Using for loop to create multiple ggplots

Question

p[[2]]<-ggplot(data = icadata, aes(x = icadata[[2]])) + geom_histogram()    
p[[3]]<-ggplot(data = icadata, aes(x = icadata[[3]])) + geom_histogram()    
p[[4]]<-ggplot(data = icadata, aes(x = icadata[[4]])) + geom_histogram()    
p[[5]]<-ggplot(data = icadata, aes(x = icadata[[5]])) + geom_histogram()    
p[[6]]<-ggplot(data = icadata, aes(x = icadata[[6]])) + geom_histogram()    
p[[7]]<-ggplot(data = icadata, aes(x = icadata[[7]])) + geom_histogram()    
p[[8]]<-ggplot(data = icadata, aes(x = icadata[[8]])) + geom_histogram()    
p[[9]]<-ggplot(data = icadata, aes(x = icadata[[9]])) + geom_histogram()    
p[[10]]<-ggplot(data = icadata, aes(x = icadata[[10]])) + geom_histogram()    
p[[11]]<-ggplot(data = icadata, aes(x = icadata[[11]])) + geom_histogram()    
p[[12]]<-ggplot(data = icadata, aes(x = icadata[[12]])) + geom_histogram()    
p[[13]]<-ggplot(data = icadata, aes(x = icadata[[13]])) + geom_histogram()    
p[[16]]<-ggplot(data = icadata, aes(x = icadata[[16]])) + geom_histogram()    

p <- list()    
for(i in c(2:13,16) ) {      
           p[[i]]<-ggplot(data = icadata, aes(x = icadata[[i]])) + geom_histogram()    
}     

What's the difference between the codes above?
Why do I get always the same picture after runnning the for loop code?

Answer 1

if you look at the plot you get, it is the last plot that appears. ggplot2 only evaluates the function when you call it. And by entering the x value as icadata[[i]], ggplot2 only plots the icadata[[16]].

So if you do need to use the for loop, it is much better to point it directly to the column inside icadata:

icadata = data.frame(sapply(1:16,function(i)rnorm(100,i,1)))

for(i in c(2:13,16) ) {
COLUMN=colnames(icadata)[i]
p[[i]]<-ggplot(data = icadata, aes_(x = as.name(COLUMN))) + geom_histogram()  
# or use 
# p[[i]]<-ggplot(data = icadata, aes_string(x = COLUMN)) + geom_histogram()    
}  

grid.arrange(grobs=p[c(2:13,16)])

Answer 2

library(reshape2)
d <- melt(icadata[,-c(1,14,15)])
ggplot(d,aes(x = value)) + 
  facet_wrap(~variable,scales = "free_x") + 
  geom_histogram()

I tried facets instead.It worked.But I am still confused why I can't get a plot list using that for-loop code unless I plot them one-by-one like the very beginning of my code.

Answer 3

I have been able to reproduce the observed behaviour using the mtcars dataset. To me, it seems that the way how aes(x = icadata[[2]]) is specified might be the issue.

The code below does work for me:

icadata <- as.data.frame(mtcars) # create reproducible data
library(ggplot2)

p <- list()    
for(i in c(2:3, 6)) {
  p[[i]] <- ggplot(data = icadata, aes(x = !!sym(names(icadata)[i]))) + geom_histogram()    
}    

Output is a list as requested by the OP

class(p)

 [1] "list"

with the list elements either empty or a ggplot object

lapply(p, class)

 [[1]] [1] "NULL" [[2]] [1] "gg" "ggplot" [[3]] [1] "gg" "ggplot" [[4]] [1] "NULL" [[5]] [1] "NULL" [[6]] [1] "gg" "ggplot"

I am able to plot the whole list by

lapply(p, print)

or a single graph with, eg,

p[[3]]

Please, note that the x-axis is labeled by the column name, eg, disp instead of icadata[[3]] .

---

If desired, the for loop can be replaced by a call to lapply() which returns a list object without empty elements. In addition, the list elements can be named appropriately.

cols <- names(icadata)[c(2:3, 6)]
p2 <- lapply(
  cols, 
  function(col) ggplot(data = icadata, aes(x = !!sym(col))) + geom_histogram()
  )
names(p2) <- paste0(cols, "_histogram")

lapply(p2, class)

 $cyl_histogram [1] "gg" "ggplot" $disp_histogram [1] "gg" "ggplot" $wt_histogram [1] "gg" "ggplot"