Dynamically naming variables in a loop to create 30 ggplots - r

Question

I have a dataframe with 30 columns and I would like to create 30 (gg)plots based on these columns. When creating a plot through ggplot, you have to create a variable to which all the information of the plot is added.
Is there a way how I can create 30 of such variable names in a for loop (so that I don't have to create and store them all locally?
In earlier code I repeated the below steps 30 times:
In earlier code, I had the following:
a1 = ggplot(data = results_round_one,
aes(results_round_one$`R-0,01`))
a1 = a1 + geom_histogram()
a1 = a1 + xlim(0.46, 0.55)
a1 = a1 + geom_vline(xintercept= mean(results_round_one$`R-0,01`),
col = 'blue')
a1 = a1 + geom_vline(xintercept = max(results_round_one$`R-0,01`),
col = 'red')
a1= a1 + labs(y = 'Frequency',
x= 'Validated accuracy',
title = 'Optimizer = RMSProp',
subtitle = 'Learning rate = 0.01')
However, since I only have to change the aes and the labels, I think I should be able to do this process in a for loop as well.

Answer 1

In absence of some example data, here is some code that would loop through iris columns, creating density plots:
library(purrr)
library(dplyr)
df <- iris %>%
select(Sepal.Length:Petal.Width)
df %>%
map2(names(df), ~ .x %>%
as.data.frame %>%
set_names(.y) %>%
ggplot(aes_string(.y)) + geom_density() + ggtitle(.y))
Using your code, something along the lines of:
results_round_one %>%
map2(names(results_round_one), ~ .x %>%
as.data.frame %>%
set_names(.y) %>%
ggplot(aes_string(.y)) +
geom_histogram() +
xlim(0.46, 0.55) +
geom_vline(xintercept = mean(.x), col = 'blue') +
geom_vline(xintercept = max(.x), col = 'red') +
labs(y = 'Frequency',
x= 'Validated accuracy',
title = 'Optimizer = RMSProp',
subtitle = 'Learning rate = 0.01'))

Answer 2

You could apply histogram function:
getImage <- function(col){
a1 = ggplot(data = results_round_one,
aes(results_round_one[, col])) +
geom_histogram() +
xlim(0.46, 0.55) +
geom_vline(xintercept= mean(results_round_one[, col]),
col = 'blue') +
labs(y = 'Frequency',
x= 'Validated accuracy',
title = 'Optimizer = RMSProp',
subtitle = 'Learning rate = 0.01')
return(a1)
}
to a vector of columns iteratively. In this case col_30 is a vector of column names
# e.g. col_30 = c("col1", "col2") etc.
for(col in col_30){
getImage(col)
}
This would generate different plots.