Read Text Documents into R

A small collection of convenience tools for reading text documents into R.

textreadr

textreadr is a small collection of convenience tools for reading text documents into R. This is not meant to be an exhaustive collection; for more see the tm package.

Table of Contents

• Functions
• Installation
• Contact
• Demonstration
  • Load the Packages/Data
  • Download & Browse
    • Download
    • Browse
  • Generic Document Reading
  • Read Directory Contents
  • Read .docx
  • Read .doc
  • Read .rtf
  • Read .pdf
    • Image Based pdf: OCR
  • Read .html
  • Read Transcripts
    • docx Simple
    • docx With Skip
    • docx With Dash Separator
    • xls and xlsx
    • doc
    • rtf
    • Reading Text
    • Authentic Interview
  • Pairing textreadr

Functions

Most jobs in my workflow can be completed with read_document and read_dir. The former generically reads in a .docx, .doc, .pdf, .html, or .txt file without specifying the extension. The latter reads in multiple .docx, .doc, .rtf, .pdf, .html, or .txt files from a directory as a data.frame with a file and text column. This workflow is effective because most text documents I encounter are stored as a .docx, .doc, .rtf, .pdf, .html, or .txt file. The remaining common storage formats I encounter include .csv, .xlsx, XML, structured .html, and SQL. For these first 4 forms the readr, readxl, xml2, and rvest. For SQL:

These packages are already specialized to handle these very specific data formats. textreadr provides the basic reading tools that work with the five basic file formats in which text data is stored.

The main functions, task category, & descriptions are summarized in the table below:

Installation

To download the development version of textreadr:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the pacman package to install the development version:

if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/textreadr")

Contact

You are welcome to:

• submit suggestions and bug-reports at: https://github.com/trinker/textreadr/issues
• send a pull request on: https://github.com/trinker/textreadr/
• compose a friendly e-mail to: [email protected]

Demonstration

Load the Packages/Data

if (!require("pacman")) install.packages("pacman")
pacman::p_load(textreadr, magrittr)
pacman::p_load_gh("trinker/pathr")
## package 'downloader' successfully unpacked and MD5 sums checked
## package 'influenceR' successfully unpacked and MD5 sums checked
## package 'rgexf' successfully unpacked and MD5 sums checked
## package 'DiagrammeR' successfully unpacked and MD5 sums checked
## package 'data.tree' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\trinker\AppData\Local\Temp\RtmpgVzUqi\downloaded_packages

trans_docs <- dir(
    system.file("docs", package = "textreadr"), 
    pattern = "^trans",
    full.names = TRUE
)

docx_doc <- system.file("docs/Yasmine_Interview_Transcript.docx", package = "textreadr")
doc_doc <- system.file("docs/Yasmine_Interview_Transcript.doc", package = "textreadr")
pdf_doc <- system.file("docs/rl10075oralhistoryst002.pdf", package = "textreadr")
html_doc <- system.file('docs/textreadr_creed.html', package = "textreadr")
txt_doc <- system.file('docs/textreadr_creed.txt', package = "textreadr")

rtf_doc <- download(
    'https://raw.githubusercontent.com/trinker/textreadr/master/inst/docs/trans7.rtf'
)

pdf_doc_img <- system.file("docs/McCune2002Choi2010.pdf", package = "textreadr")

Download & Browse

The download and browse functions are utilities for downloading and opening files and directories.

Download

download is simply a wrapper for curl::curl_download that allows multiple documents to be download, has the tempdir pre-set as the destfile (named loc in textreadr), and also returns the path to the file download for easy use in a magrittr chain.

Here I download a .docx file of presidential debated from 2012.

'https://github.com/trinker/textreadr/raw/master/inst/docs/pres.deb1.docx' %>%
    download() %>%
    read_docx() %>%
    head(3)

## pres.deb1.docx read into C:\Users\trinker\AppData\Local\Temp\RtmpgVzUqi

## [1] "LEHRER: We'll talk about -- specifically about health care in a moment. But what -- do you support the voucher system, Governor?"                           
## [2] "ROMNEY: What I support is no change for current retirees and near-retirees to Medicare. And the president supports taking $716 billion out of that program."
## [3] "LEHRER: And what about the vouchers?"

Browse

browse is a system dependent tool for opening files and directories. In the example below we open the directory that contains the example documents used in this README.

system.file("docs", package = "textreadr") %>%
    browse()

We can open files as well:

html_doc %>%
    browse()

Generic Document Reading

The read_document is a generic wrapper for read_docx, read_doc, read_html, and read_pdf that detects the file extension and chooses the correct reader. For most tasks that require reading a .docx, .doc, .html, .pdf, or .txt file this is the go-to function to get the job done. Below I demonstrate reading each of these five file formats with read_document.

docx_doc %>%
    read_document() %>%
    head(3)

## [1] "JRMC2202 Audio Project"      "Interview Transcript"       
## [3] "Interviewer: Yasmine Hassan"

doc_doc %>%
    read_document() %>%
    head(3)

## [1] "JRMC2202 Audio Project"      "Interview Transcript"       
## [3] "Interviewer: Yasmine Hassan"

rtf_doc %>%
    read_document() %>%
    head(3)

## [1] "Researcher 2:\tOctober 7, 1892."          
## [2] "Teacher 4:\tStudents it’s time to learn." 
## [3] "[Student discussion; unintelligible]"

pdf_doc %>%
    read_document() %>%
    head(3)

## [1] "Interview with Mary Waters Spaulding, August 8, 2013"                                          
## [2] "CRAIG BREADEN: My name is Craig Breaden. I’m the audiovisual archivist at Duke University,"    
## [3] "and I’m with Kirston Johnson, the curator of the Archive of Documentary Arts at Duke. The date"

html_doc %>%
    read_document() %>%
    head(3)

## [1] "textreadr Creed"                                                                                                
## [2] "The textreadr package aims to be a lightweight tool kit that handles 80% of an analyst’s text reading in needs."
## [3] "The package handles .docx, .doc, .pdf, .html, and .txt."

txt_doc %>%
    read_document() %>%
    paste(collapse = "\n") %>%
    cat()

## The textreadr package aims to be a lightweight
## tool kit that handles 80% of an analyst's text
## reading in needs.
## The package handles .docx, .doc, .pdf, .html, and .txt.
## If you have another format there is likely already
## another popular R package that specializes in this
## read in task.  For example, got XML, use the xml2
## package, authored by Hadley Wickham, Jim Hester, &
## Jeroen Ooms.  Need special handling for .html?  Use
## Hadley Wickham's rvest package.  Got SQL?  Oh boy
## there's a bunch of great ways to read it into R.
## | R Package   | SQL                    |
## |-------------|------------------------|
## | ROBDC       | Microsoft SQL Server   |
## | RMySQL      | MySQL                  |
## | ROracle     | Oracle                 |
## | RJDBC       | JDBC                   |

Read Directory Contents

Often there is a need to read multiple files in from a single directory. The read_dir function wraps other textreadr functions and lapply to create a data frame with a document and text column (one row per document). We will read the following documents from the 'pos' directory in textreadr's system file:

levelName
pos          
  |--0_9.txt  
  |--1_7.txt  
  |--10_9.txt 
  |--11_9.txt 
  |--12_9.txt 
  |--13_7.txt 
  |--14_10.txt
  |--15_7.txt 
  |--16_7.txt 
  |--17_9.txt 
  |--18_7.txt 
  |--19_10.txt
  |--2_9.txt  
  |--3_10.txt 
  |--4_8.txt  
  |--5_10.txt 
  |--6_10.txt 
  |--7_7.txt  
  |--8_7.txt  
  \--9_7.txt

Here we have read the files in, one row per file.

system.file("docs/Maas2011/pos", package = "textreadr") %>%
    read_dir() %>%
    peek(Inf, 40)

## Table: [20 x 2]
## 
##    document content                                 
## 1  0_9      Bromwell High is a cartoon comedy. It ra
## 2  1_7      If you like adult comedy cartoons, like 
## 3  10_9     I'm a male, not given to women's movies,
## 4  11_9     Liked Stanley & Iris very much. Acting w
## 5  12_9     Liked Stanley & Iris very much. Acting w
## 6  13_7     The production quality, cast, premise, a
## 7  14_10    This film has a special place in my hear
## 8  15_7     I guess if a film has magic, I don't nee
## 9  16_7     I found this to be a so-so romance/drama
## 10 17_9     This is a complex film that explores the
## 11 18_7     `Stanley and Iris' is a heart warming fi
## 12 19_10    I just read the comments of TomReynolds2
## 13 2_9      Bromwell High is nothing short of brilli
## 14 3_10     "All the world's a stage and its people 
## 15 4_8      FUTZ is the only show preserved from the
## 16 5_10     I came in in the middle of this film so 
## 17 6_10     Fair drama/love story movie that focuses
## 18 7_7      Although I didn't like Stanley & Iris tr
## 19 8_7      Very good drama although it appeared to 
## 20 9_7      Working-class romantic drama from direct
## .. ...      ...

Read .docx

A .docx file is nothing but a fancy container. It can be parsed via XML. The read_docx function allows the user to read in a .docx file as plain text. Elements are essentially the p tags (explicitly //w:p) in the markup.

docx_doc %>%
    read_docx() %>%
    head(3)

## [1] "JRMC2202 Audio Project"      "Interview Transcript"       
## [3] "Interviewer: Yasmine Hassan"

docx_doc %>%
    read_docx(15) %>%
    head(3)

## [1] "Hassan:           Could you please tell me your name, your title, your age, and your place of ref,                                   umm, residence?"
## [2] "Abd Rabou:   My name is Ahmad Abd Rabou. I’m assistant professor of comparative politics at"                                                         
## [3] "both Cairo University and The American University in Cairo. I’m 34 years old. I"

Read .doc

A .doc file is a bit trickier to read in than .docx but is made easy by the antiword package which wraps the Antiword program in an OS independent way.

doc_doc %>%
    read_doc() %>%
    head()

## [1] "JRMC2202 Audio Project"      "Interview Transcript"       
## [3] "Interviewer: Yasmine Hassan" "Narrator: Ahmad Abd Rabou"  
## [5] "Date: 16/10/2014"            "Place: Narrator's office"

doc_doc %>%
    read_doc(15) %>%
    head(7)

## [1] "Hassan:           Could you please tell me your name, your title, your age,"
## [2] "and your place of ref,"                                                     
## [3] "umm, residence?"                                                            
## [4] "Abd Rabou:   My name is Ahmad Abd Rabou. I'm assistant professor of"        
## [5] "comparative politics at"                                                    
## [6] "both Cairo University and The American University"                          
## [7] "in Cairo. I'm 34 years old. I"

Read .rtf

Rich text format (.rtf) is a plain text document with markup similar to latex. The striprtf package provides the backend for read_rtf.

rtf_doc %>%
    read_rtf() 

## [1] "Researcher 2:\tOctober 7, 1892."                                                                                                                                                                                                                                                                                                                              
## [2] "Teacher 4:\tStudents it’s time to learn."                                                                                                                                                                                                                                                                                                                     
## [3] "[Student discussion; unintelligible]"                                                                                                                                                                                                                                                                                                                        
## [4] "Multiple Students:\tYes teacher we‘re ready to learn."                                                                                                                                                                                                                                                                                                        
## [5] "Teacher 4:\tLet's read this terrific book together.  It's called Moo Baa La La La and – what was I going to …  Oh yes — The story is by Sandra Boynton."                                                                                                                                                                                                      
## [6] "“A cow says Moo. A Sheep says Baa. Three singing pigs say LA LA LA! \"No, no!\" you say, that isn't right. The pigs say oink all day and night. Rhinoceroses snort and snuff. And little dogs go ruff ruff ruff! Some other dogs go bow wow wow! And cats and kittens say Meow! Quack! Says the duck. A horse says neigh. It's quiet now. What do you say? ”"

Read .pdf

Like .docx a .pdf file is simply a container. Reading PDF's is made easier with a number of command line tools. A few methods of PDF reading have been incorporated into R. Here I wrap pdftools pdf_text to produce read_pdf, a function with sensible defaults that is designed to read PDFs into R for as many folks as possible right out of the box.

Here I read in a PDF with read_pdf. Notice the result is a data frame with meta data, including page numbers and element (row) ids.

pdf_doc %>%
    read_pdf() 

## Table: [616 x 3]
## 
##    page_id element_id text                                    
## 1  1       1          Interview with Mary Waters Spaulding, Au
## 2  1       2          CRAIG BREADEN: My name is Craig Breaden.
## 3  1       3          and I’m with Kirston Johnson, the curato
## 4  1       4          is August 8, 2013, and we are in Lexingt
## 5  1       5          life and family, and particularly about 
## 6  1       6          your full name, date of birth, and place
## 7  1       7          MARY WATERS SPAULDING: My name is Mary E
## 8  1       8          birth was Lexington, NC, on May 14, 1942
## 9  1       9          BREADEN: Can you describe what Lexington
## 10 1       10         1940’s?                                 
## .. ...     ...        ...

Image Based pdf: OCR

Image based .pdfs require optical character recognition (OCR) in order for the images to be converted to text. The ocr argument of read_pdf allows the user to read in image based .pdf files and allow the tesseract package do the heavy lifting in the backend. You can look at the .pdf we'll be using by running:

browse(pdf_doc_img)

First let's try the task without using OCR.

pdf_doc_img %>%
    read_pdf(ocr = FALSE)

## Table: [0 x 3]
## 
## [1] page_id    element_id text      
## <0 rows> (or 0-length row.names)
## ... ...        ...        ...

And now using OCR via tesseract. Note that ocr = TRUE is the default behavior of read_pdf.

pdf_doc_img %>%
    read_pdf(ocr = TRUE)

## Converting page 1 to C:\Users\AppData\Local\Temp\RtmpKeJAnL/McCune2002Choi2010_01.png... done!
## Converting page 2 to C:\Users\AppData\Local\Temp\RtmpKeJAnL/McCune2002Choi2010_02.png... done!

## Table: [104 x 3]
##
##    page_id element_id text                                           
## 1  1       1          A Survey of Binary Similarity and Distan       
## 2  1       2          Seung-Seok Choi, Sung-Hyuk Cha, Charles        
## 3  1       3          Department of Computer Science, Pace Uni       
## 4  1       4          New York, US                                   
## 5  1       5          ABSTRACT ecological 25 <U+FB01>sh species [2|].
## 6  1       6          conventional similarity measures to solv       
## 7  1       7          The binary feature vector is one of the        
## 8  1       8          representations of patterns and measurin       
## 9  1       9          distance measures play a critical role i       
## 10 1       10         such as clustering, classi<U+FB01>cation, etc. 
## .. ...     ...        ... 

Read .html

Often a researcher only wishes to grab the text from the body of .html files. The read_html function does exactly this task. For finer control over .html scraping the user may investigate the xml2 & rvest packages for parsing .html and .xml files. Here I read in HTML with read_html.

html_doc %>%
    read_html() 

##  [1] "textreadr Creed"                                                                                                                                                                                                                                                                                                                                             
##  [2] "The textreadr package aims to be a lightweight tool kit that handles 80% of an analyst’s text reading in needs."                                                                                                                                                                                                                                             
##  [3] "The package handles .docx, .doc, .pdf, .html, and .txt."                                                                                                                                                                                                                                                                                                     
##  [4] "If you have another format there is likely already another popular R package that specializes in this read in task. For example, got XML, use the xml2 package, authored by Hadley Wickham, Jim Hester, & Jeroen Ooms. Need special handling for .html? Use Hadley Wickham’s rvest package. Got SQL? Oh boy there’s a bunch of great ways to read it into R."
##  [5] "R Package"                                                                                                                                                                                                                                                                                                                                                   
##  [6] "SQL"                                                                                                                                                                                                                                                                                                                                                         
##  [7] "ROBDC"                                                                                                                                                                                                                                                                                                                                                       
##  [8] "Microsoft SQL Server"                                                                                                                                                                                                                                                                                                                                        
##  [9] "RMySQL"                                                                                                                                                                                                                                                                                                                                                      
## [10] "MySQL"                                                                                                                                                                                                                                                                                                                                                       
## [11] "ROracle"                                                                                                                                                                                                                                                                                                                                                     
## [12] "Oracle"                                                                                                                                                                                                                                                                                                                                                      
## [13] "RJDBC"                                                                                                                                                                                                                                                                                                                                                       
## [14] "JDBC"

Read Transcripts

Many researchers store their dialogue data (including interviews and observations) as a .docx or .xlsx file. Typically the data is a two column format with the person in the first column and the text in the second separated by some sort of separator (often a colon). The read_transcript wraps up many of these assumptions into a reader that will extract the data as a data frame with a person and text column. The skip argument is very important for correct parsing.

Here I read in and parse the different formats read_transcript handles. These are the files that will be read in:

basename(trans_docs)

## [1] "trans1.docx" "trans2.docx" "trans3.docx" "trans4.xlsx" "trans5.xls" 
## [6] "trans6.doc"  "trans7.rtf"  "transcripts"

docx Simple

read_transcript(trans_docs[1])

## Table: [5 x 2]
## 
##   Person            Dialogue                                
## 1 Researcher 2      October 7, 1892.                        
## 2 Teacher 4         Students it's time to learn. [Student di
## 3 Multiple Students Yes teacher we're ready to learn.       
## 4 [Cross Talk 3     00]                                     
## 5 Teacher 4         Let's read this terrific book together. 
## . ...               ...

docx With Skip

skip is important to capture the document structure. Here not skipping front end document matter throws an error, while skip = 1 correctly parses the file.

read_transcript(trans_docs[2])

## Error in data.frame(X1 = trimws(speaker), X2 = trimws(pvalues), stringsAsFactors = FALSE): arguments imply differing number of rows: 7, 8

read_transcript(trans_docs[2], skip = 1)

## Table: [5 x 2]
## 
##   Person            Dialogue                                
## 1 Researcher 2      October 7, 1892.                        
## 2 Teacher 4         Students it's time to learn. [Student di
## 3 Multiple Students Yes teacher we're ready to learn.       
## 4 [Cross Talk 3     00]                                     
## 5 Teacher 4         Let's read this terrific book together. 
## . ...               ...

docx With Dash Separator

The colon is the default separator. At times other separators may be used to separate speaker and text. Here is an example where hypens are used as a separator. Notice the poor parse with colon set as the default separator the first go round.

read_transcript(trans_docs[3], skip = 1)

## Table: [1 x 2]
## 
##   Person        Dialogue                                
## 1 [Cross Talk 3 Teacher 4-Students it's time to learn. [
## . ...           ...

read_transcript(trans_docs[3], sep = "-", skip = 1)

## Table: [3 x 2]
## 
##   Person            Dialogue                                
## 1 Teacher 4         Students it's time to learn. [Student di
## 2 Multiple Students Yes teacher we're ready to learn. [Cross
## 3 Teacher 4         Let's read this terrific book together. 
## . ...               ...

xls and xlsx

read_transcript(trans_docs[4])

## Table: [7 x 2]
## 
##   Person             Dialogue                                
## 1 Researcher 2:      October 7, 1892.                        
## 2 <NA>               NA                                      
## 3 Teacher 4:         Students it's time to learn.            
## 4 <NA>               NA NA NA                                
## 5 Multiple Students: Yes teacher we're ready to learn.       
## 6 <NA>               NA NA NA                                
## 7 Teacher 4:         Let's read this terrific book together. 
## . ...                ...

read_transcript(trans_docs[5])

## Table: [7 x 2]
## 
##   Person             Dialogue                                
## 1 Researcher 2:      October 7, 1892.                        
## 2 <NA>               NA                                      
## 3 Teacher 4:         Students it's time to learn.            
## 4 <NA>               NA NA NA                                
## 5 Multiple Students: Yes teacher we're ready to learn.       
## 6 <NA>               NA NA NA                                
## 7 Teacher 4:         Let's read this terrific book together. 
## . ...                ...

doc

read_transcript(trans_docs[6], skip = 1)

## Table: [3 x 2]
## 
##   Person            Dialogue                                
## 1 Teacher 4         Students it's time to learn. [Student di
## 2 Multiple Students Yes teacher we're ready to learn.       
## 3 Teacher 4         Let's read this terrific book together. 
## . ...               ...

rtf

read_transcript(rtf_doc, skip = 1)

## Table: [4 x 2]
## 
##   Person            Dialogue                                
## 1 Researcher 2      October 7, 1892.                        
## 2 Teacher 4         Students it's time to learn. [Student di
## 3 Multiple Students Yes teacher we're ready to learn.       
## 4 Teacher 4         Let's read this terrific book together. 
## . ...               ...

Reading Text

Like read.table, read_transcript also has a text argument which is useful for demoing code.

read_transcript(
    text=

"34    The New York Times reports a lot of words here.
12    Greenwire reports a lot of words.
31    Only three words.
 2    The Financial Times reports a lot of words.
 9    Greenwire short.
13    The New York Times reports a lot of words again.",

    col.names = c("NO", "ARTICLE"), sep = "   "
)

## Table: [6 x 2]
## 
##   NO ARTICLE                                 
## 1 34 The New York Times reports a lot of word
## 2 12 Greenwire reports a lot of words.       
## 3 31 Only three words.                       
## 4 2  The Financial Times reports a lot of wor
## 5 9  Greenwire short.                        
## 6 13 The New York Times reports a lot of word
## . .. ...

Authentic Interview

Here I read in an authentic interview transcript:

docx_doc %>%
    read_transcript(c("Person", "Dialogue"), skip = 19)

## Table: [13 x 2]
## 
##    Person    Dialogue                                
## 1  Hassan    Professor Abd Rabou, being a current pro
## 2  Abd Rabou Sure. First of all, let's look at the so
## 3  Hassan    So from this point of the differences of
## 4  Abd Rabou No. I don't--It depends --Like my--This 
## 5  Hassan    So, as political science students, does 
## 6  Abd Rabou Less, not mature, they are politically m
## 7  Hassan    Since you are an active politician and w
## 8  Abd Rabou It does somehow. What I do is--First of 
## 9  Hassan    But you are characterized with, somehow 
## 10 Abd Rabou So far I didn't get--So far--Maybe it do
## .. ...       ...

Pairing textreadr

textreadr is but one package used in the text analysis (often the first package used). It pairs nicely with a variety of other text munging and analysis packages. In the example below I show just a few other package pairings that are used to extract case names (e.g., "Jones v. State of New York") from a Supreme Court Database Code Book. I demonstrate pairings with textshape, textclean, qdapRegex, and dplyr.

if (!require("pacman")) install.packages("pacman"); library(pacman)
p_load(dplyr, qdapRegex)
p_load_current_gh(file.path('trinker', c('textreadr', 'textshape', 'textclean')))

## Read in pdf, split on variables
dat <- 'http://scdb.wustl.edu/_brickFiles/2012_01/SCDB_2012_01_codebook.pdf' %>%
    textreadr::download() %>%
    textreadr::read_pdf() %>%
    filter(page_id > 5 & page_id < 79) %>%
    mutate(
        loc = grepl('Variable Name', text, ignore.case=TRUE),
        text = textclean::replace_non_ascii(text)
    ) %>%
    textshape::split_index(which(.$loc) -1) %>%
    lapply(select, -loc)

## Function to extract cases
ex_vs <- qdapRegex::ex_(pattern = "((of|[A-Z][A-Za-z'.,-]+)\\s+)+([Vv]s?\\.\\s+)(([A-Z][A-Za-z'.,-]+\\s+)*((of|[A-Z][A-Za-z',.-]+),?($|\\s+|\\d))+)")

## Extract and filter cases
dat %>%
    lapply(function(x) {
        x$text %>%
            textshape::combine() %>%
            ex_vs()  %>% 
            c() %>% 
            textclean::mgsub(c("^[ ,]+", "[ ,0-9]+$", "^(See\\s+|E\\.g\\.?,)"), "", fixed=FALSE)
    }) %>%
    setNames(seq_along(.)) %>%
    {.[sapply(., function(x) all(length(x) > 1 | !is.na(x)))]}

## $`24`
## [1] " Townsend v. Sain"        " Simpson v. Florida"     
## [3] "McNally v. United States" "United States v. Gray"   
## 
## $`30`
## [1] "Edward V. Heck"
## 
## $`36`
## [1] "State of Colorado v. Western Alfalfa Corporation"
## 
## $`38`
## [1] "Pulliam v. Allen"   "Burnett v. Grattan"
## 
## $`40`
##  [1] " United States v. Knox"                                           
##  [2] "Lassiter v. Department of Social Services"                        
##  [3] "Arkansas v. Tennessee"                                            
##  [4] "Utah v. United States"                                            
##  [5] "Johnson v. United States"                                         
##  [6] "Baldonado v. California"                                          
##  [7] "Conway v. California Adult Authority"                             
##  [8] "Wheaton v. California"                                            
##  [9] "Maxwell v. Bishop"                                                
## [10] "National Labor Relations Board v. United Insurance Co. of America"
## [11] "United States v. King"                                            
## [12] "National Labor Relations Board v. United Insurance Co. of America"
## [13] "United States v. King"                                            
## 
## $`44`
## [1] "Grisham v. Hagan"                  
## [2] "McElroy v. Guagliardo"             
## [3] "Virginia Supreme Court v. Friedman"
## 
## $`48`
## [1] "Baker v. Carr"                     "Gray v. Sanders"                  
## [3] " Patterson v. McLean Credit Union"
## 
## $`53`
## [1] "Bates v. Arizona State Bar"
## 
## $`57`
## [1] "New York Gaslight Club, Inc. v. Carey"
## [2] "Pruneyard Shopping Center v. Robins"  
## 
## $`58`
## [1] "Mobile v. Bolden"                            
## [2] "Williams v. Brown"                           
## [3] "United States v. Havens"                     
## [4] "Parratt v. Taylor"                           
## [5] "Dougherty County Board of Education v. White"
## [6] "Jenkins v. Anderson"