I have a grid composed of several ggplots and want to add an x axis, where axis ticks and annotations are added between the plots. I could not came up with a better solution than to create a custom plot for the axis and adding it below with arrangeGrob. But they do not align with the plots (I draw arrows where the numbers should be). Also there is a large white space below which I don't want.
I will also need an analogue for the y-axis.
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(grid)
# Create a grid with several ggplots
p <-
ggplot(mtcars, aes(wt, mpg)) +
geom_point() +
theme_transparent() +
theme(plot.background = element_rect(color = "black"))
main.plot <- arrangeGrob(p, p, p, p, p, p, p, p, ncol = 4, nrow = 2)
# grid.draw(main.plot)
# Now add an x axis to the main plot
x.breaks <- c(0, 1, 2.5, 8, 10)
p.axis <- ggplot() +
ylim(-0.1, 0) +
xlim(1, length(x.breaks)) +
ggpubr::theme_transparent()
for (i in seq_along(x.breaks)) {
p.axis <- p.axis +
geom_text(aes_(x = i, y = -0.01, label = as.character(x.breaks[i])), color = "red")
}
# p.axis
final.plot <- arrangeGrob(main.plot, p.axis, nrow = 2)
grid.draw(final.plot)
Any help appreciated.
Note: In the code below, I assume each plot in your grid has equal width / height, & used equally spaced label positions. If that's not the case, you'll have to adjust the positions yourself.
Adding x-axis to main.plot:
library(gtable)
# create additional row below main plot
# height may vary, depending on your actual plot dimensions
main.plot.x <- gtable_add_rows(main.plot, heights = unit(20, "points"))
# optional: check results to verify position of the new row
dev.off(); gtable_show_layout(main.plot.x)
# create x-axis labels as a text grob
x.axis.grob <- textGrob(label = x.breaks,
x = unit(seq(0, 1, length.out = length(x.breaks)), "npc"),
y = unit(0.75, "npc"),
just = "top")
# insert text grob
main.plot.x <- gtable_add_grob(main.plot.x,
x.axis.grob,
t = nrow(main.plot.x),
l = 1,
r = ncol(main.plot.x),
clip = "off")
# check results
dev.off(); grid.draw(main.plot.x)
You can do the same for the y-axis:
# create additional col
main.plot.xy <- gtable_add_cols(main.plot.x, widths = unit(20, "points"), pos = 0)
# create y-axis labels as a text grob
y.breaks <- c("a", "b", "c") # placeholder, since this wasn't specified in the question
y.axis.grob <- textGrob(label = y.breaks,
x = unit(0.75, "npc"),
y = unit(seq(0, 1, length.out = length(y.breaks)), "npc"),
just = "right")
# add text grob into main plot's gtable
main.plot.xy <- gtable_add_grob(main.plot.xy,
y.axis.grob,
t = 1,
l = 1,
b = nrow(main.plot.xy) - 1,
clip = "off")
# check results
dev.off(); grid.draw(main.plot.xy)
(Note that the above order of x-axis followed by y-axis should not be switched blindly. If you are adding rows / columns, it's good habit to use gtable_show_layout() frequently to check the latest gtable object dimensions, & ensure that you are inserting new grobs into the right cells.)
Finally, let's add some buffer on all sides, so that the labels & plot borders don't get cut off:
final.plot <- gtable_add_padding(main.plot.xy,
padding = unit(20, "points"))
dev.off(); grid.draw(final.plot)
Related
I'd like to make a plot using ggplot2 where some of the fill values are clipped, i.e. values above or below the limits of the color scale are displayed as the minimum/maximum color. I can get this to work like this, using a combination of limit and oob (out of bounds):
library(ggplot2)
library(scales)
ggplot() + ... + scale_fill_viridis(na.value="white", limit=c(0, 10), oob=squish)
But there is no information in the colorbar that indicates there are values present outside of the limits.
How can I reproduce this matplotlib example in ggplot: https://stackoverflow.com/a/32072348
Specifically, how to get the triangles at the end of the colorbar?
As far as I'm aware there is not a package that implements triangle ends for colourbars in ggplot2 (but please let me know if there is!). However, we can implement our own. We'd need a constructor for our custom guide and a way to draw it. Most of the stuff is already implemented in guide_colourbar() and methods for their class, so what we need to do is just tag on our own class and expand the guide_gengrob method. The code below should work for vertically oriented colourbars. You'd need to know some stuff about the grid package and gtable package to follow along.
library(ggplot2)
library(gtable)
library(grid)
my_triangle_colourbar <- function(...) {
guide <- guide_colourbar(...)
class(guide) <- c("my_triangle_colourbar", class(guide))
guide
}
guide_gengrob.my_triangle_colourbar <- function(...) {
# First draw normal colourbar
guide <- NextMethod()
# Extract bar / colours
is_bar <- grep("^bar$", guide$layout$name)
bar <- guide$grobs[[is_bar]]
extremes <- c(bar$raster[1], bar$raster[length(bar$raster)])
# Extract size
width <- guide$widths[guide$layout$l[is_bar]]
height <- guide$heights[guide$layout$t[is_bar]]
short <- min(convertUnit(width, "cm", valueOnly = TRUE),
convertUnit(height, "cm", valueOnly = TRUE))
# Make space for triangles
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar] - 1)
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar])
# Draw triangles
top <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(0, 1, 0), "npc"),
gp = gpar(fill = extremes[1], col = NA)
)
bottom <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(1, 0, 1), "npc"),
gp = gpar(fill = extremes[2], col = NA)
)
# Add triangles to guide
guide <- gtable_add_grob(
guide, top,
t = guide$layout$t[is_bar] - 1,
l = guide$layout$l[is_bar]
)
guide <- gtable_add_grob(
guide, bottom,
t = guide$layout$t[is_bar] + 1,
l = guide$layout$l[is_bar]
)
return(guide)
}
You can then use your custom guide as the guide argument in a scale.
g <- ggplot(mtcars, aes(mpg, wt)) +
geom_point(aes(colour = drat))
g + scale_colour_viridis_c(
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
There isn't really a natural way to colour out-of-bounds values differently, but you can make very small slices near the extremes a different colour.
g + scale_colour_gradientn(
colours = c("red", scales::viridis_pal()(255), "hotpink"),
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
Created on 2021-07-19 by the reprex package (v1.0.0)
library(gg.layers)
library(ggplot2)
library(rcolors)
brk <- c(-Inf, -1, 0, 1, 3, 6, 9, Inf)
nbrk <- length(brk) - 1
cols <- get_color(rcolors$amwg256, nbrk)
g <- make_colorbar(
at = brk, col = cols, height = 1,
tck = 0.4,
space = "right",
legend.text.location = c(0.3, 0.5),
legend.text.just = c(0.5, 0.5),
# legend.text = list(fontfamily = "Times", cex = 1.1),
hjust = 0.05
)
p <- ggplot(mtcars, aes(mpg, disp)) + geom_point()
p + g
https://github.com/rpkgs/gg.layers
Triangles? No idea. Colors? You can set a gradient with custom values where your normal range is manually defined and your extremes are something else.
library(ggplot2)
# example taken from ?viridis::scale_colour_viridis, even if I don't use that function
dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
d <- ggplot(dsub, aes(x, y, colour=diff)) + geom_point()
d +
scale_color_gradientn(
colours=c("red", "red", "blue", "green", "yellow", "red", "red"),
values = c(0, 0.1-1e-9, 0.1, 0.5, 0.9, 0.9+1e-9, 1),
breaks = c(-0.51, -.4, 0, .4, .62),
label = function(z) replace(z, c(1, length(z)), c("Min", "Max"))) +
theme_bw()
I doubled "red" on each end so that there would be no gradient transition with the neighboring colors. You can choose a different color for one end (while in this case it's clear if it's extreme-high or extreme-low).
I chose to manually control values= and labels= to include arbitrary points and labels for the extremes. This can be improved based on your preferences.
The disadvantage to this is that you have to define the viridis colors manually; should not be too difficult. I've hastily approximated it here, I'm confident you can choose better colors for the internal gradient portion.
I am trying to arrange two ggplot2 plots side by side, i.e., in a two-column
layout using the package gridExtra. I am interested in ensuring that both
plots have equal plotting area (i.e., the gray plot panel is the same for both
plots) regardless of the height of the x-axis labels. As you can see in the
example below, when longer x-axis labels are used, gridExtra::grid.arrange()
seems to compensate this by adjusting the plotting area (i.e., the grayed out
part of the plot).
# Dummy data.
data <- data.frame(x = 1:10, y = rnorm(10))
# Dummy labels.
x_labels_short <- 1:10
x_labels_long <- 100001:100010
# Common settings for both `ggplot2` plots.
layers <- list(
labs(
x = "Plot title"
),
theme(
axis.text.x = element_text(
angle = 90,
vjust = 0.5,
hjust = 1
)
)
)
# `ggplot2 plot (a).
plot_a <- ggplot(data, aes(x, y)) +
scale_x_continuous(breaks = 1:10, labels = x_labels_short) +
layers
# `ggplo2` plot (b).
plot_b <- ggplot(data, aes(x, y)) +
scale_x_continuous(breaks = 1:10, labels = x_labels_long) +
layers
# Showing the plots side by side.
gridExtra::grid.arrange(
plot_a,
plot_b,
ncol = 2
)
Output:
What I want is for both plots to (1) have equal plotting area and (b) the x-axis
title of plot_a to be aligned with that of plot_b (i.e., the x-axis title of
plot_a to be offset based on the length of of the x-axis labels of plot_b).
If this is not clear, this is what I want to achieve would look like with base
R.
# Wrapper for convenience.
plot_gen <- function(data, labels) {
plot(
NULL,
xlim = c(1, 10),
ylim = c(min(data$y), max(data$y)),
xlab = "",
ylab = "y",
xaxt = "n"
)
axis(
side = 1,
at = 1:10,
labels = labels,
las = 2
)
title(
xlab = "Plot title",
line = 4.5
)
}
# Get `par`.
old_par = par(no.readonly = TRUE)
# Set the two-column layout.
layout(matrix(1:2, ncol = 2))
# Adjust margins.
par(mar = old_par$mar + c(1.5, 0, 0, 0))
# Plot base plot one.
plot_gen(data, x_labels_short)
# Plot base plot two.
plot_gen(data, x_labels_long)
# Restore `par`.
par(old_par)
# Restore layout.
layout(1:1)
Output:
Quick mention. I found a similar question on SO (i.e.,
How to specify the size of a graph in ggplot2 independent of axis labels), however I fail to see how the
answers address the problem. Also, the plots I am trying to arrange are based
on different data and I don't think I can use a facet_wrap approach.
One suggestion: the patchwork package.
library(patchwork)
plot_a + plot_b
It also works for more complex layouts, e.g.:
(plot_a | plot_b) / plot_a
I'd like to make a plot using ggplot2 where some of the fill values are clipped, i.e. values above or below the limits of the color scale are displayed as the minimum/maximum color. I can get this to work like this, using a combination of limit and oob (out of bounds):
library(ggplot2)
library(scales)
ggplot() + ... + scale_fill_viridis(na.value="white", limit=c(0, 10), oob=squish)
But there is no information in the colorbar that indicates there are values present outside of the limits.
How can I reproduce this matplotlib example in ggplot: https://stackoverflow.com/a/32072348
Specifically, how to get the triangles at the end of the colorbar?
As far as I'm aware there is not a package that implements triangle ends for colourbars in ggplot2 (but please let me know if there is!). However, we can implement our own. We'd need a constructor for our custom guide and a way to draw it. Most of the stuff is already implemented in guide_colourbar() and methods for their class, so what we need to do is just tag on our own class and expand the guide_gengrob method. The code below should work for vertically oriented colourbars. You'd need to know some stuff about the grid package and gtable package to follow along.
library(ggplot2)
library(gtable)
library(grid)
my_triangle_colourbar <- function(...) {
guide <- guide_colourbar(...)
class(guide) <- c("my_triangle_colourbar", class(guide))
guide
}
guide_gengrob.my_triangle_colourbar <- function(...) {
# First draw normal colourbar
guide <- NextMethod()
# Extract bar / colours
is_bar <- grep("^bar$", guide$layout$name)
bar <- guide$grobs[[is_bar]]
extremes <- c(bar$raster[1], bar$raster[length(bar$raster)])
# Extract size
width <- guide$widths[guide$layout$l[is_bar]]
height <- guide$heights[guide$layout$t[is_bar]]
short <- min(convertUnit(width, "cm", valueOnly = TRUE),
convertUnit(height, "cm", valueOnly = TRUE))
# Make space for triangles
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar] - 1)
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar])
# Draw triangles
top <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(0, 1, 0), "npc"),
gp = gpar(fill = extremes[1], col = NA)
)
bottom <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(1, 0, 1), "npc"),
gp = gpar(fill = extremes[2], col = NA)
)
# Add triangles to guide
guide <- gtable_add_grob(
guide, top,
t = guide$layout$t[is_bar] - 1,
l = guide$layout$l[is_bar]
)
guide <- gtable_add_grob(
guide, bottom,
t = guide$layout$t[is_bar] + 1,
l = guide$layout$l[is_bar]
)
return(guide)
}
You can then use your custom guide as the guide argument in a scale.
g <- ggplot(mtcars, aes(mpg, wt)) +
geom_point(aes(colour = drat))
g + scale_colour_viridis_c(
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
There isn't really a natural way to colour out-of-bounds values differently, but you can make very small slices near the extremes a different colour.
g + scale_colour_gradientn(
colours = c("red", scales::viridis_pal()(255), "hotpink"),
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
Created on 2021-07-19 by the reprex package (v1.0.0)
library(gg.layers)
library(ggplot2)
library(rcolors)
brk <- c(-Inf, -1, 0, 1, 3, 6, 9, Inf)
nbrk <- length(brk) - 1
cols <- get_color(rcolors$amwg256, nbrk)
g <- make_colorbar(
at = brk, col = cols, height = 1,
tck = 0.4,
space = "right",
legend.text.location = c(0.3, 0.5),
legend.text.just = c(0.5, 0.5),
# legend.text = list(fontfamily = "Times", cex = 1.1),
hjust = 0.05
)
p <- ggplot(mtcars, aes(mpg, disp)) + geom_point()
p + g
https://github.com/rpkgs/gg.layers
Triangles? No idea. Colors? You can set a gradient with custom values where your normal range is manually defined and your extremes are something else.
library(ggplot2)
# example taken from ?viridis::scale_colour_viridis, even if I don't use that function
dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
d <- ggplot(dsub, aes(x, y, colour=diff)) + geom_point()
d +
scale_color_gradientn(
colours=c("red", "red", "blue", "green", "yellow", "red", "red"),
values = c(0, 0.1-1e-9, 0.1, 0.5, 0.9, 0.9+1e-9, 1),
breaks = c(-0.51, -.4, 0, .4, .62),
label = function(z) replace(z, c(1, length(z)), c("Min", "Max"))) +
theme_bw()
I doubled "red" on each end so that there would be no gradient transition with the neighboring colors. You can choose a different color for one end (while in this case it's clear if it's extreme-high or extreme-low).
I chose to manually control values= and labels= to include arbitrary points and labels for the extremes. This can be improved based on your preferences.
The disadvantage to this is that you have to define the viridis colors manually; should not be too difficult. I've hastily approximated it here, I'm confident you can choose better colors for the internal gradient portion.
I am trying to combine two ggplot objects with patchwork - two plots with different subsets of data, but the same x variable (and therefore same unit). I would like to align the plots according to the x values - Each x unit should have the same physical width in the final plot.
This is very easy when actually plotting the entire width of the larger data set (see plot below) - but I struggle to plot only parts of the data and keeping the same alignment.
library(ggplot2)
library(patchwork)
library(dplyr)
p1 <-
ggplot(mtcars, aes(mpg)) +
geom_density(trim = TRUE) +
scale_x_continuous(limits = c(10,35))
p2 <-
ggplot(filter(mtcars, mpg < 20), aes(mpg)) +
geom_histogram(binwidth = 1, boundary = 1) +
scale_x_continuous(limits = c(10,35))
p1/p2
Created on 2019-08-07 by the reprex package (v0.3.0)
The desired output
That's photoshopped
adding coord_cartesian(xlim = c(10,(20 or 35)), clip = 'off'), and/or changing scale_x limits to c(0,(20 or 35)) doesn't work.
patchwork also won't let me set the widths of both plots when they are in two rows, which makes sense in a way. So I could create an empty plot for the second row and set the widths for those, but this seems a terrible hack and I feel there must be a much easier solution.
I am not restricted to patchwork, but any solution allowing to use it would be very welcome.
I modified the align_plots function from the cowplot package for this, so that its plot_grid function can now support adjustments to the dimensions of each plot.
(The main reason I went with cowplot rather than patchwork is that I haven't had much tinkering experience with the latter, and overloading common operators like + makes me slightly nervous.)
Demonstration of results
# x / y axis range of p1 / p2 have been changed for illustration purpose
p1 <- ggplot(mtcars, aes(mpg, 1 + stat(count))) +
geom_density(trim = TRUE) +
scale_x_continuous(limits = c(10,35)) +
coord_cartesian(ylim = c(1, 3.5))
p2 <- ggplot(filter(mtcars, mpg >= 15 & mpg < 30), aes(mpg)) +
geom_histogram(binwidth = 1, boundary = 1)
plot_grid(p1, p2, ncol = 1, align = "v") # plots in 1 column, x-axes aligned
plot_grid(p1, p2, nrow = 1, align = "h") # plots in 1 row, y-axes aligned
Plots in 1 column (x-axes aligned for 15-28 range):
Plots in 1 row (y-axes aligned for 1 - 3.5 range):
Caveats
This hack assumes the plots that the user intends to align (either horizontally or vertically) have reasonably similar axes of comparable magnitude. I haven't tested it on more extreme cases.
This hack expects simple non-faceted plots in Cartesian coordinates. I'm not sure what one could expect from aligning faceted plots. Similarly, I'm not considering polar coordinates (what's there to align?) or map projections (haven't looked into this, but they feel rather complicated).
This hack expects the gtable cell containing the plot panel to be in the 7th row / 5th column of the gtable object, which is based on my understanding of how ggplot objects are typically converted to gtables, and may not survive changes to the underlying code.
Code
Modified version of cowplot::align_plots:
align_plots_modified <- function (..., plotlist = NULL, align = c("none", "h", "v", "hv"),
axis = c("none", "l", "r", "t", "b", "lr", "tb", "tblr"),
greedy = TRUE) {
plots <- c(list(...), plotlist)
num_plots <- length(plots)
grobs <- lapply(plots, function(x) {
if (!is.null(x)) as_gtable(x)
else NULL
})
halign <- switch(align[1], h = TRUE, vh = TRUE, hv = TRUE, FALSE)
valign <- switch(align[1], v = TRUE, vh = TRUE, hv = TRUE, FALSE)
vcomplex_align <- hcomplex_align <- FALSE
if (valign) {
# modification: get x-axis value range associated with each plot, create union of
# value ranges across all plots, & calculate the proportional width of each plot
# (with white space on either side) required in order for the plots to align
plot.x.range <- lapply(plots, function(x) ggplot_build(x)$layout$panel_params[[1]]$x.range)
full.range <- range(plot.x.range)
plot.x.range <- lapply(plot.x.range,
function(x) c(diff(c(full.range[1], x[1]))/ diff(full.range),
diff(x)/ diff(full.range),
diff(c(x[2], full.range[2]))/ diff(full.range)))
num_widths <- unique(lapply(grobs, function(x) {
length(x$widths)
}))
num_widths[num_widths == 0] <- NULL
if (length(num_widths) > 1 || length(grep("l|r", axis[1])) > 0) {
vcomplex_align = TRUE
warning("Method not implemented for faceted plots. Placing unaligned.")
valign <- FALSE
}
else {
max_widths <- list(do.call(grid::unit.pmax,
lapply(grobs, function(x) {x$widths})))
}
}
if (halign) {
# modification: get y-axis value range associated with each plot, create union of
# value ranges across all plots, & calculate the proportional width of each plot
# (with white space on either side) required in order for the plots to align
plot.y.range <- lapply(plots, function(x) ggplot_build(x)$layout$panel_params[[1]]$y.range)
full.range <- range(plot.y.range)
plot.y.range <- lapply(plot.y.range,
function(x) c(diff(c(full.range[1], x[1]))/ diff(full.range),
diff(x)/ diff(full.range),
diff(c(x[2], full.range[2]))/ diff(full.range)))
num_heights <- unique(lapply(grobs, function(x) {
length(x$heights)
}))
num_heights[num_heights == 0] <- NULL
if (length(num_heights) > 1 || length(grep("t|b", axis[1])) > 0) {
hcomplex_align = TRUE
warning("Method not implemented for faceted plots. Placing unaligned.")
halign <- FALSE
}
else {
max_heights <- list(do.call(grid::unit.pmax,
lapply(grobs, function(x) {x$heights})))
}
}
for (i in 1:num_plots) {
if (!is.null(grobs[[i]])) {
if (valign) {
grobs[[i]]$widths <- max_widths[[1]]
# modification: change panel cell's width to a proportion of unit(1, "null"),
# then add whitespace to the left / right of the plot's existing gtable
grobs[[i]]$widths[[5]] <- unit(plot.x.range[[i]][2], "null")
grobs[[i]] <- gtable::gtable_add_cols(grobs[[i]],
widths = unit(plot.x.range[[i]][1], "null"),
pos = 0)
grobs[[i]] <- gtable::gtable_add_cols(grobs[[i]],
widths = unit(plot.x.range[[i]][3], "null"),
pos = -1)
}
if (halign) {
grobs[[i]]$heights <- max_heights[[1]]
# modification: change panel cell's height to a proportion of unit(1, "null"),
# then add whitespace to the bottom / top of the plot's existing gtable
grobs[[i]]$heights[[7]] <- unit(plot.y.range[[i]][2], "null")
grobs[[i]] <- gtable::gtable_add_rows(grobs[[i]],
heights = unit(plot.y.range[[i]][1], "null"),
pos = -1)
grobs[[i]] <- gtable::gtable_add_rows(grobs[[i]],
heights = unit(plot.y.range[[i]][3], "null"),
pos = 0)
}
}
}
grobs
}
Utilising the above modified function with cowplot package's plot_grid:
# To start using (in current R session only; effect will not carry over to subsequent session)
trace(cowplot::plot_grid, edit = TRUE)
# In the pop-up window, change `grobs <- align_plots(...)` (at around line 27) to
# `grobs <- align_plots_modified(...)`
# To stop using
untrace(cowplot::plot_grid)
(Alternatively, we can define a modified version of plot_grid function that uses align_plots_modified instead of cowplot::align_plots. Results would be the same either way.)
Here is an option with grid.arrange that does not use a blank plot, but requires a manual of adjustment of:
plot margin
x axis expansion
number of decimal places in y axis labels
library(ggplot2)
library(dplyr)
library(gridExtra)
p1 <-
ggplot(mtcars, aes(mpg)) +
geom_density(trim = TRUE) +
scale_x_continuous(limits = c(10,35), breaks=seq(10,35,5), expand = expand_scale(add=c(0,0)))
p2 <-
ggplot(filter(mtcars, mpg < 20), aes(mpg)) +
geom_histogram(binwidth = 1, boundary = 1) +
scale_x_continuous(limits = c(10,20), breaks=seq(10,20,5), expand = expand_scale(add=c(0,0))) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
theme(plot.margin = unit(c(0,1,0,0), "cm"))
grid.arrange(p1, p2,
layout_matrix = rbind(c(1, 1), c(2, NA))
)
Should make this plot:
I have created a facet chart across two dimensions Rating and Geography (Geo_class). how does one introduce spaces between the different Geography classes (panel.spacing.x), and yet avoid introducing the space between Rating classes. Sample data here https://www.dropbox.com/s/n3tbiexbvpuqm3t/Final_impact_melt_All5.csv?dl=0
in the image below, 1 to 3, 4,5,6,7 represent Ratings, Geo_Class is (Saudi Arabia, NOn GCC, Other GCC and All). Method is New or Old.
Im using the following code to generate the plot
p<-ggplot(Final_impact_melt_All5, aes(x=Method, y=Capital_Charge, fill= Capital_Charge_type))+ geom_bar(stat='Identity', width= 1)
p + facet_wrap (Geo_class ~ Ratings, nrow = 2) + scale_fill_brewer(palette ="Oranges") + theme(axis.text=element_text(size=6),panel.spacing.x=unit(0, "lines"),panel.spacing.y=unit(1, "lines"))
what id like is as separate the chart into 4 panels (one each for Geo_class ie. Saudi Arabia, Other GCC, Non GCC and All). Id like to keep spacing between the ratings to zero so that this takes on the look of a cluster stacked bar chart.
Another bonus would be if i can get rid of multiple times the Geography class is repeated and it just show up once atop each of the 4 new panels.
Is this what you want? As far as I know, what you ask for cannot be accomplished using ggplot only. The code below is not pretty. It depends of gtable and grid functions. It decomposes the strips in the ggplot plot, then constructs the new strips with appropriate spacing. The code works for the particular configuration of inner and outer strip labels in the example. If that changes, the code will break. And it might not survive the next version of ggplot.
library(ggplot2)
library(grid)
library(gtable)
# Set spacing between Geo_class and between Ratings
OuterSpacing = unit(5, "pt") # spacing between Geo_class
InnerSpacing = unit(0, "pt") # spacing between Ratings
# Your ggplot
p <- ggplot(Final_impact_melt_All5,
aes(x=Method, y=Capital_Charge, fill= Capital_Charge_type)) +
geom_bar(stat='Identity', width= 1)
plot = p +
facet_wrap (Geo_class ~ Ratings, nrow = 2) +
scale_fill_brewer(palette ="Oranges") +
theme(axis.text=element_text(size=6),
panel.spacing.x = OuterSpacing,
panel.spacing.y = unit(1, "lines"))
# Get the ggplot grob
g = ggplotGrob(plot)
# Set spacing between 'Ratings' to 'InnerSpacing'
g$widths[c(seq(6, by=4, length.out=4), seq(26, by=4, length.out=4)) ] = InnerSpacing
# Get a list of strips
strip = lapply(grep("strip-t", g$layout$name), function(x) {g$grobs[[x]]})
# Number of strips
URow = 4; LRow = 5 # Top row and Bottom row
# Construct gtable to contain the new strip
Inner = (rep(unit.c(unit(1, "null"), InnerSpacing), LRow))[-10]
newStrip = gtable(widths = (rep(unit.c(Inner, OuterSpacing), URow))[-40],
heights = strip[[1]]$heights)
## Populate the gtable
# Top Row
cols1 = seq(1, by = 5, length.out = 4)
cols2 = (seq(1, by = 10, length.out = 4))
newStrip = gtable_add_grob(newStrip, lapply(strip[cols1], `[`, 1), t = 1, l = cols2, r = cols2 + 8)
# Bottom row
cols = seq(1, by = 2, length.out = 20)
newStrip = gtable_add_grob(newStrip, lapply(strip, `[`, 2), t = 2, l = cols)
## Add the strips to the plot,
# making sure the second half go in the upper section (t=6)
# and the first half go in the lower section (t=11)
pgNew = gtable_add_grob(g, newStrip[1:2, 21:39], t = 6, l = 4, r = 40)
pgNew = gtable_add_grob(pgNew, newStrip[1:2, 1:19], t = 11, l = 4, r = 40)
# Remove the original strip
for(i in 102:121) pgNew$grobs[[i]] = nullGrob()
# Draw the plot
grid.newpage()
grid.draw(pgNew)