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Maybe you’re trying to install an old version, doing some testing, or you’d rather manually install than use a CRAN mirror. 2 A Solution 2.1 On a Mac 2.1.1 From Within R Enter at the R prompt install.packages(pkgname.tar.gz, repos = NULL, type =‘‘source’’) 2.1.2 Using Terminal 1. Download the tar.gz onto the Desktop 3.
Download Xpdf and XpdfReaderCurrent version: 4.02Released: 2019 Sep 25The GPG/PGP key used to sign the packages isavailable, or from the PGP keyservers(search for [email protected]).
Let’s say we’re interested in text mining the opinions of The Supreme Court of the United States from the 2014 term. The opinions are published as PDF files at the following web page. We would probably want to look at all 76 opinions, but for the purposes of this introductory tutorial we’ll just look at the last three of the term: (1) Glossip v. Gross, (2) State Legislature v.
Arizona Independent Redistricting Comm’n, and (3) Michigan v. These are the first three listed on the page. To follow along with this tutorial, download the three opinions by clicking on the name of the case. (If you want to download all the opinions, you may want to look into using a browser extension such as.)To begin we load the pdftools package. The pdftools package provides functions for extracting text from PDF files. # install.packages('tm')library(tm)corp.
Inspect(opinions.tdm1:10,)## ## Non-/sparse entries: 30/0## Sparsity: 0%## Maximal term length: 6## Weighting: term frequency (tf)## Sample:## Docs## Terms 13-13143ea4.pdf 14-46bqmc.pdf 14-7955aplc.pdf## —————— 26 6 21## —decid 1 1 1## “all 1 1 2## “each 5 1 1## “in 14 3 5## “is 3 1 4## “it 6 3 8## “not 1 4 6## “on 1 1 3## “that 2 1 2We see words preceded with double quotes and dashes even though we specified removePunctuation = TRUE. We even see a series of dashes being treated as a word.
What happened? It appears the pdftext function preserved the unicode curly-quotes and em-dashes used in the PDF files.One way to take care of this is to manually use the removePunctuation function with tmmap, both functions in the tm package. The removePunctuation function has an argument called ucp that when set to TRUE will look for unicode punctuation.
Here’s how we can use use it to remove punctuation from the corpus. ## ## Non-/sparse entries: 30/0## Sparsity: 0%## Maximal term length: 10## Weighting: term frequency (tf)## Sample:## Docs## Terms 13-13143ea4.pdf 14-46bqmc.pdf 14-7955aplc.pdf## abandon 1 1 8## abdic 1 1 1## absent 5 2 2## accept 6 4 12## accompani 1 2 2## accomplish 4 1 1## accord 12 10 13## account 1 26 8## accur 1 3 1## achiev 1 15 3We see, for example, that the term “abandon” appears in the third PDF file 8 times. Also notice that words have been stemmed.
The word “achiev” is the stemmed version of “achieve”, “achieved”, “achieves”, and so on.The tm package includes a few functions for summary statistics. We can use the findFreqTerms function to quickly find frequently occurring terms. To find words that occur at least 100 times.
FindFreqTerms(opinions.tdm, lowfreq = 100, highfreq = Inf)1 'also' 'amend' 'ant' 'case' 'cite' 'claus' 'congress'8 'constitut' 'cost' 'court' 'decis' 'dissent' 'district' 'effect'15 'elect' 'execut' 'feder' 'find' 'justic' 'law' 'major'22 'make' 'may' 'one' 'opinion' 'petition' 'power' 'reason'29 'requir' 'see' 'state' 'time' 'tion' 'unit' 'use'To see the counts of those words we could save the result and use it to subset the TDM. Notice we have to use as.matrix to see the print out of the subsetted TDM.