CODEWITHSUNDEEP
r
Stataq
Stataq

Reputation: 2297

how to create multiple plots using for loop

I would like to create multiple plots using for loop setup. However my code does not work. Could anyone give me some guidance on this?

for i in 1:4 {
paste0("p_carb_",i) <- ggplot(mtcars%>% filter(carb==4), aes(x = wt, y = mpg, color = disp)) 
+  geom_point()
}

Upvotes: 0

Views: 217

Answers (1)

r2evans
r2evans

Reputation: 160447

Perhaps this?

library(ggplot2)
library(dplyr)
ggs <- lapply(sort(unique(mtcars$carb)), function(crb) {
  ggplot(filter(mtcars, carb == crb), aes(x = wt, y = mpg, color = disp)) +
    geom_point()
})

This produces six plots, which when the first two are viewed side-by-side (calling ggs[[1]] and then ggs[[2]]), we see

enter image description here

An alternative might be to facet the data, as in

ggplot(mtcars, aes(x = wt, y = mpg, color = disp)) +
  facet_wrap(~ carb) +
  geom_point()

enter image description here

But the literal translation of your paste(..) <- ... code into something syntactically correct, we'd use an anti-pattern in R: assign:

for (crb in sort(unique(mtcars$carb))) {
  gg <- ggplot(filter(mtcars, carb == crb), aes(x = wt, y = mpg, color = disp)) +
    geom_point()
  assign(paste0("carb_", crb), gg)
}

Again, this is not the preferred/best-practices way of doing things. It is generally considered much better to keep like-things in a list for uniform/consistent processing of them.

Multiple IDs ... two ways:

  1. Nested lapply:

    carbs <- sort(unique(mtcars$carb))
ggs <- lapply(carbs, function(crb) {
  gears <- subset(mtcars, carb == crb)$gear
  lapply(gears, function(gr) {
    ggplot(dplyr::filter(mtcars, carb == crb, gear == gr), aes(x = wt, y = mpg, color = disp)) +
      geom_point()
  })
})

    Where ggs[[1]] is a list of lists. ggs[[1]][[1]] will be one plot.

  2. split list, one-deep:

    carbsgears <- split(mtcars, mtcars[,c("carb", "gear")], drop = TRUE)
ggs <- lapply(carbsgears, function(dat) {
  ggplot(dat, aes(x = wt, y = mpg, color = disp)) + geom_point()
})

    Here, ggs is a list only one-deep. The names are just concatenated strings of the two fields, so since we have mtcars$carb with values c(1,2,3,4,6,8) and mtcars$gear with values c(3,4,5), removing combinations without data we have names:

    names(ggs)
#  [1] "1.3" "2.3" "3.3" "4.3" "1.4" "2.4" "4.4" "2.5" "4.5" "6.5" "8.5"

    where "1.3" is carb == 1 and gear == 3. When column names have dots in them, this might become ambiguous.

Upvotes: 3

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