Adding new column names to use in spread() - r

Question

Big picture: I'm trying to set up an export that has one route as a row and columns for each value.
This code: I'm trying to select the top three transfers for each route (using slice(1:3) because I need no more than three values. top_n() allows for ties). Then, I'm trying to spread() to create 6 columns: a name and a pct for each.
If I were to spread the data right now, the names would become columns, but I need to keep the names in the rows (see Desired Output). I want to create the column names as a key column to use to spread(). My approach is creating an error. I'm having trouble thinking of another strategy.
Data frame:
# A tibble: 7 x 3
route_shortname transfer_to pct
<chr> <chr> <dbl>
1 A D 0.5
2 A E 0.5
3 B F 0.667
4 B G 0.333
5 C D 0.111
6 C E 0.111
7 C G 0.111
Desired output:
# A tibble: 3 x 7
route_shortname transfer1 transfer1_pct transfer2 transfer2_pct transfer3 transfer3_pct
<chr> <chr> <dbl> <chr> <dbl> <chr> <dbl>
1 A D 0.5 E 0.5 NA NA
2 B F 0.667 G 0.333 NA NA
3 C D 0.111 E 0.111 G 0.111
Reprex:
library(tidyverse)
sample_data <- tibble::tribble(
~route_shortname, ~transfer_to, ~pct,
"A", "D", 0.5,
"A", "E", 0.5,
"B", "F", 0.666666666666667,
"B", "G", 0.333333333333333,
"C", "D", 0.111111111111111,
"C", "E", 0.111111111111111,
"C", "G", 0.111111111111111
)
transfer_to_table <- sample_data %>%
group_by(route_shortname) %>%
mutate(key = c("transfer1", "transfer2", "transfer3"))
#> Error in mutate_impl(.data, dots): Column `key` must be length 2 (the group size) or one, not 3

Answer 1

df = read.table(text = "
route_shortname transfer_to pct
1 A D 0.5
2 A E 0.5
3 B F 0.667
4 B G 0.333
5 C D 0.111
6 C E 0.111
7 C G 0.111
", header=T)
library(tidyverse)
df %>%
group_by(route_shortname) %>%
mutate(id = paste0("transfer", row_number())) %>%
ungroup() %>%
unite(v, transfer_to, pct) %>%
spread(id, v) %>%
separate(transfer1, c("transfer1","transfer1_pct"), sep = "_", convert = T) %>%
separate(transfer2, c("transfer2","transfer2_pct"), sep = "_", convert = T) %>%
separate(transfer3, c("transfer3","transfer3_pct"), sep = "_", convert = T)
# route_shortname transfer1 transfer1_pct transfer2 transfer2_pct transfer3 transfer3_pct
# <fct> <chr> <dbl> <chr> <dbl> <chr> <dbl>
# 1 A D 0.5 E 0.5 NA NA
# 2 B F 0.667 G 0.333 NA NA
# 3 C D 0.111 E 0.111 G 0.111

Answer 2

Though you tagged this question with tidyverse packages, here is an option using dcast from data.table which let's you do the reshaping in one (admittedly long) line.
library(data.table)
setDT(sample_data)
dcast(sample_data, route_shortname ~ rowid(route_shortname), value.var = c('transfer_to', 'pct'))
# route_shortname transfer_to_1 transfer_to_2 transfer_to_3 pct_1 pct_2 pct_3
#1: A D E <NA> 0.5000000 0.5000000 NA
#2: B F G <NA> 0.6666667 0.3333333 NA
#3: C D E G 0.1111111 0.1111111 0.1111111
You could also use reshape from base R
sample_data <- as.data.frame(sample_data) # does not work with tibbles for some reason
sample_data$idx <- with(sample_data,
ave(route_shortname, route_shortname, FUN = seq_along))
reshape(sample_data, idvar = "route_shortname", timevar = "idx", direction = "wide", sep = "_")
# route_shortname transfer_to_1 pct_1 transfer_to_2 pct_2 transfer_to_3 pct_3
#1 A D 0.5000000 E 0.5000000 <NA> NA
#3 B F 0.6666667 G 0.3333333 <NA> NA
#5 C D 0.1111111 E 0.1111111 G 0.1111111
In both cases you'd need to rename columns but I that shouldn't be too hard.