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https://github.com/SystemsBioinformatics/parcr

Construct parser combinators in R
https://github.com/SystemsBioinformatics/parcr

combinators higher-order-functions parser parsing r-package

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Construct parser combinators in R

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

library(parcr)

```



[![CRAN status](https://www.r-pkg.org/badges/version/parcr)](https://cran.r-project.org/package=parcr)

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

[![R-CMD-check](https://github.com/SystemsBioinformatics/parcr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/SystemsBioinformatics/parcr/actions/workflows/R-CMD-check.yaml)



## Construct parser combinator functions for parsing character vectors



This R package contains tools to construct parser combinator functions, higher 

order functions that parse input. The main goal of this package is to simplify

the creation of *transparent* parsers for structured text files generated by 

machines like laboratory instruments. Such files consist of lines of text 

organized in higher-order structures like headers with metadata and blocks of 

measured values. To read these data into R you first need to create a parser

that processes these files and creates R-objects as output. The `parcr` package

simplifies the task of creating such parsers.



This package was inspired by the package 

["Ramble"](https://github.com/8bit-pixies/Ramble) by Chapman Siu and co-workers 

and by the paper

["Higher-order functions for parsing"](https://doi.org/10.1017/S0956796800000411) 

by [Graham Hutton](https://orcid.org/0000-0001-9584-5150) (1992).



## Installation



Install the stable version from CRAN



```

install.packages("parcr")

```



To install the development version including its vignette run the following command



```

install_github("SystemsBioinformatics/parcr", build_vignettes=TRUE)

```



## Example application: a parser for *fasta* sequence files



As an example of a realistic application we write a parser for 

fasta-formatted files for nucleotide and protein sequences. We use a few 

simplifying assumptions about this format for the sake of the example. Real 

fasta files are more complex than we pretend here.



*Please note that more background about the functions that we use here is 

available in the package documentation. Here we only present a summary.*



A fasta file with mixed sequence types could look like the example below:



```{r, echo=FALSE, comment = NA}

data("fastafile")

cat(paste0(fastafile, collapse="\n"))

```



Since fasta files are text files we could read such a file using `readLines()`

into a character vector. The package provides the data set `fastafile` which 

contains that character vector.



```{r, eval=FALSE}

data("fastafile")

```



We can distinguish the following higher order components in a fasta file:

 

- A **fasta** file: consists of one or more **sequence blocks** until the 

  **end of the file**.

- A **sequence block**: consist of a **header** and a 

  **nucleotide sequence** or a **protein sequence**. A sequence block could be

  preceded by zero or more **empty lines**.

- A **nucleotide sequence**: consists of one or more 

  **nucleotide sequence strings**.

- A **protein sequence**: consists of one or more 

  **protein sequence strings**.

- A **header** is a *string* that starts with a ">" immediately followed by

  a **title** without spaces.

- A **nucleotide sequence string** is a *string* without spaces that consists

  *entirely* of symbols from the set `{G,A,T,C}`.

- A **protein sequence string** is a *string* without spaces that consists

  *entirely* of symbols from the set `{A,R,N,D,B,C,E,Q,Z,G,H,I,L,K,M,F,P,S,T,W,Y,V}`.



It now becomes clear what we mean when we say that the package allows us

to write *transparent* parsers: the description above of the structure of fasta

files can be put straight into code for a `Fasta()` parser:



```{r}

Fasta <- function() {

  one_or_more(SequenceBlock()) %then%

    eof()

}



SequenceBlock <- function() {

  MaybeEmpty() %then% 

    Header() %then% 

    (NuclSequence() %or% ProtSequence()) %using%

    function(x) list(x)

}



NuclSequence <- function() {

  one_or_more(NuclSequenceString()) %using% 

    function(x) list(type = "Nucl", sequence = paste(x, collapse=""))

}



ProtSequence <- function() {

  one_or_more(ProtSequenceString()) %using% 

    function(x) list(type = "Prot", sequence = paste(x, collapse=""))

}

```



Functions like `one_or_more()`, `%then%`, `%or%`, `%using%`, `eof()` and

`MaybeEmpty()` are defined in the package and are the basic parsers with

which the package user can build complex parsers. The `%using%` operator uses

the function on its right-hand side to modify parser output on its left hand 

side. Please see the vignette in the `parcr` package for more explanation why

this is useful or necessary even.



Notice that the new parser functions that we define above are higher order 

functions taking no input, hence the empty argument brackets `()` behind their

names. Now we need to define the line-parsers `Header()`, `NuclSequenceString()`

and `ProtSequenceString()` that recognize and process the header line and 

single lines of nucleotide or protein sequences in the character vector 

`fastafile`. We use functions from `stringr` to do this in a few helper 

functions, and we use `match_s()` to to create `parcr` parsers from these.



```{r}

# returns the title after the ">" in the sequence header

parse_header <- function(line) {

  # Study stringr::str_match() to understand what we do here

  m <- stringr::str_match(line, "^>(\\w+)")

  if (is.na(m[1])) {

    return(list()) # signal failure: no title found

  } else {

    return(m[2])

  }

}



# returns a nucleotide sequence string

parse_nucl_sequence_line <- function(line) {

  # The line must consist of GATC from the start (^) until the end ($)

  m <- stringr::str_match(line, "^([GATC]+)$")

  if (is.na(m[1])) {

    return(list()) # signal failure: not a valid nucleotide sequence string

  } else {

    return(m[2])

  }

}



# returns a protein sequence string

parse_prot_sequence_line <- function(line) {

  # The line must consist of ARNDBCEQZGHILKMFPSTWYV from the start (^) until the

  # end ($)

  m <- stringr::str_match(line, "^([ARNDBCEQZGHILKMFPSTWYV]+)$")

  if (is.na(m[1])) {

    return(list()) # signal failure: not a valid protein sequence string

  } else {

    return(m[2])

  }

}

```



Then we define the line-parsers.



```{r}

Header <- function() {

  match_s(parse_header) %using% 

    function(x) list(title = unlist(x))

}



NuclSequenceString <- function() {

  match_s(parse_nucl_sequence_line)

}



ProtSequenceString <- function() {

  match_s(parse_prot_sequence_line)

}

```



where `match_s()` is also a parser defined in `parcr`.



Now we have all the elements that we need to apply the `Fasta()` parser.



```{r}

Fasta()(fastafile)

```



The output of the parser consists of two elements, `L` and `R`, where `L` 

contains the parsed and processed part of the input and `R` the remaining 

un-parsed part of the input. Since we explicitly demanded to parse until the 

end of the file by the `eof()` function in the definition of the `Fasta()` 

parser, the `R` element contains an empty list to signal that the parser was

indeed at the end of the input. Please see the package documentation for more

examples and explanation.



Finally, let's present the result of the parse more concisely using the names 

of the elements inside the `L` element:



```{r}

d <- Fasta()(fastafile)[["L"]]

invisible(lapply(d, function(x) {cat(x$type, x$title, x$sequence, "\n")}))

```