I want to draw two circles inside each other with ggplot2.
So far my effort is:
make a fake data and plot it with geom_line(). If I convert this with coord_polar() then I will not be able to see two different circles the one inside each other
library(ggplot2)
library(tidyverse)
x1=seq(0,6000000,1000)
y1=rep(1,length(x1))
y2=rep(2,length(x1))
data=as.data.frame(cbind(x1,y1,y2))
Created on 2021-12-25 by the reprex package (v2.0.1)
# plot the data
ggplot(data) +
geom_line(aes(x1,y1)) +
geom_line(aes(x1,y2))
#coord_polar()
I would avoid the geom_circle option and use the coord_polar option if possible.
The reason is that these two circles have some differences in the x-axis, which I would indicate after drawing the circles.
I would like my plot to look like this
The code you have with coord_polar() is correct, just the plot limits need adjusting to see both the circles, e.g.
ggplot(data) +
geom_line(aes(x1,y1)) +
geom_line(aes(x1,y2)) +
coord_polar() + ylim(c(0,NA))
The reason for using ylim is that this is the direction getting transformed to the radius by the coord_polar()
Why not use two geom_point() with different sizes and pch = 21?
library(ggplot2)
df <- tibble(x = 0, y = 0)
ggplot(df, aes(x, y)) +
geom_point(pch = 21, size = 50) +
geom_point(pch = 21, size = 40) +
theme_void()
Related
This is my dataframe:
df = data.frame(info=1:30, type=c(replicate(5,'A'), replicate(5,'B')), group= c(replicate(10,'D1'), replicate(10,'D2'), replicate(10,'D3')))
I want to make a jitter plot of my data distinguished by group (X-label) and type (colour):
ggplot()+
theme(panel.background=element_rect(colour="grey", size=0.2, fill='grey100'))+
geom_jitter(data=df, aes(x=group, y=info, color=type, shape=type), position=position_dodge(0.2), cex=2)+
scale_shape_manual(values=c(17,15,19))+
scale_color_manual(values=c(A="mediumvioletred", B="blue"))
How can I reduce the distance between the X-labels (D1, D2, D3) in the representation?
P.D. I want to do it even if I left a blank space in the graphic
Here are a few options.
# Setting up the plot
library(ggplot2)
df <- data.frame(
info=1:30,
type=c(replicate(5,'A'), replicate(5,'B')),
group= c(replicate(10,'D1'), replicate(10,'D2'), replicate(10,'D3'))
)
p <- ggplot(df, aes(group, info, colour = type, shape = type))
Option 1: increase the dodge distance. This won't put the labels closer, but it makes better use of the space available so that the labels appear less isolated.
p +
geom_point(position = position_dodge(width = 0.9))
Option 2: Expand the x-axis. Increasing the expansion factor from the default 0.5 to >0.5 increases the space at the ends of the axis, putting the labels closer.
p +
geom_point(position = position_dodge(0.2)) +
scale_x_discrete(expand = c(2, 0))
Option 3: change the aspect ratio. Depending on the plotting window size, this also visually puts the x-axis labels closer together.
p +
geom_point(position = position_dodge(0.2)) +
theme(aspect.ratio = 2)
Created on 2021-06-25 by the reprex package (v1.0.0)
Try adding coord_fixed(ratio = 0.2) and play around with the ratio.
ggplot()+
theme(panel.background=element_rect(colour="grey", size=0.2, fill='grey100'))+
geom_jitter(data=df, aes(x=group, y=info, color=type, shape=type), position=position_dodge(0.2))+
scale_shape_manual(values=c(17,15,19))+
scale_color_manual(values=c(A="mediumvioletred", B="blue")) + coord_fixed(ratio = 0.2)
The simplest solution is to resize the plot. For example if you follow your command with ggsave("my_plot.pdf", width = 3, height = 4.5) it looks like this:
Or in an Rmd file you can control the dimensions by various means: see this link.
I'm currently working on a very simple data.frame, containing three columns:
x contains x-coordinates of a set of points,
y contains y-coordinates of the set of points, and
weight contains a value associated to each point;
Now, working in ggplot2 I seem to be able to plot contour levels for these data, but i can't manage to find a way to fill the plot according to the variable weight. Here's the code that I used:
ggplot(df, aes(x,y, fill=weight)) +
geom_density_2d() +
coord_fixed(ratio = 1)
You can see that there's no filling whatsoever, sadly.
I've been trying for three days now, and I'm starting to get depressed.
Specifying fill=weight and/or color = weight in the general ggplot call, resulted in nothing. I've tried to use different geoms (tile, raster, polygon...), still nothing. Tried to specify the aes directly into the geom layer, also didn't work.
Tried to convert the object as a ppp but ggplot can't handle them, and also using base-R plotting didn't work. I have honestly no idea of what's wrong!
I'm attaching the first 10 points' data, which is spaced on an irregular grid:
x = c(-0.13397460,-0.31698730,-0.13397460,0.13397460,-0.28867513,-0.13397460,-0.31698730,-0.13397460,-0.28867513,-0.26794919)
y = c(-0.5000000,-0.6830127,-0.5000000,-0.2320508,-0.6547005,-0.5000000,-0.6830127,-0.5000000,-0.6547005,0.0000000)
weight = c(4.799250e-01,5.500250e-01,4.799250e-01,-2.130287e+12,5.798250e-01,4.799250e-01,5.500250e-01,4.799250e-01,5.798250e-01,6.618956e-01)
any advise? The desired output would be something along these lines:
click
Thank you in advance.
From your description geom_density doesn't sound right.
You could try geom_raster:
ggplot(df, aes(x,y, fill = weight)) +
geom_raster() +
coord_fixed(ratio = 1) +
scale_fill_gradientn(colours = rev(rainbow(7)) # colourmap
Here is a second-best using fill=..level... There is a good explanation on ..level.. here.
# load libraries
library(ggplot2)
library(RColorBrewer)
library(ggthemes)
# build your data.frame
df <- data.frame(x=x, y=y, weight=weight)
# build color Palette
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")
# Plot
ggplot(df, aes(x,y, fill=..level..) ) +
stat_density_2d( bins=11, geom = "polygon") +
scale_fill_gradientn(colours = myPalette(11)) +
theme_minimal() +
coord_fixed(ratio = 1)
I am trying to recreate a figure from a GGplot2 seminar http://dl.dropbox.com/u/42707925/ggplot2/ggplot2slides.pdf.
In this case, I am trying to generate Example 5, with jittered data points subject to a dodge. When I run the code, the points are centered around the correct line, but have no jitter.
Here is the code directly from the presentation.
set.seed(12345)
hillest<-c(rep(1.1,100*4*3)+rnorm(100*4*3,sd=0.2),
rep(1.9,100*4*3)+rnorm(100*4*3,sd=0.2))
rep<-rep(1:100,4*3*2)
process<-rep(rep(c("Process 1","Process 2","Process 3","Process 4"),each=100),3*2)
memorypar<-rep(rep(c("0.1","0.2","0.3"),each=4*100),2)
tailindex<-rep(c("1.1","1.9"),each=3*4*100)
ex5<-data.frame(hillest=hillest,rep=rep,process=process,memorypar=memorypar, tailindex=tailindex)
stat_sum_df <- function(fun, geom="crossbar", ...) {stat_summary(fun.data=fun, geom=geom, ...) }
dodge <- position_dodge(width=0.9)
p<- ggplot(ex5,aes(x=tailindex ,y=hillest,color=memorypar))
p<- p + facet_wrap(~process,nrow=2) + geom_jitter(position=dodge) +geom_boxplot(position=dodge)
p
In ggplot2 version 1.0.0 there is new position named position_jitterdodge() that is made for such situation. This postion should be used inside the geom_point() and there should be fill= used inside the aes() to show by which variable to dodge your data. To control the width of dodging argument dodge.width= should be used.
ggplot(ex5, aes(x=tailindex, y=hillest, color=memorypar, fill=memorypar)) +
facet_wrap(~process, nrow=2) +
geom_point(position=position_jitterdodge(dodge.width=0.9)) +
geom_boxplot(fill="white", outlier.colour=NA, position=position_dodge(width=0.9))
EDIT: There is a better solution with ggplot2 version 1.0.0 using position_jitterdodge. See #Didzis Elferts' answer. Note that dodge.width controls the width of the dodging and jitter.width controls the width of the jittering.
I'm not sure how the code produced the graph in the pdf.
But does something like this get you close to what you're after?
I convert tailindex and memorypar to numeric; add them together; and the result is the x coordinate for the geom_jitter layer. There's probably a more effective way to do it. Also, I'd like to see how dodging geom_boxplot and geom_jitter, and with no jittering, will produce the graph in the pdf.
library(ggplot2)
dodge <- position_dodge(width = 0.9)
ex5$memorypar2 <- as.numeric(ex5$tailindex) +
3 * (as.numeric(as.character(ex5$memorypar)) - 0.2)
p <- ggplot(ex5,aes(x=tailindex , y=hillest)) +
scale_x_discrete() +
geom_jitter(aes(colour = memorypar, x = memorypar2),
position = position_jitter(width = .05), alpha = 0.5) +
geom_boxplot(aes(colour = memorypar), outlier.colour = NA, position = dodge) +
facet_wrap(~ process, nrow = 2)
p
See plot here:
(from here)
How do I reproduce both the upper and lower portion of the barplot using ggplot2?
For example, I can produce the upper portion with
ggplot(data.frame(x=rnorm(1000, 5)), aes(x=x)) + geom_bar() + scale_y_reverse()
However now if I add any other geom_, such as another geom_bar() the scale for y is reversed. Is it possible to apply the scale_y_reverse() to only a specific geom_?
Another option is to make two separate plots and combine them with arrangeGrob from the gridExtra package. After playing with the plot margins, you can arrive at something that looks decent.
library(gridExtra)
library(ggplot2)
set.seed(100)
p2 <- ggplot(data.frame(x=rnorm(1000, 5)), aes(x=x)) + geom_bar() + theme(plot.margin=unit(c(0,0,0,0), 'lines'))
p1 <- p2 + scale_y_reverse() +
theme(plot.margin=unit(c(0, 0, -.8, 0), 'lines'), axis.title.x=element_blank(),
axis.text.x=element_blank(), axis.ticks.x=element_blank())
p <- arrangeGrob(p1, p2)
print(p)
ggplot only like to have one y-axis scale. The easiest thing would be to basically reshape your data yourself. Here we can use geom_rect to draw the data where ever we like and we can condition it on group time. Here's an example
#sample data
dd<-data.frame(
year=rep(2000:2014, 2),
group=rep(letters[1:2], each=15),
count=rpois(30, 20)
)
And now we can plot it. But first, let's define the offset to the top bars by finding the maxima height at a year and adding a bit of space
height <- ceiling(max(tapply(dd$count, dd$year, sum))*1.10)
And here's how we plot
ggplot(dd) +
geom_rect(aes(xmin=year-.4, xmax=year+.4,
ymin=ifelse(group=="a", 0, height-count),
ymax=ifelse(group=="a", count, height), fill=group)) +
scale_y_continuous(expand=c(0,0))
And that will give us
I'm using ggplot2 to make line graphs of some log-transformed data that all have large values (between 10^6 and 10^8); since the axes doesn't start at zero, I'd prefer not to have them intersect at the "origin."
Here's what the axes currently look like:
I'd prefer something more like one gets from base graphics (but I'm additionally using geom_ribbon and other fancy things I really like in ggplot2, so I'd prefer to find a ggplot2 solution):
Here's what I'm doing currently:
mydata <- data.frame(Day = rep(1:8, 3),
Treatment = rep(c("A", "B", "C"), each=8),
Value = c(7.415929, 7.200486, 7.040555, 7.096490, 7.056413, 7.143981, 7.429724, 7.332760, 7.643673, 7.303994, 7.343151, 6.923636, 6.923478, 7.249170, 7.513370, 7.438630, 7.209895, 7.000063, 7.160154, 6.677734, 7.026307, 6.830495, 6.863329, 7.319219))
ggplot(mydata, aes(x=Day, y=Value, group=Treatment))
+ theme_classic()
+ geom_line(aes(color = Treatment), size=1)
+ scale_y_continuous(labels = math_format(10^.x))
+ coord_cartesian(ylim = c(6.4, 7.75), xlim=c(0.5, 8))
plot(mydata$Day, mydata$Value, frame.plot = F) #non-intersecting axes
Workaround for this problem would be to remove axis lines with theme(axis.line=element_blank()) and then add false axis lines with geom_segment() - one for x axis and second for y axis. x, y , xend and yend values are determined from your plot (taken as the smallest and the largest values shown on plot for each corresponding axis) and axis limits used in coord_cartesian() (minimal value of limits to ensure that segment is plotted in place of axis).
ggplot(mydata, aes(x=Day, y=Value, group=Treatment)) +theme_classic() +
geom_line(aes(color = Treatment), size=1) +
scale_y_continuous(labels = math_format(10^.x))+
coord_cartesian(ylim = c(6.4, 7.75), xlim=c(0.5, 8))+
theme(axis.line=element_blank())+
geom_segment(x=2,xend=8,y=6.4,yend=6.4)+
geom_segment(x=0.5,xend=0.5,y=6.5,yend=7.75)
An older question. But since I was looking for this functionality recently I thought I'd flag the ggh4x package, which adds guides for truncating axes.
library(ggh4x)
#> Loading required package: ggplot2
ggplot(data.frame(x=0:10, y=0:10), aes(x, y)) +
geom_point() +
theme_classic() +
guides(x = "axis_truncated", y = "axis_truncated")
Created on 2023-02-17 with reprex v2.0.2
Apart from convenience, two nice things about the ggh4x option are that 1) it is stable across more complex plot compositions like faceting and 2) its dependencies are a subset of those belonging to ggplot2, so you aren't introducing a bunch of additional imports.
P.S. There's an open GitHub issue to bring this kind of "floating axes" functionality to the main ggplot2 library. It looks like it will eventually be incorporated.