I am trying to run a simulation, but would like to find an efficient approach to do this.
Sample data:
month <- 1:12
a <- rep(10, 12)
dat1 <- data.frame(month, a)
sim_dat <- do.call(rbind, replicate(50, dat1, simplify = FALSE)) %>%
mutate(sim_index = rep(1:50, each = nrow(dat1)))
What I want to do is create a new variable for each month and generate a random value for that month. My current approach is to use dplyr, see below:
sim_dat1 <- sim_dat %>%
group_by(sim_index) %>%
mutate(mnth1 = ifelse(month == 1, a + rnorm(n()), NA),
mnth2 = ifelse(month == 2, a + rnorm(n()), NA),
mnth3 = ifelse(month == 3, a + rnorm(n()), NA),
mnth4 = ifelse(month == 4, a + rnorm(n()), NA),
mnth5 = ifelse(month == 5, a + rnorm(n()), NA),
mnth6 = ifelse(month == 6, a + rnorm(n()), NA),
mnth7 = ifelse(month == 7, a + rnorm(n()), NA),
mnth8 = ifelse(month == 8, a + rnorm(n()), NA),
mnth9 = ifelse(month == 9, a + rnorm(n()), NA),
mnth10 = ifelse(month == 10, a + rnorm(n()), NA),
mnth11 = ifelse(month == 11, a + rnorm(n()), NA),
mnth12 = ifelse(month == 12, a + rnorm(n()), NA))
It works but it is inefficient. Any suggestions on how to improve this code?
library(dplyr); library(tidyr)
sim_dat %>%
mutate(col = paste0("mnth", month), num = a + rnorm(n())) %>%
pivot_wider(names_from = col, values_from = num)
Result
# A tibble: 600 × 15
month a sim_index mnth1 mnth2 mnth3 mnth4 mnth5 mnth6 mnth7 mnth8 mnth9 mnth10 mnth11 mnth12
<int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 10 1 9.46 NA NA NA NA NA NA NA NA NA NA NA
2 2 10 1 NA 9.35 NA NA NA NA NA NA NA NA NA NA
3 3 10 1 NA NA 10.5 NA NA NA NA NA NA NA NA NA
4 4 10 1 NA NA NA 10.9 NA NA NA NA NA NA NA NA
5 5 10 1 NA NA NA NA 8.87 NA NA NA NA NA NA NA
6 6 10 1 NA NA NA NA NA 8.12 NA NA NA NA NA NA
7 7 10 1 NA NA NA NA NA NA 10.4 NA NA NA NA NA
8 8 10 1 NA NA NA NA NA NA NA 10.4 NA NA NA NA
9 9 10 1 NA NA NA NA NA NA NA NA 12.7 NA NA NA
10 10 10 1 NA NA NA NA NA NA NA NA NA 9.12 NA NA
# ℹ 590 more rows