Plot multiple boxplot in one graph
I saved my data in as a
.csv file with 12 columns. Columns two through 11 (labeled
F1, F2, ..., F11) are
Column one contains the
label of these features either
I would like to plot a
boxplot of all these 11 features against the
label, but separate by
bad. My code so far is:
qplot(Label, F1, data=testData, geom = "boxplot", fill=Label, binwidth=0.5, main="Test") + xlab("Label") + ylab("Features")
However, this only shows
F1 against the
My question is: How to show
F2, F3, ..., F11 against the
label in one graph with some
dodge position? I have normalized the features so they are in the same scale within [0 1] range.
The test data can be found here. I have drawn something by hand to explain the problem (see below).
You should get your data in a specific format by melting your data (see below for how melted data looks like) before you plot. Otherwise, what you have done seems to be okay.
require(reshape2) df <- read.csv("TestData.csv", header=T) # melting by "Label". `melt is from the reshape2 package. # do ?melt to see what other things it can do (you will surely need it) df.m <- melt(df, id.var = "Label") > df.m # pasting some rows of the melted data.frame # Label variable value # 1 Good F1 0.64778924 # 2 Good F1 0.54608791 # 3 Good F1 0.46134200 # 4 Good F1 0.79421221 # 5 Good F1 0.56919951 # 6 Good F1 0.73568570 # 7 Good F1 0.65094207 # 8 Good F1 0.45749702 # 9 Good F1 0.80861929 # 10 Good F1 0.67310067 # 11 Good F1 0.68781739 # 12 Good F1 0.47009455 # 13 Good F1 0.95859182 # 14 Good F1 1.00000000 # 15 Good F1 0.46908343 # 16 Bad F1 0.57875528 # 17 Bad F1 0.28938046 # 18 Bad F1 0.68511766 require(ggplot2) ggplot(data = df.m, aes(x=variable, y=value)) + geom_boxplot(aes(fill=Label))
Edit: I realise that you might need to facet. Here's an implementation of that as well:
p <- ggplot(data = df.m, aes(x=variable, y=value)) + geom_boxplot(aes(fill=Label)) p + facet_wrap( ~ variable, scales="free")
Edit 2: How to add
legend heading, add a
p <- ggplot(data = df.m, aes(x=variable, y=value)) p <- p + geom_boxplot(aes(fill=Label)) p <- p + geom_jitter() p <- p + facet_wrap( ~ variable, scales="free") p <- p + xlab("x-axis") + ylab("y-axis") + ggtitle("Title") p <- p + guides(fill=guide_legend(title="Legend_Title")) p
Edit 3: How to align
geom_point() points to the center of box-plot? It could be done using
position_dodge. This should work.
require(ggplot2) p <- ggplot(data = df.m, aes(x=variable, y=value)) p <- p + geom_boxplot(aes(fill = Label)) # if you want color for points replace group with colour=Label p <- p + geom_point(aes(y=value, group=Label), position = position_dodge(width=0.75)) p <- p + facet_wrap( ~ variable, scales="free") p <- p + xlab("x-axis") + ylab("y-axis") + ggtitle("Title") p <- p + guides(fill=guide_legend(title="Legend_Title")) p
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Since you don't mention a plot package , I propose here using
Lattice version( I think there is more ggplot2 answers than lattice ones, at least since I am here in SO).
## reshaping the data( similar to the other answer) library(reshape2) dat.m <- melt(TestData,id.vars='Label') library(lattice) bwplot(value~Label |variable, ## see the powerful conditional formula data=dat.m, between=list(y=1), main="Bad or Good")
Using base graphics, we can use
at = to control box position , combined with
boxwex = for the width of the boxes. The 1st
boxplot statement creates a blank plot. Then add the 2 traces in the following two statements.
Note that in the following, we use
df[,-1] to exclude the 1st (id) column from the values to plot. With different data frames, it may be necessary to change this to subset for whichever columns contain the data you want to plot.
boxplot(df[,-1], boxfill = NA, border = NA) #invisible boxes - only axes and plot area boxplot(df[df$id=="Good", -1], xaxt = "n", add = TRUE, boxfill="red", boxwex=0.25, at = 1:ncol(df[,-1]) - 0.15) #shift these left by -0.15 boxplot(df[df$id=="Bad", -1], xaxt = "n", add = TRUE, boxfill="blue", boxwex=0.25, at = 1:ncol(df[,-1]) + 0.15) #shift to the right by +0.15
Some dummy data:
df <- data.frame( id = c(rep("Good",200), rep("Bad", 200)), F1 = c(rnorm(200,10,2), rnorm(200,8,1)), F2 = c(rnorm(200,7,1), rnorm(200,6,1)), F3 = c(rnorm(200,6,2), rnorm(200,9,3)), F4 = c(rnorm(200,12,3), rnorm(200,8,2)))
ggplot version of the lattice plot:
library(reshape2) library(ggplot2) df <- read.csv("TestData.csv", header=T) df.m <- melt(df, id.var = "Label") ggplot(data = df.m, aes(x=Label, y=value)) + geom_boxplot() + facet_wrap(~variable,ncol = 4)
I know this is a bit of an older question, but it is one I had as well, and while the accepted answers work, there is a way to do something similar without using additional packages like ggplot or lattice. It isn't quite as nice in that the boxplots overlap rather than showing side by side but:
This puts in two sets of boxplots, with the second having an outline (no fill) in red, and also puts the outliers in red. The nice thing is, it works for two different dataframes rather than trying to reshape them. Quick and dirty way.
In base R a formula interface with interactions (
:) can be used to achieve this.
df <- read.csv("~/Desktop/TestData.csv") df <- data.frame(stack(df[,-1]), Label=df$Label) # reshape to long format boxplot(values ~ Label:ind, data=df, col=c("red", "limegreen"), las=2)