Diagnosing missing values in R and Python

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

The code used in this section can be found in the R\03-diagnose-missing-values-in-r.R and Python\03-diagnose-missing-values-in-python.py files in Chapter 16. To properly run this code and the code in the following sections, you must install the required R and Python packages as follows:

1. Open Anaconda Prompt.
2. Enter the conda activate pbi_powerquery_env command.
3. Enter the pip install missingno==0.5.2 command.
4. Enter the pip install upsetplot==0.8.0 command.
5. Then, open RStudio and make sure it is referencing your latest CRAN R (version 4.4.2 in our case).
6. Click on the Console window and enter install.packages(c("naniar", "imputeTS", "forecast", "ggpubr", "missForest", "mice", "miceadds...