Mosaic plot

The spine plot you have created in the previous exercise allows you to study missing data patterns between two variables at a time. This idea is generalized to more variables in the form of a mosaic plot.

In this exercise, you will start by creating a dummy variable indicating whether the United States was involved in the production of each movie. To do this, you will use the grepl() function, which checks if the string passed as its first argument is present in the object passed as its second argument. Then, you will draw a mosaic plot to see if the subject's gender correlates with the amount of missing data on earnings for both US and non-US movies.

The biopics data as well as the VIM package are already loaded for you. Let's do some exploratory plotting!

Note that a proprietydisplay_image()function has been created to return the output from the latestVIMpackage version. Make sure to expand theHTML Viewer section.

Deze oefening maakt deel uit van de cursus

Handling Missing Data with Imputations in R

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Oefeninstructies

Feed the biopics data into the dplyr pipeline.
Create a dummy variable is_US_movie that is TRUE if country contains the string "US" and is FALSE otherwise.
Draw a mosaic plot that shows the amount of missing data in "earnings" split by"is_US_movie" and "sub_sex", while remembering to pass variable names as strings.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Prepare data for plotting and draw a mosaic plot
___ %>%
	# Create a dummy variable for US-produced movies
	mutate(is_US_movie = grepl(___, ___)) %>%
	# Draw mosaic plot
	mosaicMiss(highlight = ___, 
             plotvars = c(___, ___))

# Return plot from latest VIM package - expand the HTML viewer section
display_image()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Handling Missing Data with Imputations in R

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4.8+

Begin de cursus gratis

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.

Exercise 1: Missing data: what can go wrong Exercise 2: Linear regression with incomplete data Exercise 3: Analyzing regression output Exercise 4: Comparing models Exercise 5: Missing data mechanisms Exercise 6: Recognizing missing data mechanisms Exercise 7: t-test for MAR: data preparation Exercise 8: t-test for MAR: interpretation Exercise 9: Visualizing missing data patterns Exercise 10: Aggregation plot Exercise 11: Spine plot Exercise 12: Mosaic plot

Huidige oefening

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.

Exercise 1: Mean imputation Exercise 2: Smelling the danger of mean imputation Exercise 3: Mean-imputing the temperature Exercise 4: Assessing imputation quality with margin plot Exercise 5: Hot-deck imputation Exercise 6: Vanilla hot-deck Exercise 7: Hot-deck tricks & tips I: imputing within domains Exercise 8: Hot-deck tricks & tips II: sorting by correlated variables Exercise 9: k-Nearest-Neighbors imputation Exercise 10: Choosing the number of neighbors Exercise 11: kNN tricks & tips I: weighting donors Exercise 12: kNN tricks & tips II: sorting variables

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.

Exercise 1: Model-based imputation approach Exercise 2: Linear regression imputation Exercise 3: Initializing missing values & iterating over variables Exercise 4: Detecting convergence Exercise 5: Replicating data variability Exercise 6: Logistic regression imputation Exercise 7: Drawing from conditional distribution Exercise 8: Model-based imputation with multiple variable types Exercise 9: Tree-based imputation Exercise 10: Imputing with random forests Exercise 11: Variable-wise imputation errors Exercise 12: Speed-accuracy trade-off

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.

Exercise 1: Multiple imputation by bootstrapping Exercise 2: Wrapping imputation & modeling in a function Exercise 3: Running the bootstrap Exercise 4: Bootstrapping confidence intervals Exercise 5: Multiple imputation by chained equations Exercise 6: The mice flow: mice - with - pool Exercise 7: Choosing default models Exercise 8: Using predictor matrix Exercise 9: Putting it all together Exercise 10: Analyzing missing data patterns Exercise 11: Imputing and inspecting outcomes Exercise 12: Inference with imputed data Exercise 13: Final remarks