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Purpose

This vignette is aimed at developers who want to understand the package better and to make it easier for them to contribute.

Overview

There are only two main user-facing functions in {a11ytables}:

This vignette provides a quick look at what’s happening ‘under the hood’ in these functions.

Please add an issue to the package’s GitHub repository if you would like any of this explanation to be expanded, or provide a solution in a pull request.

Files

First it’s worth explaining how the source files are laid out. There are four major groups of scripts in the R/ directory of the package:

  1. Code to make a11ytable-class objects: a11ytable.R and utils-a11ytable.R contain code for handling the a11ytable class, most importantly the create_a11ytable() function, but also coercion with as_a11ytable(), checking with is_a11ytable(), a summary() method and a print() method, which takes advantage of the {pillar} package for prettier outputs.
  2. Code to make Workbook-class objects: workbook.R, utils-workbook.R and utils-workbook-style.R contain the code for creating and styling a Workbook-class object with the generate_workbook() function.
  3. Code to produce demo datasets: data.R contains the documentation for demo datasets, which are created in the data-raw/ directory with the files stored in the data/ directory.
  4. Code that creates the RStudio Addin: addin.R and utils-addin.R contain code for the RStudio Addin (the .dcf file for which is in the inst/rstudio/ directory).

You’ll also find the a11ytables-package.R file in the R/ directory, which provides a package-level help page derived from the DESCRIPTION file when ?a11ytables is run by the user. It doesn’t need to be edited.

Code

This sections below focus on the create_a11ytable() and generate_workbook() functions, which are the primary and most complex functions in the package.

The code that underpins these functions is modularised to aid with bug-catching and testing, but also to make it easier for developers to understand how the code fits together. Internal sub-functions are consistently-named and begin with verbs, which should help you better understand their purpose.

Note that {a11ytables} uses a convention that internal functions (i.e. those not presented to the user, but accessed via the ::: qualifier) are prefixed with a period (i.e. .f()) to make it clearer that they are internal to the package. The exported user-facing functions do not use a leading period.

To create a11ytables

Actually, create_a11ytable() itself only does one thing: it takes user inputs from the arguments and combines them into a dataframe. It then passes this off to the most important function in the package, as_a11ytable(), which is responsible for coercing the dataframe to a11ytable class and performing checks on its content.

Basically, as_a11ytable() creates an S3-class object with classes ‘data.frame’ and ‘tbl’ (i.e. tibble) and an additional ‘a11ytable’ class.

library(a11ytables)
my_a11ytable <- as_a11ytable(mtcars_df)
class(my_a11ytable)
# [1] "a11ytable"  "tbl"        "data.frame"

The object can be manipulated like a ‘normal’ dataframe and—thanks to the {pillar} package and the tbl class—it can be printed in compact form without the need for the whole of the {tibble} package to be imported.

my_a11ytable
# # a11ytable: 5 x 6
#   tab_title sheet_type sheet_title               blank_cells        source table
#   <chr>     <chr>      <chr>                     <chr>              <chr>  <lis>
# 1 Cover     cover      The 'mtcars' Demo Dataset NA                 NA     <df> 
# 2 Contents  contents   Table of contents         NA                 NA     <df> 
# 3 Notes     notes      Notes                     NA                 NA     <df> 
# 4 Table_1   tables     Table 1: Car Road Tests 1 A blank cell in t… Motor… <df> 
# 5 Table_2   tables     Table 2: Car Road Tests 2 NA                 Motor… <df>

Compare this to its appearance as a regular data.frame, which is trickier to understand:

as.data.frame(my_a11ytable)
#   tab_title sheet_type               sheet_title
# 1     Cover      cover The 'mtcars' Demo Dataset
# 2  Contents   contents         Table of contents
# 3     Notes      notes                     Notes
# 4   Table_1     tables Table 1: Car Road Tests 1
# 5   Table_2     tables Table 2: Car Road Tests 2
#                                                                      blank_cells
# 1                                                                           <NA>
# 2                                                                           <NA>
# 3                                                                           <NA>
# 4 A blank cell in the Notes column indicates that there is no note for that row.
# 5                                                                           <NA>
#                source
# 1                <NA>
# 2                <NA>
# 3                <NA>
# 4 Motor Trend (1974).
# 5 Motor Trend (1974).
#                                                                                                                                                                           table
# 1 Description, Properties, Contact, Aspects of automobile design and performance., Suppressed values are replaced with the value '[c]'., The mtcars Team, telephone 0123456789.
# 2                                              Notes, Table 1, Table 2, Notes used in the statistical tables of this workbook, Car Road Tests (demo 1), Car Road Tests (demo 2)
# 3                                                                                       [note 1], [note 2], US gallons., Retained to enable comparisons with previous analyses.
# 4       Mazda RX4, Mazda RX4 Wag, Datsun 710, Hornet 4 Drive, Hornet Sportabout, Valiant, 6, 6, [c], 6, 8, 6, 21, 21, [c], 21.4, 18.7, 18.1, [note 2], [note 2], NA, NA, NA, NA
# 5                               Mazda RX4, Mazda RX4 Wag, Datsun 710, Hornet 4 Drive, Hornet Sportabout, Valiant, 110, 110, 93, 110, 175, 105, 3.9, 3.9, 3.85, 3.08, 3.15, 2.76

Within as_a11ytable() itself are two major functions that help ensure proper construction of an a11ytable object:

  • .validate_a11ytable() will generate errors if basic structural expectations of an a11ytable aren’t met (e.g. if ‘cover’, ‘contents’ or ‘notes’ have been provided more than once to the sheet_type argument)
  • .warn_a11ytable() checks for things that the user may have forgotten and prints warnings about them (e.g. if 5 notes are declared in the notes sheet but there are fewer in the tables themselves)

Advanced users can create a correctly-formatted data.frame on the fly and convert it to an a11ytable with as_a11ytable() directly. The as_a11ytable() function mainly exists to make testing easier, i.e. you can pass to the pre-prepared mtcars_df dataset.

Methods

There’s a few methods for a11ytables that are also found in R/a11ytables.R.

is_a11ytable() is a classic logical test that checks for the a11ytable class in the object provided to it.

is_a11ytable(my_a11ytable)
# [1] TRUE

The summary() method prints a very simple ‘long’ overview of a provided a11ytable.

summary(my_a11ytable)
# # An a11ytable with 5 sheets
#  * Tab titles: 
#   - Cover
#   - Contents
#   - Notes
#   - Table_1
#   - Table_2 
#  * Sheet types: 
#   - cover
#   - contents
#   - notes
#   - tables
#   - tables 
#  * Sheet titles: 
#   - The 'mtcars' Demo Dataset
#   - Table of contents
#   - Notes
#   - Table 1: Car Road Tests 1
#   - Table 2: Car Road Tests 2 
#  * Table sizes: 
#   - 3 x 2
#   - 3 x 2
#   - 2 x 2
#   - 6 x 4
#   - 6 x 3

The tbl_sum() method is provided via the {pillar} package, with the goal of providing a bespoke header to the printed a11ytable.

pillar::tbl_sum(my_a11ytable)
# a11ytable 
#   "5 x 6"

To create workbooks

The generate_workbook() function sets up an {openxlsx} Workbook-class object and fills it by iterating over a user-supplied the a11ytable-class object.

my_wb <- generate_workbook(my_a11ytable)
class(my_wb)
# [1] "Workbook"
# attr(,"package")
# [1] "openxlsx"

You can see how the Workbook-class object carries information that will determine the structure and style of the final spreadsheet output.

my_wb
# A Workbook object.
#  
# Worksheets:
#  Sheet 1: "Cover"
#  
#   Custom row heights (row: height)
#    2: 34, 4: 34, 6: 34 
#   Custom column widths (column: width)
#     1: 72 
#  
# 
#  Sheet 2: "Contents"
#  
#   Custom column widths (column: width)
#     1: 16, 2: 56 
#  
# 
#  Sheet 3: "Notes"
#  
#   Custom column widths (column: width)
#     1: 16, 2: 56 
#  
# 
#  Sheet 4: "Table_1"
#  
#   Custom column widths (column: width)
#     1: 16, 2: 16, 3: 16, 4: 16 
#  
# 
#  Sheet 5: "Table_2"
#  
#   Custom column widths (column: width)
#     1: 16, 2: 16, 3: 16 
#  
# 
#  
#  Worksheet write order: 1, 2, 3, 4, 5
#  Active Sheet 1: "Cover" 
#   Position: 1

Several internal sub-functions within generate_workbook().add_*(), .insert_*() and .style_*()—are responsible for adding these sheets, inserting sheet elements and styling them, respectively.

Add sheets

A Workbook-class object is first created with openxlsx::createWorkbook() and then sheets are added based on the contents of the user-supplied a11ytable.

The following functions add sheets and sheet elements into the workbook:

  • .add_tabs() adds the required number of tabs into the workbook with openxlsx::addWorksheet() (as per the tab_title column of the supplied a11ytable)
  • .add_cover() and .add_contents() add the information needed for the cover and contents sheets (as per the required ‘cover’ and ‘contents’ supplied in the sheet_type column of an a11ytable)
  • .add_notes() if a notes sheet exists (i.e. a row in the supplied a11ytable with a sheet_type of ‘notes’)
  • .add_table() adds sheets for each statistical table (as per rows of supplied a11ytable with a sheet_type of ‘table’)

As sheets are added, content is inserted and styles are applied with the:

  • .insert_*() functions, which insert sheet elements (title, source statement, table, etc) to each sheet
  • .style_*() functions, which apply formatting to each sheet (e.g. bold sheet titles with larger font) and the workbook (e.g. Arial font)
Insert sheet elements

There are several .insert_*() functions that add information to each sheet depending on the sheet_type of the provided a11ytable, as well as the content, if any, of its sheet_title, blank_cells, source and table columns.

The following functions insert ‘pre-table’ elements in this order:

  • .insert_title() to place the sheet title in cell A1
  • .insert_table_count() to add a statement about the number of tables in the sheet
  • .insert_notes_statement() if a sheet_type of ‘notes’ is provided in the user’s a11ytable
  • .insert_blanks_message() if content is provided in the blanks_cells column of the user’s a11ytable
  • .insert_source() if content is provided in the source column of the user’s a11ytable

A table of data is added under the metadata with .insert_table(), which is provided in the table column of the user’s a11ytable object.

The exact .insert_*() functions called depend on the sheet_type declared in the a11ytable:

  • meta sheets (cover, contents and notes) need only .insert_title() and .insert_table_count()
  • statistical tables also require .insert_source() and .insert_blanks_message() if provided, as well as .insert_notes_statement() if there are notes

Simple logic is used to check for the presence of meta elements with the .has_*() functions, while the get_start_row_*() functions handle the cell to which each message should be inserted.

For example, if all the elements are supplied, then the table would begin in row 6 (i.e. after the sheet title, table count, note presence, meaning of blank cells and source), but it’s possible that the table would have to be inserted to row 3 if only the sheet title and statement are required. This avoids inaccessible blank rows and redundant statements like ‘This table has no source statement’.

Apply styles

There are a few .style_*() functions that create styles and apply them on the basis of the sheet_type provided in the a11ytable.

  • .style_create() creates an easily-referenced lookup of styles, which is created with openxlsx::createStyle()
  • .style_workbook() applies defaults for the whole workbook (i.e. to set the font style to Arial size 12)
  • .style_cover(), .style_contents() and .style_notes() all apply styles to specific sheets
  • .style_sheet_title() and .style_table() apply styles to particular sheet elements (e.g. the title is larger and bolder than the default font)

Contribute

To contribute, please add an issue or a pull request after reading the code of conduct and contributing guidance.