I first make a plot
df <- data.frame(x = c(1:40, rep(1:20, 3), 15:40))
p <- ggplot(df, aes(x=x, y = x)) +
stat_density2d(aes(fill='red',alpha=..level..),geom='polygon', show.legend = F)
Then I want to change the geom_density values and use these in another plot.
# build plot
q <- ggplot_build(p)
# Change density
dens <- q$data[[1]]
dens$y <- dens$y - dens$x
Build the other plot using the changed densities, something like this:
# Built another plot
ggplot(df, aes(x=x, y =1)) +
geom_point(alpha = 0.3) +
geom_density2d(dens)
This does not work however is there a way of doing this?
EDIT: doing it when there are multiple groups:
df <- data.frame(x = c(1:40, rep(1:20, 3), 15:40), group = c(rep('A',40), rep('B',60), rep('C',26)))
p <- ggplot(df, aes(x=x, y = x)) +
stat_density2d(aes(fill=group,alpha=..level..),geom='polygon', show.legend = F)
q <- ggplot_build(p)
dens <- q$data[[1]]
dens$y <- dens$y - dens$x
ggplot(df, aes(x=x, y =1)) +
geom_point(aes(col = group), alpha = 0.3) +
geom_polygon(data = dens, aes(x, y, fill = fill, group = piece, alpha = alpha)) +
scale_alpha_identity() +
guides(fill = F, alpha = F)
Results when applied to my own dataset
Although this is exactly what I'm looking for the fill colors seem not to correspond to the initial colors (linked to A, B and C):
Like this? It is possible to plot a transformation of the shapes plotted by geom_density. But that's not quite the same as manipulating the underlying density...
ggplot(df, aes(x=x, y =1)) +
geom_point(alpha = 0.3) +
geom_polygon(data = dens, aes(x, y, fill = fill, group = piece, alpha = alpha)) +
scale_alpha_identity() +
guides(fill = F, alpha = F)
Edit - OP now has multiple groups. We can plot those with the code below, which produces an artistic plot of questionably utility. It does what you propose, but I would suggest it would be more fruitful to transform the underlying data and summarize that, if you are looking for representative output.
ggplot(df, aes(x=x, y =1)) +
geom_point(aes(col = group), alpha = 0.3) +
geom_polygon(data = dens, aes(x, y, fill = group, group = piece, alpha = alpha)) +
scale_alpha_identity() +
guides(fill = F, alpha = F) +
theme_minimal()
Related
I have the following graph and code:
Graph
ggplot(long2, aes(x = DATA, y = value, fill = variable)) + geom_area(position="fill", alpha=0.75) +
scale_y_continuous(labels = scales::comma,n.breaks = 5,breaks = waiver()) +
scale_fill_viridis_d() +
scale_x_date(date_labels = "%b/%Y",date_breaks = "6 months") +
ggtitle("Proporcions de les visites, només 9T i 9C") +
xlab("Data") + ylab("% visites") +
theme_minimal() + theme(legend.position="bottom") + guides(fill=guide_legend(title=NULL)) +
annotate("rect", fill = "white", alpha = 0.3,
xmin = as.Date.character("2020-03-16"), xmax = as.Date.character("2020-06-22"),
ymin = 0, ymax = 1)
But it has some sawtooth, how am I supposed to smooth it out?
I believe your situation is roughly analogous to the following, wherein we have missing x-positions for one group, but not the other at the same position. This causes spikes if you set position = "fill".
library(ggplot2)
x <- seq_len(100)
df <- data.frame(
x = c(x[-c(25, 75)], x[-50]),
y = c(cos(x[-c(25, 75)]), sin(x[-50])) + 5,
group = rep(c("A", "B"), c(98, 99))
)
ggplot(df, aes(x, y, fill = group)) +
geom_area(position = "fill")
To smooth out these spikes, it has been suggested to linearly interpolate the data at the missing positions.
# Find all used x-positions
ux <- unique(df$x)
# Split data by group, interpolate data groupwise
df <- lapply(split(df, df$group), function(xy) {
approxed <- approx(xy$x, xy$y, xout = ux)
data.frame(x = ux, y = approxed$y, group = xy$group[1])
})
# Recombine data
df <- do.call(rbind, df)
# Now without spikes :)
ggplot(df, aes(x, y, fill = group)) +
geom_area(position = "fill")
Created on 2022-06-17 by the reprex package (v2.0.1)
P.S. I would also have expected a red spike at x=50, but for some reason this didn't happen.
I am creating several plots in order to create frames for a gif. It is supposed to show growing points over time. (see plot 1 and 2 - the values increase). Using size aesthetic is problematic, because the scaling is done for each plot individually.
I tried to set breaks with scale_size_area() to provide a sequence of absolute values, in order to scale on 'all values' rather than only the values present in each plot. (no success).
Plot 3 shows how the points should be scaled, but this scaling should be achieved in each plot.
library(tidyverse)
df1 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25)
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
ggplot(df1, aes(x, y, size = size2)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
df2 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25) %>% gather(key, value, y:size2)
ggplot(df2, aes(x, value, size = value)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
Created on 2019-05-12 by the reprex package (v0.2.1)
Pass lower and upper bound to limits argument in scale_size_area function:
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
labs(
title = "Y on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
ggplot(df1, aes(x, y, size = size2 )) +
geom_point() +
labs(
title = "size2 on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
How about this?
library("ggplot2")
df1 <- data.frame(x = letters[1:5],
y = 1:5)
ggplot(data = df1,
aes(x = x,
y = y,
size = y)) +
geom_point() +
scale_size_area(breaks = seq(1,25,1),
limits = c(1, 25))
I want to overlay two plots: one is a simple point plot where a variable is used to control the dot size; and another is a simple curve.
Here is a dummy example for the first plot;
library(ggplot2)
x <- seq(from = 1, to = 10, by = 1)
df = data.frame(x=x, y=x^2, v=2*x)
ggplot(df, aes(x, y, size = v)) + geom_point() + theme_classic() + scale_size("blabla")
Now lets overlay a curve to this plot with data from another dataframe:
df2 = data.frame(x=x, y=x^2-x+2)
ggplot(df, aes(x, y, size = v)) + geom_point() + theme_classic() + scale_size("blabla") + geom_line(data=df2, aes(x, y), color = "blue") + scale_color_discrete(name = "other", labels = c("nanana"))
It produces the error:
Error in FUN(X[[i]], ...) : object 'v' not found
The value in v is not used to draw the intended curse, but anyway, I added a dummy v to df2.
df2 = data.frame(x=x, y=x^2-x+2, v=replicate(length(x),0)) # add a dummy v
ggplot(df, aes(x, y, size = v)) + geom_point() + theme_classic() + scale_size("blabla") + geom_line(data=df2, aes(x, y), color = "blue") + scale_color_discrete(name = "other", labels = c("nanana"))
An the result has a messed legend:
What is the right way to achieve the desired plot?
You can put the size aes in the geom_point() call to make it so that you don't need the dummy v in df2.
Not sure exactly what you want regarding the legend. If you replace the above, then the blue portion goes away. If you want to have a legend for the line color, then you have to place color inside the geom_line aes call.
x <- seq(from = 1, to = 10, by = 1)
df = data.frame(x=x, y=x^2, v=2*x)
df2 = data.frame(x=x, y=x^2-x+2)
ggplot(df, aes(x, y)) +
geom_point(aes(size = v)) +
theme_classic() +
scale_size("blabla") +
geom_line(data=df2, aes(x, y, color = "blue")) +
scale_color_manual(values = "blue", labels = "nanana", name = "other")
Example data frame (if there's a better/more idiomatic way to do this, let me know):
n <- 10
group <- rep(c("A","B","C"),each = n)
x <- rep(seq(0,1,length = n),3)
y <- ifelse(group == "A",1+x,ifelse(group == "B",2+2*x,3+3*x))
df <- data.frame(group,x,y)
xd <- 0.5
des <- data.frame(xd)
I want to plot create point-line plots for the data in df, add a vertical curve at the x location indicated by xd, and get readable legends for both. I tried the following:
p <- ggplot(data = df, aes(x = x, y = y, color = group)) + geom_point() + geom_line(aes(linetype=group))
p <- p + geom_vline(data = des, aes(xintercept = xd), color = "blue")
p
Not quite what I had in mind, there's no legend for the vertical line.
A small modification (I don't understand why geom_vline is one of the few geometries with a show.legend parameter, which moreover defaults to FALSE!):
p <- ggplot(data = df, aes(x = x, y = y, color = group)) + geom_point() + geom_line(aes(linetype=group))
p <- p + geom_vline(data = des, aes(xintercept = xd), color = "blue", show.legend = TRUE)
p
At least now the vertical bar is showing in the legend, but I don't want it to go in the same "category" (?) as group. I would like another legend entry, titled Design, and containing only the vertical line. How can I achieve this?
A possible approach is to add an extra dummy aesthetic like fill =, which we'll subsequently use to create the second legend in combination with scale_fill_manual() :
ggplot(data = df, aes(x = x, y = y, color = group)) +
geom_point() +
geom_line(aes(linetype=group), show.legend = TRUE) +
geom_vline(data = des,
aes(xintercept = xd, fill = "Vertical Line"), # add dummy fill
colour = "blue") +
scale_fill_manual(values = 1, "Design", # customize second legend
guide = guide_legend(override.aes = list(colour = c("blue"))))
I'm plotting 11 curves and the program bellow works well. BUT I'm not able two change the wild colors to plot 11 black curves:
library(ggplot2)
#library(latex2exp)
library(reshape)
fn <- "img/plot.eps"
fct1 <- function(x0 ){
return(1/sin(x0)+1/tan(x0))
}
fct2 <- function(beta, t ){
return(2*atan(exp(t)/beta))
}
t<-seq(from=0,to=10,by=0.01)
s1<-cbind(t, fct2(fct1(-pi+0.0001),t),
fct2(fct1(-1.5),t),
fct2(fct1(-0.5),t),
fct2(fct1(-0.05),t),
fct2(fct1(-0.01),t),
fct2(fct1(0),t),
fct2(fct1(0.01),t),
fct2(fct1(0.05),t),
fct2(fct1(0.5),t),
fct2(fct1(1.5),t),
fct2(fct1(pi),t))
colnames(s1)<-c("time","y1","y2","y3","y4","y5","y6","y7","y8","y9","y10","y11")
s2 <- melt(as.data.frame(s1), id = "time")
q <- ggplot(s2, aes(x = time, y = value, color = variable))
q <- q + geom_line() + ylab("y") + xlab("t")+ ylab("x(t)")+
theme_bw(base_size = 7) + guides(colour = FALSE)
ggsave(file = fn, width = 2, height = 1)
q
EDIT Now the code should be reproducible
You need to map the variable to the grouping, and it will produce black lines by default.
q <- ggplot() +
geom_line(data = s2, aes(x = time, y = value,
group = variable)) +
xlab("t")+ ylab("x(t)") +
theme_bw(base_size = 7) + guides(colour = FALSE)
q
To be perfectly clear, it is possible to map the color to the variable, which can produce black lines, but not without changing the legend. Here is how you would amend the colors after the fact, if you wanted to, having already mapped the color to the variable.
q <- ggplot() +
geom_line(data = s2, aes(x = time, y = value,
color = variable)) +
xlab("t")+ ylab("x(t)") +
theme_bw(base_size = 7) + guides(colour = FALSE) +
scale_color_manual(values = rep("black",11))
q