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Dealing With Missing Data in R

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4 hours
4,350 XP
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Course Description

Missing data is part of any real world data analysis. It can crop up in unexpected places, making analyses challenging to understand. In this course, you will learn how to use tidyverse tools and the naniar R package to visualize missing values. You'll tidy missing values so they can be used in analysis and explore missing values to find bias in the data. Lastly, you'll reveal other underlying patterns of missingness. You will also learn how to "fill in the blanks" of missing values with imputation models, and how to visualize, assess, and make decisions based on these imputed datasets.
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  1. 1

    Why care about missing data?


    Chapter 1 introduces you to missing data, explaining what missing values are, their behavior in R, how to detect them, and how to count them. We then introduce missing data summaries and how to summarise missingness across cases, variables, and how to explore across groups within the data. Finally, we discuss missing data visualizations, how to produce overview visualizations for the entire dataset and over variables, cases, and other summaries, and how to explore these across groups.

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    Introduction to missing data
    50 xp
    Using and finding missing values
    100 xp
    How many missing values are there?
    100 xp
    Working with missing values
    50 xp
    Why care about missing values?
    50 xp
    Summarizing missingness
    100 xp
    Tabulating Missingness
    100 xp
    Other summaries of missingness
    100 xp
    How do we visualize missing values?
    50 xp
    Your first missing data visualizations
    100 xp
    Visualizing missing cases and variables
    100 xp
    Visualizing missingness patterns
    100 xp
  2. 2

    Wrangling and tidying up missing values

    In chapter two, you will learn how to uncover hidden missing values like "missing" or "N/A" and replace them with `NA`. You will learn how to efficiently handle implicit missing values - those values implied to be missing, but not explicitly listed. We also cover how to explore missing data dependence, discussing Missing Completely at Random (MCAR), Missing At Random (MAR), Missing Not At Random (MNAR), and what they mean for your data analysis.

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  3. 3

    Testing missing relationships

    In this chapter, you will learn about workflows for working with missing data. We introduce special data structures, the shadow matrix, and nabular data, and demonstrate how to use them in workflows for exploring missing data so that you can link summaries of missingness back to values in the data. You will learn how to use ggplot to explore and visualize how values changes as other variables go missing. Finally, you learn how to visualize missingness across two variables, and how and why to visualize missings in a scatterplot.

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  4. 4

    Connecting the dots (Imputation)

    In this chapter, you will learn about filling in the missing values in your data, which is called imputation. You will learn how to impute and track missing values, and what the good and bad features of imputations are so that you can explore, visualise, and evaluate the imputed data against the original values. You will learn how to use, evaluate, and compare different imputation models, and explore how different imputation models affect the inferences you can draw from the models.

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In the following tracks

Intermediate Tidyverse Toolbox


Collaborator's avatar
David Campos
Collaborator's avatar
Shon Inouye
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Chester Ismay

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