With R ====== Introduction ------------ `R `_ is a free software environment for statistical computing and graphics. The `reticulate `_ package provides a comprehensive set of tools for seamless interoperability between Python and R. It basically allows for execution of Python code inside an R session, so that Python packages can be used with minimal adaptations, which is ideal for those who would rather operate from R than having to go back and forth between languages and environments. The package provides several ways to integrate Python code into R projects: - Python in R Markdown - Importing Python modules - Sourcing Python scripts - An interactive Python console within R. Complete vignette: `Calling Python from R `_. This tutorial shows how to import a Python scraper straight from R and use the results directly with the usual R syntax: `Web scraping with R: Text and metadata extraction `_. Installation ------------ The reticulate package can be easily installed from CRAN as follows: .. code-block:: R > install.packages("reticulate") A recent version of Python 3 is necessary. Some systems already have such an environment installed, to check it just run the following command in a terminal window: .. code-block:: bash $ python3 --version Python 3.8.6 # version 3.6 or higher is fine In case Python is not installed, please refer to the excellent [Djangogirls tutorial: Python installation](https://tutorial.djangogirls.org/en/python_installation/). ``Trafilatura`` has to be installed with `pip `_, `conda `_, or `py_install `_. Skip the installation of Miniconda if it doesn't seem necessary, you should only be prompted once; or see `Installing Python Packages `_. Here is a simple example using the `py_install()` function included in ``reticulate``: .. code-block:: R > library(reticulate) > py_install("trafilatura") Download and extraction ----------------------- Text extraction from HTML documents (including downloads) is available in a straightforward way: .. code-block:: R # getting started > install.packages("reticulate") > library(reticulate) > trafilatura <- import("trafilatura") # get a HTML document as string > url <- "https://example.org/" > downloaded <- trafilatura$fetch_url(url) # extraction > trafilatura$extract(downloaded) [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..." # extraction with arguments > trafilatura$extract(downloaded, output_format="xml", url=url) [1] "\n
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\n Example Domain\n

This domain is for use in illustrative examples in documents. You may use this\ndomain in literature without prior coordination or asking for permission.

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More information...

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\n \n" For a full list of arguments see `extraction documentation `_. 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 `_. Other functions --------------- 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 `_. .. code-block:: R # using the code for link discovery in sitemaps > sitemapsfunc <- py_run_string("from trafilatura.sitemaps import sitemap_search") > sitemapsfunc$sitemap_search("https://www.sitemaps.org/") [1] "https://www.sitemaps.org" [2] "https://www.sitemaps.org/protocol.html" [3] "https://www.sitemaps.org/faq.html" [4] "https://www.sitemaps.org/terms.html" ... # import the metadata part of the package as a function > metadatafunc <- py_run_string("from trafilatura.metadata import extract_metadata") > downloaded <- trafilatura$fetch_url("https://github.com/rstudio/reticulate") > metadatafunc$extract_metadata(downloaded) $title [1] "rstudio/reticulate" $author [1] "Rstudio" $url [1] "https://github.com/rstudio/reticulate" $hostname [1] "github.com" ... Going further ------------- - `Basic Text Processing in R `_ - `Quanteda `_ is an R package for managing and analyzing text: - `Quickstart `_ - `Quanteda tutorials `_ - `Advancing Text Mining with R and quanteda `_