Spine plot

Il grafico di aggregazione che hai disegnato nell’esercizio precedente ti ha dato una panoramica ad alto livello dei dati mancanti. Se ti interessa l’interazione tra variabili specifiche, uno spine plot è la scelta giusta. Ti permette di studiare la percentuale di valori mancanti in una variabile al variare dei valori dell’altra, concettualmente molto simile ai t-test che hai eseguito nella lezione precedente.

In questo esercizio, disegnerai uno spine plot per analizzare la percentuale di dati mancanti in earnings per le diverse categorie di sub_race. Ci sono più dati mancanti sui guadagni per alcune razze specifiche del personaggio principale del film? Scopriamolo! Il pacchetto VIM è già stato caricato per te.

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Gestione dei dati mancanti con imputazioni in R

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Draw a spine plot to analyse missing values in earnings by sub_race
biopics %>% 
	select(___, ___) %>%
	___()

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Questo esercizio fa parte del corso

Gestione dei dati mancanti con imputazioni in R

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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: Dati mancanti: cosa può andare storto Exercise 2: Regressione lineare con dati incompleti Exercise 3: Analizzare l'output della regressione Exercise 4: Confrontare i modelli Exercise 5: Meccanismi dei dati mancanti Exercise 6: Riconoscere i meccanismi dei dati mancanti Exercise 7: t-test per MAR: preparazione dei dati Exercise 8: t-test per MAR: interpretazione Exercise 9: Visualizzare i pattern dei dati mancanti Exercise 10: Grafico di aggregazione Exercise 11: Spine plot

Esercizio in corso

Exercise 12: Grafico a mosaico

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