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I suggest you take a look at the TidyTuesday repo, where every week they post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. The repo also contains other resources, like data science books. Together with the repo, I suggest the TidyTuesday videos by David Robinson, where he creates screencasts of complete data ...


Use mutate in combination with row_number as follows: df %>% mutate(row = row_number())


As far as I'm aware there is no correct/standard way to apply topic modelling, most decisions depend on the specifics of the case. So below I just give my opinion about these points: I have removed, before cleaning the data (removing mentions, stopwords, weird characters, numbers etc), all duplicate instances (having all three columns in common), in order ...


I can think of two approaches. You could use term-frequency/inverse-document-frequency (tf-idf) to cluster the vectors. Personally, I would start by first clustering the full text of the original job vacancies and then use this to assign clusters to the vectors. I have the feeling that it will outperform clustering directly the vectors. There are ...

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