179 lines
6.6 KiB
ReStructuredText
179 lines
6.6 KiB
ReStructuredText
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With R
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.. meta::
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:description lang=en:
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Trafilatura extends its download and extractions capabilities to the R community.
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Discover how to use Trafilatura in your R projects with this dedicated guide.
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Introduction
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------------
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R is a free software environment for statistical computing and graphics. `Reticulate <https://rstudio.github.io/reticulate/>`_ is an R package that enables easy interoperability between R and Python. With Reticulate, you can import Python modules as if they were R packages and call Python functions from R.
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This allows R users to leverage the vast array of Python packages and tools and basically allows for execution of Python code inside an R session. Python packages can then be used with minimal adaptations rather than having to go back and forth between languages and environments.
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Installation
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------------
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Both R and Python installed on your system, for the latter see `installation page <installation.html>`_.
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Reticulate
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^^^^^^^^^^
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The reticulate package can be easily installed from CRAN and then loaded into your R session:
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.. code-block:: R
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> install.packages("reticulate")
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> library(reticulate)
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A recent version of Python is necessary. Some systems already have such an environment installed, to check it just run the following command in a terminal window:
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.. code-block:: bash
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$ python3 --version
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Python 3.10.12 # version 3.6 or higher is fine
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By default, reticulate will use the Python executable found on your system's PATH. You can use the ``use_python()`` function to set the Python version and path that you want to use: ``use_python("/path/to/python/executable")``.
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You can also use the ``py_config()`` function to check your current Python configuration.
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Trafilatura
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^^^^^^^^^^^
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The most convenient way to install Python packages is to use the `reticulate::py_install() <https://rstudio.github.io/reticulate/reference/py_install.html>`_ function. Of course ``Trafilatura`` can also be installed with `pip <installation.html>`_ as any other Python package. Skip the installation of Miniconda if it doesn't seem necessary, you should only be prompted once; or see `Installing Python Packages <https://rstudio.github.io/reticulate/articles/python_packages.html>`_.
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Here is a simple example using the ``py_install()`` function included in ``reticulate``:
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.. code-block:: R
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> library(reticulate)
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> py_install("trafilatura")
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Here is how to do it with the ``pip`` Python package manager:
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.. code-block:: bash
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$ pip install trafilatura
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Once you have installed a Python package, you can use it from R with the ``import()`` function.
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Download and extraction
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-----------------------
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This section demonstrates how to use functions in a R environment. Beyond the examples below, all functions in these documentation pages should be available as well.
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R syntax
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^^^^^^^^
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Text extraction from HTML documents (including downloads) is available in a straightforward way:
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.. code-block:: R
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# getting started
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> install.packages("reticulate")
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> library(reticulate)
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# loading the Trafilatura module
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> trafilatura <- import("trafilatura")
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# fetching a web page
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> url <- "https://example.org/"
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> downloaded <- trafilatura$fetch_url(url)
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# extracting the text content
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> text <- trafilatura$extract(downloaded)
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> cat(text)
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[1] "This domain is for use in illustrative examples in documents. You may use this domain in literature without prior coordination or asking for permission.\nMore information..."
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# extraction with arguments
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> trafilatura$extract(downloaded, output_format="xml", url=url)
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[1] "<doc sitename=\"example.org\" title=\"Example Domain\" source=\"https://example.org/\" hostname=\"example.org\" categories=\"\" tags=\"\" fingerprint=\"lxZaiIwoxp80+AXA2PtCBnJJDok=\">\n <main>\n <div>\n <head>Example Domain</head>\n <p>This domain is for use in illustrative examples in documents. You may use this\ndomain in literature without prior coordination or asking for permission.</p>\n <p>More information...</p>\n </div>\n </main>\n <comments/>\n</doc>"
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For a full list of arguments see `extraction documentation <corefunctions.html#extraction>`_.
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Already stored documents can also be read directly from R, for example with CSV/TSV output and ``read_delim()``, see information on `data import in R <https://r4ds.had.co.nz/data-import.html>`_.
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The ``html2txt`` function extracts all possible text on the webpage, it can be used as follows:
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.. code-block:: R
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> trafilatura$html2txt(downloaded)
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Python syntax
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^^^^^^^^^^^^^
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You can also use Python functions and objects from R. For example:
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.. code-block:: R
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> py_run_string("import trafilatura")
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> url <- "https://www.example.com"
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> py_df <- py_run_string("trafilatura.extract(url)")
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> df <- py_to_r(py_df)
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Other functions
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---------------
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Specific parts of the package can also be imported on demand, which provides access to functions not directly exported by the package. For a list of relevant functions and arguments see `core functions <corefunctions.html>`_.
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.. code-block:: R
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# using the code for link discovery in sitemaps
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> sitemapsfunc <- py_run_string("from trafilatura.sitemaps import sitemap_search")
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> sitemapsfunc$sitemap_search("https://www.sitemaps.org/")
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[1] "https://www.sitemaps.org"
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[2] "https://www.sitemaps.org/protocol.html"
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[3] "https://www.sitemaps.org/faq.html"
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[4] "https://www.sitemaps.org/terms.html"
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# and so on...
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# import the metadata part of the package as a function
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> metadatafunc <- py_run_string("from trafilatura.metadata import extract_metadata")
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> downloaded <- trafilatura$fetch_url("https://github.com/rstudio/reticulate")
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> metadatafunc$extract_metadata(downloaded)
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$title
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[1] "rstudio/reticulate"
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$author
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[1] "Rstudio"
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$url
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[1] "https://github.com/rstudio/reticulate"
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$hostname
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[1] "github.com"
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# and so on...
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Going further
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-------------
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By combining the web scraping capabilities of Trafilatura with the data analysis capabilities of R, you can create powerful workflows for extracting and analyzing data from web pages.
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Further resources:
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- Complete vignette: `Calling Python from R <https://rstudio.github.io/reticulate/articles/calling_python.html>`_.
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- Tutorial showing how to import a Python scraper and use the results directly with the usual R syntax: `Web scraping with R: Text and metadata extraction <https://adrien.barbaresi.eu/blog/web-scraping-text-metadata-r.html>`_.
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Working with the content:
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- `Basic Text Processing in R <https://programminghistorian.org/en/lessons/basic-text-processing-in-r>`_
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