R assigning ggplot objects to list in loop

Question

I\'m using a for loop to assign ggplots to a list, which is then passed to plot_grid() (package cowplot). plot_grid

Answer 1

I think ggplot is getting confused by looking for your x and y variables inside of dfrm even though you are actually defining them on the fly. If you change the for loop slightly to build a new sub data.frame as the first line it works just fine.

myplots <- list()
count = 1

for(i in v){
    df <- data.frame(x = 1:dfmsize, y = dfrm[,i])
    myplots[[count]] <- ggplot(df, aes(x=x, y=y)) + geom_point() + labs(y=i)
    count = count + 1
}
plot_grid(plotlist=myplots)

Answer 2

There is a nice explanation of what happens with ggplot2's lazy evaluation and for loops in [this answer](https://stackoverflow.com/a/26246791/2461552.

I usually switch to aes_string or aes_ for situations like this so I can use variables as strings in ggplot2.

I find lapply loops easier than a for loop in your case as initializing the list and using the counter can be avoided.

First, I add the x variable to the dataset.

dfrm$index = 1:nrow(dfrm)

Now, the lapply loop, looping through the columns in v.

myplots = lapply(v, function(x) {
    ggplot(dfrm, aes_string(x = "index", y = x)) + 
        geom_point() +
        labs(y = x)
})

plot_grid(plotlist = myplots)

Answer 3

The answers so far are very close, but unsatisfactory in my opinion. The problem is the following - after your for loop:

myplots[[1]]$mapping
#* x -> 1:dfmsize
#* y -> dfrm[, i]
myplots[[1]]$plot_env
#<environment: R_GlobalEnv>

myplots[[2]]$mapping
#* x -> 1:dfmsize
#* y -> dfrm[, i]
myplots[[2]]$plot_env
#<environment: R_GlobalEnv>

i
#[1] "B"

As the other answers mention, ggplot doesn't actually evaluate those expressions until plotting, and since these are all in the global environment, and the value of i is "B", you get the undesirable results.

There are several ways of avoiding this issue, the simplest of which in fact simplifies your expressions:

myplots = lapply(v, function(col)
            ggplot(dfrm, aes(x=1:dfmsize, y=dfrm[,col])) + geom_point() + labs(y=col))

The reason this works, is because the environment is different for each of the values in the lapply loop:

myplots[[1]]$mapping
#* x -> 1:dfmsize
#* y -> dfrm[, col]
myplots[[1]]$plot_env
#<environment: 0x000000000bc27b58>

myplots[[2]]$mapping
#* x -> 1:dfmsize
#* y -> dfrm[, col]
myplots[[2]]$plot_env
#<environment: 0x000000000af2ef40>

eval(quote(dfrm[, col]), env = myplots[[1]]$plot_env)
#[1]  1  2  3  4  5  6  7  8  9 10
eval(quote(dfrm[, col]), env = myplots[[2]]$plot_env)
#[1] 10  9  8  7  6  5  4  3  2  1

So even though the expressions are the same, the results are different.

And in case you're wondering what exactly is stored/copied to the environment of lapply - unsurprisingly it's just the column name:

ls(myplots[[1]]$plot_env)
#[1] "col"

Answer 4

I believe the problem here is that the non-standard evaluation of the aes method delays evaluating i until the plot is actually plotted. By the time of plotting, i is the last value (in the toy example "B") and thus the y aesthetic mapping for all plots refers to that last value. Meanwhile, the labs call uses standard evaluation and so the labels correctly refer to each iteration of i in the loop.

This can be fixed by simply using the standard evaluation version of the mapping function, aes_q:

require(cowplot)

dfrm <- data.frame(A=1:10, B=10:1)

v <- c("A","B")
dfmsize <- nrow(dfrm)
myplots <- vector("list",2)

count = 1
for(i in v){
    myplots[[count]] <- ggplot(dfrm, aes_q(x=1:dfmsize, y=dfrm[,i])) + geom_point() + labs(y=i)
    count = count +1
}
plot_grid(plotlist=myplots)