Plots are generated with the use of R’s ggplot2 data visualization package. # 15 am 15 head(greenEggs_wordcounts_filt, n = 15) # A tibble: 15 x 2Ĭase One: Wordcounts Plot and Wordcloud With Stopwords Head(greenEggs_wordcounts, n = 15) # A tibble: 15 x 2 GreenEggs_wordcounts_filt % count(word, sort = TRUE) # Word Counts in Fox In Socks (Stopwords removed) GreenEggs_wordcounts % count(word, sort = TRUE) # Word Counts in Fox In Socks (No stopwords) With the use of dplyr’s pipe operator (%>%) and its count() function, counts for each word can be obtained for the filtered case and the non-filtered case. # Stop words include me, you, for, myself, he, sheĪnti_join(stop_words) # Joining, by = "word" # Remove English stop words from Fox In Socks: The variable which is associated with the filtered text is greenEggs_words_filt.
To filter out the stop words the anti_join() function from R’s dplyr package is used.
This time around, I will obtain word counts in Green Eggs & Ham when the stopwords are filtered out and the word counts of the original book itself. Normally, I want to remove stopwords from the text as they carry very little meaning on their own. Head(greenEggs_words, n = 10) # A tibble: 10 x 1 Unnest_tokens(output = word, input = Text) # Unnest tokens: Have each word in a row:
To read in the file, use the readLines() function in R.įrom the tidytext package, the unnest_tokens() function converts the text in a way such that each row is just a single word. This text file is the book itself so there is no need for data cleaning. There is a text version of the Green Eggs & Ham book online here.
My other text mining posts mention creating wordclouds with the use of the tm package but in this case I am using the tidytext and wordcloud packages. To be able to generate wordclouds, you would require the wordcloud R package. With the tidytext package in R, you can obtain wordcounts from pieces of text. Wordcounts & Wordclouds In Green Eggs & Ham
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