How to add a row to each group and assign values - r

Question

I have this tibble:
library(tibble)
library(dplyr)
df <- tibble(id = c("one", "two", "three"),
A = c(1,2,3),
B = c(4,5,6))
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 two 2 5
3 three 3 6
I want to add a row to each group AND assign values to the new column BUT with a function (here the new row in each group should get A=4 B = the first group value of column B USING first(B)-> desired output:
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 one 4 4
3 three 3 6
4 three 4 6
5 two 2 5
6 two 4 5
I have tried so far:
If I add a row in a ungrouped tibble with add_row -> this works perfect!
df %>%
add_row(A=4, B=4)
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 two 2 5
3 three 3 6
4 NA 4 4
If I try to use add_row in a grouped tibble -> this works not:
df %>%
group_by(id) %>%
add_row(A=4, B=4)
Error: Can't add rows to grouped data frames.
Run `rlang::last_error()` to see where the error occurred.
According to this post Add row in each group using dplyr and add_row() we could use group_modify -> this works great:
df %>%
group_by(id) %>%
group_modify(~ add_row(A=4, B=4, .x))
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 one 4 4
3 three 3 6
4 three 4 4
5 two 2 5
6 two 4 4
I want to assign to column B the first value of column B (or it can be any function min(B), max(B) etccc.) -> this does not work:
df %>%
group_by(id) %>%
group_modify(~ add_row(A=4, B=first(B), .x))
Error in h(simpleError(msg, call)) :
Fehler bei der Auswertung des Argumentes 'x' bei der Methodenauswahl für Funktion 'first': object 'B' not found

Answer 1

library(tidyverse)
df <- tibble(id = c("one", "two", "three"),
A = c(1,2,3),
B = c(4,5,6))
df %>%
group_by(id) %>%
summarise(add_row(cur_data(), A = 4, B = first(cur_data()$B)))
#> `summarise()` has grouped output by 'id'. You can override using the `.groups` argument.
#> # A tibble: 6 × 3
#> # Groups: id [3]
#> id A B
#> <chr> <dbl> <dbl>
#> 1 one 1 4
#> 2 one 4 4
#> 3 three 3 6
#> 4 three 4 6
#> 5 two 2 5
#> 6 two 4 5
Or
df %>%
group_by(id) %>%
group_split() %>%
map_dfr(~ add_row(.,id = first(.$id), A = 4, B = first(.$B)))
#> # A tibble: 6 × 3
#> id A B
#> <chr> <dbl> <dbl>
#> 1 one 1 4
#> 2 one 4 4
#> 3 three 3 6
#> 4 three 4 6
#> 5 two 2 5
#> 6 two 4 5
Created on 2022-01-02 by the reprex package (v2.0.1)

Answer 2

Maybe this is an option
library(dplyr)
df %>%
group_by(id) %>%
summarise( A=c(A,4), B=c(B,first(B)) ) %>%
ungroup
`summarise()` has grouped output by 'id'. You can override using the `.groups` argument.
# A tibble: 6 x 3
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 one 4 4
3 three 3 6
4 three 4 6
5 two 2 5
6 two 4 5

Answer 3

According to the documentation of the function group_modify, if you use a formula, you must use ". or .x to refer to the subset of rows of .tbl for the given group;" that's why you used .x inside the add_row function. To be entirely consistent, you have to do it also within the first function.
df %>%
group_by(id) %>%
group_modify(~ add_row(A=4, B=first(.x$B), .x))
# A tibble: 6 x 3
# Groups: id [3]
id A B
<chr> <dbl> <dbl>
1 one 1 4
2 one 4 4
3 three 3 6
4 three 4 6
5 two 2 5
6 two 4 5
Using first(.$B) or first(df$B) will provide the same results.

Answer 4

A possible solution:
library(tidyverse)
df <- tibble(id = c("one", "two", "three"),
A = c(1,2,3),
B = c(4,5,6))
df %>%
group_by(id) %>%
slice(rep(1,2)) %>% mutate(A = if_else(row_number() > 1, first(df$B), A)) %>%
ungroup
#> # A tibble: 6 × 3
#> id A B
#> <chr> <dbl> <dbl>
#> 1 one 1 4
#> 2 one 4 4
#> 3 three 3 6
#> 4 three 4 6
#> 5 two 2 5
#> 6 two 4 5