R/plotH-M.R
In theMILOlab/SPATA2: A Toolbox for Spatial Expression Analysis
Defines functions
plotMosaicplot
plot_model_comparison_dotplot
plotLoglik
plotHistogram
Documented in
plotHistogram
plotLoglik
plot_model_comparison_dotplot
plotMosaicplot
# plotH -------------------------------------------------------------------
#' @rdname plotBoxplot
#' @export
plotHistogram <- function(object,
variables,
across = NULL,
across_subset = NULL,
relevel = NULL,
clrp = NULL,
clrp_adjust = NULL,
scales = "free_x",
nrow = NULL,
ncol = NULL,
method_gs = NULL,
normalize = NULL,
verbose = NULL,
...){
hlpr_assign_arguments(object)
var_levels <- base::unique(variables)
spata_df <-
joinWithVariables(
object = object,
spata_df = getSpataDf(object),
variables = variables,
smooth = FALSE,
normalize = normalize
) %>%
dplyr::select(-barcodes, -sample)
confuns::plot_histogram(
df = spata_df,
variables = var_levels,
across = across,
across.subset = across_subset,
relevel = relevel,
scales = scales,
nrow = nrow,
ncol = ncol,
clrp = clrp,
clrp.adjust = clrp_adjust,
verbose = verbose,
...
)
}
# plotI -------------------------------------------------------------------
#' @title Plot histology image
#'
#' @description Plots the histology image with `ggplot2`.
#'
#' @param unit Character value. Units of x- and y-axes. Defaults
#' to *'px'*. If a SI unit is specified, uses [`ggpLayerAxesSI()`] with
#' default parameters. Add the layer manually with `+ ggpLayerAxesSI(...)` for
#' more control.
#' @param ... Additional arguments given to `ggpLayerZoom()`.
#'
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @inheritSection section_dummy Distance measures
#' @inheritSection section_dummy Image visualization with ggplot2
#'
#' @export
#'
#' @inherit ggpLayerRect examples
#'
setGeneric(name = "plotImage", def = function(object, ...){
standardGeneric(f = "plotImage")
})
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "SPATA2",
definition = function(object,
img_name = activeImage(object),
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 0.5,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
...){
deprecated(...)
getSpatialData(object) %>%
plotImage(
object = .,
img_name = img_name,
fragments = fragments,
outline = outline,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
img_alpha = img_alpha,
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
...
)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 0.5,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
unit = "px",
...){
out <-
getHistoImage(object, img_name = img_name) %>%
plotImage(
object = .,
by_section = by_section,
fragments = fragments,
outline = outline,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
img_alpha = img_alpha,
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
...
)
if(unit != "px"){
out <-
out +
ggpLayerAxesSI(object, unit = unit)
}
return(out)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "HistoImage",
definition = function(object,
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 1,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
display_subtitle = FALSE,
...){
layer_coord_equal <- ggplot2::coord_equal(expand = FALSE)
layer_coord_equal$default <- TRUE
if(base::isTRUE(display_subtitle)){
subtitle <- object@name
} else {
subtitle <- NULL
}
out <-
ggplot2::ggplot() +
ggpLayerImage(
object = object,
transform = transform,
scale_fct = scale_fct,
img_alpha = img_alpha
) +
theme_image() +
layer_coord_equal +
ggplot2::labs(
subtitle = subtitle,
x = "Width [pixel]",
y = "Height [pixel]"
)
if(base::isTRUE(outline)){
out <-
out +
ggpLayerTissueOutline(
object = object,
by_section = by_section,
fragments = fragments,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
scale_fct = scale_fct
)
}
if(!base::is.null(xrange) & !base::is.null(yrange)){
out <-
out +
ggpLayerZoom(
object = object,
xrange = xrange,
yrange = yrange,
...
)
}
return(out)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "Image",
definition = function(object, scale_fct = 1, img_alpha = 1, ...){
ggplot2::ggplot() +
ggpLayerImage(object, scale_fct = scale_fct, img_alpha = img_alpha) +
ggplot2::coord_equal() +
theme_image()
}
)
#' @title Plot image with R base plotting
#'
#' @description Plots the histology image as a raster.
#'
#' @inherit argument_dummy params
#'
#' @return A plot that is immediately plotted.
#'
setGeneric(name = "plotImageBase", def = function(object, ...){
standardGeneric(f = "plotImageBase")
})
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "SPATA2",
definition = function(object, xrange = NULL, yrange = NULL, axes = FALSE, ...){
img <- getImageRaster(object, xrange = xrange, yrange = yrange)
coords_df <- getCoordsDf(object)
if(base::is.numeric(xrange)){
coords_df <- dplyr::filter(coords_df, dplyr::between(x = x, left = xrange[1], right = xrange[2]))
}
if(base::is.numeric(yrange)){
coords_df <- dplyr::filter(coords_df, dplyr::between(x = y, left = yrange[1], right = yrange[2]))
}
if(!base::is.numeric(xrange)){
xrange <- getImageRange(object)$x
}
if(!base::is.numeric(yrange)){
yrange <- getImageRange(object)$y
}
graphics::plot.new()
graphics::par(pty = "s", ...)
graphics::plot(
x = coords_df$x,
y = coords_df$y,
col = ggplot2::alpha("white", 0),
axes = axes,
xlab = NA_character_,
ylab = NA_character_,
xlim = xrange,
ylim = yrange
)
graphics::rasterImage(
image = img,
xleft = xrange[1],
xright = xrange[2],
ybottom = yrange[1],
ytop = yrange[2]
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
xrange = NULL,
yrange = NULL,
scale_fct = 1,
axes = TRUE,
...){
plotImageBase(
object = getHistoImage(object, img_name = img_name),
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
axes = axes,
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "HistoImage",
definition = function(object,
img_name = activeImage(object),
xrange = NULL,
yrange = NULL,
scale_fct = 1,
axes = TRUE,
...){
if(!base::is.null(xrange)){
xrange <- as_pixel(input = xrange, object = object)
}
if(!base::is.null(yrange)){
yrange <- as_pixel(input = yrange, object = object)
}
getImage(
object = object,
img_name = img_name,
xrange = xrange,
yrange = yrange
) %>%
plotImageBase(
object = .,
xrange = xrange,
yrange = yrange,
scale_fct = scale_fct,
axes = axes
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "Image",
definition = function(object,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
axes = TRUE,
...){
# scale
image <-
scale_image(
image = object,
scale_fct = scale_fct
)
# get dims if not provided
dims <- base::dim(image)
# if specified xrange and yrange are not scaled!
if(base::is.null(xrange)){
xrange <- c(0, dims[1])
}
if(base::is.null(yrange)){
yrange <- c(0, dims[2])
}
# plot
graphics::plot.new()
graphics::par(pty = "s", ...)
graphics::plot(
x = c(0, dims[1]),
y = c(0, dims[2]),
col = ggplot2::alpha("white", alpha = 0),
xlab = NA_character_,
ylab = NA_character_,
xlim = xrange,
ylim = yrange,
axes = axes
)
graphics::rasterImage(
image = image,
xleft = 0,
xright = dims[1],
ybottom = 0,
ytop = dims[2]
)
})
#' @title Plot pixel content
#'
#' @description Visualizes the results of [`identifyPixelContent()`].
#' \itemize{
#' \item{`plotImageMask()`:}{ Distinguishes pixel in back- and foreground. Foreground being the tissue.}
#' \item{`plotPixelContent():`}{ Visualizes the classification of each pixel in detail.}
#' }
#'
#' @param clr_fg,clr_bg Character values. Color with which to display
#' foreground and background of the mask.
#' @param clr_artefact,clr_fragments,clr_tissue Character values. Colors
#' with which to display the content type if `type = FALSE`.
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @note Always plots the original justification of the image without
#' transformations.
#'
#' @export
#'
setGeneric(name = "plotImageMask", def = function(object, ...){
standardGeneric(f = "plotImageMask")
})
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "SPATA2",
definition = function(object,
img_name = activeImage(object),
clr_fg = "black",
clr_bg = "white"){
getSpatialData(object) %>%
plotImageMask(object = ., img_name = img_name, clr_fg = clr_fg, clr_bg = clr_bg)
}
)
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
clr_fg = "black",
clr_bg = "white"){
getHistoImage(object, img_name = img_name) %>%
plotImageMask(object = ., clr_fg = clr_fg, clr_bg = clr_bg)
}
)
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "HistoImage",
definition = function(object,
clr_fg = "black",
clr_bg = "white"){
pxl_df <-
getPixelDf(object, content = TRUE, transform = FALSE) %>%
dplyr::mutate(
Mask = content != "background",
MasK = base::as.character(Mask)
)
ggplot2::ggplot() +
ggplot2::geom_raster(
data = pxl_df,
mapping = ggplot2::aes(x = width, y = height, fill = Mask)
) +
ggplot2::scale_fill_manual(
values = c("TRUE" = clr_fg, "FALSE" = clr_bg),
guide = "none"
) +
theme_image(panel.border = ggplot2::element_rect(color = "black")) +
ggplot2::coord_equal(expand = FALSE) +
ggplot2::labs(x = "Width [pixel]", y = "Height [pixel]")
})
#' @title Plot histology images
#'
#' @description Plots all images registered the `SPATA2` object.
#'
#' @param img_names Character vector or `NULL`. If character, specifies the images
#' by name. If `NULL`, all images are plotted.
#' @param outline Logical value. If `TRUE`, all images are plotted with the outline
#' identified by `identifyTissueOutline(..., method = "image")`.
#' @param outline_ref Logical value. If `TRUE`, all images plotted with
#' the outline identified by `identifyTissueOutline(..., method = "image")` of the
#' reference image.
#' @param by_section Logical value. If `TRUE`, the default, the outline for every
#' tissue section identified is used.
#' @param line_color,line_color_ref Character value. The transparency for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param line_color,line_color_ref Character value. The color for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param line_size,line_size_ref Character value. The linewidth for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param ... Additional arguments given to `plotImage()`.
#' @param transform Logical value. Should the instructions for image transformation
#' set with [`alignImage()`] be applied?
#' @inherit argument_dummy params
#'
#' @return A ggplot assembled with via `patchwork::wrap_plots()`.
#'
#' @inheritSection section_dummy Distance measures
#' @inheritSection section_dummy Image visualization with ggplot2
#'
#' @seealso [`getImageDirectories()`], [`identifyPixelContent()`], [`identifyTissueOutline()`]
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' plotImages(object)
#'
#' @export
setGeneric(name = "plotImages", def = function(object, ...){
standardGeneric(f = "plotImages")
})
#' @rdname plotImages
#' @export
setMethod(
f = "plotImages",
signature = "SPATA2",
definition = function(object,
img_names = getImageNames(object),
by_section = TRUE,
outline = FALSE,
outline_ref = FALSE,
fragments = TRUE,
line_alpha = line_alpha_ref*0.75,
line_alpha_ref = 1,
line_color = "black",
line_color_ref = "red",
line_size = 0.5,
line_size_ref = line_size * 1.5,
transform = TRUE,
img_alpha = 1,
against_ref = FALSE,
alignment_eval = FALSE,
ncol = NULL,
nrow = NULL,
verbose = TRUE){
hlpr_assign_arguments(object)
getSpatialData(object) %>%
plotImages(
object = .,
img_names = img_names,
ncol = ncol,
nrow = nrow,
image = TRUE,
outline = outline,
outline_ref = outline_ref,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha,
line_alpha_ref = line_alpha_ref,
line_color = line_color,
line_color_ref = line_color_ref,
line_size = line_size,
line_size_ref = line_size_ref,
transform = transform,
img_alpha = img_alpha,
against_ref = against_ref,
alignment_eval = alignment_eval,
verbose = verbose
)
}
)
#' @rdname plotImages
#' @export
setMethod(
f = "plotImages",
signature = "SpatialData",
definition = function(object,
img_names = NULL,
ncol = NULL,
nrow = NULL,
image = TRUE,
outline = FALSE,
outline_ref = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = line_alpha_ref*0.75,
line_alpha_ref = 1,
line_color = "black",
line_color_ref = "red",
line_size = 0.5,
line_size_ref = line_size * 1.5,
transform = TRUE,
img_alpha = 1,
against_ref = FALSE,
alignment_eval = FALSE,
verbose = TRUE){
ref_name <- object@name_img_ref
if(base::is.null(img_names)){
img_names <- getImageNames(object)
} else {
confuns::check_one_of(
input = img_names,
against = getImageNames(object)
)
}
if(base::isTRUE(against_ref) & !(ref_name %in% img_names)){
img_names <- base::unique(c(img_names, ref_name))
}
image_list <-
purrr::map(
.x = img_names,
.f = function(name){
# adjust title
if(name == getHistoImage(object)@name){
if(name == ref_name){
title_add <- "(Active Image, Reference Image)"
} else {
title_add <- "(Active Image)"
}
hist_img <- getHistoImage(object)
} else if(name == ref_name) {
title_add <- "(Reference Image)"
hist_img <- getHistoImageRef(object)
} else {
hist_img <- getHistoImage(object, img_name = name)
title_add <- ""
}
if(base::isTRUE(alignment_eval)){
if(base::isTRUE(hist_img@aligned) & base::isTRUE(transform)){
ares <- base::round(hist_img@overlap[[2]], digits = 2)*100
title_add <- stringr::str_c(title_add, " - Aligned (", ares, "%)")
} else if(base::isTRUE(transform) & name != ref_name){
title_add <- stringr::str_c(title_add, " - Not aligned")
} else {
# title_add stays as is
}
}
title <- stringr::str_c(hist_img@name, " ", title_add)
p <-
ggplot2::ggplot() +
theme_image() +
ggplot2::coord_equal(expand = FALSE) +
ggplot2::labs(subtitle = title, x = NULL, y = NULL)
transform_checked <- transform | name == ref_name
# first add image
if(base::isTRUE(image)){
# ggpLayerImage loads the image if slot @image is empty
p <-
p +
ggpLayerImage(
object = hist_img,
transform = transform_checked,
img_alpha = img_alpha
)
}
# second add reference outline in specified color
if(base::isTRUE(outline_ref)){
hist_img_ref <- getHistoImageRef(object)
scale_fct <-
compute_img_scale_fct(
hist_img1 = hist_img_ref,
hist_img2 = hist_img
)
p <-
p +
ggpLayerTissueOutline(
object = hist_img_ref,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha_ref,
line_color = line_color_ref,
line_size = line_size_ref,
transform = TRUE, # no transformation needed as its the reference
scale_fct = scale_fct
)
}
# third add image outline allow normal outline of reference if needed
if((base::isTRUE(outline) & name != ref_name) |
(base::isTRUE(outline) & base::isFALSE(outline_ref) & name == ref_name)){
if(containsTissueOutline(hist_img)){
p <-
p +
ggpLayerTissueOutline(
object = hist_img,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
transform = transform_checked,
scale_fct = 1
)
} else {
warning(glue::glue("No tissue outline identified for image '{hist_img@name}'."))
}
}
return(p)
}
) %>%
purrr::set_names(nm = img_names)
if(ref_name %in% img_names){
image_list <- image_list[c(ref_name, img_names[img_names != ref_name])]
}
if(base::isTRUE(against_ref) && ref_name %in% img_names){
p1 <- image_list[[ref_name]]
p2 <-
confuns::lselect(image_list, -{{ref_name}}) %>%
patchwork::wrap_plots(ncol = ncol, nrow = nrow)
out <- p1|p2
} else {
out <- patchwork::wrap_plots(image_list, ncol = ncol, nrow = nrow)
}
return(out)
}
)
# plotL -------------------------------------------------------------------
#' @title Plot Bayes Space logliks
#'
#' @description Visualizes the results of `BayesSpace::qTune()` to determine
#' the optimal number of clusters.
#'
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @details For this function to work the results of [`runBayesSpaceClustering()`]
#' are required.
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' # this might take some time...
#' object <- runBayesSpaceClustering(object, name = "bspace", qs = 3:15)
#'
#' plotLoglik(object)
#'
#' @export
#'
plotLoglik <- function(object, elbow = TRUE){
ma <- getAssay(object, assay_name = "transcriptomics")
df <- ma@analysis$bayes_space$logliks
if(purrr::is_empty(df)){
stop("No logliks found. Use `runBayesSpaceClustering()` first.")
}
if(base::isTRUE(elbow)){
elbow_add_on <- ggplot2::geom_vline(xintercept = find_elbow_point(df))
} else {
elbow_add_on <- NULL
}
ggplot2::ggplot(data = df, mapping = ggplot2::aes(x = q, y = -loglik)) +
elbow_add_on +
ggplot2::geom_path() +
ggplot2::geom_point() +
ggplot2::theme_minimal()
}
# plotM -------------------------------------------------------------------
#' @title Plot Model Comparison Dotplot
#'
#' @description Overview dotplot to compare screening results of selected models.
#'
#' @param data Output of `spatialAnnotationScreening()` or `spatialTrajectoryScreening()`
#' in the form of `screening_results@results`.
#' @param model_remove (Optional) A character vector specifying models to remove from the plot.
#' @param scale_factor A numeric value to scale the point sizes. Default is 1.
#' @param pt_size The size of the points in the plot. Default is 0.1.
#' @param label_vars The number of top variables to label for each model. Default is 2.
#' @param label_size The size of the labels. Default is 4.
#' @param threshold_pval The p-value threshold for coloring points. Default is 0.05.
#' @param label_color The color of labels. Default is "#4d4d4d".
#' @param x_label The label for the x-axis. Default is "Gene-model correlation".
#'
#' @return A dotplot comparing model screening results.
#'
#' @examples
#' # Example usage:
#' pl <- plot_model_comparison_dotplot(screening_1@results, model_remove = c("peak"), label_vars = 3)
#' pl + coord_cartesian(xlim = c(0.5, 1))
#'
#' @keywords internal
#'
plot_model_comparison_dotplot <- function(data,
eval = "mae",
pval = "p_value",
model_subset = NULL,
model_remove = NULL,
scale_factor = 1,
pt_size = 1.5,
label_vars = 2,
label_size = 4,
threshold_pval = 0.05,
label_color = "#4d4d4d"
) {
data <- data[data$corr >= 0,]
# Scale point size based on p-values
max_size <- scale_factor * max(-log10(data[[pval]]))
if(!base::is.null(model_remove)){
data <- data[!base::grepl(paste(model_remove, collapse = "|"), data$models), ]
}
if(!base::is.null(model_subset)){
data <- dplyr::filter(data, stringr::str_detect(models, pattern = model_subset))
}
# Select top variables to label for each model
data <-
dplyr::group_by(data, variables) %>%
dplyr::slice_min(!!rlang::sym(eval), n = 1)
labeled_data <-
dplyr::group_by(data, models) %>%
dplyr::slice_min(!!rlang::sym(eval), n = label_vars, with_ties = FALSE)
ggplot2::ggplot(
data = data,
mapping = ggplot2::aes(x = .data[[eval]], y = reorder(models, -log10(.data[[pval]])))
) +
ggplot2::geom_point(
data = data,
size = pt_size,
color = dplyr::if_else(data[[pval]] < threshold_pval, "#ff7256", "grey50")
) +
ggplot2::scale_color_manual(values = c("grey50", "#ff7256"), labels = c(paste0(">= ", threshold_pval), paste0("< ", threshold_pval))) +
ggplot2::scale_size_continuous(range = c(pt_size, max_size)) +
#ggplot2::scale_x_continuous(limits = c(0,1)) +
ggplot2::labs(x = eval, y = "Model") +
ggplot2::theme_minimal() +
ggplot2::guides(size = guide_legend(override.aes = list(size = c(pt_size, mean(c(pt_size, max_size)), max_size)))) + # Adjust dot size in legend
ggplot2::theme(panel.grid.major.y = element_blank()) +
ggrepel::geom_text_repel(
data = labeled_data,
mapping = ggplot2::aes(label = ifelse(((.data[[eval]] >= 0)), variables, '')),
color = label_color,
size = label_size
)
}
#' @title Plot mosaic plot
#'
#' @description Plots a mosaic plot of two grouping variables.
#'
#' @param grouping Character value. The grouping variable that is
#' plotted on the x-axis.
#' @param fill_by Character value. The grouping variable that is used to
#' fill the mosaic.
#'
#' @inherit confuns::plot_mosaic params
#' @inherit argument_dummy params
#' @inherit plotBarchart params return
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' plotMosaicPlot(object, grouping = "seurat_clusters", fill_by = "bayes_space")
#'
#' @export
#'
plotMosaicplot <- function(object,
grouping,
fill_by,
clrp = NULL,
clrp_adjust = NULL,
...){
require(ggmosaic)
deprecated(...)
hlpr_assign_arguments(object)
confuns::check_one_of(
input = c(grouping, fill_by),
against = getGroupingOptions(object),
suggest = TRUE
)
df <- getMetaDf(object)
confuns::plot_mosaic(
df = df,
x = grouping,
fill.by = fill_by,
clrp = clrp,
clrp.adjust = clrp_adjust
) +
ggplot2::theme(
panel.background = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank()
) +
ggplot2::labs(
x = grouping,
fill = fill_by
)
}
theMILOlab/SPATA2 documentation built on Feb. 8, 2025, 11:41 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions plotMosaicplot plot_model_comparison_dotplot plotLoglik plotHistogram
Documented in plotHistogram plotLoglik plot_model_comparison_dotplot plotMosaicplot
# plotH -------------------------------------------------------------------
#' @rdname plotBoxplot
#' @export
plotHistogram <- function(object,
variables,
across = NULL,
across_subset = NULL,
relevel = NULL,
clrp = NULL,
clrp_adjust = NULL,
scales = "free_x",
nrow = NULL,
ncol = NULL,
method_gs = NULL,
normalize = NULL,
verbose = NULL,
...){
hlpr_assign_arguments(object)
var_levels <- base::unique(variables)
spata_df <-
joinWithVariables(
object = object,
spata_df = getSpataDf(object),
variables = variables,
smooth = FALSE,
normalize = normalize
) %>%
dplyr::select(-barcodes, -sample)
confuns::plot_histogram(
df = spata_df,
variables = var_levels,
across = across,
across.subset = across_subset,
relevel = relevel,
scales = scales,
nrow = nrow,
ncol = ncol,
clrp = clrp,
clrp.adjust = clrp_adjust,
verbose = verbose,
...
)
}
# plotI -------------------------------------------------------------------
#' @title Plot histology image
#'
#' @description Plots the histology image with `ggplot2`.
#'
#' @param unit Character value. Units of x- and y-axes. Defaults
#' to *'px'*. If a SI unit is specified, uses [`ggpLayerAxesSI()`] with
#' default parameters. Add the layer manually with `+ ggpLayerAxesSI(...)` for
#' more control.
#' @param ... Additional arguments given to `ggpLayerZoom()`.
#'
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @inheritSection section_dummy Distance measures
#' @inheritSection section_dummy Image visualization with ggplot2
#'
#' @export
#'
#' @inherit ggpLayerRect examples
#'
setGeneric(name = "plotImage", def = function(object, ...){
standardGeneric(f = "plotImage")
})
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "SPATA2",
definition = function(object,
img_name = activeImage(object),
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 0.5,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
...){
deprecated(...)
getSpatialData(object) %>%
plotImage(
object = .,
img_name = img_name,
fragments = fragments,
outline = outline,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
img_alpha = img_alpha,
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
...
)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 0.5,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
unit = "px",
...){
out <-
getHistoImage(object, img_name = img_name) %>%
plotImage(
object = .,
by_section = by_section,
fragments = fragments,
outline = outline,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
img_alpha = img_alpha,
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
...
)
if(unit != "px"){
out <-
out +
ggpLayerAxesSI(object, unit = unit)
}
return(out)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "HistoImage",
definition = function(object,
outline = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = 0.9,
line_color = "black",
line_size = 1,
line_type = "solid",
transform = TRUE,
img_alpha = 1,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
display_subtitle = FALSE,
...){
layer_coord_equal <- ggplot2::coord_equal(expand = FALSE)
layer_coord_equal$default <- TRUE
if(base::isTRUE(display_subtitle)){
subtitle <- object@name
} else {
subtitle <- NULL
}
out <-
ggplot2::ggplot() +
ggpLayerImage(
object = object,
transform = transform,
scale_fct = scale_fct,
img_alpha = img_alpha
) +
theme_image() +
layer_coord_equal +
ggplot2::labs(
subtitle = subtitle,
x = "Width [pixel]",
y = "Height [pixel]"
)
if(base::isTRUE(outline)){
out <-
out +
ggpLayerTissueOutline(
object = object,
by_section = by_section,
fragments = fragments,
transform = transform,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
line_type = line_type,
scale_fct = scale_fct
)
}
if(!base::is.null(xrange) & !base::is.null(yrange)){
out <-
out +
ggpLayerZoom(
object = object,
xrange = xrange,
yrange = yrange,
...
)
}
return(out)
}
)
#' @rdname plotImage
#' @export
setMethod(
f = "plotImage",
signature = "Image",
definition = function(object, scale_fct = 1, img_alpha = 1, ...){
ggplot2::ggplot() +
ggpLayerImage(object, scale_fct = scale_fct, img_alpha = img_alpha) +
ggplot2::coord_equal() +
theme_image()
}
)
#' @title Plot image with R base plotting
#'
#' @description Plots the histology image as a raster.
#'
#' @inherit argument_dummy params
#'
#' @return A plot that is immediately plotted.
#'
setGeneric(name = "plotImageBase", def = function(object, ...){
standardGeneric(f = "plotImageBase")
})
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "SPATA2",
definition = function(object, xrange = NULL, yrange = NULL, axes = FALSE, ...){
img <- getImageRaster(object, xrange = xrange, yrange = yrange)
coords_df <- getCoordsDf(object)
if(base::is.numeric(xrange)){
coords_df <- dplyr::filter(coords_df, dplyr::between(x = x, left = xrange[1], right = xrange[2]))
}
if(base::is.numeric(yrange)){
coords_df <- dplyr::filter(coords_df, dplyr::between(x = y, left = yrange[1], right = yrange[2]))
}
if(!base::is.numeric(xrange)){
xrange <- getImageRange(object)$x
}
if(!base::is.numeric(yrange)){
yrange <- getImageRange(object)$y
}
graphics::plot.new()
graphics::par(pty = "s", ...)
graphics::plot(
x = coords_df$x,
y = coords_df$y,
col = ggplot2::alpha("white", 0),
axes = axes,
xlab = NA_character_,
ylab = NA_character_,
xlim = xrange,
ylim = yrange
)
graphics::rasterImage(
image = img,
xleft = xrange[1],
xright = xrange[2],
ybottom = yrange[1],
ytop = yrange[2]
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
xrange = NULL,
yrange = NULL,
scale_fct = 1,
axes = TRUE,
...){
plotImageBase(
object = getHistoImage(object, img_name = img_name),
scale_fct = scale_fct,
xrange = xrange,
yrange = yrange,
axes = axes,
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "HistoImage",
definition = function(object,
img_name = activeImage(object),
xrange = NULL,
yrange = NULL,
scale_fct = 1,
axes = TRUE,
...){
if(!base::is.null(xrange)){
xrange <- as_pixel(input = xrange, object = object)
}
if(!base::is.null(yrange)){
yrange <- as_pixel(input = yrange, object = object)
}
getImage(
object = object,
img_name = img_name,
xrange = xrange,
yrange = yrange
) %>%
plotImageBase(
object = .,
xrange = xrange,
yrange = yrange,
scale_fct = scale_fct,
axes = axes
)
}
)
#' @rdname plotImageBase
#' @export
setMethod(
f = "plotImageBase",
signature = "Image",
definition = function(object,
scale_fct = 1,
xrange = NULL,
yrange = NULL,
axes = TRUE,
...){
# scale
image <-
scale_image(
image = object,
scale_fct = scale_fct
)
# get dims if not provided
dims <- base::dim(image)
# if specified xrange and yrange are not scaled!
if(base::is.null(xrange)){
xrange <- c(0, dims[1])
}
if(base::is.null(yrange)){
yrange <- c(0, dims[2])
}
# plot
graphics::plot.new()
graphics::par(pty = "s", ...)
graphics::plot(
x = c(0, dims[1]),
y = c(0, dims[2]),
col = ggplot2::alpha("white", alpha = 0),
xlab = NA_character_,
ylab = NA_character_,
xlim = xrange,
ylim = yrange,
axes = axes
)
graphics::rasterImage(
image = image,
xleft = 0,
xright = dims[1],
ybottom = 0,
ytop = dims[2]
)
})
#' @title Plot pixel content
#'
#' @description Visualizes the results of [`identifyPixelContent()`].
#' \itemize{
#' \item{`plotImageMask()`:}{ Distinguishes pixel in back- and foreground. Foreground being the tissue.}
#' \item{`plotPixelContent():`}{ Visualizes the classification of each pixel in detail.}
#' }
#'
#' @param clr_fg,clr_bg Character values. Color with which to display
#' foreground and background of the mask.
#' @param clr_artefact,clr_fragments,clr_tissue Character values. Colors
#' with which to display the content type if `type = FALSE`.
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @note Always plots the original justification of the image without
#' transformations.
#'
#' @export
#'
setGeneric(name = "plotImageMask", def = function(object, ...){
standardGeneric(f = "plotImageMask")
})
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "SPATA2",
definition = function(object,
img_name = activeImage(object),
clr_fg = "black",
clr_bg = "white"){
getSpatialData(object) %>%
plotImageMask(object = ., img_name = img_name, clr_fg = clr_fg, clr_bg = clr_bg)
}
)
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "SpatialData",
definition = function(object,
img_name = activeImage(object),
clr_fg = "black",
clr_bg = "white"){
getHistoImage(object, img_name = img_name) %>%
plotImageMask(object = ., clr_fg = clr_fg, clr_bg = clr_bg)
}
)
#' @rdname plotImageMask
#' @export
setMethod(
f = "plotImageMask",
signature = "HistoImage",
definition = function(object,
clr_fg = "black",
clr_bg = "white"){
pxl_df <-
getPixelDf(object, content = TRUE, transform = FALSE) %>%
dplyr::mutate(
Mask = content != "background",
MasK = base::as.character(Mask)
)
ggplot2::ggplot() +
ggplot2::geom_raster(
data = pxl_df,
mapping = ggplot2::aes(x = width, y = height, fill = Mask)
) +
ggplot2::scale_fill_manual(
values = c("TRUE" = clr_fg, "FALSE" = clr_bg),
guide = "none"
) +
theme_image(panel.border = ggplot2::element_rect(color = "black")) +
ggplot2::coord_equal(expand = FALSE) +
ggplot2::labs(x = "Width [pixel]", y = "Height [pixel]")
})
#' @title Plot histology images
#'
#' @description Plots all images registered the `SPATA2` object.
#'
#' @param img_names Character vector or `NULL`. If character, specifies the images
#' by name. If `NULL`, all images are plotted.
#' @param outline Logical value. If `TRUE`, all images are plotted with the outline
#' identified by `identifyTissueOutline(..., method = "image")`.
#' @param outline_ref Logical value. If `TRUE`, all images plotted with
#' the outline identified by `identifyTissueOutline(..., method = "image")` of the
#' reference image.
#' @param by_section Logical value. If `TRUE`, the default, the outline for every
#' tissue section identified is used.
#' @param line_color,line_color_ref Character value. The transparency for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param line_color,line_color_ref Character value. The color for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param line_size,line_size_ref Character value. The linewidth for the outline if
#' `outline = TRUE` and/or `outline_ref = TRUE`.
#' @param ... Additional arguments given to `plotImage()`.
#' @param transform Logical value. Should the instructions for image transformation
#' set with [`alignImage()`] be applied?
#' @inherit argument_dummy params
#'
#' @return A ggplot assembled with via `patchwork::wrap_plots()`.
#'
#' @inheritSection section_dummy Distance measures
#' @inheritSection section_dummy Image visualization with ggplot2
#'
#' @seealso [`getImageDirectories()`], [`identifyPixelContent()`], [`identifyTissueOutline()`]
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' plotImages(object)
#'
#' @export
setGeneric(name = "plotImages", def = function(object, ...){
standardGeneric(f = "plotImages")
})
#' @rdname plotImages
#' @export
setMethod(
f = "plotImages",
signature = "SPATA2",
definition = function(object,
img_names = getImageNames(object),
by_section = TRUE,
outline = FALSE,
outline_ref = FALSE,
fragments = TRUE,
line_alpha = line_alpha_ref*0.75,
line_alpha_ref = 1,
line_color = "black",
line_color_ref = "red",
line_size = 0.5,
line_size_ref = line_size * 1.5,
transform = TRUE,
img_alpha = 1,
against_ref = FALSE,
alignment_eval = FALSE,
ncol = NULL,
nrow = NULL,
verbose = TRUE){
hlpr_assign_arguments(object)
getSpatialData(object) %>%
plotImages(
object = .,
img_names = img_names,
ncol = ncol,
nrow = nrow,
image = TRUE,
outline = outline,
outline_ref = outline_ref,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha,
line_alpha_ref = line_alpha_ref,
line_color = line_color,
line_color_ref = line_color_ref,
line_size = line_size,
line_size_ref = line_size_ref,
transform = transform,
img_alpha = img_alpha,
against_ref = against_ref,
alignment_eval = alignment_eval,
verbose = verbose
)
}
)
#' @rdname plotImages
#' @export
setMethod(
f = "plotImages",
signature = "SpatialData",
definition = function(object,
img_names = NULL,
ncol = NULL,
nrow = NULL,
image = TRUE,
outline = FALSE,
outline_ref = FALSE,
by_section = TRUE,
fragments = TRUE,
line_alpha = line_alpha_ref*0.75,
line_alpha_ref = 1,
line_color = "black",
line_color_ref = "red",
line_size = 0.5,
line_size_ref = line_size * 1.5,
transform = TRUE,
img_alpha = 1,
against_ref = FALSE,
alignment_eval = FALSE,
verbose = TRUE){
ref_name <- object@name_img_ref
if(base::is.null(img_names)){
img_names <- getImageNames(object)
} else {
confuns::check_one_of(
input = img_names,
against = getImageNames(object)
)
}
if(base::isTRUE(against_ref) & !(ref_name %in% img_names)){
img_names <- base::unique(c(img_names, ref_name))
}
image_list <-
purrr::map(
.x = img_names,
.f = function(name){
# adjust title
if(name == getHistoImage(object)@name){
if(name == ref_name){
title_add <- "(Active Image, Reference Image)"
} else {
title_add <- "(Active Image)"
}
hist_img <- getHistoImage(object)
} else if(name == ref_name) {
title_add <- "(Reference Image)"
hist_img <- getHistoImageRef(object)
} else {
hist_img <- getHistoImage(object, img_name = name)
title_add <- ""
}
if(base::isTRUE(alignment_eval)){
if(base::isTRUE(hist_img@aligned) & base::isTRUE(transform)){
ares <- base::round(hist_img@overlap[[2]], digits = 2)*100
title_add <- stringr::str_c(title_add, " - Aligned (", ares, "%)")
} else if(base::isTRUE(transform) & name != ref_name){
title_add <- stringr::str_c(title_add, " - Not aligned")
} else {
# title_add stays as is
}
}
title <- stringr::str_c(hist_img@name, " ", title_add)
p <-
ggplot2::ggplot() +
theme_image() +
ggplot2::coord_equal(expand = FALSE) +
ggplot2::labs(subtitle = title, x = NULL, y = NULL)
transform_checked <- transform | name == ref_name
# first add image
if(base::isTRUE(image)){
# ggpLayerImage loads the image if slot @image is empty
p <-
p +
ggpLayerImage(
object = hist_img,
transform = transform_checked,
img_alpha = img_alpha
)
}
# second add reference outline in specified color
if(base::isTRUE(outline_ref)){
hist_img_ref <- getHistoImageRef(object)
scale_fct <-
compute_img_scale_fct(
hist_img1 = hist_img_ref,
hist_img2 = hist_img
)
p <-
p +
ggpLayerTissueOutline(
object = hist_img_ref,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha_ref,
line_color = line_color_ref,
line_size = line_size_ref,
transform = TRUE, # no transformation needed as its the reference
scale_fct = scale_fct
)
}
# third add image outline allow normal outline of reference if needed
if((base::isTRUE(outline) & name != ref_name) |
(base::isTRUE(outline) & base::isFALSE(outline_ref) & name == ref_name)){
if(containsTissueOutline(hist_img)){
p <-
p +
ggpLayerTissueOutline(
object = hist_img,
by_section = by_section,
fragments = fragments,
line_alpha = line_alpha,
line_color = line_color,
line_size = line_size,
transform = transform_checked,
scale_fct = 1
)
} else {
warning(glue::glue("No tissue outline identified for image '{hist_img@name}'."))
}
}
return(p)
}
) %>%
purrr::set_names(nm = img_names)
if(ref_name %in% img_names){
image_list <- image_list[c(ref_name, img_names[img_names != ref_name])]
}
if(base::isTRUE(against_ref) && ref_name %in% img_names){
p1 <- image_list[[ref_name]]
p2 <-
confuns::lselect(image_list, -{{ref_name}}) %>%
patchwork::wrap_plots(ncol = ncol, nrow = nrow)
out <- p1|p2
} else {
out <- patchwork::wrap_plots(image_list, ncol = ncol, nrow = nrow)
}
return(out)
}
)
# plotL -------------------------------------------------------------------
#' @title Plot Bayes Space logliks
#'
#' @description Visualizes the results of `BayesSpace::qTune()` to determine
#' the optimal number of clusters.
#'
#' @inherit argument_dummy params
#' @inherit ggplot_dummy return
#'
#' @details For this function to work the results of [`runBayesSpaceClustering()`]
#' are required.
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' # this might take some time...
#' object <- runBayesSpaceClustering(object, name = "bspace", qs = 3:15)
#'
#' plotLoglik(object)
#'
#' @export
#'
plotLoglik <- function(object, elbow = TRUE){
ma <- getAssay(object, assay_name = "transcriptomics")
df <- ma@analysis$bayes_space$logliks
if(purrr::is_empty(df)){
stop("No logliks found. Use `runBayesSpaceClustering()` first.")
}
if(base::isTRUE(elbow)){
elbow_add_on <- ggplot2::geom_vline(xintercept = find_elbow_point(df))
} else {
elbow_add_on <- NULL
}
ggplot2::ggplot(data = df, mapping = ggplot2::aes(x = q, y = -loglik)) +
elbow_add_on +
ggplot2::geom_path() +
ggplot2::geom_point() +
ggplot2::theme_minimal()
}
# plotM -------------------------------------------------------------------
#' @title Plot Model Comparison Dotplot
#'
#' @description Overview dotplot to compare screening results of selected models.
#'
#' @param data Output of `spatialAnnotationScreening()` or `spatialTrajectoryScreening()`
#' in the form of `screening_results@results`.
#' @param model_remove (Optional) A character vector specifying models to remove from the plot.
#' @param scale_factor A numeric value to scale the point sizes. Default is 1.
#' @param pt_size The size of the points in the plot. Default is 0.1.
#' @param label_vars The number of top variables to label for each model. Default is 2.
#' @param label_size The size of the labels. Default is 4.
#' @param threshold_pval The p-value threshold for coloring points. Default is 0.05.
#' @param label_color The color of labels. Default is "#4d4d4d".
#' @param x_label The label for the x-axis. Default is "Gene-model correlation".
#'
#' @return A dotplot comparing model screening results.
#'
#' @examples
#' # Example usage:
#' pl <- plot_model_comparison_dotplot(screening_1@results, model_remove = c("peak"), label_vars = 3)
#' pl + coord_cartesian(xlim = c(0.5, 1))
#'
#' @keywords internal
#'
plot_model_comparison_dotplot <- function(data,
eval = "mae",
pval = "p_value",
model_subset = NULL,
model_remove = NULL,
scale_factor = 1,
pt_size = 1.5,
label_vars = 2,
label_size = 4,
threshold_pval = 0.05,
label_color = "#4d4d4d"
) {
data <- data[data$corr >= 0,]
# Scale point size based on p-values
max_size <- scale_factor * max(-log10(data[[pval]]))
if(!base::is.null(model_remove)){
data <- data[!base::grepl(paste(model_remove, collapse = "|"), data$models), ]
}
if(!base::is.null(model_subset)){
data <- dplyr::filter(data, stringr::str_detect(models, pattern = model_subset))
}
# Select top variables to label for each model
data <-
dplyr::group_by(data, variables) %>%
dplyr::slice_min(!!rlang::sym(eval), n = 1)
labeled_data <-
dplyr::group_by(data, models) %>%
dplyr::slice_min(!!rlang::sym(eval), n = label_vars, with_ties = FALSE)
ggplot2::ggplot(
data = data,
mapping = ggplot2::aes(x = .data[[eval]], y = reorder(models, -log10(.data[[pval]])))
) +
ggplot2::geom_point(
data = data,
size = pt_size,
color = dplyr::if_else(data[[pval]] < threshold_pval, "#ff7256", "grey50")
) +
ggplot2::scale_color_manual(values = c("grey50", "#ff7256"), labels = c(paste0(">= ", threshold_pval), paste0("< ", threshold_pval))) +
ggplot2::scale_size_continuous(range = c(pt_size, max_size)) +
#ggplot2::scale_x_continuous(limits = c(0,1)) +
ggplot2::labs(x = eval, y = "Model") +
ggplot2::theme_minimal() +
ggplot2::guides(size = guide_legend(override.aes = list(size = c(pt_size, mean(c(pt_size, max_size)), max_size)))) + # Adjust dot size in legend
ggplot2::theme(panel.grid.major.y = element_blank()) +
ggrepel::geom_text_repel(
data = labeled_data,
mapping = ggplot2::aes(label = ifelse(((.data[[eval]] >= 0)), variables, '')),
color = label_color,
size = label_size
)
}
#' @title Plot mosaic plot
#'
#' @description Plots a mosaic plot of two grouping variables.
#'
#' @param grouping Character value. The grouping variable that is
#' plotted on the x-axis.
#' @param fill_by Character value. The grouping variable that is used to
#' fill the mosaic.
#'
#' @inherit confuns::plot_mosaic params
#' @inherit argument_dummy params
#' @inherit plotBarchart params return
#'
#' @examples
#' library(SPATA2)
#'
#' data("example_data")
#'
#' object <- example_data$object_UKF275T_diet
#'
#' plotMosaicPlot(object, grouping = "seurat_clusters", fill_by = "bayes_space")
#'
#' @export
#'
plotMosaicplot <- function(object,
grouping,
fill_by,
clrp = NULL,
clrp_adjust = NULL,
...){
require(ggmosaic)
deprecated(...)
hlpr_assign_arguments(object)
confuns::check_one_of(
input = c(grouping, fill_by),
against = getGroupingOptions(object),
suggest = TRUE
)
df <- getMetaDf(object)
confuns::plot_mosaic(
df = df,
x = grouping,
fill.by = fill_by,
clrp = clrp,
clrp.adjust = clrp_adjust
) +
ggplot2::theme(
panel.background = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank()
) +
ggplot2::labs(
x = grouping,
fill = fill_by
)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.