Extract Markdown Tables from Markdown Files

Usage

extract_md_tables(file, ...)

extract_md_table(file, ...)

Arguments

file

Either a path to a file, a connection, or literal data (either a single string or a raw vector). Files starting with http://, https://, ftp://, or ftps:// will be automatically downloaded.

...

Arguments passed on to readr::read_delim

quote

Single character used to quote strings.

escape_backslash

Does the file use backslashes to escape special characters? This is more general than escape_double as backslashes can be used to escape the delimiter character, the quote character, or to add special characters like \\n.

escape_double

Does the file escape quotes by doubling them? i.e. If this option is TRUE, the value """" represents a single quote, \".

col_names

Either TRUE, FALSE or a character vector of column names.

If TRUE, the first row of the input will be used as the column names, and will not be included in the data frame. If FALSE, column names will be generated automatically: X1, X2, X3 etc.

If col_names is a character vector, the values will be used as the names of the columns, and the first row of the input will be read into the first row of the output data frame.

Missing (NA) column names will generate a warning, and be filled in with dummy names ...1, ...2 etc. Duplicate column names will generate a warning and be made unique, see name_repair to control how this is done.

col_types

One of NULL, a cols() specification, or a string. See vignette("readr") for more details.

If NULL, all column types will be inferred from guess_max rows of the input, interspersed throughout the file. This is convenient (and fast), but not robust. If the guessed types are wrong, you'll need to increase guess_max or supply the correct types yourself.

Column specifications created by list() or cols() must contain one column specification for each column. If you only want to read a subset of the columns, use cols_only().

Alternatively, you can use a compact string representation where each character represents one column:

c = character
i = integer
n = number
d = double
l = logical
f = factor
D = date
T = date time
t = time
? = guess
_ or - = skip

By default, reading a file without a column specification will print a message showing what readr guessed they were. To remove this message, set show_col_types = FALSE or set options(readr.show_col_types = FALSE).

col_select

Columns to include in the results. You can use the same mini-language as dplyr::select() to refer to the columns by name. Use c() to use more than one selection expression. Although this usage is less common, col_select also accepts a numeric column index. See ?tidyselect::language for full details on the selection language.

id

The name of a column in which to store the file path. This is useful when reading multiple input files and there is data in the file paths, such as the data collection date. If NULL (the default) no extra column is created.

locale

The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.

na

Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.

quoted_na

Should missing values inside quotes be treated as missing values (the default) or strings. This parameter is soft deprecated as of readr 2.0.0.

comment

A string used to identify comments. Any text after the comment characters will be silently ignored.

skip

Number of lines to skip before reading data. If comment is supplied any commented lines are ignored after skipping.

n_max

Maximum number of lines to read.

guess_max

Maximum number of lines to use for guessing column types. Will never use more than the number of lines read. See vignette("column-types", package = "readr") for more details.

name_repair

Handling of column names. The default behaviour is to ensure column names are "unique". Various repair strategies are supported:

"minimal": No name repair or checks, beyond basic existence of names.
"unique" (default value): Make sure names are unique and not empty.
"check_unique": No name repair, but check they are unique.
"unique_quiet": Repair with the unique strategy, quietly.
"universal": Make the names unique and syntactic.
"universal_quiet": Repair with the universal strategy, quietly.
A function: Apply custom name repair (e.g., name_repair = make.names for names in the style of base R).
A purrr-style anonymous function, see rlang::as_function().

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

num_threads

The number of processing threads to use for initial parsing and lazy reading of data. If your data contains newlines within fields the parser should automatically detect this and fall back to using one thread only. However if you know your file has newlines within quoted fields it is safest to set num_threads = 1 explicitly.

progress

Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The automatic progress bar can be disabled by setting option readr.show_progress to FALSE.

show_col_types

If FALSE, do not show the guessed column types. If TRUE always show the column types, even if they are supplied. If NULL (the default) only show the column types if they are not explicitly supplied by the col_types argument.

skip_empty_rows

Should blank rows be ignored altogether? i.e. If this option is TRUE then blank rows will not be represented at all. If it is FALSE then they will be represented by NA values in all the columns.

lazy

Read values lazily? By default, this is FALSE, because there are special considerations when reading a file lazily that have tripped up some users. Specifically, things get tricky when reading and then writing back into the same file. But, in general, lazy reading (lazy = TRUE) has many benefits, especially for interactive use and when your downstream work only involves a subset of the rows or columns.

Learn more in should_read_lazy() and in the documentation for the altrep argument of vroom::vroom().

Value

A tibble or list of tibbles extracted from the markdown tables in file.

Details

extract_md_tables captures all the markdown tables from file and returns a tibble or list of tibbles.

Examples

md <-
"# Heading 1

This example splits the `mtcars` dataset into several different tables
with the same header.

## Table 1
The first table contains the initial four rows of the `mtcars` dataset.

|model              |mpg |cyl|disp |hp |drat|wt   |qsec |vs |am |gear|carb|
|-------------------|----|---|-----|---|----|-----|-----|---|---|----|----|
|Mazda RX4          |21  |6  |160  |110|3.9 |2.62 |16.46|0  |1  |4   |4   |
|Mazda RX4 Wag      |21  |6  |160  |110|3.9 |2.875|17.02|0  |1  |4   |4   |
|Datsun 710         |22.8|4  |108  |93 |3.85|2.32 |18.61|1  |1  |4   |1   |
|Hornet 4 Drive     |21.4|6  |258  |110|3.08|3.215|19.44|1  |0  |3   |1   |

## Table 2
The second table includes the next four rows of the dataset.

|model              |mpg |cyl|disp |hp |drat|wt   |qsec |vs |am |gear|carb|
|-------------------|----|---|-----|---|----|-----|-----|---|---|----|----|
|Hornet Sportabout  |18.7|8  |360  |175|3.15|3.44 |17.02|0  |0  |3   |2   |
|Valiant            |18.1|6  |225  |105|2.76|3.46 |20.22|1  |0  |3   |1   |
|Duster 360         |14.3|8  |360  |245|3.21|3.57 |15.84|0  |0  |3   |4   |
|Merc 240D          |24.4|4  |146.7|62 |3.69|3.19 |20   |1  |0  |4   |2   |

## Tables 3 and 4
The last two tables contain four and six rows, respectively.

|model              |mpg |cyl|disp |hp |drat|wt   |qsec |vs |am |gear|carb|
|-------------------|----|---|-----|---|----|-----|-----|---|---|----|----|
|Cadillac Fleetwood |10.4|8  |472  |205|2.93|5.25 |17.98|0  |0  |3   |4   |
|Lincoln Continental|10.4|8  |460  |215|3   |5.424|17.82|0  |0  |3   |4   |
|Chrysler Imperial  |14.7|8  |440  |230|3.23|5.345|17.42|0  |0  |3   |4   |
|Fiat 128           |32.4|4  |78.7 |66 |4.08|2.2  |19.47|1  |1  |4   |1   |

|model              |mpg |cyl|disp |hp |drat|wt   |qsec |vs |am |gear|carb|
|-------------------|----|---|-----|---|----|-----|-----|---|---|----|----|
|Porsche 914-2      |26  |4  |120.3|91 |4.43|2.14 |16.7 |0  |1  |5   |2   |
|Lotus Europa       |30.4|4  |95.1 |113|3.77|1.513|16.9 |1  |1  |5   |2   |
|Ford Pantera L     |15.8|8  |351  |264|4.22|3.17 |14.5 |0  |1  |5   |4   |
|Ferrari Dino       |19.7|6  |145  |175|3.62|2.77 |15.5 |0  |1  |5   |6   |
|Maserati Bora      |15  |8  |301  |335|3.54|3.57 |14.6 |0  |1  |5   |8   |
|Volvo 142E         |21.4|4  |121  |109|4.11|2.78 |18.6 |1  |1  |4   |2   |

# Conclusion
These four markdown tables contain the classic `mtcars` dataset."

# Extract tables from the markdown file
tables <- extract_md_tables(md, show_col_types = FALSE)

# Display the 2nd table in the list
tables[[2]]
#> # A tibble: 4 × 12
#>   model          mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Hornet Spor…  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#> 2 Valiant       18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#> 3 Duster 360    14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#> 4 Merc 240D     24.4     4  147.    62  3.69  3.19  20       1     0     4     2