Get Mutate Error When Applying Purrr::Map on Grouped Data - r

Question

Hi I am trying to apply a very simple function by using purrr::map however i keep getting the error Error in mutate_impl(.data, dots) :
Evaluation error: unused argument (.x[[i]]).
The codes are as below:
data = data.frame(name = c('A', 'B', 'C'), metric = c(0.29, 0.39,0.89))
get_sample_size = function(metric, threshold = 0.01){
sample_size = ceiling((1.96^2)*(metric*(1-metric))/(threshold^2))
return(data.frame(sample_size))
}
data %>% group_by(name) %>% tidyr::nest() %>%
dplyr::mutate(result = purrr::map( .x = data, .f = get_sample_size, metric = metric, threshold = 0.01 ))

Answer 1

You don't need nest. The metric argument from get_sample_size function should be a numeric vector, but if you do nest, the data column is a list of data frame, which cannot be the input for the metric argument.
I think you can use summarize and map to apply your function to the metric column.
library(tidyverse)
data %>%
group_by(name) %>%
summarize(result = purrr::map(.x = metric,
.f = get_sample_size,
threshold = 0.01))
# # A tibble: 3 x 2
# name result
# <fct> <list>
# 1 A <data.frame [1 x 1]>
# 2 B <data.frame [1 x 1]>
# 3 C <data.frame [1 x 1]>

Answer 2

When you pass metric in the ... part of map, it's not clear that that is a column in the nested data frame. But once you nest the data like you've done, metric isn't a column in data, it's a column in the nested frame...also called "data." (This is a good example of why you want more specific variable names btw.)
If you're mapping over the data column, you can use $metric to point to that column, either in writing out a function, as I've done here (such as df$metric), or in formula notation (such as .$metric).
As #www said, you don't need nested data frames in this case. But for a more complicated case, you might need nested data frames to work with, such as for building models, so it's good to know how to reference exactly the data you want.
library(tidyverse)
data %>%
group_by(name) %>%
tidyr::nest() %>%
mutate(result = map(data, function(df) {
get_sample_size(metric = df$metric, threshold = 0.01)
}))
#> # A tibble: 3 x 3
#> name data result
#> <fct> <list> <list>
#> 1 A <tibble [1 × 1]> <data.frame [1 × 1]>
#> 2 B <tibble [1 × 1]> <data.frame [1 × 1]>
#> 3 C <tibble [1 × 1]> <data.frame [1 × 1]>
Created on 2019-01-16 by the reprex package (v0.2.1)