Stack Exchange
How to use geom_errorbar with facet_wrap in ggplot2
I am facing a problem adding error bars to my plots. I have a data frame like this:  
> str(bank1)
'data.frame':   24 obs. of  4 variables:
 $ site    : Factor w/ 12 levels "BED","BEU","EB",..: 8 9 10 3 11 1 6 7 5 4 ...
 $ canopy  : Factor w/ 3 levels "M_Closed","M_Open",..: 3 3 3 3 2 2 2 2 1 1 ...
 $ variable: Factor w/ 2 levels "depth5","depth10": 1 1 1 1 1 1 1 1 1 1 ...
 $ value   : int  200 319 103 437 33 51 165 38 26 29 ...

I plot it like this:  
gs1 <- ggplot(bank1, aes(x = canopy, y= value , fill = variable)) + 
  geom_bar(stat='identity', position = 'dodge', fill = 'darkgray')+
  xlab("Canopy cover")+ylab("Seed Bank")+ 
  facet_wrap(~variable,nrow=1)
gs1  

This gives a plot like this:

My problem is when I want to add the error bars (standard deviation), the code does not run. I use this code:  
bank2 <- bank1
bank2.mean = ddply(bank2, .(canopy, variable), summarize,
                  plant.mean = mean(value), plant.sd = sd(value))

gs1 <- ggplot(bank1, aes(x = canopy, y= value , fill = variable)) + 
  geom_bar(stat='identity', position = 'dodge', fill = 'darkgray')+ 
  geom_errorbar(aes(ymin=plant.mean-plant.sd, ymax = plant.mean +
                      plant.sd), width = 0.5)+
  xlab("Canopy cover")+ylab("Seed Bank")+ 
  facet_wrap(~variable,nrow=1)
gs1  

I searched for help here, here, here and here but I did not understand how to proceed.
Kindly help!  
Here I reproduce an example:  
> set.seed(1)
> Data1 <- data.frame(
+   site= c("KOA","KOB","KOO","EB","PNS","BED","KB","KER","KAU","KAD","RO","BEU"),
+   variable = sample(c("depth5", "depth10"), 12, replace = TRUE),
+   canopy=sample(c("open", "M_open", "M_closed"), 12, replace = TRUE),
+   value=sample(c(100,500,50,20,112,200,230,250,300,150,160,400))
+ )
> Data1
   site variable   canopy value
1   KOA   depth5 M_closed    20
2   KOB   depth5   M_open   112
3   KOO  depth10 M_closed   100
4    EB  depth10   M_open   400
5   PNS   depth5 M_closed   230
6   BED  depth10 M_closed    50
7    KB  depth10   M_open   250
8   KER  depth10 M_closed   200
9   KAU  depth10 M_closed   500
10  KAD   depth5     open   150
11   RO   depth5   M_open   300
12  BEU   depth5     open   160
> gs1 <- ggplot(Data1, aes(x = canopy, y= value , fill = variable)) + 
+   geom_bar(stat='identity', position = 'dodge', fill = 'darkgray')+
+   xlab("Canopy cover")+ylab("Seed Bank")+ 
+   facet_wrap(~variable,nrow=1)
> gs1
> Data2 <- Data1
> data2.mean = ddply(Data2, .(canopy, variable), summarize,
+                    plant.mean = mean(value), plant.sd = sd(value))
> gs1 <- ggplot(Data2, aes(x = canopy, y= value , fill = variable)) + 
+   geom_bar(stat='identity', position = 'dodge', fill = 'darkgray')+ 
+   geom_errorbar(aes(ymin=plant.mean-plant.sd, ymax = plant.mean +
+                       plant.sd), width = 0.5)+
+   xlab("Canopy cover")+ylab("Seed Bank")+ 
+   facet_wrap(~variable,nrow=1)
> gs1
Error in FUN(X[[i]], ...) : object 'plant.mean' not found

I get the same error with my original data

What do you mean it doesn't run? What happens? We don't have a sample of your data, so we can't reproduce it to see what the error is

I have reproduced an example now @camille

As the error message states: you do not have plant.mean in the data. plant.mean is available in data2.mean but not in Data2. It is Data2, you provide to ggplot, and as it is not in there, it cannot find it.

@KoenV. I tried changing it to Data1 but same error!

Data1 doesn't have a column with that name either...

Are you still looking for a solution in this matter?

@KoenV. I have solved my problem on my own. I will upload the answer after some time. Thank you so much for asking.

@KoenV. I have added the answer below. If you or anybody else has a different way or more easy way to do the same he/she is welcome to add an answer.

Congrats. This is basically the same as I intended to do.(The call to plyr is not needed).  You might consider cleaning up your question a little bit, but that's up to you.

@KoenV. Thank you. You are right. plyr is not needed. I am removing it from my answer.

The solution to my problem is here. The way I wanted. You need these packages  
library(ggplot2)
library(dplyr)

My data frame bank1 was piped into a new data frame cleandata to calculate the mean, sd and se and summarize the results
cleandata <- bank1 %>%
  group_by(canopy, variable) %>%
  summarise(mean.value = mean(value),
            sd.value = sd(value), count = n(),
            se.mean = sd.value/sqrt(count))

The summarized results look like this:  
> head(cleandata)
# A tibble: 6 x 6
# Groups:   canopy [3]
  canopy   variable mean.value sd.value count se.mean
  <fct>    <fct>         <dbl>    <dbl> <int>   <dbl>
1 Open     depth5        265.    145.       4   72.4 
2 Open     depth10        20.5    12.8      4    6.41
3 M_Open   depth5         71.8    62.6      4   31.3 
4 M_Open   depth10         6.5     4.20     4    2.10
5 M_Closed depth5         20       8.98     4    4.49
6 M_Closed depth10         0.5     1        4    0.5   

Finally, the plotting was done with this piece of code:  
gs1 <- ggplot(cleandata, aes(x=canopy, y=mean.value)) + 
  geom_bar(stat = "identity", color = "black", position = position_dodge())+  
  geom_errorbar(aes(ymin = mean.value - sd.value, ymax = mean.value + sd.value), 
                width=0.2)+
  xlab("Canopy cover")+ylab("Seed Bank")+ 
  facet_wrap(~variable,nrow=1)
gs1  

This gives a graph with error bars (standard deviation) as given below
 
Problem solved! Cheers!
