- using R version 4.4.3 (2025-02-28)
- using platform: aarch64-apple-darwin20
- R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
- running under: macOS Ventura 13.7.8
- using session charset: UTF-8
- checking for file ‘tm/DESCRIPTION’ ... OK
- this is package ‘tm’ version ‘0.7-17’
- checking package namespace information ... OK
- checking package dependencies ... NOTE
Packages suggested but not available for checking:
'Rcampdf', 'tm.lexicon.GeneralInquirer'
- checking if this is a source package ... OK
- checking if there is a namespace ... OK
- checking for executable files ... OK
- checking for hidden files and directories ... OK
- checking for portable file names ... OK
- checking for sufficient/correct file permissions ... OK
- checking whether package ‘tm’ can be installed ... [5s/8s] OK
See the install log for details.
- used C compiler: ‘Apple clang version 14.0.3 (clang-1403.0.22.14.1)’
- used C++ compiler: ‘Apple clang version 14.0.3 (clang-1403.0.22.14.1)’
- used SDK: ‘MacOSX11.3.sdk’
- checking installed package size ... OK
- checking package directory ... OK
- checking ‘build’ directory ... OK
- checking DESCRIPTION meta-information ... OK
- checking top-level files ... OK
- checking for left-over files ... OK
- checking index information ... OK
- checking package subdirectories ... OK
- checking code files for non-ASCII characters ... OK
- checking R files for syntax errors ... OK
- checking whether the package can be loaded ... [0s/0s] OK
- checking whether the package can be loaded with stated dependencies ... [0s/0s] OK
- checking whether the package can be unloaded cleanly ... [0s/0s] OK
- checking whether the namespace can be loaded with stated dependencies ... [0s/0s] OK
- checking whether the namespace can be unloaded cleanly ... [0s/0s] OK
- checking loading without being on the library search path ... [0s/0s] OK
- checking dependencies in R code ... OK
- checking S3 generic/method consistency ... OK
- checking replacement functions ... OK
- checking foreign function calls ... OK
- checking R code for possible problems ... [2s/2s] OK
- checking Rd files ... [0s/0s] OK
- checking Rd metadata ... OK
- checking Rd cross-references ... OK
- checking for missing documentation entries ... OK
- checking for code/documentation mismatches ... OK
- checking Rd \usage sections ... OK
- checking Rd contents ... OK
- checking for unstated dependencies in examples ... OK
- checking contents of ‘data’ directory ... OK
- checking data for non-ASCII characters ... [0s/0s] OK
- checking data for ASCII and uncompressed saves ... OK
- checking line endings in C/C++/Fortran sources/headers ... OK
- checking compiled code ... OK
- checking sizes of PDF files under ‘inst/doc’ ... OK
- checking installed files from ‘inst/doc’ ... OK
- checking files in ‘vignettes’ ... OK
- checking examples ... [0s/0s] ERROR
Running examples in ‘tm-Ex.R’ failed
The error most likely occurred in:
> ### Name: readPDF
> ### Title: Read In a PDF Document
> ### Aliases: readPDF
> ### Keywords: file
>
> ### ** Examples
>
> uri <- paste0("file://",
+ system.file(file.path("doc", "tm.pdf"), package = "tm"))
> engine <- if(nzchar(system.file(package = "pdftools"))) {
+ "pdftools"
+ } else {
+ "ghostscript"
+ }
> reader <- readPDF(engine)
> pdf <- reader(elem = list(uri = uri), language = "en", id = "id1")
> cat(content(pdf)[1])
Introduction to the tm Package
Text Mining in R
Ingo Feinerer
December 10, 2025
Introduction
This vignette gives a short introduction to text mining in R utilizing the text mining framework provided by
the tm package. We present methods for data import, corpus handling, preprocessing, metadata management,
and creation of term-document matrices. Our focus is on the main aspects of getting started with text mining
in R—an in-depth description of the text mining infrastructure offered by tm was published in the Journal of
Statistical Software (Feinerer et al., 2008). An introductory article on text mining in R was published in R
News (Feinerer, 2008).
Data Import
The main structure for managing documents in tm is a so-called Corpus, representing a collection of text
documents. A corpus is an abstract concept, and there can exist several implementations in parallel. The
default implementation is the so-called VCorpus (short for Volatile Corpus) which realizes a semantics as known
from most R objects: corpora are R objects held fully in memory. We denote this as volatile since once the
R object is destroyed, the whole corpus is gone. Such a volatile corpus can be created via the constructor
VCorpus(x, readerControl). Another implementation is the PCorpus which implements a Permanent Corpus
semantics, i.e., the documents are physically stored outside of R (e.g., in a database), corresponding R objects
are basically only pointers to external structures, and changes to the underlying corpus are reflected to all R
objects associated with it. Compared to the volatile corpus the corpus encapsulated by a permanent corpus
object is not destroyed if the corresponding R object is released.
Within the corpus constructor, x must be a Source object which abstracts the input location. tm provides a
set of predefined sources, e.g., DirSource, VectorSource, or DataframeSource, which handle a directory, a vector
interpreting each component as document, or data frame like structures (like CSV files), respectively. Except
DirSource, which is designed solely for directories on a file system, and VectorSource, which only accepts (char-
acter) vectors, most other implemented sources can take connections as input (a character string is interpreted
as file path). getSources() lists available sources, and users can create their own sources.
The second argument readerControl of the corpus constructor has to be a list with the named components
reader and language. The first component reader constructs a text document from elements delivered by
a source. The tm package ships with several readers (e.g., readPlain(), readPDF(), readDOC(), . . . ). See
getReaders() for an up-to-date list of available readers. Each source has a default reader which can be
overridden. E.g., for DirSource the default just reads in the input files and interprets their content as text.
Finally, the second component language sets the texts’ language (preferably using ISO 639-2 codes).
In case of a permanent corpus, a third argument dbControl has to be a list with the named components
dbName giving the filename holding the sourced out objects (i.e., the database), and dbType holding a valid
database type as supported by package filehash. Activated database support reduces the memory demand,
however, access gets slower since each operation is limited by the hard disk’s read and write capabilities.
So e.g., plain text files in the directory txt containing Latin (lat) texts by the Roman poet Ovid can be
read in with following code:
> txt <- system.file("texts", "txt", package = "tm")
> (ovid <- VCorpus(DirSource(txt, encoding = "UTF-8"),
+ readerControl = list(language = "lat")))
<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 5
1
> VCorpus(URISource(uri, mode = ""),
+ readerControl = list(reader = readPDF(engine = "ghostscript")))
sh: : command not found
Error in system2(gs_cmd, c("-dNODISPLAY -q", sprintf("-sFile=%s", shQuote(file)), :
error in running command
Calls: VCorpus ... mapply -> <Anonymous> -> <Anonymous> -> pdf_info -> system2
Execution halted
- checking for unstated dependencies in ‘tests’ ... OK
- checking tests ... [0s/0s] OK
Running ‘testthat.R’ [0s/0s]
- checking for unstated dependencies in vignettes ... OK
- checking package vignettes ... OK
- checking re-building of vignette outputs ... [10s/24s] OK
- checking PDF version of manual ... [3s/3s] OK
- DONE
Status: 1 ERROR, 1 NOTE
- using check arguments '--no-clean-on-error '