21.33 Letter Frequency

Next we want to review the frequency of letters across all of the words in the discourse. Some data preparation will transform the vector of words into a list of letters, which we then construct a frequency count for, and pass this on to be plotted.

We again use a pipeline to string together the operations on the data. Starting from the vector of words stored in word we split the words into characters using stringr::str_split() from (Wickham 2025), removing the first string (an empty string) from each of the results (using base::sapply()). Reducing the result into a simple vector, using base::unlist(), we then generate a data frame recording the letter frequencies, using qdap::dist_tab() from . We can then plot the letter proportions.

References

———. 2025. Stringr: Simple, Consistent Wrappers for Common String Operations. https://stringr.tidyverse.org.


If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0