Nothing
## ----eval=FALSE----------------------------------------------------------
# install.packages("preText")
## ----eval=FALSE----------------------------------------------------------
# install.packages("devtools")
## ----eval=FALSE----------------------------------------------------------
# devtools::install_github("matthewjdenny/preText")
## ----eval=FALSE----------------------------------------------------------
# library(preText)
## ----eval=TRUE, fig.width=6, fig.height=6, fig.align ='center'-----------
library(preText)
library(quanteda)
# load in U.S. presidential inaugural speeches from Quanteda example data.
documents <- data_corpus_inaugural
# use first 10 documents for example
documents <- documents[1:10,]
# take a look at the document names
print(names(documents))
## ----eval=TRUE, fig.width=6, fig.height=6, fig.align ='center'-----------
preprocessed_documents <- factorial_preprocessing(
documents,
use_ngrams = FALSE,
infrequent_term_threshold = 0.2,
verbose = FALSE)
## ----eval=TRUE, fig.width=6, fig.height=6, fig.align ='center'-----------
names(preprocessed_documents)
head(preprocessed_documents$choices)
## ----eval=TRUE, fig.width=6, fig.height=6, fig.align ='center'-----------
preText_results <- preText(
preprocessed_documents,
dataset_name = "Inaugural Speeches",
distance_method = "cosine",
num_comparisons = 20,
verbose = FALSE)
## ----eval=TRUE, fig.width=6, fig.height=16, fig.align ='center'----------
preText_score_plot(preText_results)
## ----eval=TRUE, fig.width=6, fig.height=4, fig.align ='center'-----------
regression_coefficient_plot(preText_results,
remove_intercept = TRUE)
## ----eval=FALSE, fig.width=6, fig.height=4, fig.align ='center'----------
# # load the package
# library(preText)
# # load in the data
# data("UK_Manifestos")
# # preprocess data
# preprocessed_documents <- factorial_preprocessing(
# UK_Manifestos,
# use_ngrams = TRUE,
# infrequent_term_threshold = 0.02,
# verbose = TRUE)
# # run preText
# preText_results <- preText(
# preprocessed_documents,
# dataset_name = "UK Manifestos",
# distance_method = "cosine",
# num_comparisons = 100,
# verbose = TRUE)
# # generate preText score plot
# preText_score_plot(preText_results)
# # generate regression results
# regression_coefficient_plot(preText_results,
# remove_intercept = TRUE)
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