# Small Multiples

#### Duncan Garmonsway

#### 2019-03-30

This vignette for the unpivotr package demonstrates unpivoting multiple similar tables from a spreadsheet via the tidyxl package. It is best read with the spreadsheet open in a spreadsheet program, e.g. Excel, LibreOffice Calc or Gnumeric.

## Introduction

The spreadsheet is from the famous Enron subpoena, made available by Felienne Hermans, and has has previously been publicised by Jenny Bryan and David Robinson, in particular in Robinson’s article ‘Tidying an untidyable dataset’.

Here’s a screenshot:

    knitr::include_graphics("enron-screenshot.png")

## Preparation

This vignette uses several common packages.

    library(unpivotr)
    library(tidyxl)
    library(dplyr)
    library(purrr)

    ##
    ## Attaching package: 'purrr'

    ## The following object is masked from 'package:rvest':
    ##
    ##     pluck

    library(tidyr)
    library(stringr)

The spreadsheet is distributed with the unpivotr package, so can be loaded as a system file.

    path <- system.file("extdata/enron.xlsx", package = "unpivotr")

## 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)`.

    formatting <- 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.

    partitions <- partition(cells, title)

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

## 34-line code listing

    library(unpivotr)
    library(tidyxl)
    library(dplyr)
    library(purrr)
    library(tidyr)
    library(stringr)

    cells <-
      xlsx_cells(system.file("extdata/enron.xlsx", package = "unpivotr")) %>%
      dplyr::filter(!is_blank, between(row, 14L, 56L), col <= 20) %>%
      select(row, col, data_type, numeric, character, date)

    row_headers <-
      dplyr::filter(cells, between(row, 17, 56), between(col, 2, 4)) %>%
      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)

    titles <-
      dplyr::filter(cells, character == "Fixed Price") %>%
      select(row, col) %>%
      mutate(row = row - 1L) %>%
      inner_join(cells, by = c("row", "col"))

    partition(cells, titles)$cells %>%
      purrr::map_dfr(~ .x %>%
                     behead("NNW", "title") %>%
                     behead("NNW", "price") %>%
                     behead("N", "bid_offer")) %>%
      select(-data_type, -character, -date) %>%
      left_join(row_headers, by = "row")