This R-package offers functions to facilitate the handling of bad values.
- I choose the term "bad values" for cells in a data frame that should be used under reserve or even removed from the data.
- The bad value concept should enhance the traceability: Instead of simply removing those values after detection they can be marked for later removal.
- Therefore it is straightforward to keep the whole data frame 'as-is' while removing the "bad values" only when needed.
-
Add a column for indices of bad values to your data frame (e.g. mydata$BADVAL <- NA)
-
Use the following function to add bad value indices to your badval_column. Check output and update the column of your dataframe.
badval_add_index()
- Clean the bad values of your data. It is possible to clean all bad values at once or specify a pattern or an exact string for specific column names:
badval_rm_data()
- Remove the bad values from the badval_column if you do not need them anymore:
badval_rm_index()
For a more in depth Introduction read the vignette (either via the package or see: https://sachserf.github.io/vignettes.html)