Diagnosing missing values in R and Python

Before thinking about imputing missing values in a dataset, we must first know the extent to which the missing values affect each individual variable.

You can find the code used in this section in the Chapter12\R\03-diagnose-missing-values-in-r.R and Chapter12\Python\03-diagnose-missing-values-in-python.py files. In order to properly run the code and the code of the following sections, you need to install the requisite R and Python packages as follows:

1. Open the Anaconda prompt.
2. Enter the conda activate pbi_powerquery_env command.
3. Enter the pip install missingno command.
4. Enter the pip install upsetplot command.
5. Then, open RStudio and make sure it is referencing your latest CRAN R (version 4.0.2 in our case).
6. Click on the Console window and enter install.packages('naniar'). Then press Enter.
7. Enter install.packages('imputeTS'). Then press Enter.
8. Enter install.packages('forecast&apos...