purrr combine pmap and nest

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)))