https://www.r-bloggers.com/building-wordclouds-in-r/
Sumber http://engdashboard.blogspot.com/
In this article, I volition exhibit you lot how to purpose text information to construct discussion clouds inward R. We volition purpose a dataset containing around 200k Jeopardy questions. The dataset tin sack travel downloaded here (thanks to reddit user trexmatt for providing the dataset).
We volition require iii packages for this:
tm, SnowballC, and wordcloud. First, let’s charge the required libraries as well as read inward the data.
library(tm) library(SnowballC) library(wordcloud) jeopQ <- read.csv('JEOPARDY_CSV.csv', stringsAsFactors = FALSE) The actual questions are available inward the Question column.
Now, nosotros volition perform a serial of operations on the text information to simplify it.
First, nosotros take to create a corpus.
First, nosotros take to create a corpus.
jeopCorpus <- Corpus(VectorSource(jeopQ$Question))
Next, nosotros volition convert the corpus to a evidently text document.
jeopCorpus <- tm_map(jeopCorpus, PlainTextDocument)
Then, nosotros volition take away all punctuation as well as stopwords. Stopwords are normally used words inward the English linguistic communication language such equally I, me, my, etc. You tin sack run into the amount listing of stopwords using
stopwords('english').jeopCorpus <- tm_map(jeopCorpus, removePunctuation) jeopCorpus <- tm_map(jeopCorpus, removeWords, stopwords('english')) Next, nosotros volition perform stemming. This agency that all the words are converted to their stalk (Ex: learning -> learn, walked -> walk, etc.). This volition ensure that dissimilar forms of the discussion are converted to the same shape as well as plotted exclusively 1 time inward the wordcloud.
jeopCorpus <- tm_map(jeopCorpus, stemDocument)
Now, nosotros volition plot the wordcloud.
wordcloud(jeopCorpus, max.words = 100, random.order = FALSE)
This volition create the next wordcloud:
If you lot desire to take away the words ‘the’ as well as ‘this’, you lot tin sack include them inward the removeWords business office equally follows:
jeopCorpus <- tm_map(jeopCorpus, removeWords, c('the', 'this', stopwords('english'))) There are a few ways to customize it.
- scale: This is used to betoken the attain of sizes of the words.
- max.words as well as min.freq: These parameters are used to limit the divulge of words plotted. max.words volition plot the specified divulge of words as well as discard to the lowest degree frequent terms, whereas, min.freq volition discard all damage whose frequency is below the specified value.
- random.order: By setting this to FALSE, nosotros arrive then that the words amongst the highest frequency are plotted first. If nosotros don’t gear upward this, it volition plot the words inward a random order, as well as the highest frequency words may non necessarily look inward the center.
- rot.per: This value determines the fraction of words that are plotted vertically.
- colors: The default value is black. If you lot desire to purpose dissimilar colors based on frequency, you lot tin sack specify a vector of colors, or purpose 1 of the pre-defined color palettes. You tin sack discovery a list here.
That brings us to the cease of this article. I promise you lot enjoyed it! As always, if you lot create got questions, experience gratis to acquire out a comment or attain out to me on Twitter.
Note: I learnt this technique inward The Analytics Edge class offered yesteryear MIT on edX. It is a peachy class as well as I highly recommend that you lot accept it if you lot are interested inward Data Science!