Related
I am trying to make my beautiful ggplot map interactive with a tooltip using ggplotly. But the map rendered with ggploty is not beautiful.
Here is a picture of my map with only ggplot:
Here is a picture of my map when using ggplotly. It removes the legend and make the map ugly:
Is there another way of making my ggplot map interactive with a tooltip? And also ggplotly takes some time to render the interactive map:
Here is my sample code for my ggplot:
ggplot(data = sdpf_f, aes( fill = n,x = long, y = lat, group = group, text = tooltip)) +
geom_polygon(color = "white") +
theme_void() +
scale_fill_continuous(low="#c3ffff", high="#0291da",
guide = guide_legend(title.position = "top", label.position = "bottom", keywidth = 2,
keyheight = 0.5,
title = "Number of agreements"),na.value="lightgrey"
) +
theme(legend.position="bottom") +
coord_map()
Thanks & kind regards,
Akshay
I don't have your data and this isn't exactly the same, but it's fairly close to what I think you're expecting.
The libraries:
I called tidyverse for the plotting and piping. I called maps for the data I used and plotly for the Plotly graph.
I used a function that is derived from one of the ways ggplot sets the aspect ratio. I know I found this function on SO, but I don't remember who wrote it.
library(tidyverse)
library(maps)
library(plotly)
map_aspect = function(x, y) {
x.center <- sum(range(x)) / 2
y.center <- sum(range(y)) / 2
x.dist <- ggplot2:::dist_central_angle(x.center + c(-0.5, 0.5), rep(y.center, 2))
y.dist <- ggplot2:::dist_central_angle(rep(x.center, 2), y.center + c(-0.5, 0.5))
y.dist / x.dist
}
I had to create data as your question is not reproducible. I figured I would include it, so that my answer was reproducible.
ms <- map_data("state") %>%
mutate(n = ifelse(str_detect(region, "^a"), 1.0,
ifelse(str_detect(region, "^o"), 1.5,
ifelse(str_detect(region, "^t"), 2.0,
ifelse(str_detect(region, "^s"), 2.5,
ifelse(str_detect(region, "^w"),
3.0,
NA))))))
I modified your ggplot call. The only change is coord_fixed instead of coord_map. I did this so that Plotly could interpret the aspect ratio correctly. Within coord_fixed, I used the function map_aspect.
gp <- ggplot(data = ms, aes(fill = n, x = long, y = lat,
group = group, text = tooltip)) +
geom_polygon(color = "white") +
theme_void() +
scale_fill_continuous(low="#c3ffff", high="#0291da",
guide = guide_legend(title.position = "top",
label.position = "bottom",
keywidth = 2,
keyheight = 0.5,
title = "Number of agreements"),
na.value="lightgrey"
) +
theme(legend.position="bottom") +
coord_fixed(with(ms, map_aspect(long, lat)))
Then I created a Plotly object. I set some requirements for the layout, as well (horizontal legend at the bottom, with the legend title above the elements—similar to the legend in your ggplot call).
pp <- ggplotly(gp) %>%
layout(legend = list(orientation = "h", valign = "bottom",
title = list(side = "top"),
x = .02),
xaxis = list(showgrid = F), yaxis = list(showgrid = F),
showlegend = T)
Next, I needed to add the information for the legend. I chose to name the traces (which are split by color). I started by creating a vector of the names of the traces (which is what you see in the legend). I added a "" at the end, so that the NA-colored states wouldn't have a named trace (so they won't show in the legend).
This is likely something you'll need to change for your data.**
# the color values and the last one, which is for NAs, no legend
nm <- seq(1, 3, by = .5) %>% sprintf(fmt = "%.1f") %>% append(., "")
Then I wanted to ensure that showlegend was true for all but the NA (light gray) states. I created a vector of T and F.
legs <- c(rep(TRUE, 5), FALSE)
Then I added these to the plot.
invisible(lapply(1:length(pp$x$data),
function(i){
pp$x$data[[i]]$name <<- nm[i]
pp$x$data[[i]]$showlegend <<- legs[i]
}))
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'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 admit this question has already been asked at least two times, but none of the provided answers worked for me.
My data is very similar to the ggbiplot wine example, so I'll just use this to illustrate my problem.
I want my datapoints to be differently coloured AND shaped, so they look nice when printed in colour but can still be identified when printed in b/w. This actually works just fine, but the legend doesn't play along.
require(scales)
library(devtools)
install_github("vqv/ggbiplot")
library(ggbiplot)
data(wine)
wine.pca <- prcomp(wine, scale. = TRUE)
ggbiplot(wine.pca, obs.scale = 1, var.scale = 1,
groups = wine.class, ellipse = TRUE, circle = TRUE, var.axes=FALSE) +
scale_color_discrete(name = '') +
geom_point(aes(colour=wine.class,shape=wine.class),size=2) +
theme(legend.direction = 'horizontal', legend.position = 'top')
produces this:
The legend is obviously not what I want. The shapes and colours should be in the same legend.
(As a bonus, I'd also like to remove the small lines from the points, but if that's not easily doable I can live with them. )
I already tried what was suggested here and here.
The first suggestion
data(wine)
wine.pca <- prcomp(wine, scale. = TRUE)
ggbiplot(wine.pca, obs.scale = 1, var.scale = 1,
groups = wine.class, ellipse = TRUE, circle = TRUE, var.axes=FALSE) +
scale_color_discrete(name = '') +
geom_point(aes(colour = wine.class), size = "2") +
scale_color_manual(values=c(16,17,18)) +
theme(legend.direction = 'horizontal', legend.position = 'top')
produces this error message (and no plot at all)
Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
Fehler in coords$size * .pt :
nicht-numerisches Argument für binären Operator
(the last two lines translate to "error in coords$size * .pt : non-numeric argument for binary operator)
The second suggestion
data(wine)
wine.pca <- prcomp(wine, scale. = TRUE)
ggbiplot(wine.pca, obs.scale = 1, var.scale = 1,
groups = wine.class, ellipse = TRUE, circle = TRUE, var.axes=FALSE) +
scale_color_discrete(name = '') +
geom_point(size = 2) +
scale_colour_manual(name = "Wines",
labels = c("barolo","grignolino","barbera"),
values = c("blue", "red", "green")) +
scale_shape_manual(name = "Wines",
labels = c("barolo","grignolino","barbera"),
values = c(17,18,19))
gives me this error message
Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
and a funny plot with coloured ellipses, legend with coloured points, but black datapoints.
I'm afraid my knowledge of R plotting isn't enough to make anything of this. Can someone point me in the right way?
I would like to use my own specific color in my image plot. I am very new in ggplot2 so had a look at its manual from here - the old link does not exist and now is here - and tried to repeat some of the stuff; but I couldn't figure out how to supply my colorbar as I did for the graphics plot.
library(reshape2)
library(ggplot2)
#my specific color list
mycol <- vector(length=512, mode = "numeric")
for (k in 1:256) mycol[k] <- rgb(255, k - 1, k - 1, maxColorValue=255)
for (k in 257:512) mycol[k] <- rgb(511 - (k - 1), 511 - (k - 1), 255, maxColorValue=255)
mycol <- rev(mycol)
ncolors <- length(mycol)
# graphics plot
par(mar = c(5, 13, 1, 6))
image(1:ncol(volcano), 1:nrow(volcano), t(volcano), zlim = c(0, ncolors), col=mycol, axes=FALSE, main="W Matrix", sub = "", xlab= "Components", ylab="Genes")
axis(2, at=1:nrow(volcano), labels=row.names(volcano), adj= 0.5, tick=FALSE, las = 1, cex.axis=0.25, font.axis=1, line=-1)
axis(1, at=1:ncol(volcano), labels=colnames(volcano), adj= 0.5, tick=FALSE,las = 3, cex=1, cex.axis=0.5, font.axis=1, line=-1)
# ggplot2
library(reshape2)
library(ggplot2)
library(ez)
ggplot(melt(volcano), aes(x=Var1, y=Var2, fill=value)) + geom_tile() + scale_color_gradient2(low = muted("red"), mid = "white", high = muted("blue"), midpoint = 0, space = "rgb", guide = "colourbar") # the code does not really use my color bar
Error in unit(tic_pos.c, "mm") : 'x' and 'units' must have length > 0
Just to clarify #Didzis' answer, which works but may not produce the gradient you're looking for...
'midpoint' refers to the numerical value at which you want the color specified by 'mid' to appear. So, instead of setting the 'midpoint' argument to 256 (which falls outside the range of value, which is the vector you're coloring by), it's wise to set it to a value somewhere in the middle of the range of values you are coloring by, otherwise you aren't using the entire gradient you specified with 'low' and 'high', which defeats the purpose of scale_color_gradient2. The exact value depends on what you are trying to communicate visually, but usually the mean or median is used. Here, I edited #Didzis' code with 'midpoint' set to the median of value
v <- melt(volcano)
ggplot(v, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
scale_fill_gradient2(low = "#0000FF", mid = "#FFFFFF", high ="#FF0000",
midpoint = median(v$value), space = "rgb", guide = "colourbar")
This gives a plot with a much wider gradient:
I think that you should change values for low=, mid= and high= values in scale_fill_gradient2(). For low= I used first value of mycol, for high= last value of mycol and for mid= used 256. value (middle). Also changed midpoint= to 256 as this is midpoint of your number of colors.
ggplot(melt(volcano), aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
scale_fill_gradient2(low = "#0000FF", mid = "#FFFFFF", high ="#FF0000",
midpoint = 256.5, space = "rgb", guide = "colourbar")