Main
Importing the data
Spreadsheet cells are imported with the xlsx_cells()
function, which returns a data frame of all the cells in all the requested sheets. By default, every sheet is imported, but we don’t have to worry about that in this case because there is only one sheet in the file. We can also straightaway discard rows above 14 and below 56, and columns beyond 20.
cells <-
xlsx_cells(path) %>%
dplyr::filter(!is_blank, between(row, 14L, 56L), col <= 20) %>%
select(row, col, data_type, numeric, character, date)
Cell formatting isn’t required for this vignette, but if it were, it would be imported via xlsx_formats(path)
.
Importing one of the multiples
The small multiples each have exactly one ‘Fixed Price’ header cell, so begin by filtering for those cells, and then move the selection up one row to get the title cells. The title cells are the top-left corner cell of each table.
title <-
dplyr::filter(cells, character == "Fixed Price") %>%
select(row, col) %>%
mutate(row = row - 1L) %>%
inner_join(cells, by = c("row", "col"))
Use these title cells to partition the sheet.
Taking one of the partitions, unpivot with behead()
. The compass directions "NNW"
and "N"
express the direction from each data cell to its header. "NNW"
means “look up and then left to find the nearest header.”
partitions$cells[[1]] %>%
behead("NNW", "title") %>%
behead("NNW", "price") %>%
behead("N", "bid_offer") %>%
print(n = Inf)
## # A tibble: 24 x 9
## row col data_type numeric character date title price
## <int> <int> <chr> <dbl> <chr> <dttm> <chr> <chr>
## 1 17 17 numeric 1.89 <NA> NA IF N… Fixe…
## 2 17 18 numeric 1.91 <NA> NA IF N… Fixe…
## 3 18 17 numeric 2.06 <NA> NA IF N… Fixe…
## 4 18 18 numeric 2.08 <NA> NA IF N… Fixe…
## 5 19 17 numeric 2.40 <NA> NA IF N… Fixe…
## 6 19 18 numeric 2.42 <NA> NA IF N… Fixe…
## 7 20 17 numeric 2.59 <NA> NA IF N… Fixe…
## 8 20 18 numeric 2.61 <NA> NA IF N… Fixe…
## 9 21 17 numeric 2.58 <NA> NA IF N… Fixe…
## 10 21 18 numeric 2.60 <NA> NA IF N… Fixe…
## 11 22 17 numeric 3.36 <NA> NA IF N… Fixe…
## 12 22 18 numeric 3.38 <NA> NA IF N… Fixe…
## 13 23 17 numeric 2.63 <NA> NA IF N… Fixe…
## 14 23 18 numeric 2.65 <NA> NA IF N… Fixe…
## 15 19 19 numeric -0.565 <NA> NA IF N… Basis
## 16 19 20 numeric -0.545 <NA> NA IF N… Basis
## 17 20 19 numeric -0.494 <NA> NA IF N… Basis
## 18 20 20 numeric -0.474 <NA> NA IF N… Basis
## 19 21 19 numeric -0.585 <NA> NA IF N… Basis
## 20 21 20 numeric -0.565 <NA> NA IF N… Basis
## 21 22 19 numeric -0.295 <NA> NA IF N… Basis
## 22 22 20 numeric -0.275 <NA> NA IF N… Basis
## 23 23 19 numeric -0.530 <NA> NA IF N… Basis
## 24 23 20 numeric -0.510 <NA> NA IF N… Basis
## # … with 1 more variable: bid_offer <chr>
The same procedure can be mapped to every small multiple.
unpivoted <-
purrr::map_dfr(partitions$cells,
~ .x %>%
behead("NNW", "title") %>%
behead("NNW", "price") %>%
behead("N", "bid_offer")) %>%
select(-data_type, -character, -date)
unpivoted
## # A tibble: 240 x 6
## row col numeric title price bid_offer
## <int> <int> <dbl> <chr> <chr> <chr>
## 1 17 17 1.89 IF NWPL Rocky Mountains Fixed Price BID
## 2 17 18 1.91 IF NWPL Rocky Mountains Fixed Price OFFER
## 3 18 17 2.06 IF NWPL Rocky Mountains Fixed Price BID
## 4 18 18 2.08 IF NWPL Rocky Mountains Fixed Price OFFER
## 5 19 17 2.40 IF NWPL Rocky Mountains Fixed Price BID
## 6 19 18 2.42 IF NWPL Rocky Mountains Fixed Price OFFER
## 7 20 17 2.59 IF NWPL Rocky Mountains Fixed Price BID
## 8 20 18 2.61 IF NWPL Rocky Mountains Fixed Price OFFER
## 9 21 17 2.58 IF NWPL Rocky Mountains Fixed Price BID
## 10 21 18 2.60 IF NWPL Rocky Mountains Fixed Price OFFER
## # … with 230 more rows
So far, only the column headers have been joined, but there are also row headers on the left-hand side of the spreadsheet. The following code incorporates these into the final dataset.
row_headers <-
cells %>%
dplyr::filter(between(row, 17, 56), between(col, 2, 4)) %>%
# Concatenate rows like "Dec-01", "to", "Mar-02"
mutate(character = ifelse(!is.na(character),
character,
format(date, origin="1899-12-30", "%b-%y"))) %>%
select(row, col, character) %>%
nest(-row) %>%
mutate(row_header = map(data,
~ str_trim(paste(.x$character, collapse = " ")))) %>%
unnest(row_header) %>%
mutate(col = 2L) %>%
select(row, row_header)
unpivoted <- left_join(unpivoted, row_headers, by = "row")
unpivoted
## # A tibble: 240 x 7
## row col numeric title price bid_offer row_header
## <int> <int> <dbl> <chr> <chr> <chr> <chr>
## 1 17 17 1.89 IF NWPL Rocky Mo… Fixed Pr… BID Cash
## 2 17 18 1.91 IF NWPL Rocky Mo… Fixed Pr… OFFER Cash
## 3 18 17 2.06 IF NWPL Rocky Mo… Fixed Pr… BID ROM
## 4 18 18 2.08 IF NWPL Rocky Mo… Fixed Pr… OFFER ROM
## 5 19 17 2.40 IF NWPL Rocky Mo… Fixed Pr… BID Dec-01
## 6 19 18 2.42 IF NWPL Rocky Mo… Fixed Pr… OFFER Dec-01
## 7 20 17 2.59 IF NWPL Rocky Mo… Fixed Pr… BID Dec-01 to Mar…
## 8 20 18 2.61 IF NWPL Rocky Mo… Fixed Pr… OFFER Dec-01 to Mar…
## 9 21 17 2.58 IF NWPL Rocky Mo… Fixed Pr… BID Apr-02 to Oct…
## 10 21 18 2.60 IF NWPL Rocky Mo… Fixed Pr… OFFER Apr-02 to Oct…
## # … with 230 more rows