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I have data that look like this (thanks once again dput!):

dat <- structure(list(vars = c("var_1", "var_2"), data = list(structure(list(
 time = 1:10, value = c(1:10
 )), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
 )), structure(list(time = 1:10, value = c(11:20
 )), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"
 ))), mu = c(1, 2), stdev = c(1,2)), class = c("tbl_df", "tbl", "data.frame"),
 row.names = c(NA,-2L))

I am trying to mutate an extra column which maps a function over each row. e.g calculate dnorm for each element of the nested variable in dat$data[[1]]$value using dat$mu[1] and dat$stdev[1] and the go on to do the same for row two.

The column I would like to mutate is a tibble [10 x 1] for each row containing this as the output:

dnorm(dat$data[[1]]$value, mean = dat$mu[1], sd = dat$stdev[1])
dnorm(dat$data[[2]]$value, mean = dat$mu[2], sd = dat$stdev[2])

Things I have tried that don't work but might be close?:

# This alternates between mean and stdev for each element of each nested variable
dat_1 <- dat %>% 
 mutate(z = map(data, ~ dnorm(.x$value, mean = dat$mu, sd = dat$stdev)))
# apply by row has structure issues
dat_2 <- dat %>% 
 apply(MARGIN = 1, function(x)
 mutate(x, z = map(data, ~ dnorm(.x$value, mean = dat$mu, sd = dat$stdev)))
 )

a basic map function like this dat_3 <- dat %>% mutate(sigma = map(data, ~ sum(.x$value))) works fine without referencing other values in the df. This is early days for me using nested data and map in this way - been looking at the documentation for all the map functions to try solve this but no luck yet! If that's clear as mud I can try clarify - thanks in advance!

We can use a parallel map:

library(purrr)
library(dplyr)

expected_out1 <- dnorm(dat$data[[1]]$value, mean = dat$mu[1], sd = dat$stdev[1])
expected_out2 <- dnorm(dat$data[[2]]$value, mean = dat$mu[2], sd = dat$stdev[2])

out <- 
 dat %>% 
 mutate(z = pmap(list(map(data, "value"), mu, stdev), dnorm))

all.equal(out$z, list(expected_out1, expected_out2))
# [1] TRUE