data frame, for dplyr-chain
mydf <-
stats::na.omit(data.frame(
grp = sjlabelled::as_numeric(grp, keep.labels = F),
xpos = x,
dep = sjlabelled::as_numeric(y, keep.labels = F)
))
# recode dependent variable's categorues
# max and all others, so we have proportion
# between maximux value and rest
mydf$dep <- sjmisc::rec(mydf$dep, rec = "max=1;else=0", append = FALSE)
# group data by grouping variable, and inside
# groups, group the x-variable
newdf <- mydf %>%
dplyr::group_by(.data$grp, .data$xpos) %>%
dplyr::summarise(ypos = mean(.data$dep))
# group data by grouping variable,
# and summarize N per group and chisq.test
# of grp and x within each group
pvals <- mydf %>%
dplyr::group_by(.data$grp) %>%
dplyr::summarise(N = dplyr::n(), p = suppressWarnings(stats::chisq.test(table(.data$xpos, .data$dep))$p.value))
# copy p values
for (i in seq_len(length(pvals$grp))) group.p[i] <- get_p_stars(pvals$p[i])
# copy N
for (i in seq_len(length(pvals$grp)))
group.n[i] <- prettyNum(pvals$N[i], big.mark = ",", scientific = F)
# if we want total line, repeat all for
# complete data frame
if (show.total) {
tmp <- mydf %>%
dplyr::group_by(.data$xpos) %>%
dplyr::summarise(ypos = mean(.data$dep))
# pvalues and N
pvals <- mydf %>%
dplyr::summarise(N = dplyr::n(), p = suppressWarnings(stats::chisq.test(table(.data$xpos, .data$dep))$p.value))
# bind total row to final df
newdf <- dplyr::bind_rows(newdf, tmp)
# copy p values
group.p <- c(group.p, get_p_stars(pvals$p))
# copy N
group.n <- c(group.n, prettyNum(pvals$N, big.mark = ",", scientific = F))
# add "total" to axis labels
axis.labels <- c(axis.labels, "Total")
}
# make group variables categorical
newdf$grp <- suppressMessages(sjmisc::to_factor(newdf$grp))
newdf$xpos <- suppressMessages(sjmisc::to_factor(newdf$xpos))
# proportion needs to be numeric
newdf$ypos <- sjlabelled::as_numeric(newdf$ypos, keep.labels = F)
# add N and p-values to axis labels?
if (show.n) axis.labels <- paste0(axis.labels, " (n=", group.n, ")")
if (show.p) axis.labels <- paste0(axis.labels, " ", group.p)
# Set up axis limits
if (is.null(axis.lim)) axis.lim <- c(0, max(pretty(max(newdf$ypos, na.rm = TRUE), 10)))
# Set up grid breaks
if (is.null(grid.breaks))
gridbreaks <- waiver()
else
gridbreaks <- seq(axis.lim[1], axis.lim[2], by = grid.breaks)
# Set up geom colors
pal.len <- length(legend.labels)
geom.colors <- col_check2(colors, pal.len)
# Set up plot
p <- ggplot(newdf, aes(x = rev(.data$grp), y = .data$ypos, colour = .data$xpos, shape = .data$xpos)) +
geom_point(size = geom.size, fill = shape.fill.color) +
scale_y_continuous(labels = scales::percent, breaks = gridbreaks, limits = axis.lim) +
scale_x_discrete(labels = rev(axis.labels)) +
scale_shape_manual(name = legend.title, labels = legend.labels, values = shapes[1:pal.len]) +
scale_colour_manual(name = legend.title, labels = legend.labels, values = geom.colors) +
labs(x = axisTitle.x, y = axisTitle.y, title = title) +
coord_flip()
# Annotate total line?
if (show.total && annotate.total)
p <- p + annotate("rect", xmin = 0.5, xmax = 1.5, ymin = -Inf, ymax = Inf, alpha = 0.15)
p
}