Data Analytics Workouts

This was a little bit of fun, after reading a few more chapters of Text Analysis with R for Students of Literature . Spinoza is a current interest, as I am also reading Radical Enlightenment: Philosophy and the Making of Modernity 1650-1750 . Example Code (Common to Subsections) # Text for this can be acquired as below Project Gutenberg, as below # http://www.gutenberg.org/cache/epub/989/pg989.txt, # or via a Sample Data at the end of this post: textToRead = 'pg989.txt' # Text for this can be acquired via Matthew Jockers site, as below, # http://www.matthewjockers.net/macroanalysisbook/expanded-stopwords-list/, # or via a Sample Data at the end of this post: exclusionFile = 'StopList_Extended.csv' # Read Text text.scan <- scan(file = textToRead, what = 'char') text.scan <- tolower(text.scan) # Create list text.list <- strsplit(text.scan, '\\W+', perl = TRUE) text.vector <- unlist(text.list) # Create ...

OESMN is an acronym, for the elements of data science: Obtaining data Scrubbing data Exploring data Modeling data iNterpreting data As part of the Data science is OSEMN module  for Obtaining Data I performed the examples myself, as well as with variants. Example Code # Data science is OSEMN """http://people.duke.edu/~ccc14/sta-663/DataProcessingSolutions.html#data-science-is-osemn""" # Acquiring data """Plain text files We can open plain text files with the open function. This is a common and very flexible format, but because no structure is involved, custom processing methods to extract the information needed may be necessary. Example 1: Suppose we want to find out how often the words alice and drink occur in the same sentence in Alice in Wonderland. This example uses Gutenberg: http://www.gutenberg.org/wiki/Main_Page, a good source for files""" # the data file is linked below...