Onnowpurbo at 02:49, 2 December 2019

2019-12-02T02:49:36Z

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	# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd		# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd

			# The tidy text format {#tidytext}
			library(knitr)
			opts_chunk$set(message = FALSE, warning = FALSE, cache = TRUE)
			options(width = 100, dplyr.width = 100)
			library(ggplot2)
			theme_set(theme_light())


			## The `unnest_tokens` function
			text <- c("Because I could not stop for Death -",
			"He kindly stopped for me -",
			"The Carriage held but just Ourselves -",
			"and Immortality")
			text

			library(dplyr)
			text_df <- tibble(line = 1:4, text = text)
			text_df


			# Within our tidy text framework, we need to both break the text
			# into individual tokens (a process called tokenization) and
			# transform it to a tidy data structure.
			# To do this, we use tidytext's `unnest_tokens()` function.
			library(tidytext)
			text_df %>%
			unnest_tokens(word, text)




			## Tidying the works of Jane Austen {#tidyausten}
			library(janeaustenr)
			library(dplyr)
			library(stringr)
			original_books <- austen_books() %>%
			group_by(book) %>%
			mutate(linenumber = row_number(),
			chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
			ignore_case = TRUE)))) %>%
			ungroup()
			original_books


			# To work with this as a tidy dataset,
			# we need to restructure it in the one-token-per-row format,
			# which as we saw earlier is done with the `unnest_tokens()` function.
			library(tidytext)
			tidy_books <- original_books %>%
			unnest_tokens(word, text)
			tidy_books


			# Now that the data is in one-word-per-row format,
			# we can manipulate it with tidy tools like dplyr.
			# Often in text analysis, we will want to remove stop words;
			# stop words are words that are not useful for an analysis,
			# typically extremely common words such as "the", "of", "to", and so forth in English.
			# We can remove stop words (kept in the tidytext dataset `stop_words`) with an `anti_join()`.
			data(stop_words)
			tidy_books <- tidy_books %>%
			anti_join(stop_words)

			# We can also use dplyr's `count()` to find the most common words in all the books
			# as a whole.
			tidy_books %>%
			count(word, sort = TRUE)

			# Because we've been using tidy tools, our word counts are stored in a tidy data frame.
			# This allows us to pipe this directly to the ggplot2 package,
			# for example to create a visualization of the most common words
			# (Figure \@ref(fig:plotcount)).
			library(ggplot2)
			tidy_books %>%
			count(word, sort = TRUE) %>%
			filter(n > 600) %>%
			mutate(word = reorder(word, n)) %>%
			ggplot(aes(word, n)) +
			geom_col() +
			xlab(NULL) +
			coord_flip()

Onnowpurbo: Created page with "# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd ==Referensi== * https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rm..."

2019-12-02T02:19:21Z

Created page with "# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd ==Referensi== * https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rm..."

New page

# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd

==Referensi==

* https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd

==Pranala Menarik==

* [[R]]

R: tidytext: tidytext - Revision history

Onnowpurbo at 02:49, 2 December 2019

Onnowpurbo: Created page with "# Ref: https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rmd ==Referensi== * https://github.com/dgrtwo/tidy-text-mining/blob/master/01-tidy-text.Rm..."