parsermd
The goal of parsermd is to extract the content of an R Markdown file to allow for programmatic interactions with the document’s contents (i.e. code chunks and markdown text). The goal is to capture the fundamental structure of the document and as such we do not attempt to parse every detail of the Rmd. Specifically, the yaml front matter, markdown text, and R code are read as text lines allowing them to be processed using other tools.
The package supports both traditional chunk options (specified in the chunk header) and YAML-style chunk options (specified as special comments within chunks). When both formats are used for the same option, YAML options take precedence.
Installation
parsermd can be installed from CRAN with:
install.packages("parsermd")You can install the latest development version of parsermd from GitHub with:
remotes::install_github("rundel/parsermd")Parsing Rmds
This is a basic example which shows you the basic abstract syntax tree (AST) that results from parsing a simple Rmd file,
rmd = parsermd::parse_rmd(system.file("examples/minimal.Rmd", package = "parsermd"))The R Markdown document is parsed and stored in a flat, ordered list object containing tagged elements. By default the package will present a hierarchical view of the document where chunks and markdown text are nested within headings, which is shown by the default print method for rmd_ast objects.
print(rmd)
#> ├── YAML [4 fields]
#> ├── Heading [h1] - Setup
#> │ └── Chunk [r, 1 line] - setup
#> └── Heading [h1] - Content
#> ├── Heading [h2] - R Markdown
#> │ ├── Markdown [5 lines]
#> │ ├── Chunk [r, 1 line] - cars
#> │ └── Chunk [r, 1 line] - unnamed-chunk-1
#> └── Heading [h2] - Including Plots
#> ├── Markdown [1 line]
#> ├── Chunk [r, 1 line] - pressure
#> └── Markdown [2 lines]If you would prefer to see the underlying flat structure, this can be printed by setting flat = TRUE with print.
print(rmd, flat = TRUE)
#> ├── YAML [4 fields]
#> ├── Heading [h1] - Setup
#> ├── Chunk [r, 1 line] - setup
#> ├── Heading [h1] - Content
#> ├── Heading [h2] - R Markdown
#> ├── Markdown [5 lines]
#> ├── Chunk [r, 1 line] - cars
#> ├── Chunk [r, 1 line] - unnamed-chunk-1
#> ├── Heading [h2] - Including Plots
#> ├── Markdown [1 line]
#> ├── Chunk [r, 1 line] - pressure
#> └── Markdown [2 lines]Additionally, to ease the manipulation of the AST the package supports the transformation of the object into a tidy tibble with as_tibble or as.data.frame (both return a tibble).
as_tibble(rmd)
#> # A tibble: 12 × 5
#> sec_h1 sec_h2 type label ast
#> <chr> <chr> <chr> <chr> <list>
#> 1 <NA> <NA> rmd_yaml <NA> <yaml>
#> 2 Setup <NA> rmd_heading <NA> <heading [h1]>
#> 3 Setup <NA> rmd_chunk setup <chunk [r]>
#> 4 Content <NA> rmd_heading <NA> <heading [h1]>
#> 5 Content R Markdown rmd_heading <NA> <heading [h2]>
#> 6 Content R Markdown rmd_markdown <NA> <markdown>
#> 7 Content R Markdown rmd_chunk cars <chunk [r]>
#> 8 Content R Markdown rmd_chunk unnamed-chunk-1 <chunk [r]>
#> 9 Content Including Plots rmd_heading <NA> <heading [h2]>
#> 10 Content Including Plots rmd_markdown <NA> <markdown>
#> 11 Content Including Plots rmd_chunk pressure <chunk [r]>
#> 12 Content Including Plots rmd_markdown <NA> <markdown>and it is possible to convert from these data frames back into an rmd_ast.
as_ast( as_tibble(rmd) )
#> ├── YAML [4 fields]
#> ├── Heading [h1] - Setup
#> │ └── Chunk [r, 1 line] - setup
#> └── Heading [h1] - Content
#> ├── Heading [h2] - R Markdown
#> │ ├── Markdown [5 lines]
#> │ ├── Chunk [r, 1 line] - cars
#> │ └── Chunk [r, 1 line] - unnamed-chunk-1
#> └── Heading [h2] - Including Plots
#> ├── Markdown [1 line]
#> ├── Chunk [r, 1 line] - pressure
#> └── Markdown [2 lines]Finally, we can also convert the rmd_ast back into an R Markdown document via as_document
cat(
as_document(rmd),
sep = "\n"
)#> ---
#> title: Minimal
#> author: Colin Rundel
#> date: 7/21/2020
#> output: html_document
#> ---
#>
#> # Setup
#>
#> ```{r setup}
#> #| include: false
#> knitr::opts_chunk$set(echo = TRUE)
#> ```
#>
#> # Content
#>
#> ## R Markdown
#>
#> This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML,
#> PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
#>
#> When you click the **Knit** button a document will be generated that includes both content as well
#> as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
#>
#>
#> ```{r cars}
#> summary(cars)
#> ```
#>
#> ```{r unnamed-chunk-1}
#> knitr::knit_patterns$get()
#> ```
#>
#> ## Including Plots
#>
#> You can also embed plots, for example:
#>
#>
#> ```{r pressure}
#> #| echo: false
#> plot(pressure)
#> ```
#>
#> Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code
#> that generated the plot.Working with the AST
Once we have parsed an R Markdown document, there are a variety of things that we can do with our new abstract syntax tree (ast). Below we will demonstrate some of the basic functionality within parsermd to manipulate and edit these objects as well as check their properties.
rmd = parse_rmd(system.file("examples/hw01-student.Rmd", package="parsermd"))
rmd
#> ├── YAML [2 fields]
#> ├── Heading [h3] - Load packages
#> │ └── Chunk [r, 2 lines] - load-packages
#> ├── Heading [h3] - Exercise 1
#> │ ├── Markdown [1 line]
#> │ └── Heading [h4] - Solution
#> │ └── Markdown [4 lines]
#> ├── Heading [h3] - Exercise 2
#> │ ├── Markdown [1 line]
#> │ └── Heading [h4] - Solution
#> │ ├── Markdown [1 line]
#> │ ├── Chunk [r, 5 lines] - plot-dino
#> │ ├── Markdown [1 line]
#> │ └── Chunk [r, 2 lines] - cor-dino
#> └── Heading [h3] - Exercise 3
#> ├── Markdown [1 line]
#> └── Heading [h4] - Solution
#> ├── Chunk [r, 5 lines] - plot-star
#> └── Chunk [r, 2 lines] - cor-starSay we were interested in examining the solution a student entered for Exercise 1 - we can get access to this using the rmd_select function and its selection helper functions, specifically the by_section helper.
rmd_select(rmd, by_section( c("Exercise 1", "Solution") ))
#> ├── YAML [2 fields]
#> └── Heading [h3] - Exercise 1
#> └── Heading [h4] - Solution
#> └── Markdown [4 lines]To view the content instead of the AST we can use the as_document() function,
rmd_select(rmd, by_section( c("Exercise 1", "Solution") )) |>
as_document()
#> [1] "---"
#> [2] "title: Lab 01 - Hello R"
#> [3] "output: html_document"
#> [4] "---"
#> [5] ""
#> [6] "### Exercise 1"
#> [7] ""
#> [8] "#### Solution"
#> [9] ""
#> [10] "2 columns, 13 rows, 3 variables: "
#> [11] "dataset: indicates which dataset the data are from "
#> [12] "x: x-values "
#> [13] "y: y-values "
#> [14] ""
#> [15] ""Note that this gives us the Exercise 1 and Solution headings and the contained markdown text, if we only wanted the markdown text then we can refine our selector to only include nodes with the type rmd_markdown via the has_type helper.
rmd_select(rmd, by_section(c("Exercise 1", "Solution")) & has_type("rmd_markdown")) |>
as_document()
#> [1] "---"
#> [2] "title: Lab 01 - Hello R"
#> [3] "output: html_document"
#> [4] "---"
#> [5] ""
#> [6] "2 columns, 13 rows, 3 variables: "
#> [7] "dataset: indicates which dataset the data are from "
#> [8] "x: x-values "
#> [9] "y: y-values "
#> [10] ""
#> [11] ""This approach uses the tidyselect & operator within the selection to find the intersection of the selectors by_section(c("Exercise 1", "Solution")) and has_type("rmd_markdown"). Alternative the same result can be achieved by chaining multiple rmd_selects together,
rmd_select(rmd, by_section(c("Exercise 1", "Solution"))) |>
rmd_select(has_type("rmd_markdown")) |>
as_document()
#> [1] "---"
#> [2] "title: Lab 01 - Hello R"
#> [3] "output: html_document"
#> [4] "---"
#> [5] ""
#> [6] "2 columns, 13 rows, 3 variables: "
#> [7] "dataset: indicates which dataset the data are from "
#> [8] "x: x-values "
#> [9] "y: y-values "
#> [10] ""
#> [11] ""Wildcards
One useful feature of the by_section() and has_label() selection helpers is that they support glob style pattern matching. As such we can do the following to extract all of the solutions from our document:
rmd_select(rmd, by_section(c("Exercise *", "Solution")))
#> ├── YAML [2 fields]
#> ├── Heading [h3] - Exercise 1
#> │ └── Heading [h4] - Solution
#> │ └── Markdown [4 lines]
#> ├── Heading [h3] - Exercise 2
#> │ └── Heading [h4] - Solution
#> │ ├── Markdown [1 line]
#> │ ├── Chunk [r, 5 lines] - plot-dino
#> │ ├── Markdown [1 line]
#> │ └── Chunk [r, 2 lines] - cor-dino
#> └── Heading [h3] - Exercise 3
#> └── Heading [h4] - Solution
#> ├── Chunk [r, 5 lines] - plot-star
#> └── Chunk [r, 2 lines] - cor-starSimilarly, if we wanted to just extract the chunks that involve plotting we can match for chunk labels with a “plot” prefix,
rmd_select(rmd, has_label("plot*"))
#> ├── YAML [2 fields]
#> ├── Chunk [r, 5 lines] - plot-dino
#> └── Chunk [r, 5 lines] - plot-star