Creating barplot with standard errors plotted in R

问题

I am trying to find the best way to create barplots in R with standard errors displayed. I have seen other articles but I cannot figure out the code to use with my own data (having not used ggplot before and this seeming to be the most used way and barplot not cooperating with dataframes). I need to use this in two cases for which I have created two example dataframes:

Plot df1 so that the x-axis has sites a-c, with the y-axis displaying the mean value for V1 and the standard errors highlighted, similar to this example with a grey colour. Here, plant biomass should the mean V1 value and treatments should be each of my sites.

Plot df2 in the same way, but so that before and after are located next to each other in a similar way to this, so pre-test and post-test equate to before and after in my example.

x <- factor(LETTERS[1:3])
site <- rep(x, each = 8)
values <- as.data.frame(matrix(sample(0:10, 3*8, replace=TRUE), ncol=1))
df1 <- cbind(site,values)
z <- factor(c("Before","After"))
when <- rep(z, each = 4)
df2 <- data.frame(when,df1)

Apologies for the simplicity for more experienced R users and particuarly those that use ggplot but I cannot apply snippets of code that I have found elsewhere to my data. I cannot even get enough code together to produce a start to a graph so I hope my descriptions are sufficient. Thank you in advance.

回答1:

I used group_by and summarise_each function for this and std.error function from package plotrix

library(plotrix) # for std error function
library(dplyr) # for group_by and summarise_each function
library(ggplot2) # for creating ggplot

For df1 plot

# Group data by when and site
grouped_df1<-group_by(df1,site)

#summarise grouped data and calculate mean and standard error using function mean and std.error(from plotrix)
summarised_df1<-summarise_each(grouped_df1,funs(mean=mean,std_error=std.error))


# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)

#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df1,aes(site,mean))

#Creating bar to show the factor variable position_dodge 
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())

#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g

For df2 plot

# Group data by when and site
grouped_df2<-group_by(df2,when,site)

#summarise grouped data and calculate mean and standard error using function mean and std.error
summarised_df2<-summarise_each(grouped_df2,funs(mean=mean,std_error=std.error))

# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)

#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df2,aes(site,mean,fill=when))

#Creating bar to show the factor variable position_dodge 
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())

#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g

回答2:

Something like this?

library(ggplot2)
get.se <- function(y) {
 se <- sd(y)/sqrt(length(y))
 mu <- mean(y)
 c(ymin=mu-se, ymax=mu+se)
}
ggplot(df1, aes(x=site, y=V1)) +
  stat_summary(fun.y=mean, geom="bar", fill="lightgreen", color="grey70")+
  stat_summary(fun.data=get.se, geom="errorbar", width=0.1)

ggplot(df2, aes(x=site, y=V1, fill=when)) +
  stat_summary(fun.y=mean, geom="bar", position="dodge", color="grey70")+
  stat_summary(fun.data=get.se, geom="errorbar", width=0.1, position=position_dodge(width=0.9))

So this takes advantage of the stat_summary(...) function in ggplot to, first, summarize y for given x using mean(...) (for the bars), and then to summarize y for given x using the get.se(...) function for the error-bars. Another option would be to summarize your data prior to using ggplot, and then use geom_bar(...) and geom_errorbar(...).

Also, plotting +/- 1 se is not a great practice (although it's used often enough). You'd be better served plotting legitimate confidence limits, which you could do, for instance, using the built-in mean_cl_normal function instead of the contrived get.se(...). mean_cl_normal returns the 95% confidence limits based on the assumption that the data is normally distributed (or you can set the CL to something else; read the documentation).

来源：https://stackoverflow.com/questions/32468497/creating-barplot-with-standard-errors-plotted-in-r

标签

ggplot2

bar-chart