# Tidyxl

#### Duncan Garmonsway

#### 2023-12-21

# tidyxl

tidyxl imports non-tabular data from Excel files into R. It exposes cell content, position, formatting and comments in a tidy structure for further manipulation, especially by the unpivotr package. It supports the xml-based file formats ‘.xlsx’ and ‘.xlsm’ via the embedded RapidXML C++ library. It does not support the binary file formats ‘.xlsb’ or ‘.xls’.

It also provides a function `xlex()` for tokenizing formulas. See the vignette for details. It is useful for detecting ‘spreadsheet smells’ (poor practice such as embedding constants in formulas, or using deep levels of nesting), and for understanding the dependency structures within spreadsheets.

## Mailing list

For bugs and/or issues, create a new issue on GitHub For other questions or comments, please subscribe to the tidyxl-devel mailing list. You must be a member to post messages, but anyone can read the archived discussions.

## Installation

    devtools::install_github("nacnudus/tidyxl")

## Examples

The package includes a spreadsheet, ‘titanic.xlsx’, which contains the following pivot table:

    ftable(Titanic, row.vars = 1:2)
    #>              Age      Child     Adult
    #>              Survived    No Yes    No Yes
    #> Class Sex
    #> 1st   Male                0   5   118  57
    #>       Female              0   1     4 140
    #> 2nd   Male                0  11   154  14
    #>       Female              0  13    13  80
    #> 3rd   Male               35  13   387  75
    #>       Female             17  14    89  76
    #> Crew  Male                0   0   670 192
    #>       Female              0   0     3  20

The multi-row column headers make this difficult to import. A popular package for importing spreadsheets coerces the pivot table into a dataframe. It treats the second header row as though it were observations.

    titanic <- system.file("extdata/titanic.xlsx", package = "tidyxl")
    readxl::read_excel(titanic)
    #> New names:
    #> • `` -> `...1`
    #> • `` -> `...2`
    #> • `` -> `...5`
    #> • `` -> `...7`
    #> # A tibble: 10 × 7
    #>    ...1  ...2   Age      Child ...5  Adult ...7
    #>    <chr> <chr>  <chr>    <chr> <chr> <chr> <chr>
    #>  1 <NA>  <NA>   Survived No    Yes   No    Yes
    #>  2 Class Sex    <NA>     <NA>  <NA>  <NA>  <NA>
    #>  3 1st   Male   <NA>     0     5     118   57
    #>  4 <NA>  Female <NA>     0     1     4     140
    #>  5 2nd   Male   <NA>     0     11    154   14
    #>  6 <NA>  Female <NA>     0     13    13    80
    #>  7 3rd   Male   <NA>     35    13    387   75
    #>  8 <NA>  Female <NA>     17    14    89    76
    #>  9 Crew  Male   <NA>     0     0     670   192
    #> 10 <NA>  Female <NA>     0     0     3     20

tidyxl doesn’t coerce the pivot table into a data frame. Instead, it represents each cell in its own row, where it describes the cell’s address, value and other properties.

    library(tidyxl)
    x <- xlsx_cells(titanic)
    dplyr::glimpse(x)
    #> Rows: 60
    #> Columns: 24
    #> $ sheet               <chr> "Sheet1", "Sheet1", "Sheet1", "Sheet1", "Sheet1", …
    #> $ address             <chr> "C1", "D1", "E1", "F1", "G1", "C2", "D2", "E2", "F…
    #> $ row                 <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4,…
    #> $ col                 <int> 3, 4, 5, 6, 7, 3, 4, 5, 6, 7, 1, 2, 1, 2, 4, 5, 6,…
    #> $ is_blank            <lgl> FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FAL…
    #> $ content             <chr> "0", "1", NA, "2", NA, "3", "4", "5", "4", "5", "6…
    #> $ data_type           <chr> "character", "character", "blank", "character", "b…
    #> $ error               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ logical             <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ numeric             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ date                <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    #> $ character           <chr> "Age", "Child", NA, "Adult", NA, "Survived", "No",…
    #> $ character_formatted <list> [<tbl_df[1 x 14]>], [<tbl_df[1 x 14]>], <NULL>, […
    #> $ formula             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ is_array            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…
    #> $ formula_ref         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ formula_group       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ comment             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ height              <dbl> 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15…
    #> $ width               <dbl> 8.38, 8.38, 8.38, 8.38, 8.38, 8.38, 8.38, 8.38, 8.…
    #> $ row_outline_level   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
    #> $ col_outline_level   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
    #> $ style_format        <chr> "Normal", "Normal", "Normal", "Normal", "Normal", …
    #> $ local_format_id     <int> 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 1, 1, 1,…

In this structure, the cells can be found by filtering.

    x[x$data_type == "character", c("address", "character")]
    #> # A tibble: 22 × 2
    #>    address character
    #>    <chr>   <chr>
    #>  1 C1      Age
    #>  2 D1      Child
    #>  3 F1      Adult
    #>  4 C2      Survived
    #>  5 D2      No
    #>  6 E2      Yes
    #>  7 F2      No
    #>  8 G2      Yes
    #>  9 A3      Class
    #> 10 B3      Sex
    #> # ℹ 12 more rows
    x[x$row == 4, c("address", "character", "numeric")]
    #> # A tibble: 6 × 3
    #>   address character numeric
    #>   <chr>   <chr>       <dbl>
    #> 1 A4      1st            NA
    #> 2 B4      Male           NA
    #> 3 D4      <NA>            0
    #> 4 E4      <NA>            5
    #> 5 F4      <NA>          118
    #> 6 G4      <NA>           57

Specific sheets can be requested using `xlsx_cells(file, sheet)`, and the names of all sheets in a file are given by `xlsx_sheet_names()`.

### Formatting

The original spreadsheet has formatting applied to the cells. This can also be retrieved using tidyxl, with the `xlsx_formats()` function.

Formatting is available by using the columns `local_format_id` and `style_format` as indexes into a separate list-of-lists structure. ‘Local’ formatting is the most common kind, applied to individual cells. ‘Style’ formatting is usually applied to blocks of cells, and defines several formats at once. Here is a screenshot of the styles buttons in Excel.

Formatting can be looked up as follows.

    # Bold
    formats <- xlsx_formats(titanic)
    formats$local$font$bold
    #> [1] FALSE  TRUE FALSE FALSE
    x[x$local_format_id %in% which(formats$local$font$bold),
      c("address", "character")]
    #> # A tibble: 4 × 2
    #>   address character
    #>   <chr>   <chr>
    #> 1 C1      Age
    #> 2 C2      Survived
    #> 3 A3      Class
    #> 4 B3      Sex

    # Yellow fill
    formats$local$fill$patternFill$fgColor$rgb
    #> [1] NA         NA         NA         "FFFFFF00"
    x[x$local_format_id %in%
      which(formats$local$fill$patternFill$fgColor$rgb == "FFFFFF00"),
      c("address", "numeric")]
    #> # A tibble: 2 × 2
    #>   address numeric
    #>   <chr>     <dbl>
    #> 1 F11           3
    #> 2 G11          20

    # Styles by name
    formats$style$font$name["Normal"]
    #>    Normal
    #> "Calibri"
    head(x[x$style_format == "Normal", c("address", "character")])
    #> # A tibble: 6 × 2
    #>   address character
    #>   <chr>   <chr>
    #> 1 C1      Age
    #> 2 D1      Child
    #> 3 E1      <NA>
    #> 4 F1      Adult
    #> 5 G1      <NA>
    #> 6 C2      Survived

    # In-cell formatting is available in the `character_formatted` column as a data
    # frame, one row per substring.
    examples <- system.file("/extdata/examples.xlsx", package = "tidyxl")
    xlsx_cells(examples)$character_formatted[77]
    #> [[1]]
    #> # A tibble: 16 × 14
    #>    character bold  italic underline strike vertAlign  size color_rgb color_theme
    #>    <chr>     <lgl> <lgl>  <chr>     <lgl>  <chr>     <dbl> <chr>           <int>
    #>  1 "in-cell" FALSE FALSE  <NA>      FALSE  <NA>          0 <NA>               NA
    #>  2 "bold"    TRUE  FALSE  <NA>      FALSE  <NA>          0 FF000000            2
    #>  3 "italic"  FALSE TRUE   <NA>      FALSE  <NA>          0 FF000000            2
    #>  4 "underli… FALSE FALSE  single    FALSE  <NA>          0 FF000000            2
    #>  5 "underli… FALSE FALSE  double    FALSE  <NA>          0 FF000000            2
    #>  6 "singlea… FALSE FALSE  singleAc… FALSE  <NA>          0 FF000000            2
    #>  7 "doublea… FALSE FALSE  doubleAc… FALSE  <NA>          0 FF000000            2
    #>  8 "striket… FALSE FALSE  <NA>      TRUE   <NA>          0 FF000000            2
    #>  9 "subscri… FALSE FALSE  <NA>      FALSE  subscript     0 FF000000            2
    #> 10 "supersc… FALSE FALSE  <NA>      FALSE  superscr…     0 FF000000            2
    #> 11 "red"     FALSE FALSE  <NA>      FALSE  <NA>          0 FFFF0000           NA
    #> 12 "theme"   FALSE FALSE  <NA>      FALSE  <NA>          0 FFC0504D            6
    #> 13 "tint"    FALSE FALSE  <NA>      FALSE  <NA>          0 FFC0504D            6
    #> 14 "size"    FALSE FALSE  <NA>      FALSE  <NA>          0 FFFF0000           NA
    #> 15 "arial"   FALSE FALSE  <NA>      FALSE  <NA>          0 FFFF0000           NA
    #> 16 "UTF8Sté… FALSE FALSE  <NA>      FALSE  <NA>          0 FFFF0000           NA
    #> # ℹ 5 more variables: color_indexed <int>, color_tint <dbl>, font <chr>,
    #> #   family <int>, scheme <chr>

To see all the available kinds of formats, use `str(formats)`.

### Comments

Comments are available alongside cell values.

    x[!is.na(x$comment), c("address", "comment")]
    #> # A tibble: 1 × 2
    #>   address comment
    #>   <chr>   <chr>
    #> 1 G11     All women in the crew worked in the victualling department.

### Formulas

Formulas are available, but with a few quirks.

    options(width = 120)
    y <- xlsx_cells(examples, "Sheet1")
    y[!is.na(y$formula),
      c("address", "formula", "is_array", "formula_ref", "formula_group",
        "error", "logical", "numeric", "date", "character")]
    #> # A tibble: 32 × 10
    #>    address formula             is_array formula_ref formula_group error   logical numeric date                character
    #>    <chr>   <chr>               <lgl>    <chr>               <int> <chr>   <lgl>     <dbl> <dttm>              <chr>
    #>  1 A1      "1/0"               FALSE    <NA>                   NA #DIV/0! NA           NA NA                  <NA>
    #>  2 A14     "1=1"               FALSE    <NA>                   NA <NA>    TRUE         NA NA                  <NA>
    #>  3 A15     "A4+1"              FALSE    <NA>                   NA <NA>    NA         1338 NA                  <NA>
    #>  4 A16     "DATE(2017,1,18)"   FALSE    <NA>                   NA <NA>    NA           NA 2017-01-18 00:00:00 <NA>
    #>  5 A17     "\"Hello, World!\"" FALSE    <NA>                   NA <NA>    NA           NA NA                  Hello, Wo…
    #>  6 A19     "$A$18+1"           FALSE    <NA>                   NA <NA>    NA            2 NA                  <NA>
    #>  7 B19     "A18+2"             FALSE    <NA>                   NA <NA>    NA            3 NA                  <NA>
    #>  8 A20     "$A$18+1"           FALSE    A20:A21                 0 <NA>    NA            2 NA                  <NA>
    #>  9 B20     "A19+2"             FALSE    B20:B21                 1 <NA>    NA            4 NA                  <NA>
    #> 10 A21     "$A$18+1"           FALSE    <NA>                    0 <NA>    NA            2 NA                  <NA>
    #> # ℹ 22 more rows

The top five cells show that the results of formulas are available as usual in the columns `error`, `logical`, `numeric`, `date`, and `character`.

#### Shared formulas

Cells `A20` and `A21` illustrate how formulas are normalised before being written to file, using the `formula_ref` and `formula_group` columns. When there is a group of cells whose formulas only differ by cell reference (e.g. “=A1+1”, “=A2+1”, “=A3+1”, etc.), only one formula in each group is written to the file, so `tidyxl` infers what the formulas in the other cells in the group must be, from their relative positions.

#### Array formulas

There are two kinds of array formulas: ones that compute over arrays, and ones whose output is an array (of cells).

Both kinds are distinguished in spreadsheet programs by curly braces, e.g. `{SUM(A19:A21*B19:B21)}`. In `tidyxl`, the curly braces are ommitted (as they are from the file itself), and instead the `is_array` column has the value `TRUE`.

The first kind (those that compute over arrays) is illustrated by cell `A22`.

The second kind (those whose value is spread across an array of cells) is illustrated by cells `A23` and `A24`. The formula is only given in the top-left cell (`A23`), which is also the only cell that describes the range of cells containing the result, in the `formula-ref` column. The results themselves are stored in all relevant cells (`A23` and `A24`). Unlike shared formulas, there is no `formula_group` to associate the cells of an array formula’s result. If you need to do identify those cells, use the cellranger package and the `formula_ref` column.

#### Formulas referring to other files

Cell `A25` contains a formula that refers to another file. The `[1]` is an index into a table of files. The roadmap for tidyxl includes de-referencing such numbers.

#### Tokenizing formulas

The function `xlex()` separates formulas into tokens of different types, and gives their depth within a nested formula. Its name is a bad pun on ‘Excel’ and ‘lexer’. Try the online demo or install the more experimental lexl package to run `demo_lexl()` locally.

It is useful for detecting spreadsheet smells, which are poor practices in spreadsheet design, such as deep nests of functions, or embedding constants in formulas.

    x <- xlex("MIN(3,MAX(2,A1))")
    x
    #> root
    #> ¦-- MIN         function
    #> °-- (           fun_open
    #>     ¦-- 3       number
    #>     ¦-- ,       separator
    #>     ¦-- MAX     function
    #>     °-- (       fun_open
    #>         ¦-- 2   number
    #>         ¦-- ,   separator
    #>         °-- A1  ref
    #>     °-- )       fun_close
    #> °-- )           fun_close

See the vignette for more examples and details.

### Named ranges

Names are imported with `xlsx_names()`. Also called ‘named formulas’ and ‘defined names’, these are usually used to name particular cells or ranges, making formulas that refer to them more readable. Ones that are ranges are identifed by the `is_range` column (using `is_range()`), making it easier to match the names to the cells returned by `xlsx_cells()` – e.g. by using the `cellranger` package.

When the scope of the name is within a particular sheet, rather than global, the sheet name is given.

    xlsx_names(examples)
    #>   rId    sheet                 name                     formula    comment hidden is_range
    #> 1   1   Sheet1  named_local_formula MAX(Sheet1!$A$129:$A$130)+1       <NA>  FALSE    FALSE
    #> 2   4 E09904.2   sheet_beyond_chart E09904.2!$A$1,E09904.2!$C$1       <NA>  FALSE     TRUE
    #> 3  NA     <NA>         intersection   Sheet1!$B:$B Sheet1!$8:$8       <NA>  FALSE     TRUE
    #> 4  NA     <NA> named_global_formula             Sheet1!$A$129-1       <NA>  FALSE    FALSE
    #> 5  NA     <NA>          named_range               Sheet1!$A$129 My comment  FALSE     TRUE

### Data validation rules

Data validation rules are imported with `xlsx_validation()`. These rules control what values may be entered into a cell, and are often used to create a drop-down list in a cell. Read the vignette for details.

    xlsx_validation(examples)
    #> # A tibble: 15 × 14
    #>    sheet  ref            type       operator   formula1 formula2 allow_blank show_input_message prompt_title prompt_body
    #>    <chr>  <chr>          <chr>      <chr>      <chr>    <chr>    <lgl>       <lgl>              <chr>        <chr>
    #>  1 Sheet1 A106           whole      between    0        9        TRUE        TRUE               message tit… message bo…
    #>  2 Sheet1 A108           list       <NA>       $B$108   <NA>     TRUE        TRUE               <NA>         <NA>
    #>  3 Sheet1 A110           date       between    2017-01… 2017-01… TRUE        TRUE               <NA>         <NA>
    #>  4 Sheet1 A111           time       between    00:00:00 09:00:00 TRUE        TRUE               <NA>         <NA>
    #>  5 Sheet1 A112           textLength between    0        9        TRUE        TRUE               <NA>         <NA>
    #>  6 Sheet1 A114           whole      notBetween 0        9        TRUE        TRUE               <NA>         <NA>
    #>  7 Sheet1 A115,A121:A122 whole      equal      0        <NA>     TRUE        TRUE               <NA>         <NA>
    #>  8 Sheet1 A116           whole      notEqual   0        <NA>     TRUE        TRUE               <NA>         <NA>
    #>  9 Sheet1 A117           whole      greaterTh… 0        <NA>     TRUE        TRUE               <NA>         <NA>
    #> 10 Sheet1 A119           whole      greaterTh… 0        <NA>     TRUE        TRUE               <NA>         <NA>
    #> 11 Sheet1 A120           whole      lessThanO… 0        <NA>     TRUE        TRUE               <NA>         <NA>
    #> 12 Sheet1 A118           whole      lessThan   0        <NA>     TRUE        TRUE               <NA>         <NA>
    #> 13 Sheet1 A107           decimal    notBetween 0        9        FALSE       FALSE              <NA>         <NA>
    #> 14 Sheet1 A113           custom     <NA>       A113<=L… <NA>     TRUE        TRUE               <NA>         <NA>
    #> 15 Sheet1 A109           list       <NA>       $B$108   <NA>     TRUE        TRUE               <NA>         <NA>
    #> # ℹ 4 more variables: show_error_message <lgl>, error_title <chr>, error_body <chr>, error_symbol <chr>

## Philosophy

Information in in many spreadsheets cannot be easily imported into R. Why?

Most R packages that import spreadsheets have difficulty unless the layout of the spreadsheet conforms to a strict definition of a ‘table’, e.g.:

- observations in rows
- variables in columns
- a single header row
- all information represented by characters, whether textual, logical, or numeric

These rules are designed to eliminate ambiguity in the interpretation of the information. But most spreadsheeting software relaxes these rules in a trade of ambiguity for expression via other media:

- proximity (other than headers, i.e. other than being the first value at the top of a column)
- formatting (colours and borders)

Humans can usually resolve the ambiguities with contextual knowledge, but computers are limited by their ignorance. Programmers are hampered by:

- their language’s expressiveness
- loss of information in transfer from spreadsheet to programming library

Information is lost when software discards it in order to force the data into tabular form. Sometimes date formatting is retained, but mostly formatting is lost, and position has to be inferred again.

tidyxl addresses the programmer’s problems by not discarding information. It imports the content, position and formatting of cells, leaving it up to the user to associate the different forms of information, and to re-encode them in tabular form without loss. The unpivotr package has been developed to assist with that step.

## Similar projects

tidyxl was originally derived from readxl and still contains some of the same code, hence it inherits the GPL-3 licence. readxl is intended for importing tabular data with a single row of column headers, whereas tidyxl is more general, and less magic.

The rsheets project of several R packages is in the early stages of importing spreadsheet information from Excel and Google Sheets into R, manipulating it, and potentially parsing and processing formulas and writing out to spreadsheet files. In particular, jailbreaker attempts to extract non-tabular data from spreadsheets into tabular structures automatically via some clever algorithms.

tidyxl differs from rsheets in scope (tidyxl will never import charts, for example), and implementation (tidyxl is implemented mainly in C++ and is quite fast, only a little slower than readxl). unpivotr is a package related to tidyxl that provides tools for unpivoting complex and non-tabular data layouts using I not AI (intelligence, not artificial intelligence). In this way it corresponds to jailbreaker, but with a different philosophy.