R: Gather/Spread/Reshape 21 Columns Based on 21 Other Column s - r

Question

I would like to create columns based on values in some fields, populated by values in other fields. For example column1_time has value "1030" and column1_status has value "booked". I would like to pivot those into a new field time1030 with value "booked." There are 21 unique columns with times, (the times are only listed once per row, so they are unique across the 21 columns) -- and there are 21 unique columns with statuses that map back to the time columns. So these 42 time+status columns should be rearranged to one column per unique time, being populated by that time's corresponding status.
I have data that looks like this:
I would like to utilize R's gather/spread or reshape2 (legacy) functionality to transpose this data to look like this:
I tinkered around with gather and spread for a few hours but couldn't figure it out. I thought setting the key to ends_with('_time') and the value to ends_with('_status') might work but it did not from my attempts.
For a reproducible example of the data:
structure(list(appointment1_time = c("1030", "1030"), appointment2_time = c("1100",
"1100"), appointment3_time = c("1130", "1130"), appointment4_time = c("1200",
"1200"), appointment5_time = c("1230", "1230"), appointment6_time = c("0100",
"0100"), appointment7_time = c("0130", "0130"), appointment8_time = c("0200",
"0200"), appointment9_time = c("0230", "0230"), appointment10_time = c("0300",
"0300"), appointment11_time = c("0330", "0330"), appointment12_time = c("0400",
"0400"), appointment13_time = c("0430", "0430"), appointment14_time = c("0500",
"0500"), appointment15_time = c("0530", "0530"), appointment16_time = c("0600",
""), appointment17_time = c("0630", ""), appointment18_time = c("0700",
""), appointment19_time = c("0730", ""), appointment20_time = c(NA_character_,
NA_character_), appointment21_time = c(NA_character_, NA_character_
), appointment1_status = c("booked", "available"), appointment2_status = c("booked",
"available"), appointment3_status = c("booked", "available"),
appointment4_status = c("booked", "available"), appointment5_status = c("booked",
"available"), appointment6_status = c("booked", "available"
), appointment7_status = c("booked", "available"), appointment8_status = c("booked",
"available"), appointment9_status = c("booked", "available"
), appointment10_status = c("booked", "available"), appointment11_status = c("booked",
"available"), appointment12_status = c("available", "available"
), appointment13_status = c("available", "available"), appointment14_status = c("available",
"available"), appointment15_status = c("booked", "available"
), appointment16_status = c("available", ""), appointment17_status = c("available",
""), appointment18_status = c("available", ""), appointment19_status = c("available",
""), appointment20_status = c(NA_character_, NA_character_
), appointment21_status = c(NA_character_, NA_character_)), row.names = 1:2, class = "data.frame")

Answer 1

A solution using tidyverse.
library(tidyverse)
# Get the time order
ord <- dat %>% select(ends_with("time")) %>% slice(1) %>% unlist()
# Remove NA
ord <- ord[!is.na(ord)]
dat2 <- dat %>%
rowid_to_column() %>%
gather(Column, Value, -rowid) %>%
separate(Column, into = c("Apt", "time/status"), sep = "_") %>%
spread(`time/status`, Value) %>%
# Remove NA or "" in the status column
filter(!is.na(status) & !status %in% "") %>%
mutate(Apt = str_c("apt_slot", time, sep = "_")) %>%
select(-time) %>%
spread(Apt, status) %>%
select(-rowid) %>%
# Reorder the column
select(str_c("apt_slot", ord, sep = "_"))
dat2
# apt_slot_1030 apt_slot_1100 apt_slot_1130 apt_slot_1200 apt_slot_1230 apt_slot_0100 apt_slot_0130
# 1 booked booked booked booked booked booked booked
# 2 available available available available available available available
# apt_slot_0200 apt_slot_0230 apt_slot_0300 apt_slot_0330 apt_slot_0400 apt_slot_0430 apt_slot_0500
# 1 booked booked booked booked available available available
# 2 available available available available available available available
# apt_slot_0530 apt_slot_0600 apt_slot_0630 apt_slot_0700 apt_slot_0730
# 1 booked available available available available
# 2 available <NA> <NA> <NA> <NA>