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Side-by-side plots with ggplot2
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I want the six plots in one plot. And I would like to specify the titles of each plot. How can I do that?
p<-ggplot(df, aes(x=COD_NEIGHB))+
geom_bar(stat="count", width=0.3, fill="steelblue")+
theme_minimal()
# histogram of the strata in the whole dataset
s<-ggplot(data = df, mapping = aes(x = COD_NEIGHB)) +
geom_bar(stat="count", width=0.3, fill="steelblue")+
facet_wrap(~ fold)
plot_grid(p, s, ncol=2,label_size = 2)
After that, I did the suggestion
df$fold <- as.character(df$fold)
# Duplicate data. Set category in the duplicated dataset to "all"
df_all <- df
df_all$fold <- "all"
# Row bind the datasets
df_all <- rbind(df, df_all)
ggplot(df_all, aes(x=COD_NEIGHB)) +
geom_bar(stat="count", width=0.3, fill="steelblue")+
facet_wrap(~fold)
But now the problem is the scale. y-axis has to be on the proper scale.
any idea for that?
Thanks in advance!!!!
If I got you right you want a plot with facets by categories plus an additonal facet showing the total data. One option to achieve this is to duplicate your dataset to add an addtional category "all".
As no example data was provided I make use of mtcars to show you the basic idea:
library(ggplot2)
mtcars$cyl <- as.character(mtcars$cyl)
# Duplicate data. Set category in the duplicated dataset to "all"
mtcars_all <- mtcars
mtcars_all$cyl <- "all"
# Row bind the datasets
mtcars_all <- rbind(mtcars, mtcars_all)
ggplot(mtcars_all, aes(hp, mpg)) +
geom_point() +
facet_wrap(~cyl)
Here is another useful tool with the help of the ggarrange() function from the ggpubr package. You can arrange multiple plots on one page or multiple pages. You can also create a common, unique legend once you merge all your plots together.
Similar to previous answers, I used mtcars to demonstrate a simple use case:
#install.packages("ggpubr")
#library(ggpubr)
p1 <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
theme_minimal()
p2 <- ggplot(mtcars, aes(y = mpg, x = cyl)) +
geom_boxplot() +
theme_minimal()
ggarrange(p1, p2, ncol = 2)
I'm preparing an appendix plot for a revised manuscript where I need to give information of the within-year ranges (variability) of several variables between years and sites.
I figured the tidiest way to do this (I have 7 sites, 21 years, and 5 variables...) would be to use a rose plot using coord_polar. However, I stumbled upon something that has always frustrated me about ggplot - the default ordering assumptions. While factors are easily reordered based on some value, this seems to only work in a fixed fashion: as far as I've understood, the order needs to apply throughout the data frame.
In this plot, the ordering needs to depend on a value which changes between years, and therefore the colour and fill values need to change in plotting order within the panel.
To demonstrate, I've created a reproducible example coded below (pictured in the way it should not work)
Basically, I always need the Site with the minimum value within a given Year to be plotted first (in the centre), followed outwards by the increase in value of the other sites, in order of the original value (see order and diff columns of the data frame). In other words, some years Site a will be at the centre, some years Site c will be in the centre, etc.
Any help would be massively appreciated.
library('ggplot2')
library('reshape2')
library("plyr")
## reproducible example of problem: create dummy data
madeup <- data.frame(Year = rep(2000:2015, each=20), Site=rep(c("a","b","c","d"), each=5, times=16),
var1 = rnorm(n=16*20, mean=20, sd=5), var2= rnorm(n=16*20, mean=50, sd=1))
## create ranges of the data by Year and Site
myRange <- function(dat) {range=max(dat, na.rm=TRUE)-min(dat,na.rm = TRUE)}
vardf <- ddply(madeup, .(Site, Year), summarise, var1=myRange(var1),
var2=myRange(var2))
varmelt <- melt(vardf, id.vars = c("Site","Year"))
varmelt$Site <- as.character(varmelt$Site) # this to preserve the new order when rbind called
varmelt <- by(varmelt, list(varmelt$Year, varmelt$variable), function(x) {x <- x[order(x$value),]
x$order <- 1:nrow(x)
return(x)})
varmelt <- do.call(rbind, varmelt)
## create difference between these values so that each site gets plotted cumulatively on the rose plot
## (otherwise areas close to the centre become uninterpretable)
vartest <- by(varmelt, list(varmelt$Year, varmelt$variable), function(x) {
x$diff <- c(x$value[1], diff(x$value))
return(x)
})
vartest <- do.call(rbind,vartest)
## plot rose plot to display how ranges in variables vary by year and between sites
## for this test example we'll just take one variable, but the idea is to facet by variable
max1 <- max(vartest$value[vartest$variable=='var1'])
yearlength <- length(2000:2015)
ggplot(vartest[vartest$variable=="var1",], aes(x=factor(Year), y=diff)) +
theme_bw() +
geom_hline(yintercept = seq(0,max1, by=1), size=0.3, col="grey60",lty=3) +
geom_vline(xintercept=seq(1,yearlength,1), size=0.3, col='grey30', lty=2) +
geom_bar(stat='identity', width=1, size=0.5, aes(col=Site, fill=Site)) +
scale_x_discrete() +
coord_polar() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
As long as you don't use stacked bars (position = "stack", which is the default for geom_bar), ggplot2 will actually use the order of the rows in your data for the plotting order. So all you need to do, is use the original values for the y-axis (rather than the cumulatively differenced ones) along with position = "identity", and order your data from largest to smallest value before plotting:
ordered_data <- vartest[order(-vartest$value), ]
ggplot(ordered_data, aes(factor(Year), value)) +
geom_col(aes(fill = Site), position = "identity", width = 1) +
coord_polar() +
facet_wrap(~ variable)
Created on 2018-02-17 by the reprex package (v0.2.0).
PS. When generating random data for an example, consider using set.seed so that your results can be reproduced exactly.
You can start with a single plot of the largest site, and then layer smaller sites on top like so:
a <- ggplot(vartest[vartest$variable=="var1"& vartest$order==4,], aes(x=factor(Year), y=value,group=order)) +
theme_bw() +
geom_hline(yintercept = seq(0,max1, by=1), size=0.3, col="grey60",lty=3) +
geom_vline(xintercept=seq(1,yearlength,1), size=0.3, col='grey30', lty=2) +
geom_bar(stat='identity', width=1, size=0.5, aes(col=Site, fill=Site)) +
scale_x_discrete() +
coord_polar() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
b <- a + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==3,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
c <- b + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==2,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
c + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==1,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
This produces the following:
Is that what you wanted?
I'm having quite the time understanding geom_bar() and position="dodge". I was trying to make some bar graphs illustrating two groups. Originally the data was from two separate data frames. Per this question, I put my data in long format. My example:
test <- data.frame(names=rep(c("A","B","C"), 5), values=1:15)
test2 <- data.frame(names=c("A","B","C"), values=5:7)
df <- data.frame(names=c(paste(test$names), paste(test2$names)), num=c(rep(1,
nrow(test)), rep(2, nrow(test2))), values=c(test$values, test2$values))
I use that example as it's similar to the spend vs. budget example. Spending has many rows per names factor level whereas the budget only has one (one budget amount per category).
For a stacked bar plot, this works great:
ggplot(df, aes(x=factor(names), y=values, fill=factor(num))) +
geom_bar(stat="identity")
In particular, note the y value maxes. They are the sums of the data from test with the values of test2 shown on blue on top.
Based on other questions I've read, I simply need to add position="dodge" to make it a side-by-side plot vs. a stacked one:
ggplot(df, aes(x=factor(names), y=values, fill=factor(num))) +
geom_bar(stat="identity", position="dodge")
It looks great, but note the new max y values. It seems like it's just taking the max y value from each names factor level from test for the y value. It's no longer summing them.
Per some other questions (like this one and this one, I also tried adding the group= option without success (produces the same dodged plot as above):
ggplot(df, aes(x=factor(names), y=values, fill=factor(num), group=factor(num))) +
geom_bar(stat="identity", position="dodge")
I don't understand why the stacked works great and the dodged doesn't just put them side by side instead of on top.
ETA: I found a recent question about this on the ggplot google group with the suggestion to add alpha=0.5 to see what's going on. It isn't that ggplot is taking the max value from each grouping; it's actually over-plotting bars on top of one another for each value.
It seems that when using position="dodge", ggplot expects only one y per x. I contacted Winston Chang, a ggplot developer about this to confirm as well as to inquire if this can be changed as I don't see an advantage.
It seems that stat="identity" should tell ggplot to tally the y=val passed inside aes() instead of individual counts which happens without stat="identity" and when passing no y value.
For now, the workaround seems to be (for the original df above) to aggregate so there's only one y per x:
df2 <- aggregate(df$values, by=list(df$names, df$num), FUN=sum)
p <- ggplot(df2, aes(x=Group.1, y=x, fill=factor(Group.2)))
p <- p + geom_bar(stat="identity", position="dodge")
p
I think the problem is that you want to stack within values of the num group, and dodge between values of num.
It might help to look at what happens when you add an outline to the bars.
library(ggplot2)
set.seed(123)
df <- data.frame(
id = 1:18,
names = rep(LETTERS[1:3], 6),
num = c(rep(1, 15), rep(2, 3)),
values = sample(1:10, 18, replace=TRUE)
)
By default, there are a lot of bars stacked - you just don't see that they're separate unless you have an outline:
# Stacked bars
ggplot(df, aes(x=factor(names), y=values, fill=factor(num))) +
geom_bar(stat="identity", colour="black")
If you dodge, you get bars that are dodged between values of num, but there may be multiple bars within each value of num:
# Dodged on 'num', but some overplotted bars
ggplot(df, aes(x=factor(names), y=values, fill=factor(num))) +
geom_bar(stat="identity", colour="black", position="dodge", alpha=0.1)
If you also add id as a grouping var, it'll dodge all of them:
# Dodging with unique 'id' as the grouping var
ggplot(df, aes(x=factor(names), y=values, fill=factor(num), group=factor(id))) +
geom_bar(stat="identity", colour="black", position="dodge", alpha=0.1)
I think what you want is to both dodge and stack, but you can't do both.
So the best thing is to summarize the data yourself.
library(plyr)
df2 <- ddply(df, c("names", "num"), summarise, values = sum(values))
ggplot(df2, aes(x=factor(names), y=values, fill=factor(num))) +
geom_bar(stat="identity", colour="black", position="dodge")
Folks,
I am plotting histograms using geom_histogram and I would like to label each histogram with the mean value (I am using mean for the sake of this example). The issue is that I am drawing multiple histograms in one facet and I get labels overlapping. This is an example:
library(ggplot2)
df <- data.frame (type=rep(1:2, each=1000), subtype=rep(c("a","b"), each=500), value=rnorm(4000, 0,1))
plt <- ggplot(df, aes(x=value, fill=subtype)) + geom_histogram(position="identity", alpha=0.4)
plt <- plt + facet_grid(. ~ type)
plt + geom_text(aes(label = paste("mean=", mean(value)), colour=subtype, x=-Inf, y=Inf), data = df, size = 4, hjust=-0.1, vjust=2)
Result is:
The problem is that the labels for Subtypes a and b are overlapping. I would like to solve this.
I have tried the position, both dodge and stack, for example:
plt + geom_text(aes(label = paste("mean=", mean(value)), colour=subtype, x=-Inf, y=Inf), position="stack", data = df, size = 4, hjust=-0.1, vjust=2)
This did not help. In fact, it issued warning about the width.
Would you pls help ?
Thx,
Riad.
I think you could precalculate mean values before plotting in new data frame.
library(plyr)
df.text<-ddply(df,.(type,subtype),summarise,mean.value=mean(value))
df.text
type subtype mean.value
1 1 a -0.003138127
2 1 b 0.023252169
3 2 a 0.030831337
4 2 b -0.059001888
Then use this new data frame in geom_text(). To ensure that values do not overlap you can provide two values in vjust= (as there are two values in each facet).
ggplot(df, aes(x=value, fill=subtype)) +
geom_histogram(position="identity", alpha=0.4)+
facet_grid(. ~ type)+
geom_text(data=df.text,aes(label=paste("mean=",mean.value),
colour=subtype,x=-Inf,y=Inf), size = 4, hjust=-0.1, vjust=c(2,4))
Just to expand on #Didzis:
You actually have two problems here. First, the text overlaps, but more importantly, when you use aggregating functions in aes(...), as in:
geom_text(aes(label = paste("mean=", mean(value)), ...
ggplot does not respect the subsetting implied in the facets (or in the groups for that matter). So mean(value) is based on the full dataset regardless of faceting or grouping. As a result, you have to use an auxillary table, as #Didzis shows.
BTW:
df.text <- aggregate(df$value,by=list(type=df$type,subtype=df$subtype),mean)
gets you the means and does not require plyr.
I am trying to plot side by side the following datasets
dataset1=data.frame(obs=runif(20,min=1,max=10))
dataset2=data.frame(obs=runif(20,min=1,max=20))
dataset3=data.frame(obs=runif(20,min=5,max=10))
dataset4=data.frame(obs=runif(20,min=8,max=10))
I've tried to add the option position="dodge" for geom_histogram with no luck. How can I change the following code to plot the histograms columns side by side without overlap ??
ggplot(data = dataset1,aes_string(x = "obs",fill="dataset")) +
geom_histogram(binwidth = 1,colour="black", fill="blue")+
geom_histogram(data=dataset2, aes_string(x="obs"),binwidth = 1,colour="black",fill="green")+
geom_histogram(data=dataset3, aes_string(x="obs"),binwidth = 1,colour="black",fill="red")+
geom_histogram(data=dataset4, aes_string(x="obs"),binwidth = 1,colour="black",fill="orange")
ggplot2 works best with "long" data, where all the data is in a single data frame and different groups are described by other variables in the data frame. To that end
DF <- rbind(data.frame(fill="blue", obs=dataset1$obs),
data.frame(fill="green", obs=dataset2$obs),
data.frame(fill="red", obs=dataset3$obs),
data.frame(fill="orange", obs=dataset3$obs))
where I've added a fill column which has the values that you used in your histograms. Given that, the plot can be made with:
ggplot(DF, aes(x=obs, fill=fill)) +
geom_histogram(binwidth=1, colour="black", position="dodge") +
scale_fill_identity()
where position="dodge" now works.
You don't have to use the literal fill color as the distinction. Here is a version that uses the dataset number instead.
DF <- rbind(data.frame(dataset=1, obs=dataset1$obs),
data.frame(dataset=2, obs=dataset2$obs),
data.frame(dataset=3, obs=dataset3$obs),
data.frame(dataset=4, obs=dataset3$obs))
DF$dataset <- as.factor(DF$dataset)
ggplot(DF, aes(x=obs, fill=dataset)) +
geom_histogram(binwidth=1, colour="black", position="dodge") +
scale_fill_manual(breaks=1:4, values=c("blue","green","red","orange"))
This is the same except for the legend.