| Title: |
Read Markdown Tables into Tibbles |
| Version: |
0.3.2 |
| Description: |
Efficient reading of raw markdown tables into tibbles. Designed to
accept content from strings, files, and URLs with the ability to extract
and read multiple tables from markdown for analysis. |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
cli, httr2, readr, purrr, stringr |
| URL: |
https://github.com/jrdnbradford/readMDTable,
https://jrdnbradford.github.io/readMDTable/ |
| BugReports: |
https://github.com/jrdnbradford/readMDTable/issues |
| License: |
GPL (≥ 3) |
| Encoding: |
UTF-8 |
| RoxygenNote: |
7.3.2 |
| Suggests: |
covr, devtools, ggplot2, knitr, lubridate, microbenchmark,
precommit, rmarkdown, rvest, testthat (≥ 3.0.0), tibble,
usethis |
| Config/testthat/edition: |
3 |
| VignetteBuilder: |
knitr |
| NeedsCompilation: |
no |
| Packaged: |
2025-05-05 20:33:43 UTC; bradfojb |
| Author: |
Jordan Bradford 
[aut, cre, cph] |
| Maintainer: |
Jordan Bradford <jrdnbradford@gmail.com> |
| Repository: |
CRAN |
| Date/Publication: |
2025-05-05 20:50:02 UTC |
readMDTable: Read Markdown Tables into Tibbles
Description
Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.
Author(s)
Maintainer: Jordan Bradford jrdnbradford@gmail.com (ORCID) [copyright holder]
See Also
Useful links:
Description
Extract Markdown Tables from Markdown Files
Usage
extract_md_tables(file, ...)
extract_md_table(file, ...)
Arguments
|
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
quoteSingle character used to quote strings.
escape_backslashDoes 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_doubleDoes the file escape quotes by doubling them?
i.e. If this option is TRUE, the value """" represents
a single quote, \".
col_namesEither 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_typesOne 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_selectColumns 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.
idThe 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.
localeThe 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.
naCharacter vector of strings to interpret as missing values. Set this
option to character() to indicate no missing values.
quoted_na![[Deprecated]](./figures/lifecycle-deprecated.svg) 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.
commentA string used to identify comments. Any text after the
comment characters will be silently ignored.
skipNumber of lines to skip before reading data. If comment is
supplied any commented lines are ignored after skipping.
n_maxMaximum number of lines to read.
guess_maxMaximum 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_repairHandling 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_threadsThe 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.
progressDisplay 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_typesIf 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_rowsShould 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.
lazyRead 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().
|
Details
extract_md_tables captures all the markdown tables
from file and returns a tibble or list of tibbles.
Value
A tibble or list of tibbles extracted from the
markdown tables in file.
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]]
Read a Markdown Table into a Tibble
Description
Read a Markdown Table into a Tibble
Usage
read_md_table(file, warn = TRUE, force = FALSE, ...)
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.
|
warn |
Boolean. Should warnings be raised if file does not
appear to be a markdown table? Defaults to TRUE.
|
force |
Boolean. Should read_md_table attempt to read in a table
that does not fit the regex? This param should be used carefully as it
may cause read_md_table to return unexpected data. Defaults to FALSE.
|
... |
Arguments passed on to readr::read_delim
quoteSingle character used to quote strings.
escape_backslashDoes 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_doubleDoes the file escape quotes by doubling them?
i.e. If this option is TRUE, the value """" represents
a single quote, \".
col_namesEither 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_typesOne 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_selectColumns 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.
idThe 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.
localeThe 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.
naCharacter 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.
commentA string used to identify comments. Any text after the
comment characters will be silently ignored.
skipNumber of lines to skip before reading data. If comment is
supplied any commented lines are ignored after skipping.
n_maxMaximum number of lines to read.
guess_maxMaximum 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_repairHandling 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_threadsThe 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.
progressDisplay 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_typesIf 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_rowsShould 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.
lazyRead 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().
|
Details
read_md_table reads a markdown table into a tibble from a string,
file, or URL. It uses readr::read_delim to efficiently read in data.
read_md_table expects file to be a raw markdown table. If file is a
markdown file that contains more than just a table or tables, the table(s)
should be read in with extract_md_tables instead.
If warn is TRUE, read_md_table will warn if there are potential
issues with the provided markdown table. Depending on the issue,
read_md_table may still correctly read the table if force is
TRUE. readr::read_delim will provide its own warnings if
there are potential issues.
Value
A tibble created from the markdown table, or NULL.
Examples
# Read from a file
read_md_table(read_md_table_example("mtcars.md"))
# Read from a string
read_md_table(
"| H1 | H2 | \n|-----|-----|\n| R1C1 | R1C2 |\n| R2C1 | R2C2 |",
warn = FALSE,
force = TRUE
)
# Read from a URL
read_md_table(
"https://raw.githubusercontent.com/jrdnbradford/readMDTable/main/inst/extdata/iris.md"
)
# Get warning for malformed tables
read_md_table(
"| Name | Age | City | Date |
|-------|-----|-------------|------------|
| Alice | 30 | New York | 2021/01/08 |
| Bob | 25 | Los Angeles | 2023/07/22 |
Carol | 27 | Chicago | 2022/11/01 ",
force = TRUE
)
Get Path to readMDTable Examples
Description
Get Path to readMDTable Examples
Usage
read_md_table_example(file = NULL)
Arguments
file |
Name of file. If NULL, the example files will be listed.
|
Details
readMDTable comes with a number of well-known datasets as example
markdown tables in the inst/extdata directory. read_md_table_example
will list the file names or return the path of a specified file.
Value
Vector of example file names if file is NULL, else the path
to the example markdown table file.
Examples
# List the available example files
read_md_table_example()
# Get the path to the mtcars example file
read_md_table_example("mtcars.md")
# Read in an example file
mtcars_path <- read_md_table_example("mtcars.md")
read_md_table(mtcars_path)