I have a dataset which looks like this one below. I am trying to make a barplot with the grouping variable gender, with all the variables side by side on the x axis (grouped by gender as filler with different colors), and mean values of variables on the y axis (which basically represents percentages)
tea coke beer water gender
14.55 26.50793651 22.53968254 40 1
24.92997199 24.50980392 26.05042017 24.50980393 2
23.03732304 30.63063063 25.41827542 20.91377091 1
225.51781276 24.6064623 24.85501243 50.80645161 1
24.53662842 26.03706973 25.24271845 24.18358341 2
In the end I want to get a barplot like this
any suggestions how to do that? I made some searches but I only find examples for factors on the x axis, not variables grouped by a factor. any help will be appreciated!
The answer is
You can use aggregate to calculate the means:
means<-aggregate(df,by=list(df$gender),mean)
Group.1 tea coke beer water gender
1 1 87.70171 27.24834 24.27099 37.24007 1
2 2 24.73330 25.27344 25.64657 24.34669 2
Get rid of the Group.1 column
means<-means[,2:length(means)]
Then you have reformat the data to be in long format:
library(reshape2)
means.long<-melt(means,id.vars="gender")
gender variable value
1 1 tea 87.70171
2 2 tea 24.73330
3 1 coke 27.24834
4 2 coke 25.27344
5 1 beer 24.27099
6 2 beer 25.64657
7 1 water 37.24007
8 2 water 24.34669
Finally, you can use ggplot2 to create your plot:
library(ggplot2)
ggplot(means.long,aes(x=variable,y=value,fill=factor(gender)))+
geom_bar(stat="identity",position="dodge")+
scale_fill_discrete(name="Gender",
breaks=c(1, 2),
labels=c("Male", "Female"))+
xlab("Beverage")+ylab("Mean Percentage")
Using reshape2 and dplyr. Your data:
df <- read.table(text=
"tea coke beer water gender
14.55 26.50793651 22.53968254 40 1
24.92997199 24.50980392 26.05042017 24.50980393 2
23.03732304 30.63063063 25.41827542 20.91377091 1
225.51781276 24.6064623 24.85501243 50.80645161 1
24.53662842 26.03706973 25.24271845 24.18358341 2", header=TRUE)
Getting data into correct form:
library(reshape2)
library(dplyr)
df.melt <- melt(df, id="gender")
bar <- group_by(df.melt, variable, gender)%.%summarise(mean=mean(value))
Plotting:
library(ggplot2)
ggplot(bar, aes(x=variable, y=mean, fill=factor(gender)))+
geom_bar(position="dodge", stat="identity")
You can plot the means without resorting to external calculations and additional tables using stat_summary(...). In fact, stat_summary(...) was designed for exactly what you are doing.
library(ggplot2)
library(reshape2) # for melt(...)
gg <- melt(df,id="gender") # df is your original table
ggplot(gg, aes(x=variable, y=value, fill=factor(gender))) +
stat_summary(fun.y=mean, geom="bar",position=position_dodge(1)) +
scale_color_discrete("Gender")
stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
color="grey80",position=position_dodge(1), width=.2)
To add "error bars" you cna also use stat_summary(...) (here, I'm using the min and max value rather than sd because you have so little data).
ggplot(gg, aes(x=variable, y=value, fill=factor(gender))) +
stat_summary(fun.y=mean, geom="bar",position=position_dodge(1)) +
stat_summary(fun.ymin=min,fun.ymax=max,geom="errorbar",
color="grey40",position=position_dodge(1), width=.2) +
scale_fill_discrete("Gender")
Source: Stackoverflow.com