Until now, even though we merged the two data sets, analyzing the Netflix movies was possible by only using each one. Let’s change that! Generate a word cloud with your personal most watched genres! Is it what you expected it to be?
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Install the package “wordcloud” and start preparing your joined dataset. We are only interested in the “listed_in” column now. Use the mutate function to do a structure split, so each genre will get its row instead of being separated by a comma. Add unnest to your call. Extract the column and fix possible issues (e.g., white space). An easy way is to use the table() call on the listed_in and change the table directly into a data frame. For the word cloud, set a seed if you want to get the same cloud every time you run the code. The call wordcloud only needs to be filled with the respective word and frequency column. Feel free to spice it up!
Use the Wordcloud package that can be installed by using: from wordcloud import WordCloud. Stopwords are words, that will not be shown on the wordcloud.
Install the package “wordcloud” and start preparing your joined dataset. We are only interested in the “listed_in” column now. Use the mutate function to do a structure split, so each genre will get its row instead of being separated by a comma. Add unnest to your call. Extract the column and fix possible issues (e.g., white space). An easy way is to use the table() call on the listed_in and change the table directly into a data frame. For the word cloud, set a seed if you want to get the same cloud every time you run the code. The call wordcloud only needs to be filled with the respective word and frequency column. Feel free to spice it up!
Use the Wordcloud package that can be installed by using: from wordcloud import WordCloud. Stopwords are words, that will not be shown on the wordcloud.
