Transform data to long with grouped columns - r

Question

For this week's tidytuesday challenge, for some reason, I am not able to group the column names in R which I was doing with pivot_longer function from tidyr previously. So, here is my code and I do not get it why it does throw an error and not give what I want.
library(tidyverse)
tuesdata <- tidytuesdayR::tt_load(2023, week = 7)
age_gaps <- tuesdata$age_gaps
df_long <- age_gaps %>%
pivot_longer(cols= actor_1_name:actor_2_name, names_to = "actornumber", values_to = "actorname") %>%
pivot_longer(cols= character_1_gender:character_2_gender, names_to = "gendernumber", values_to = "gender") %>%
pivot_longer(cols= actor_1_age:actor_2_age, names_to = "agenumber", values_to = "age") %>%
select(movie_name, release_year, director, age_difference, actorname, gender, age)
As seen from the code, the initial data has 1155 rows and after doing the quick data wrangling, I am expecting to get a data of 1155x2=2310 rows as I would like to merge the columns on actor names and their relevant information such as age and birthdate. Yet, the code does not give me the expected outcome and I am wondering why and how can I solve this problem. Thank you for your attention beforehand.
Example data (first 6 rows)
age_gaps <- structure(list(movie_name = c("Harold and Maude", "Venus", "The Quiet American",
"The Big Lebowski", "Beginners", "Poison Ivy"), release_year = c(1971,
2006, 2002, 1998, 2010, 1992), director = c("Hal Ashby", "Roger Michell",
"Phillip Noyce", "Joel Coen", "Mike Mills", "Katt Shea"), age_difference = c(52,
50, 49, 45, 43, 42), couple_number = c(1, 1, 1, 1, 1, 1), actor_1_name = c("Ruth Gordon",
"Peter O'Toole", "Michael Caine", "David Huddleston", "Christopher Plummer",
"Tom Skerritt"), actor_2_name = c("Bud Cort", "Jodie Whittaker",
"Do Thi Hai Yen", "Tara Reid", "Goran Visnjic", "Drew Barrymore"
), character_1_gender = c("woman", "man", "man", "man", "man",
"man"), character_2_gender = c("man", "woman", "woman", "woman",
"man", "woman"), actor_1_birthdate = structure(c(-26725, -13666,
-13442, -14351, -14629, -13278), class = "Date"), actor_2_birthdate = structure(c(-7948,
4536, 4656, 2137, 982, 1878), class = "Date"), actor_1_age = c(75,
74, 69, 68, 81, 59), actor_2_age = c(23, 24, 20, 23, 38, 17)), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))

Answer 1

You could set ".value" in names_to and supply one of names_sep or names_pattern to specify how the column names should be split.
library(tidyr)
age_gaps %>%
pivot_longer(actor_1_name:actor_2_age,
names_prefix = "(actor|character)_",
names_to = c("actor", ".value"),
names_sep = '_')
# A tibble: 12 × 10
movie_name release_year director age_difference couple_number actor name gender birthdate age
<chr> <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <date> <dbl>
1 Harold and Maude 1971 Hal Ashby 52 1 1 Ruth Gordon woman 1896-10-30 75
2 Harold and Maude 1971 Hal Ashby 52 1 2 Bud Cort man 1948-03-29 23
3 Venus 2006 Roger Michell 50 1 1 Peter O'Toole man 1932-08-02 74
4 Venus 2006 Roger Michell 50 1 2 Jodie Whittaker woman 1982-06-03 24
5 The Quiet American 2002 Phillip Noyce 49 1 1 Michael Caine man 1933-03-14 69
6 The Quiet American 2002 Phillip Noyce 49 1 2 Do Thi Hai Yen woman 1982-10-01 20
7 The Big Lebowski 1998 Joel Coen 45 1 1 David Huddleston man 1930-09-17 68
8 The Big Lebowski 1998 Joel Coen 45 1 2 Tara Reid woman 1975-11-08 23
9 Beginners 2010 Mike Mills 43 1 1 Christopher Plummer man 1929-12-13 81
10 Beginners 2010 Mike Mills 43 1 2 Goran Visnjic man 1972-09-09 38
11 Poison Ivy 1992 Katt Shea 42 1 1 Tom Skerritt man 1933-08-25 59
12 Poison Ivy 1992 Katt Shea 42 1 2 Drew Barrymore woman 1975-02-22 17