I'm using the "tips" data set in ggplot2. If I do
sp = ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_point(shape=1) +
facet_grid(sex ~ day)
The plot comes out fine. But I now want to change the panel background for just the plots under "Fri". Is there a way to do this?
Even better, can I conditionally change colors by passing parameters? For example if more than 3 points are below 0.1, then change panel background (for just that panel) to a certain color while all others remain the default light grey?
The general rule for doing anything in ggplot2 is to,
Create a data frame that encodes the information you want to plot
Pass that data frame to a geom
This is made a bit more complicated in this case because of the particular aspect of the plot you want to alter. The Powers That Be designed ggplot2 in a way that separates data elements of the plot (i.e. geom's) from non-data elements (i.e. theme's), and it so happens that the plot background falls under the "non-data" category.
There is always the option of modifying the underlying grid object manually but this is tedious and the details may change with different versions of ggplot2. Instead, we'll employ the "hack" that Hadley refers to in this question.
#Create a data frame with the faceting variables
# and some dummy data (that will be overwritten)
tp <- unique(tips[,c('sex','day')])
tp$total_bill <- tp$tip <- 1
#Just Fri
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = subset(tp,day == 'Fri'),aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
#Each panel
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = tp,aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
I cannot comment yet.. so here is an additional answer to joran his answer.
If you are having trouble with the transparency setting, like setting alpha = 0.2 but not noticing any difference, it might be because of the data that you give to ggplot.
"Thanks for clarifying your question. This was puzzling to me, so I went to google, and ended up learning something new (after working around some vagaries in their examples). Apparently what you are doing is drawing many rectangles on top of each other, effectively nullifying the semi-transparency you want. So, the only ways to overcome this are to hard-code the rectangle coordinates in a separate df"
This answer comes from
geom_rect and alpha - does this work with hard coded values?
Related
I'm using the "tips" data set in ggplot2. If I do
sp = ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_point(shape=1) +
facet_grid(sex ~ day)
The plot comes out fine. But I now want to change the panel background for just the plots under "Fri". Is there a way to do this?
Even better, can I conditionally change colors by passing parameters? For example if more than 3 points are below 0.1, then change panel background (for just that panel) to a certain color while all others remain the default light grey?
The general rule for doing anything in ggplot2 is to,
Create a data frame that encodes the information you want to plot
Pass that data frame to a geom
This is made a bit more complicated in this case because of the particular aspect of the plot you want to alter. The Powers That Be designed ggplot2 in a way that separates data elements of the plot (i.e. geom's) from non-data elements (i.e. theme's), and it so happens that the plot background falls under the "non-data" category.
There is always the option of modifying the underlying grid object manually but this is tedious and the details may change with different versions of ggplot2. Instead, we'll employ the "hack" that Hadley refers to in this question.
#Create a data frame with the faceting variables
# and some dummy data (that will be overwritten)
tp <- unique(tips[,c('sex','day')])
tp$total_bill <- tp$tip <- 1
#Just Fri
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = subset(tp,day == 'Fri'),aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
#Each panel
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = tp,aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
I cannot comment yet.. so here is an additional answer to joran his answer.
If you are having trouble with the transparency setting, like setting alpha = 0.2 but not noticing any difference, it might be because of the data that you give to ggplot.
"Thanks for clarifying your question. This was puzzling to me, so I went to google, and ended up learning something new (after working around some vagaries in their examples). Apparently what you are doing is drawing many rectangles on top of each other, effectively nullifying the semi-transparency you want. So, the only ways to overcome this are to hard-code the rectangle coordinates in a separate df"
This answer comes from
geom_rect and alpha - does this work with hard coded values?
I have an ethogram-like ggplot where I plot the value of a factor quadrant (1 to 4), which is plotted for each frame of a movie (frameID). The color is given by 3 animals that are being tracked.
I am fairly satisfied with the graph but the amount of points makes it difficult to see, even with alpha. I was wondering how to add position_dodge in a way that doesn't destroy the plot.
ggplot(dataframe) ,
aes(frameID, quadrant, color=animal)) +
geom_jitter(alpha=0.5) +
scale_color_manual(values = c("#1334C1","#84F619", "#F43900")) +
theme_classic()+
theme(legend.position = 'none')
This link has useful info about dodging using geom_point.
R: How to spread (jitter) points with respect to the x axis?
I can change to geom_point with height, which works but it produces something awful.
+ geom_point(position = position_jitter(w = 0, h = 2))
Update
Data lives in GitHub
Lowering the alpha or changing size helps, adds trouble when rescaling the image.
https://github.com/matiasandina/MLA2_Tracking/blob/master/demo_data/sample_data.csv
Update 2022
It's been a while since I posted this initially, my original thoughts changed and are better reflected here, but I am still looking for a ggplot2 version of doing this!
I want to compare the distribution of several variables (here X1 and X2) with a single value (here bm). The issue is that these variables are too many (about a dozen) to use a single boxplot.
Additionaly the levels are too different to use one plot. I need to use facets to make things more organised:
However with this plot my benchmark category (bm), which is a single value in X1 and X2, does not appear in X1 and seems to have several values in X2. I want it to be only this green line, which it is in the first plot. Any ideas why it changes? Is there any good workaround? I tried the options of facet_wrap/facet_grid, but nothing there delivered the right result.
I also tried combining a bar plot with bm and three empty categories with the boxplot. But firstly it looked terrible and secondly it got similarly screwed up in the facetting. Basically any work around would help.
Below the code to create the minimal example displayed here:
# Creating some sample data & loading libraries
library(ggplot2)
library(RColorBrewer)
set.seed(10111)
x=matrix(rnorm(40),20,2)
y=rep(c(-1,1),c(10,10))
x[y==1,]=x[y==1,]+1
x[,2]=x[,2]+20
df=data.frame(x,y)
# creating a benchmark point
benchmark=data.frame(y=rep("bm",2),key=c("X1","X2"),value=c(-0.216936,20.526312))
# melting the data frame, rbinding it with the benchmark
test_dat=rbind(tidyr::gather(df,key,value,-y),benchmark)
# Creating a plot
p_box <- ggplot(data = test_dat, aes(x=key, y=value,color=as.factor(test_dat$y))) +
geom_boxplot() + scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1"))
# The first line delivers the first plot, the second line the second plot
p_box
p_box + facet_wrap(~key,scales = "free",drop = FALSE) + theme(legend.position = "bottom")
The problem only lies int the use of test_dat$y inside the color aes. Never use $ in aes, ggplot will mess up.
Anyway, I think you plot would improve if you use a geom_hline for the benchmark, instead of hacking in a single value boxplot:
library(ggplot2)
library(RColorBrewer)
ggplot(tidyr::gather(df,key,value,-y)) +
geom_boxplot(aes(x=key, y=value, color=as.factor(y))) +
geom_hline(data = benchmark, aes(yintercept = value), color = '#4DAF4A', size = 1) +
scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1")) +
facet_wrap(~key,scales = "free",drop = FALSE) +
theme(legend.position = "bottom")
I would like to use customized linetypes in ggplot. If that is impossible (which I believe to be true), then I am looking for a smart hack to plot arrowlike symbols above, or below, my line.
Some background:
I want to plot some water quality data and compare it to the standard (set by the European Water Framework Directive) in a red line. Here's some reproducible data and my plot:
df <- data.frame(datum <- seq.Date(as.Date("2014-01-01"),
as.Date("2014-12-31"),by = "week"),y=rnorm(53,mean=100,sd=40))
(plot1 <-
ggplot(df, aes(x=datum,y=y)) +
geom_line() +
geom_point() +
theme_classic()+
geom_hline(aes(yintercept=70),colour="red"))
However, in this plot it is completely unclear if the Standard is a maximum value (as it would be for example Chloride) or a minimum value (as it would be for Oxygen). So I would like to make this clear by adding small pointers/arrows Up or Down. The best way would be to customize the linetype so that it consists of these arrows, but I couldn't find a way.
Q1: Is this at all possible, defining custom linetypes?
All I could think of was adding extra points below the line:
extrapoints <- data.frame(datum2 <- seq.Date(as.Date("2014-01-01"),
as.Date("2014-12-31"),by = "week"),y2=68)
plot1 + geom_point(data=extrapoints, aes(x=datum2,y=y2),
shape=">",size=5,colour="red",rotate=90)
However, I can't seem to rotate these symbols pointing downward. Furthermore, this requires calculating the right spacing of X and distance to the line (Y) every time, which is rather inconvenient.
Q2: Is there any way to achieve this, preferably as automated as possible?
I'm not sure what is requested, but it sounds as though you want arrows at point up or down based on where the y-value is greater or less than some expected value. If that's the case, then this satisfies using geom_segment:
require(grid) # as noted by ?geom_segment
(plot1 <-
ggplot(df, aes(x=datum,y=y)) + geom_line()+
geom_segment(data = data.frame( df$datum, y= 70, up=df$y >70),
aes(xend = datum , yend =70 + c(-1,1)[1+up]*5), #select up/down based on 'up'
arrow = arrow(length = unit(0.1,"cm"))
) + # adjust units to modify size or arrow-heads
geom_point() +
theme_classic()+
geom_hline(aes(yintercept=70),colour="red"))
If I'm wrong about what was desired and you only wanted a bunch of down arrows, then just take out the stuff about creating and using "up" and use a minus-sign.
I'm using the "tips" data set in ggplot2. If I do
sp = ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_point(shape=1) +
facet_grid(sex ~ day)
The plot comes out fine. But I now want to change the panel background for just the plots under "Fri". Is there a way to do this?
Even better, can I conditionally change colors by passing parameters? For example if more than 3 points are below 0.1, then change panel background (for just that panel) to a certain color while all others remain the default light grey?
The general rule for doing anything in ggplot2 is to,
Create a data frame that encodes the information you want to plot
Pass that data frame to a geom
This is made a bit more complicated in this case because of the particular aspect of the plot you want to alter. The Powers That Be designed ggplot2 in a way that separates data elements of the plot (i.e. geom's) from non-data elements (i.e. theme's), and it so happens that the plot background falls under the "non-data" category.
There is always the option of modifying the underlying grid object manually but this is tedious and the details may change with different versions of ggplot2. Instead, we'll employ the "hack" that Hadley refers to in this question.
#Create a data frame with the faceting variables
# and some dummy data (that will be overwritten)
tp <- unique(tips[,c('sex','day')])
tp$total_bill <- tp$tip <- 1
#Just Fri
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = subset(tp,day == 'Fri'),aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
#Each panel
ggplot(tips,aes(x=total_bill, y = tip/total_bill)) +
geom_rect(data = tp,aes(fill = day),xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf,alpha = 0.3) +
geom_point(shape=1) +
facet_grid(sex ~ day)
I cannot comment yet.. so here is an additional answer to joran his answer.
If you are having trouble with the transparency setting, like setting alpha = 0.2 but not noticing any difference, it might be because of the data that you give to ggplot.
"Thanks for clarifying your question. This was puzzling to me, so I went to google, and ended up learning something new (after working around some vagaries in their examples). Apparently what you are doing is drawing many rectangles on top of each other, effectively nullifying the semi-transparency you want. So, the only ways to overcome this are to hard-code the rectangle coordinates in a separate df"
This answer comes from
geom_rect and alpha - does this work with hard coded values?