Why the list size is too large?

General
#1

I am trying to run a loop on a data frame containing 27820 items. My code looks like

total.male <-  list()
for(yr in 1:32) {
  num.year = 0
  for(i in 1:27820) {
    if (suiciderate[i,"sex"] == "male" && suiciderate[i, "Year"] == yr) {
      num.year <- num.year + 1 
    }
    total.male <- list(total.male, num.year)
  }
}

When I try this, R studio pop up an error and terminate, saying fatal error. I guess maybe there are so much data that leads to this problem. So I reduce this to run the first 1000 items but
Screen Shot 2020-05-06 at 7.44.09 PM
why the list is large like this? It's so weird that the size is 3.7MB with only two elements?

Thank you

#2

If you run this code, you will see that you end up with a deeply nested structure. I do not know why it is so big in your case but it would certainly be hard to work with.

total.male <- list()
total.male <- list(total.male, 1)
total.male <- list(total.male, 2)
total.male <- list(total.male, 3)
str(total.male)

I suggest you use functions from dplyr to do this calculation rather than a for loop. Here is an example with toy data.

set.seed(1)
suiciderate <- data.frame(sex = sample(c("F", "M"), 10, replace = TRUE),
                          Year = sample(1:2, 10, replace = TRUE))
suiciderate
#>    sex Year
#> 1    F    1
#> 2    M    1
#> 3    F    1
#> 4    F    1
#> 5    M    1
#> 6    F    2
#> 7    F    2
#> 8    F    2
#> 9    M    2
#> 10   M    1
library(dplyr)
Stats <- suiciderate %>% group_by(Year) %>% summarize(Males = sum(sex == "M"))
Stats
#> # A tibble: 2 x 2
#>    Year Males
#>   <int> <int>
#> 1     1     3
#> 2     2     1

Created on 2020-05-06 by the reprex package (v0.3.0)

2 Likes
#5

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

#3

Thanks for your answer! Is there any other ways people usually do to replace a tedious tow loop in R?

#4

I have often read that the use of explicit loops is less common in R than in other languages. In base R there is the apply family of functions (apply(), lapply(), sapply(), tapply() ...) and in the tidyverse the packages tidyr, dplyr and purrr have many functions for shaping and analyzing data. I suggest you look at the free book R for Data Science as a starting point for learning about tidy functions.

1 Like