Small Multiples

Duncan Garmonsway

2018-06-26

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.

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.

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.”

## # 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.

## # 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.

## # 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 Mountains Fixed… BID       Cash      
##  2    17    18    1.91 IF NWPL Rocky Mountains Fixed… OFFER     Cash      
##  3    18    17    2.06 IF NWPL Rocky Mountains Fixed… BID       ROM       
##  4    18    18    2.08 IF NWPL Rocky Mountains Fixed… OFFER     ROM       
##  5    19    17    2.40 IF NWPL Rocky Mountains Fixed… BID       Dec-01    
##  6    19    18    2.42 IF NWPL Rocky Mountains Fixed… OFFER     Dec-01    
##  7    20    17    2.59 IF NWPL Rocky Mountains Fixed… BID       Dec-01 to…
##  8    20    18    2.61 IF NWPL Rocky Mountains Fixed… OFFER     Dec-01 to…
##  9    21    17    2.58 IF NWPL Rocky Mountains Fixed… BID       Apr-02 to…
## 10    21    18    2.60 IF NWPL Rocky Mountains Fixed… OFFER     Apr-02 to…
## # ... 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")) %>%
  filter(!is_blank, between(row, 14L, 56L), col <= 20) %>%
  select(row, col, data_type, numeric, character, date)

row_headers <-
  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 <-
  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")