Recently I started with a wonderful course titled “MITx-15.071X – The Analytics Edge” on edX. In my experience it is the best course for getting a quick hands on experience with the real world data science applications. If you have already done the course on Machine Learning by Stanford on Coursera, then I would say that its a great follow up course to learn and apply the algorithms on R by doing this course.
Now coming to the main point at hand – Wordcloud. Visualizations are a great way to present information in layman’s term to people who might not be too scientifically or mathematically oriented. Imagine you have to find the most important words in a text and present them. You could create a table of it, but it would be too dull and might not be too appealing to everyone. Wordclouds are a great way to overcome this issue. R provides an extremely simple way to create wordclouds with just 10 lines of code. So lets dive into it.
Step 1: Save your text in a simple notepad text file. For this post I will use an excerpt from the Military-Industrial Complex Speech by Dwight D. Eisenhower, in 1961, which can be found here: http://coursesa.matrix.msu.edu/~hst306/documents/indust.html
Save the text in a simple .txt file and add an empty line at the end. The reason for this will become clear in the next step.
Step 2: Open the file in R using the command
speech = readLines(“Eisenhower.txt”)
If you had not added an empty line there would be a warning message saying that
incomplete final line found on 'Eisnehower.txt'
This is because readLines() requires an empty line at the end of the file to detect the end.
Step 3: Now we need to download and install 3 packages in R.
Then load these packages using:
library(tm) … and so on
Step 4: This is one of the most important steps in the process. We will use the text-mining package that we just loaded and use it to modify and clean out our text.
First we convert our text to a specific class of R which provides infrastructure for natural language text called Corpus.
eisen = Corpus(VectorSource(speech))
Then we remove all the whitespaces from the text.
eisen = tm_map(eisen, stripWhitespace)
Next we convert all the letters to their lowercase and remove all punctuations.
eisen = tm_map(eisen, tolower)
eisen = tm_map(eisen, removePunctuation)
A speech will contain many typical english words like “I”, “me”, “my”, “and”, “to”, etc. We don’t want these to clutter our cloud and so we must remove them. Fortunately for us R has a list of some typical english words that can be accessed using stopwords(“english”). We will use this directly.
eisen = tm_map(eisen, removeWords, stopwords(“english”))
Looking at the speech I decided to remove three more words using
eisen = tm_map(eisen, removeWords, c(“must”,”will”,”also”))
Next we convert our eisen variable into a plain test format which is necessary in the newer versions of the tm package.
eisen = tm_map(eisen, PlainTextDocument)
Now we will convert this to a nice table like format which will help us get all the words and their frequencies.
dtmEisen = DocumentTermMatrix(eisen)
eisenFinal = as.data.frame(as.matrix(dtmEisen))
You can see the count of various words in the table by using the colnames() and colSums() functions.
Here the words are given in rows and their counts in the columns.
Now lets us plot this using a simple wordcloud.
You will get a very basic wordcloud as such:
We can use the other parameters of the wordcloud function by looking at the doucumentation.
Lets use them
wordcloud(colnames(eisenFinal), colSums(eisenFinal),scale=c(4,.5),min.freq=1,max.words=Inf, random.order=FALSE, random.color=FALSE, rot.per=.5, colors=brewer.pal(12, "Paired"), ordered.colors=FALSE, fixed.asp=TRUE)
To find out what each of these parameters do, please refer to its documentation. Its extremely simple.
Our new plot looks something like this:
You can also type
to view the different color combinations to give to “colors” parameter and experiment with various combinations.
Well there you go. You can now create and publish exciting wordclouds within seconds using R.