1. Learn
    2. /
  2. Courses
    3. /
  3. Handling Missing Data with Imputations in R
  • 1

    The Problem of Missing Data

    Free

    In this chapter, you’ll find out why missing data can be a risk when analyzing a dataset. You’ll be introduced to the three missing data mechanisms and learn how to recognize them using statistical tests and visualization tools.

  • 2

    Donor-Based Imputation

    Get to know the taxonomy of imputation methods and learn three donor-based techniques: mean, hot-deck, and k-Nearest-Neighbors imputation. You’ll look under the hood to see how these methods work, before learning how to apply them to a real-world tropical weather dataset. Along the way, you’ll also learn useful tricks that you can use to make them work even better for your problems.

  • 3

    Model-Based Imputation

    It’s time to learn how to use statistical and machine learning models, such as linear regression, logistic regression, and random forests, to impute missing data. In this chapter, you’ll look into how the models make their predictions and use this knowledge to draw the imputed values from conditional distributions. This is important as it ensures your imputations are more varied and plausible, making them more similar to the true data.

  • 4

    Uncertainty from Imputation

    Imputed values are not set in stone. They are just estimates and estimates come with some uncertainty. In this final chapter, you’ll discover how bootstrapping and chained equation using the mice package can be used to incorporate imputation uncertainty into your models and analyses to make them more reliable and robust.

Initializing

Exercise

Model-based imputation with multiple variable types

Great job on writing the function to implement logistic regression imputation with drawing from conditional distribution. That's pretty advanced statistics you have coded! In this exercise, you will combine what you learned so far about model-based imputation to impute different types of variables in the tao data.

Your task is to iterate over variables just like you have done in the previous chapter and impute two variables:

  • is_hot, a new binary variable that was created out of air_temp, which is 1 if air_temp is at or above 26 degrees and is 0 otherwise;
  • humidity, a continuous variable you are already familiar with.

You will have to use the linear regression function you have learned before, as well as your own function for logistic regression. Let's get to it!

Instructions

100 XP
  • Set is_hot to NA in places where it was originally missing.
  • Impute is_hot with logistic regression, using sea_surface_temp as the only predictor; use your function impute_logreg().
  • Set humidity to NA in places where it was originally missing.
  • Impute humidity with linear regression, using sea_surface_temp and air_temp as predictors.