The following produces a data frame with <int[]> and <list[]> fields:
library(tidyverse)
set.seed(123)
s <- 4
data <- data.frame(
lamda = c(5, 2, 3),
meanlog = c(9, 10, 11),
sdlog = c(2, 2.1, 2.2)
)
data2 <- data %>%
mutate(
freq = map(lamda, ~rpois(s, .x)),
freqsev = map(freq, ~map(.x, function(k) rlnorm(k, meanlog, sdlog)))
)
Output:
as_tibble(data2)
lamda meanlog sdlog freq freqsev
<dbl> <dbl> <dbl> <list> <list>
1 5 9 2 <int [4]> <list [4]>
2 2 10 2.1 <int [4]> <list [4]>
3 3 11 2.2 <int [4]> <list [4]>
I would like to create new fields with the mean of freq (producing a double), averaging over simulation s, and the sum of freqsev (producing <dbl [4]> where the [4] is the index of s) i.e. we sum over the number of occurrences e.g.
For data2$freq[[1]] I would expect the mean.
For data2$freqsev[[1]][[1]] I would expect the sum.
Additionally, I would like to create a field or preferably a handful of fields based on the the percentile function applied to freq; something like:
quantile(data2$freq[[1]], c(0.5, 0.75, 0.9))
producing fields quantile_0.5, quantile_0.75, quantile_0.9 of <dbl>
I have tried the purrr map function but I don't know how to choose the specific dimension to apply the operation over. Thank you.
