We can then subset the list of table nodes prior to parsing the data with html_table(). In this example it appears that tbls list items 3 and 4 correspond with Table 2 and Table 3, respectively. First, we can assess the previous tbls list and try to identify the table(s) of interest. Net birth/death estimates by industry supersector, April – December 2014 (in thousands) Nonfarm employment benchmarks by industry, March 2014 (in thousands) and Lets assume we want to parse the second and third tables on the webpage: More often than not we want to parse specific tables. table of contents, table of figures, footers). However, rarely do we need to scrape every HTML table from a page, especially since some HTML tables don’t catch any information we are likely interested in (i.e. To parse the HTML table data we use html_table(), which would create a list containing 15 data frames. Remember that html_nodes() does not parse the data rather, it acts as a CSS selector. #
illustrate, I will focus on the BLS employment statistics webpage which contains multiple HTML tables from which we can scrape data. The simplest approach to scraping HTML table data directly into R is by using either the rvest package or the XML package. This section reiterates some of the information from the previous section however, we focus solely on scraping data from HTML tables. Another common structure of information storage on the Web is in the form of HTML tables.
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