purrr combine pmap and nest - r

Question

I am trying to learn purrr to simulate data using rnorm with different means, sd, and n in each iteration.
This code generates my dataframe:
parameter = crossing(n = c(60,80,100),
agegroup = c("a", "b","c"),
effectsize = c(0.2, 0.5, 0.8),
sd =2
) %>%
# create a simulation id number
group_by(agegroup) %>%
mutate(sim= row_number())%>%
ungroup() %>%
mutate(# change effect size so that one group has effect, others d=0
effectsize= if_else(agegroup == "a", effectsize, 0),
# calculate the mean for the distribution from effect size
mean =effectsize*sd)
Now I want to iterate over the different simulations and for each row, generate data according to mean, sd and r using rnorm
# create a nested dataframe to iterate over each simulation and agegroup
nested_df = parameter %>%
group_by(sim, agegroup, effectsize)%>%
nest() %>% arrange(sim)
This is what my dataframe then looks like:
Now I want create normally-distributed data with the mean, sd, and n given in the "data" column
nested_df = nested_df %>%
mutate(data_points = pmap(data,rnorm))
However the code above gives an error that I haven't been able to find a solution to:
Error in mutate_impl(.data, dots) :
Evaluation error: unused arguments
I read the Iteration chapter in R for Data Science and googled a bunch, but I can't figure out how to combine pmap and nest. The reason I would like to use those functions is that it would make it easier to keep the parameters, simulated data, and output all in one dataframe.

Answer 1

You don't necessarily need to nest the parameters. For example:
parameter %>%
# Use `pmap` because we explicitly specify three arguments
mutate(data_points = pmap(list(n, mean, sd), rnorm))
# A tibble: 27 x 7
# n agegroup effectsize sd sim mean data_points
# <dbl> <chr> <dbl> <dbl> <int> <dbl> <list>
# 1 60 a 0.2 2 1 0.4 <dbl [60]>
# 2 60 a 0.5 2 2 1 <dbl [60]>
# 3 60 a 0.8 2 3 1.6 <dbl [60]>
With the nested data frame, you can use map rather than pmap:
nested_df %>%
# Use `map` because there is really one argument, `data`,
# but then refer to three different columns of `data`.
mutate(data_points = map(data, ~ rnorm(.$n, .$mean, .$sd)))

Answer 2

first, it is okay to use pmap like this:
x <- tibble(n = 100, mean = 5, sd = 0.1)
pmap(x, rnorm)
which is very similar to use do.call:
do.call(rnorm, x)
However, if you want to use pmap inside mutate you bring the inputs for the function .f into the right shape.
Writing
nested_df %>%
mutate(y = pmap(x, f))
means that f expects input x.
In your case, rnorm expects three inputs, but only gets one.
So if you insist on nesting the inputs you can do this:
nested_df %>%
mutate(data_points = pmap(list(data), function(z) pmap(z, rnorm))[[1]])
or
nested_df %>%
mutate(data_points = pmap(list(data), function(z) do.call(rnorm, z))).
However I would recommend to do it a little bit differently:
parameter %>%
mutate(data_points = pmap(list(n, mean, sd), rnorm))
Hope this helps a little.