R: tidytext RPJP BAPPENAS

install.packages("xlsx")
install.packages("tm")
install.packages("wordcloud")
install.packages("ggplot2")

library(xlsx)
library(tm)
library(wordcloud)
library(ggplot2)

library(tidyverse)
library(tidytext)
library(tm)
directory <- "data-pdf"

# create corpus from pdfs
docs <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF))

# docs <- VCorpus(DirSource("data", recursive=TRUE))
# Get the document term matrices

# dengan Stemming
#
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
dtm_unigram <- DocumentTermMatrix(docs, control = list(tokenize="words", 
    removePunctuation = TRUE, 
    stopwords = c(stopwords::stopwords("id", source = "stopwords-iso"),"tabel","pada","dan"),
    stemming = TRUE))
dtm_bigram <- DocumentTermMatrix(docs, control = list(tokenize = BigramTokenizer,
    removePunctuation = TRUE,
    stopwords = c(stopwords::stopwords("id", source = "stopwords-iso"),"tabel","pada","dan"),
    stemming = TRUE))

# tanpa Stemming
#
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
dtm_unigram <- DocumentTermMatrix(docs, control = list(tokenize="words", 
    removePunctuation = TRUE, 
    stopwords = c(stopwords::stopwords("id", source = "stopwords-iso"),"tabel","pada","dan")))
dtm_bigram <- DocumentTermMatrix(docs, control = list(tokenize = BigramTokenizer,
    removePunctuation = TRUE,
    stopwords = c(stopwords::stopwords("id", source = "stopwords-iso"),"tabel","pada","dan")))

inspect(dtm_unigram)
inspect(dtm_bigram)

converted %>%
  tidy() %>%
  filter(!grepl("[0-9]+", term))
# converted adalah DocumentTermMatrix

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