R Add a column in nested data sets - r

Question

As a part of a more complex procedure, I found myself lost in this passage. Below, a reproducible example of what I am dealing with. I need to add a column to each nested dataset with the same number within but a different number between them. Specifically, the number has to be what is written in c1$Age. The code cbind(k, AgeGroup = 3) is only for demonstration. In fact when I used cbind(k, AgeGroup = Age), R gives me the following error Error in mutate_impl(.data, dots): Evaluation error: arguments imply differing number of rows: 5, 2.
library(dplyr)
library(purrr)
library(magrittr)
library(tidyr)
c <- read.table(header = TRUE, text = "Age Verbal Fluid Speed
2 89 94 103
1 98 88 100
1 127 115 102
2 83 101 71
2 102 92 87
1 91 97 120
1 96 129 98
2 79 92 84
2 107 95 102")
c1 <- c %>%
group_by(Age) %>%
nest() %>%
dplyr::mutate(db = data %>% map(function(k) cbind(k, AgeGroup = 3)))
#> c1
# A tibble: 2 x 3
# Age data db
# <int> <list> <list>
#1 2 <tibble [5 x 3]> <data.frame [5 x 4]>
#2 1 <tibble [4 x 3]> <data.frame [4 x 4]>
This is what I have now:
#> c1$db
#[[1]]
# Verbal Fluid Speed AgeGroup
#1 89 94 103 3
#2 83 101 71 3
#3 102 92 87 3
#4 79 92 84 3
#5 107 95 102 3
#
#[[2]]
# Verbal Fluid Speed AgeGroup
#1 98 88 100 3
#2 127 115 102 3
#3 91 97 120 3
#4 96 129 98 3
This is what I would like to get.
#> c1$db
#[[1]]
# Verbal Fluid Speed AgeGroup
#1 89 94 103 2
#2 83 101 71 2
#3 102 92 87 2
#4 79 92 84 2
#5 107 95 102 2
#
#[[2]]
# Verbal Fluid Speed AgeGroup
#1 98 88 100 1
#2 127 115 102 1
#3 91 97 120 1
#4 96 129 98 1

Answer 1

You could replace map by map2 and in this way maintain the knowledge of the corresponding value of Age:
c1 <- c %>% group_by(Age) %>% nest() %>%
dplyr::mutate(db = data %>% map2(Age, function(k, age) cbind(k, AgeGroup = age)))
c1$db
# [[1]]
# Verbal Fluid Speed AgeGroup
# 1 89 94 103 2
# 2 83 101 71 2
# 3 102 92 87 2
# 4 79 92 84 2
# 5 107 95 102 2
#
# [[2]]
# Verbal Fluid Speed AgeGroup
# 1 98 88 100 1
# 2 127 115 102 1
# 3 91 97 120 1
# 4 96 129 98 1
When you tried cbind(k, AgeGroup = Age) directly, the problem was that Age was a vector 2:1, rather than a single corresponding value.

Answer 2

We can use map2 to loop through both Age and data columns and update the data columns using mutate.
library(dplyr)
library(purrr)
library(magrittr)
library(tidyr)
c1 <- c %>%
group_by(Age) %>%
nest()
c2 <- c1 %>%
mutate(data = map2(data, Age, ~mutate(.x, AgeGroup = .y)))
c2$data
# [[1]]
# # A tibble: 5 x 4
# Verbal Fluid Speed AgeGroup
# <int> <int> <int> <int>
# 1 89 94 103 2
# 2 83 101 71 2
# 3 102 92 87 2
# 4 79 92 84 2
# 5 107 95 102 2
#
# [[2]]
# # A tibble: 4 x 4
# Verbal Fluid Speed AgeGroup
# <int> <int> <int> <int>
# 1 98 88 100 1
# 2 127 115 102 1
# 3 91 97 120 1
# 4 96 129 98 1

Answer 3

you can use dplyr::rowwise now, i.e.
c <- read.table(header = TRUE, text = "Age Verbal Fluid Speed
2 89 94 103
1 98 88 100
1 127 115 102
2 83 101 71
2 102 92 87
1 91 97 120
1 96 129 98
2 79 92 84
2 107 95 102")
c1 <- c %>%
group_by(Age) %>%
nest() %>%
rowwise() %>%
mutate(db = list(cbind(data, Age)))