What is the best way to wrangle this data with multiple headers?

Hi all,

I'm working with an untidy excel file with multiple headers and I would love to learn how to parse the data with pivot_longer, if possible.

The data looks something like this:

At first I thought I would be able to load the data and skip the first five rows with read_excel("data.xlsx", skip = 5), though that did not work as the Range eff. information is needed for functions elsewhere. I've used pivot_longer in the past for ggplot2 purposes, but I cannot wrap my head around how to use it here, especially since the sheet has merged cells.

The solution I came up with can be found below, but it'd be great to know if there is a more simple/tidy approach to this. Any insight would be greatly appreciated.

rangeratefunction <- function(nextcoladate) {
  #enter date of next cola in YYYY-MM-DD format
  nextcoladate <- as_date(nextcoladate)
  rangerates <- read_excel("hourlyrates.xlsx", range = "Ranges!A6:H24")
  rangerates <- bind_rows(rangerates, read_excel("hourlyrates.xlsx", range = "Ranges!A27:H74"))
  rangecola1 <- bind_rows(read_excel("hourlyrates.xlsx", range = "Ranges!I6:N24"), read_excel("hourlyrates.xlsx", range = "Ranges!I27:N74"))
  rangecola2 <- bind_rows(read_excel("hourlyrates.xlsx", range = "Ranges!O6:T24"), read_excel("hourlyrates.xlsx", range = "Ranges!O27:T74"))
  rangerates <- if (today() < nextcoladate) {
    bind_cols(rangerates, rangecola1)
  } else {
    bind_cols(rangerates, rangecola2)
  rangerates <- rangerates %>% filter(SetID == "COMMN")


Without a toy dataset to play with, it would be fairly difficult to provide help. I recommend you make your question reproducible:


tidyxl may help when reading the data:


I highly recommend the free, online book by tidyxl's author, Spreadsheet Munging Strategies:

As well as the worked examples here:


Thank you, Martin! I'll check out tidyxl.

Thank you, Mara! I'll check out the examples and documentation.

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