gofastr is designed to do one thing really well...make a
DocumentTermMatrix. It harnesses the power
quanteda (which in turn wraps
data.table, stringi, & Matrix) to quickly generate tm
DocumentTermMatrix and TermDocumentMatrix data structures. There are
two ways in which time is meaingingful to an analyst: (a) coding time,
or the time spent writing code and (b) computational run time, or the
time the computer takes to run the code. Ideally, we want to minimize
both of these sources of time expenditures. The gofaster package is
my attempt to reduce the time an analysts takes to turn raw text into an
analysis ready data format and relies on quanteda to minimize the
run time.
In my work I often get data in the form of large .csv files or SQL
databases. Additionally, most of the higher level analysis of text I
undertake utilizes a TermDocumentMatrix or DocumentTermMatrix as the
input data. Generally, the tm package's Corpus structure is an
unnecessary step in building a usable data structure that requires
additional coding and run time. gofastr skips this step and uses
quanteda to quickly make the
DocumentTermMatrix or TermDocumentMatrix structures that are fast to
code up and fast for the computer to build.
- Function Usage
- Installation
- Contact
- Demonstration
Functions typically fall into the task category of matrix (1) creation & (2) manipulating. The main functions, task category, & descriptions are summarized in the table below:
| Function | Category | Description |
|---|---|---|
q_tdm & q_tdm_stem |
creation | TermDocumentMatrix from string vector |
q_dtm & q_dtm_stem |
creation | DocumentTermMatrix from string vector |
remove_stopwords |
manipulation | Remove stopwords and minimal character words from TermDocumentMatrix/DocumentTermMatrix |
filter_words |
manipulation | Filter words from TermDocumentMatrix/DocumentTermMatrix |
filter_tf_idf |
manipulation | Filter low tf-idf words from TermDocumentMatrix/DocumentTermMatrix |
filter_documents |
manipulation | Filter documents from a TermDocumentMatrix/DocumentTermMatrix |
select_documents |
manipulation | Select documents from TermDocumentMatrix/DocumentTermMatrix |
sub_in_na |
manipulation | Sub missing (NA) for regex matches (default: non-content elements) |
To download the development version of gofastr:
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/gofastr")
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/trinker/gofastr/issues
- send a pull request on: https://github.com/trinker/gofastr/
- compose a friendly e-mail to: tyler.rinker@gmail.com
if (!require("pacman")) install.packages("pacman")
pacman::p_load(gofastr, tm, magrittr)
(w <-with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_"))))
## <<DocumentTermMatrix (documents: 2912, terms: 3377)>>
## Non-/sparse entries: 42058/9791766
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
(x <- with(presidential_debates_2012, q_tdm(dialogue, paste(time, tot, sep = "_"))))
## <<TermDocumentMatrix (terms: 3377, documents: 2912)>>
## Non-/sparse entries: 42058/9791766
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
Stopwords are those words that we want to remove from the analysis
because they give little information gain. These words occur so
frequently in all documents or give very content information (i.e.,
function words) and thus are excluded. The remove_stopwords function
allows the user to remove stopwords using three approaches/arguments:
stopwords- A vector of common + resercher defined words (see lexicon package)min.char/max.char- Automatic removal of words less/greater than n characters in lengthdenumber- Removal of words that are numbers
By default stopwords = tm::stopwords("english"), min.char = 3, and
denumber =TRUE.
with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_"))) %>%
remove_stopwords()
## <<DocumentTermMatrix (documents: 2912, terms: 3180)>>
## Non-/sparse entries: 19014/9241146
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
with(presidential_debates_2012, q_tdm(dialogue, paste(time, tot, sep = "_"))) %>%
remove_stopwords()
## <<TermDocumentMatrix (terms: 3180, documents: 2912)>>
## Non-/sparse entries: 19014/9241146
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
As the output from gofastr matrix create functions is a true tm object, weighting is done in the standard way using tm's built in weighting functions. This is done post-hoc of creation.
with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_"))) %>%
tm::weightTfIdf()
## <<DocumentTermMatrix (documents: 2912, terms: 3377)>>
## Non-/sparse entries: 42058/9791766
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
To stem words utilize q_dtm_stem and q_tdm_stem which utilize
SnowballC's stemmer under the hood.
with(presidential_debates_2012, q_dtm_stem(dialogue, paste(time, tot, sep = "_"))) %>%
remove_stopwords()
## <<DocumentTermMatrix (documents: 2912, terms: 2261)>>
## Non-/sparse entries: 19557/6564475
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
To filter out words with counts below a threshold we use filter_words.
with(presidential_debates_2012, q_dtm(dialogue, paste(time, person, sep = "_"))) %>%
filter_words(5)
## <<DocumentTermMatrix (documents: 10, terms: 967)>>
## Non-/sparse entries: 5021/4649
## Sparsity : 48%
## Maximal term length: 14
## Weighting : term frequency (tf)
To filter out words with high/low frequency in all documents (thus low
information) use filter_tf_idf. The default min uses the tf-idf's
median per Grüen & Hornik's (2011) demonstration.
with(presidential_debates_2012, q_dtm(dialogue, paste(time, person, sep = "_"))) %>%
filter_tf_idf()
## <<DocumentTermMatrix (documents: 10, terms: 1689)>>
## Non-/sparse entries: 4024/12866
## Sparsity : 76%
## Maximal term length: 16
## Weighting : term frequency (tf)
*Grüen, B. & Hornik, K. (2011). topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software, 40(13), 1-30. http://www.jstatsoft.org/article/view/v040i13/v40i13.pdf
To filter out documents with word counts below a threshold use
filter_documents. Remember the warning from above:
Warning message:In tm::weightTfIdf(.) : empty document(s): time 1_88.1 time 2_52.1
Here we use filter_documents' default (a document must have a
row/column sum greater than 1) to eliminate the warning:
with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_"))) %>%
filter_documents() %>%
tm::weightTfIdf()
## <<DocumentTermMatrix (documents: 2912, terms: 3377)>>
## Non-/sparse entries: 42058/9791766
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
To select only documents matching a regex use the select_documents
function. This is useful for selecting only particular documents within
the corpus.
with(presidential_debates_2012, q_dtm(dialogue, paste(time, person, sep = "_"))) %>%
select_documents('romney', ignore.case=TRUE)
## <<DocumentTermMatrix (documents: 3, terms: 3377)>>
## Non-/sparse entries: 3404/6727
## Sparsity : 66%
## Maximal term length: 16
## Weighting : term frequency (tf)
with(presidential_debates_2012, q_dtm(dialogue, paste(time, person, sep = "_"))) %>%
select_documents('^(?!.*romney).*$', ignore.case = TRUE)
## <<DocumentTermMatrix (documents: 7, terms: 3377)>>
## Non-/sparse entries: 4960/18679
## Sparsity : 79%
## Maximal term length: 16
## Weighting : term frequency (tf)
Of course we can chain matrix creation functions with several of the manipulation function to quickly prepare data for analysis. Here I demonstrate preparing data for a topic model using gofastr and then the analysis. Finally, I plot the results and use the LDAvis package to interact with the results. Note that this is meant to demonstrate the types of analysis that gofastr may be of use to; the methods and parameters/hyper-parameters are selected with little regard to analysis.
pacman::p_load(tm, topicmodels, dplyr, tidyr, gofastr, devtools, LDAvis, ggplot2)
## Source topicmodels2LDAvis function
devtools::source_url("https://gist.githubusercontent.com/trinker/477d7ae65ff6ca73cace/raw/79dbc9d64b17c3c8befde2436fdeb8ec2124b07b/topicmodels2LDAvis")
## SHA-1 hash of file is f9a066b61c9f992daff3991a3293e18897268598
data(presidential_debates_2012)
## Generate Stopwords
stops <- c(
tm::stopwords("english"),
"governor", "president", "mister", "obama","romney"
) %>%
prep_stopwords()
## Create the DocumentTermMatrix
doc_term_mat <- presidential_debates_2012 %>%
with(q_dtm_stem(dialogue, paste(person, time, sep = "_"))) %>%
remove_stopwords(stops) %>%
filter_tf_idf() %>%
filter_words(4) %>%
filter_documents()
## Run the Model
lda_model <- topicmodels::LDA(doc_term_mat, 10, control = list(seed=100))
## Plot the Topics Per Person_Time
topics <- posterior(lda_model, doc_term_mat)$topics
topic_dat <- tibble::rownames_to_column(as.data.frame(topics), "Person_Time")
colnames(topic_dat)[-1] <- apply(terms(lda_model, 10), 2, paste, collapse = ", ")
gather(topic_dat, Topic, Proportion, -c(Person_Time)) %>%
separate(Person_Time, c("Person", "Time"), sep = "_") %>%
mutate(Person = factor(Person,
levels = c("OBAMA", "ROMNEY", "LEHRER", "SCHIEFFER", "CROWLEY", "QUESTION" ))
) %>%
ggplot(aes(weight=Proportion, x=Topic, fill=Topic)) +
geom_bar() +
coord_flip() +
facet_grid(Person~Time) +
guides(fill=FALSE) +
xlab("Proportion")
The output from LDAvis is not easily embedded within an R markdown document, thus the reader will need to run the code below to interact with the results.
lda_model %>%
topicmodels2LDAvis() %>%
LDAvis::serVis()
On a smaller 2912 rows these are the time comparisons between
gofastr and tm using Sys.time. Notice the gofaster runs
faster (the creation of a corpus is expensive) and requires
significantly less code.
pacman::p_load(gofastr, tm)
pd <- as.data.frame(presidential_debates_2012, stringsAsFactors = FALSE)
## tm Timing
tic <- Sys.time()
rownames(pd) <- paste("docs", 1:nrow(pd))
pd[['groups']] <- with(pd, paste(time, tot, sep = "_"))
pd <- Corpus(DataframeSource(setNames(pd[, 5:6, drop=FALSE], c('text', 'doc_id'))))
(out <- DocumentTermMatrix(pd,
control = list(
tokenize=scan_tokenizer,
stopwords=TRUE,
removeNumbers = TRUE,
removePunctuation = TRUE,
wordLengths=c(3, Inf)
)
) )
## <<DocumentTermMatrix (documents: 2912, terms: 3141)>>
## Non-/sparse entries: 19349/9127243
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
difftime(Sys.time(), tic)
## Time difference of 0.09306598 secs
## gofastr Timing
tic <- Sys.time()
x <-with(presidential_debates_2012, q_dtm(dialogue, paste(time, tot, sep = "_")))
remove_stopwords(x)
## <<DocumentTermMatrix (documents: 2912, terms: 3180)>>
## Non-/sparse entries: 19014/9241146
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
difftime(Sys.time(), tic)
## Time difference of 0.255193 secs
pacman::p_load(gofastr, tm)
pd <- as.data.frame(presidential_debates_2012, stringsAsFactors = FALSE)
## tm Timing
tic <- Sys.time()
rownames(pd) <- paste("docs", 1:nrow(pd))
pd[['groups']] <- with(pd, paste(time, tot, sep = "_"))
pd <- Corpus(DataframeSource(setNames(pd[, 5:6, drop=FALSE], c('text', 'doc_id'))))
pd <- tm_map(pd, stemDocument)
(out <- DocumentTermMatrix(pd,
control = list(
tokenize=scan_tokenizer,
stopwords=TRUE,
removeNumbers = TRUE,
removePunctuation = TRUE,
wordLengths=c(3, Inf)
)
) )
## <<DocumentTermMatrix (documents: 2912, terms: 2855)>>
## Non-/sparse entries: 19468/8294292
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
difftime(Sys.time(), tic)
## Time difference of 0.205143 secs
## gofastr Timing
tic <- Sys.time()
x <-with(presidential_debates_2012, q_dtm_stem(dialogue, paste(time, tot, sep = "_")))
remove_stopwords(x, stem=TRUE)
## <<DocumentTermMatrix (documents: 2912, terms: 2249)>>
## Non-/sparse entries: 19776/6529312
## Sparsity : 100%
## Maximal term length: 16
## Weighting : term frequency (tf)
difftime(Sys.time(), tic)
## Time difference of 0.200129 secs