10.57 A Template for Data Preparation

Through this chapter we have built a template for data preparation. An actual knitr template based on this chapter for data preparation is available as http://HandsOnDataScience.com/scripts/data.Rnw. An automatically derived version including just the R code is also available as http://HandsOnDataScience.com/scripts/data.R. Notice that we would not necessarily perform all of the steps, such as normalising the variable names, imputing missing values, omitting observations with missing values, and so on. Instead we pick and choose as is appropriate to our situation and specific datasets. Also, some data specific transformations are not included in the template and there may be other transforms we need to perform that we have not covered here. As we discover new tools to support the data scientist we can add them into our own templates.

dsname        <- "weatherAUS"
ds            <- get(dsname)
vnames        <- names(ds)
names(ds)    %<>% normVarNames()
names(vnames) <- names(ds)

Your donation will support ongoing development and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0.