Raw data has column labels spread across two rows - r

Question

I have a problem with the humans here; they're giving me Citizen Science data in spreadsheets formatted to be attractive and legible. I figured out the right sequence of pivots _longer and _wider to get it into an analyzable format but first I had to do a whole bunch of hand edits to make the column labels usable. I've just been given a corrected spreadsheet so now I have to do the same hand edits all over. Can I avoid this?
reprex <- read_csv("reprex.csv", col_names = FALSE)
gives:
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
1 NA NA 2014 NA NA 2015 NA NA 2016 NA
2 NA Total F M Total F M Total F M
3 SiteA 180 92 88 134 40 94 34 20 14
4 SiteB NA NA NA 247 143 104 8 8 0
5 SiteC 237 194 43 220 95 125 62 45 17
I want column labels like "2014 Total", "2014 F", ... like so:
Location `2014 Total` `2014 F` `2014 M` `2015 Total` `2015 F` `2015 M` `2016 Total` `2016 F` `2016 M`
1 SiteA 180 92 88 134 40 94 34 20 14
2 SiteB NA NA NA 247 143 104 8 8 0
3 SiteC 237 194 43 220 95 125 62 45 17
...which would allow me to twist it up until I get to something like:
Location date Total F M
1 SiteA 2014 180 92 88
2 SiteB 2014 NA NA NA
3 SiteC 2014 237 194 43
4 SiteA 2015 134 40 94
5 SiteB 2015 247 143 104
6 SiteC 2015 220 95 125
7 SiteA 2016 34 20 14
8 SiteB 2016 8 8 0
9 SiteC 2016 62 45 17
The part from the second table to the third I've got; the problem is in how to get from the first table to the second. It would seem like you could pivot the first and then fill in the missing dates with fill(.direction="updown") except that the dates are the grouping value you need to be following.

Answer 1

For this example we could do like this:
library(tidyverse)
df_helper <- df %>%
slice(1:2) %>%
pivot_longer(cols= everything()) %>%
fill(value, .direction = "up") %>%
mutate(x = lead(value, 11)) %>%
drop_na() %>%
unite("name", c(value, x), sep = " ", remove = FALSE) %>%
pivot_wider(names_from = name)
df %>%
setNames(names(df_helper)) %>%
rename(Location = x) %>%
slice(-c(1:2))
Location 2014 Total 2014 F 2014 M 2015 Total 2015 F 2015 M 2016 Total 2016 F 2016 M
3 SiteA 180 92 88 134 40 94 34 20 14
4 SiteB <NA> <NA> <NA> 247 143 104 8 8 0
5 SiteC 237 194 43 220 95 125 62 45 17