How to mutate to input the mean as a column after using nest?-CodePudding

I'm trying to learn how to use nest(), and I'm trying to nest by once of 3 time periods participants could be in and I want to add two columns. The first column is the overall mean, which I have figured out. Then, I want to nest by the time variable and create 3 datasets (which I have figured out) and then compute the group mean. I read that you should create a function (here, section 6.3.1), but my function keeps failing. How would I do this?

Also, please use nest or nest_by in the solution. I know I could use group_by(), like someone else did here, but in my actual data, I need these to be 3 separate datasets due to other computations that I need to do.

#Here's my setup and sample data
library(dplyr)
library(purrr)
library(tidyr)

set.seed(1414)
test <- tibble(id = c(1:100),
               condition = c(rep(c("pre", "post"), 50)),
               time = c(case_when(condition == "pre" ~ 0,
                                  condition == "post" ~ sample(c(1, 2), size = c(100), replace = TRUE))),
               score = case_when(time == 0 ~ 1,
                                 time == 1 ~ 10,
                                 time == 2 ~ 100))


#Here's what I tried

#Nesting the data (works)
nested_test <- test %>%
  unite(col = "all_combos", c(condition, time)) %>%
  mutate(score2 = mean(score)) %>%
  nest_by(all_combos)

#Make mean function and map it (doesn't work)

my_mean <- function(data) {
  mean(score, na.rm = T)
}

nested_test %>%
  mutate(score3 = map(data, my_mean))

CodePudding user response：

We may need to ungroup as there is rowwise attribute and then loop over the data with map and create the column with mutate on the nested data

library(dplyr)
library(purrr)
nested_test_new <- nested_test %>%
  ungroup %>%
   mutate(data = map(data, ~ .x %>%
    mutate(score3 = mean(score, na.rm = TRUE))))

-output

nested_test_new
# A tibble: 3 × 2
  all_combos data             
  <chr>      <list>           
1 post_1     <tibble [19 × 4]>
2 post_2     <tibble [31 × 4]>
3 pre_0      <tibble [50 × 4]>
> nested_test_new$data
[[1]]
# A tibble: 19 × 4
      id score score2 score3
   <int> <dbl>  <dbl>  <dbl>
 1     2    10   33.4     10
 2     4    10   33.4     10
 3    14    10   33.4     10
 4    16    10   33.4     10
 5    18    10   33.4     10
 6    28    10   33.4     10
 7    30    10   33.4     10
 8    32    10   33.4     10
 9    38    10   33.4     10
10    44    10   33.4     10
11    48    10   33.4     10
12    60    10   33.4     10
13    64    10   33.4     10
14    78    10   33.4     10
15    80    10   33.4     10
16    86    10   33.4     10
17    92    10   33.4     10
18    96    10   33.4     10
19   100    10   33.4     10

[[2]]
# A tibble: 31 × 4
      id score score2 score3
   <int> <dbl>  <dbl>  <dbl>
 1     6   100   33.4    100
 2     8   100   33.4    100
 3    10   100   33.4    100
 4    12   100   33.4    100
...

Or another option is nest_mutate from nplyr

library(nplyr)
test %>%
  unite(col = "all_combos", c(condition, time)) %>%
  mutate(score2 = mean(score)) %>%
  nest(data = -all_combos) %>%
  nest_mutate(data, score3 = mean(score, na.rm = TRUE))

-output

# A tibble: 3 × 2
  all_combos data             
  <chr>      <list>           
1 pre_0      <tibble [50 × 4]>
2 post_1     <tibble [19 × 4]>
3 post_2     <tibble [31 × 4]>