How to align scale transformation across geoms? - r

I have a geom_foo() which will do some transformation of the input data and I have a scale transformation. My problem is that these work not as I would expect together with other geom_*s in terms of scaling.
To illustrate the behavior, consider foo() which will be used in the setup_data method of GeomFoo, defined at the end of the question.
foo <- function(x, y) {
data.frame(
x = x + 2,
y = y + 2
)
}
foo(1, 1)
The transformer is:
foo_trans <- scales::trans_new(
name = "foo",
transform = function(x) x / 5,
inverse = function(x) x * 5
)
Given this input data:
df1 <- data.frame(x = c(1, 2), y = c(1, 2))
Here is a basic plot:
library(ggplot2)
ggplot(df1, aes(x = x, y = y)) +
geom_foo()
When I apply the transformation to the vertical scale, I get this
ggplot(df1, aes(x = x, y = y)) +
geom_foo() +
scale_y_continuous(trans = foo_trans)
What I can say is that the y-axis limits are calculate as 11 = 1 + (2*5) and 12 = 2 + (2*5), where 1 and 2 are df1$y, and (2 * 5) are taken from the setup_data method and from trans_foo.
My real problem is, that I would like add a text layer with labels. These labels and their coordinates come from another dataframe, as below.
df_label <- foo(df1$x, df1$y)
df_label$label <- c("A", "B")
Label and point layers are on same x-y positions without the scale transformation
p <- ggplot(df1, aes(x = x, y = y)) +
geom_foo(color = "red", size = 6) +
geom_text(data = df_label, aes(x, y, label = label))
p
But when I apply the transformation, the coordinates do not match anymore
p +
scale_y_continuous(trans = foo_trans)
How do I get the to layer to match in x-y coordinates after the transformation? Thanks
ggproto object:
GeomFoo <- ggproto("GeomFoo", GeomPoint,
setup_data = function(data, params) {
cols_to_keep <- setdiff(names(data), c("x", "y"))
cbind(
foo(data$x, data$y),
data[, cols_to_keep]
)
}
)
geom constructor:
geom_foo <- function(mapping = NULL, data = NULL, ...,
na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = "identity",
geom = GeomFoo,
position = "identity",
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
na.rm = na.rm,
...
)
)
}

Doing data transformations isn't really the task of a geom, but a task of a stat instead. That said, the larger issue is that scale transformations are applied before the GeomFoo$setup_data() method is called. There are two ways one could accomplish this task that I could see.
Apply foo() before scale transformation. I don't think geoms or stats ever have access to the data before scale transformation. A possible place for this is in the ggplot2:::Layer$setup_layer() method. However, this isn't exported, which probably means the devs would like to discourage this even before we make an attempt.
Inverse the scale transformation, apply foo(), and transform again. For this, you need a method with access to the scales. AFAIK, no geom method has this access. However Stat$compute_panel() does have access, so we can use this.
To give an example of (2), I think you could get away with the following:
StatFoo <- ggproto(
"StatFoo", Stat,
compute_panel = function(self, data, scales) {
cols_to_keep <- setdiff(names(data), c("x", "y"))
food <- foo(scales$x$trans$inverse(data$x),
scales$y$trans$inverse(data$y))
cbind(
data.frame(x = scales$x$trans$transform(food$x),
y = scales$y$trans$transform(food$y)),
data[, cols_to_keep]
)
}
)
geom_foo <- function(mapping = NULL, data = NULL, ...,
na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = StatFoo,
geom = GeomPoint,
position = "identity",
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
na.rm = na.rm,
...
)
)
}
If someone else has brighter ideas to do this, I'd also like to know!

Related

Extending ggplot2: How to build a geom and stat?

I am in the early stages of learning how to extend ggplot2. I would like to create a custom geom and associated stat. My starting point was the vignette. In addition, I have benefited from this and this. I'm trying to put together a template to teach myself and hopefully others.
Main question:
Inside my function calculate_shadows() the needed parameter params$anchor is NULL. How can I access it?
The goal described below is intended solely for learning how to create custom stat and geom functions, it's not a real goal: as you can see from the screenshots, I do know how to leverage the power of ggplot2 to make the graphs.
The geom will read the data and for the supplied variables ("x", "y") will plot (for want of a better word) shadows: a horizontal line min(x)--max(x) at the default y=0 and a vertical line min(y)--max(y) at the default x=0. If an option is supplied, these "anchors" could be changed, e.g. if the user supplies x = 35, y = 1, the horizontal line would be drawn at the intercept y = 1 while the vertical line would be drawn at the intercept x = 35. Usage:
library(ggplot2)
ggplot(data = mtcars, aes(x = mpg, y = wt)) +
geom_point() +
geom_shadows(x = 35, y = 1)
The stat will read the data and for the supplied variables ("x", "y") will compute shadows according to the value of stat. For instance, by passing stat = "identity", the shadows would be computed for the min and max of the data (as done by geom_shadows). But by passing stat = "quartile", the shadows would be computed for first and third quartile. More generally, one could pass a function like stats::quantile with arguments args = list(probs = c(0.10, 0.90), type = 6), to compute shadows using the 10th and 90th percentiles and the quantile method of type 6. Usage:
ggplot(data = mtcars, aes(x = mpg, y = wt)) +
geom_point() +
stat_shadows(stat = "quartile")
Unfortunately, my lack of familiarity with extending ggplot2 stopped me well short of my objective. These plots were "faked" with geom_segment. Based on the tutorial and discussions cited above and inspecting existing code like stat-qq or stat-smooth, I have put together a basic architecture for this goal. It must contain many mistakes, I would be grateful for guidance. Also, note that either of these approaches would be fine: geom_shadows(anchor = c(35, 1)) or geom_shadows(x = 35, y = 1).
Now here are my efforts. First, geom-shadows.r to define geom_shadows(). Second, stat-shadows.r to define stat_shadows(). The code doesn't work as is. But if I execute its content, it does produce the desired statistics. For clarity, I have removed most of the calculations in stat_shadows(), such as quartiles, to focus on essentials. Any obvious mistake in the layout?
geom-shadows.r
#' documentation ought to be here
geom_shadows <- function(
mapping = NULL,
data = NULL,
stat = "shadows",
position = "identity",
...,
anchor = list(x = 0, y = 0),
shadows = list("x", "y"),
type = NULL,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
data = data,
mapping = mapping,
stat = stat,
geom = GeomShadows,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
anchor = anchor,
shadows = shadows,
type = type,
na.rm = na.rm,
...
)
)
}
GeomShadows <- ggproto("GeomShadows", Geom,
# set up the data, e.g. remove missing data
setup_data = function(data, params) {
data
},
# set up the parameters, e.g. supply warnings for incorrect input
setup_params = function(data, params) {
params
},
draw_group = function(data, panel_params, coord, anchor, shadows, type) {
# draw_group uses stats returned by compute_group
# set common aesthetics
geom_aes <- list(
alpha = data$alpha,
colour = data$color,
size = data$size,
linetype = data$linetype,
fill = alpha(data$fill, data$alpha),
group = data$group
)
# merge aesthetics with data calculated in setup_data
geom_stats <- new_data_frame(c(list(
x = c(data$x.xmin, data$y.xmin),
xend = c(data$x.xmax, data$y.xmax),
y = c(data$x.ymin, data$y.ymin),
yend = c(data$x.ymax, data$y.ymax),
alpha = c(data$alpha, data$alpha)
), geom_aes
), n = 2)
# turn the stats data into a GeomPath
geom_grob <- GeomSegment$draw_panel(unique(geom_stats),
panel_params, coord)
# pass the GeomPath to grobTree
ggname("geom_shadows", grobTree(geom_grob))
},
# set legend box styles
draw_key = draw_key_path,
# set default aesthetics
default_aes = aes(
colour = "blue",
fill = "red",
size = 1,
linetype = 1,
alpha = 1
)
)
stat-shadows.r
#' documentation ought to be here
stat_shadows <-
function(mapping = NULL,
data = NULL,
geom = "shadows",
position = "identity",
...,
# do I need to add the geom_shadows arguments here?
anchor = list(x = 0, y = 0),
shadows = list("x", "y"),
type = NULL,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
layer(
stat = StatShadows,
data = data,
mapping = mapping,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
# geom_shadows argument repeated here?
anchor = anchor,
shadows = shadows,
type = type,
na.rm = na.rm,
...
)
)
}
StatShadows <-
ggproto("StatShadows", Stat,
# do I need to repeat required_aes?
required_aes = c("x", "y"),
# set up the data, e.g. remove missing data
setup_data = function(data, params) {
data
},
# set up parameters, e.g. unpack from list
setup_params = function(data, params) {
params
},
# calculate shadows: returns data_frame with colnames: xmin, xmax, ymin, ymax
compute_group = function(data, scales, anchor = list(x = 0, y = 0), shadows = list("x", "y"), type = NULL, na.rm = TRUE) {
.compute_shadows(data = data, anchor = anchor, shadows = shadows, type = type)
}
)
# Calculate the shadows for each type / shadows / anchor
.compute_shadows <- function(data, anchor, shadows, type) {
# Deleted all type-checking, etc. for MWE
# Only 'type = c(double, double)' accepted, e.g. type = c(0, 1)
qs <- type
# compute shadows along the x-axis
if (any(shadows == "x")) {
shadows.x <- c(
xmin = as.numeric(stats::quantile(data[, "x"], qs[[1]])),
xmax = as.numeric(stats::quantile(data[, "x"], qs[[2]])),
ymin = anchor[["y"]],
ymax = anchor[["y"]])
}
# compute shadows along the y-axis
if (any(shadows == "y")) {
shadows.y <- c(
xmin = anchor[["x"]],
xmax = anchor[["x"]],
ymin = as.numeric(stats::quantile(data[, "y"], qs[[1]])),
ymax = as.numeric(stats::quantile(data[, "y"], qs[[2]])))
}
# store shadows in one data_frame
stats <- new_data_frame(c(x = shadows.x, y = shadows.y))
# return the statistics
stats
}
.
Until a more thorough answer comes along: You are missing
extra_params = c("na.rm", "shadows", "anchor", "type"),
inside GeomShadows <- ggproto("GeomShadows", Geom,
and possibly also inside StatShadows <- ggproto("StatShadows", Stat,.
Inside geom-.r and stat-.r there are many very useful comments that clarify how geoms and stats work. In particular (hat tips Claus Wilke over at github issues):
# Most parameters for the geom are taken automatically from draw_panel() or
# draw_groups(). However, some additional parameters may be needed
# for setup_data() or handle_na(). These can not be imputed automatically,
# so the slightly hacky "extra_params" field is used instead. By
# default it contains `na.rm`
extra_params = c("na.rm"),

Q-Q plot with ggplot2::stat_qq, colours, single group

I'm looking for a more convenient way to get a Q-Q plot in ggplot2 where the quantiles are computed for the data set as a whole. but I can use mappings (colour/shapes) for groups in the data.
library(dplyr)
library(ggplot2)
library(broom) ## for augment()
Make up some data:
set.seed(1001)
N <- 1000
G <- 10
dd <- data_frame(x=runif(N),
f=factor(sample(1:G,size=N,replace=TRUE)),
y=rnorm(N)+2*x+as.numeric(f))
m1 <- lm(y~x,data=dd)
dda <- cbind(augment(m1),f=dd$f)
Basic plot:
ggplot(dda)+stat_qq(aes(sample=.resid))
if I try to add colour, the groups get separated for the quantile computation (which I don't want):
ggplot(dda)+stat_qq(aes(sample=y,colour=f))
If I use stat_qq(aes(sample=y,colour=f,group=1)) ggplot ignores the colour specification and I get the first plot back.
I want a plot where the points are positioned as in the first case, but coloured as in the second case. I have a qqnorm-based manual solution that I can post but am looking for something nicer ...
You could calculate the quantiles yourself and then plot using geom_point:
dda = cbind(dda, setNames(qqnorm(dda$.resid, plot.it=FALSE), c("Theoretical", "Sample")))
ggplot(dda) +
geom_point(aes(x=Theoretical, y=Sample, colour=f))
Ah, I guess I should have read to the end of your question. This is the manual solution you were referring to, right? Although you could just package it as a function:
my_stat_qq = function(data, colour.var) {
data=cbind(data, setNames(qqnorm(data$.resid, plot.it=FALSE), c("Theoretical", "Sample")))
ggplot(data) +
geom_point(aes_string(x="Theoretical", y="Sample", colour=colour.var))
}
my_stat_qq(dda, "f")
Here's a ggproto-based approach that attempts to change StatQq, since the underlying issue here (colour specification gets ignored when group is specified explicitly) is due to how its compute_group function is coded.
Define alternate version of StatQq with modified compute_group (last few lines of code):
StatQq2 <- ggproto("StatQq", Stat,
default_aes = aes(y = after_stat(sample), x = after_stat(theoretical)),
required_aes = c("sample"),
compute_group = function(data, scales, quantiles = NULL,
distribution = stats::qnorm, dparams = list(),
na.rm = FALSE) {
sample <- sort(data$sample)
n <- length(sample)
# Compute theoretical quantiles
if (is.null(quantiles)) {
quantiles <- stats::ppoints(n)
} else if (length(quantiles) != n) {
abort("length of quantiles must match length of data")
}
theoretical <- do.call(distribution, c(list(p = quote(quantiles)), dparams))
res <- ggplot2:::new_data_frame(list(sample = sample,
theoretical = theoretical))
# NEW: append remaining columns from original data
# (e.g. if there were other aesthetic variables),
# instead of returning res directly
data.new <- subset(data[rank(data$sample), ],
select = -c(sample, PANEL, group))
if(ncol(data.new) > 0) res <- cbind(res, data.new)
res
}
)
Define geom_qq2 / stat_qq2 to use modified StatQq2 instead of StatQq for their stat:
geom_qq2 <- function (mapping = NULL, data = NULL, geom = "point",
position = "identity", ..., distribution = stats::qnorm,
dparams = list(), na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(data = data, mapping = mapping, stat = StatQq2, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(distribution = distribution, dparams = dparams,
na.rm = na.rm, ...))
}
stat_qq2 <- function (mapping = NULL, data = NULL, geom = "point",
position = "identity", ..., distribution = stats::qnorm,
dparams = list(), na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(data = data, mapping = mapping, stat = StatQq2, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(distribution = distribution, dparams = dparams,
na.rm = na.rm, ...))
}
Usage:
cowplot::plot_grid(
ggplot(dda) + stat_qq(aes(sample = .resid)), # original
ggplot(dda) + stat_qq2(aes(sample = .resid, # new
color = f, group = 1))
)

Why is bins parameter unknown for the stat_density2d function? (ggmap)

I have been stuck with this for hours. When I run this :
library(ggmap)
set.seed(1)
n=100
df <- data.frame(x=rnorm(n, 0, 1), y=rnorm(n, 0, 1))
TestData <- ggplot (data = df) +
stat_density2d(aes(x = x, y = y,fill = as.factor(..level..)),bins=4, geom = "polygon",) +
geom_point(aes(x = x, y = y)) +
scale_fill_manual(values = c("yellow","red","green","royalblue", "black"))
I get this error message :
Error: Unknown parameters: bins
Does anyone know why?
Okay, adding this one as a second answer because I think the descriptions and comments in the first answer are useful and I don't feel like merging them. Basically I figured there must be an easy way to restore the regressed functionality. And after awhile, and learning some basics about ggplot2, I got this to work by overriding some ggplot2 functions:
library(ggmap)
library(ggplot2)
# -------------------------------
# start copy from stat-density-2d.R
stat_density_2d <- function(mapping = NULL, data = NULL, geom = "density_2d",
position = "identity", contour = TRUE,
n = 100, h = NULL, na.rm = FALSE,bins=0,
show.legend = NA, inherit.aes = TRUE, ...) {
layer(
data = data,
mapping = mapping,
stat = StatDensity2d,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
na.rm = na.rm,
contour = contour,
n = n,
bins=bins,
...
)
)
}
stat_density2d <- stat_density_2d
StatDensity2d <-
ggproto("StatDensity2d", Stat,
default_aes = aes(colour = "#3366FF", size = 0.5),
required_aes = c("x", "y"),
compute_group = function(data, scales, na.rm = FALSE, h = NULL,
contour = TRUE, n = 100,bins=0) {
if (is.null(h)) {
h <- c(MASS::bandwidth.nrd(data$x), MASS::bandwidth.nrd(data$y))
}
dens <- MASS::kde2d(
data$x, data$y, h = h, n = n,
lims = c(scales$x$dimension(), scales$y$dimension())
)
df <- data.frame(expand.grid(x = dens$x, y = dens$y), z = as.vector(dens$z))
df$group <- data$group[1]
if (contour) {
# StatContour$compute_panel(df, scales,bins=bins,...) # bad dots...
if (bins>0){
StatContour$compute_panel(df, scales,bins)
} else {
StatContour$compute_panel(df, scales)
}
} else {
names(df) <- c("x", "y", "density", "group")
df$level <- 1
df$piece <- 1
df
}
}
)
# end copy from stat-density-2d.R
# -------------------------------
set.seed(1)
n=100
df <- data.frame(x=rnorm(n, 0, 1), y=rnorm(n, 0, 1))
TestData <- ggplot (data = df) +
stat_density2d(aes(x = x, y = y,fill = as.factor(..level..)),bins=5,geom = "polygon") +
geom_point(aes(x = x, y = y)) +
scale_fill_manual(values = c("yellow","red","green","royalblue", "black"))
print(TestData)
Which yields the result. Note that varying the bins parameter has the desired effect now, which cannot be replicated by varying the n parameter.
update:
After an extended discussion with Roland (see the comments), he determined it is probably a regression bug and filed a bug report.
As the question is "why is the bins parameter unknown?", and I spent a fair amount of time researching it, I will answer it.
Your example obviously comes from this link from Oct 2013, where the parameter is used. How to correctly interpret ggplot's stat_density2d
However it was never a documented parameter, and it is not clear that it is being used there. It is probably a parameter that is being passed through to other libraries (like MASS) that are being used by stat_density2d.
We can get the code to work by getting rid of the scale_fill_manual call and using this:
library(ggmap)
set.seed(1)
n=100
df <- data.frame(x=rnorm(n, 0, 1), y=rnorm(n, 0, 1))
TestData <- ggplot (data = df) +
stat_density2d(aes(x = x, y = y,fill = as.factor(..level..)), geom = "polygon",) +
geom_point(aes(x = x, y = y))
# scale_fill_manual(values = c("yellow","red","green","royalblue", "black"))
print(TestData)
which yields this:
Since this looks rather different from the plot posted in the original Oct 2013 link, I would say that stat_density2d has been extensively rewritten since then, or maybe MASS:kde2d (or another MASS routine), and that the bin parameter is no longer accepted. It is also not clear that that parameter ever did anything (read the link).
You can however vary the parameters n and h - also undocumented from the stat_density2d point-of-view (as far as I can tell).

ggplot2 2.0 new stat_ function: setting default scale for given aesthetics

I try to use the new functionality of ggplot2 in R that allows creating our own stat_ functions. I'm creating a simple one to compute and plot an interpolated surface between points arranged on a 2d array.
I would like to create a stat_topo() requiring x, y, and val aesthetics, plotting a simple geom_raster of interpolated val mapped to fill.
library(ggplot2)
library(dplyr)
library(akima)
cpt_grp <- function(data, scales) {
#interpolate data in 2D
itrp <- akima::interp(data$x,data$y,data$val,linear=F,extrap=T)
out <- expand.grid(x=itrp$x, y=itrp$y,KEEP.OUT.ATTRS = F)%>%
mutate(fill=as.vector(itrp$z))
# str(out)
return(out)
}
StatTopo <- ggproto("StatTopo", Stat,
compute_group = cpt_grp,
required_aes = c("x","y","val")
)
stat_topo <- function(mapping = NULL, data = NULL, geom = "raster",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
layer(
stat = StatTopo, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
set.seed(1)
nchan <- 30
d <- data.frame(val = rnorm(nchan), # some random values to be mapped to fill color
x = 1:nchan*cos(1:nchan), # the x and y position of the points to interpolate
y = 1:nchan*sin(1:nchan))
plot(d$x,d$y)
ggplot(d,aes(x=x,y=y,val=val)) +
stat_topo() +
geom_point()
When I run this, I get the following error:
Error: numerical color values must be >= 0, found -1
I understand that this is because somehow the scale of the fill aesthetic is set to discrete.
If I enter this:
ggplot(d,aes(x=x,y=y,val=val)) +
stat_topo() +
scale_fill_continuous() +
geom_point()
I get what I wanted: the expected raster with a continuous color scale, which I want the stat_ to do by default...
So I guess the question is:
How can I prevent ggplot from setting a discrete scale here, and ideally set a default scale within the call to my new stat_ function.
Apparently, when creating a new variable inside a stat_ function, one needs to explicitly associate it to the aesthetic it will be mapped to with the parameter default_aes = aes(fill = ..fill..) within the ggproto definition.
This is telling ggplot that it is a calculated aesthetic and it will pick a scale based on the data type.
So here we need to define the stat_ as follows:
cpt_grp <- function(data, scales) {
# interpolate data in 2D
itrp <- akima::interp(data$x,data$y,data$val,linear=F,extrap=T)
out <- expand.grid(x=itrp$x, y=itrp$y,KEEP.OUT.ATTRS = F)%>%
mutate(fill=as.vector(itrp$z))
# str(out)
return(out)
}
StatTopo <- ggproto("StatTopo", Stat,
compute_group = cpt_grp,
required_aes = c("x","y","val"),
default_aes = aes(fill = ..fill..)
)
stat_topo <- function(mapping = NULL, data = NULL, geom = "raster",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
layer(
stat = StatTopo, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
Then the following code:
set.seed(1)
nchan <- 30
d <- data.frame(val = rnorm(nchan),
x = 1:nchan*cos(1:nchan),
y = 1:nchan*sin(1:nchan))
ggplot(d,aes(x=x,y=y,val=val)) +
stat_topo() +
geom_point()
Produces as expected:
Without the need to specify a scale_ manually, but leaving the possibility to adapt the scale easily as usual with e.g. scale_fill_gradient2(low = 'blue',mid='white',high='red')
I got this answer here: https://github.com/hadley/ggplot2/issues/1481
Okay, slept on it, and had an idea, and I think this might do what you want. In your stat_topo layer function instead of the ggproto I returned a list with it as the first element and then added to that list another ggproto with a call to scale_fill_continuous().
library(ggplot2)
library(dplyr)
library(akima)
cpt_grp <- function(data, scales) {
#interpolate data in 2D
itrp <- akima::interp(data$x,data$y,data$val,linear=F,extrap=T)
out <- expand.grid(x=itrp$x, y=itrp$y,KEEP.OUT.ATTRS = F)%>%
mutate(fill=as.vector(itrp$z))
return(out)
}
StatTopo <- ggproto("StatTopo", Stat,
compute_group = cpt_grp,
required_aes = c("x","y","val")
)
stat_topo <- function(mapping = NULL, data = NULL, geom = "raster",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
list(
layer(
stat = StatTopo, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm )
),
scale_fill_continuous()
)
}
set.seed(1)
nchan <- 30
d <- data.frame(val = rnorm(nchan), # some random values to be mapped to fill color
x = 1:nchan*cos(1:nchan), # the x and y position of interp points
y = 1:nchan*sin(1:nchan))
ggplot(d,aes(x=x,y=y,val=val)) +
stat_topo() +
geom_point()
yielding the same picture as above.

ggstance::geom_barh not flipping axes?

Posting this for a more general audience
Consider the following:
ggplot2::ggplot(ggplot2::mpg, ggplot2::aes(class)) + ggplot2::geom_bar()
This yields
However
ggplot2::ggplot(ggplot2::mpg, ggplot2::aes(class)) + ggstance::geom_barh()
produces the (broken!?)
Where am I getting this wrong?
Use the following stat:
stat_counth <- function(mapping = NULL, data = NULL,
geom = "fillbar", position = "stack",
...,
width = NULL,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE) {
params <- list(
na.rm = na.rm,
width = width,
...
)
if (!is.null(params$y)) {
stop("stat_count() must not be used with a y aesthetic.", call. = FALSE)
}
layer(
data = data,
mapping = mapping,
stat = StatCounth,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = params
)
}
#' #rdname ggplot2-ggproto
#' #format NULL
#' #usage NULL
#' #export
#' #include stat-.r
StatCounth <- ggproto("StatCounth", Stat,
required_aes = "y",
default_aes = aes(x = calc(count), weight = 1),
setup_params = function(data, params) {
if (!is.null(data$y)) {
stop("stat_counth() must not be used with a x aesthetic.", call. = FALSE)
}
params
},
compute_group = function(self, data, scales, width = NULL) {
y <- data$y
weight <- data$weight %||% rep(1, length(y))
width <- width %||% (resolution(y) * 0.9)
count <- as.numeric(tapply(weight, y, sum, na.rm = TRUE))
count[is.na(count)] <- 0
data.frame(
count = count,
prop = count / sum(abs(count)),
x = sort(unique(y)),
width = width
)
}
)
Then plot as follows:
ggplot2::ggplot(ggplot2::mpg, ggplot2::aes(y = class)) + ggstance::geom_barh(stat = "counth")
Edit: Actually this stat is also implemented in ggstance, seems to just have been forgotten to make it default.
This works too:
ggplot2::ggplot(ggplot2::mpg, ggplot2::aes(y = class)) + ggstance::geom_barh(stat = ggstance::StatCounth)
If you are just trying to flip the axes you can use the following:
ggplot(mpg, aes(class)) + geom_bar() + coord_flip()
I think this is a problem with ggstance::geom_barh.
Remember, with ggstance, you must supply aesthetics in their natural order. geom_barh will flip the geom only; it will not flip the axes like coord_flip(). For your example to work, you would have to do this:
ggplot(mpg, (aes(y = class) +
geom_barh()
However, this produces an error:
Error: stat_count() must not be used with a y aesthetic.

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