Quantili

Qui continuiamo con l'insieme di dati Vocab e usiamo stat_quantile() per applicare una regressione sui quantili.

La regressione lineare predice la risposta media a partire dalle variabili esplicative, mentre la regressione sui quantili predice un quantile della risposta (ad esempio la mediana) a partire dalle variabili esplicative. Puoi indicare quanti e quali quantili con l'argomento quantiles.

Specificare molti quantili e colorare i modelli in base all'anno può rendere i grafici troppo affollati. Vedremo come gestire questa situazione nel prossimo capitolo.

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Visualizzazione dei dati intermedia con ggplot2

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ggplot(Vocab, aes(x = education, y = vocabulary)) +
  geom_jitter(alpha = 0.25) +
  # Add a quantile stat, at 0.05, 0.5, and 0.95
  ___

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Questo esercizio fa parte del corso

Visualizzazione dei dati intermedia con ggplot2

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A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

Exercise 1: Statistiche con i geoms Exercise 2: Smussamento Exercise 3: Variabili di raggruppamento Exercise 4: Modificare stat_smooth Exercise 5: Modifying stat_smooth (2)Exercise 6: Statistiche: somma e quantile Exercise 7: Quantili

Esercizio in corso

Exercise 8: Uso di stat_sum Exercise 9: Statistiche al di fuori dei geoms Exercise 10: Preparativi Exercise 11: Uso degli oggetti di posizione Exercise 12: Variazioni di grafico

The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

Exercise 1: Coordinates Exercise 2: Zooming In Exercise 3: Aspect ratio I: 1:1 ratios Exercise 4: Aspect ratio II: setting ratios Exercise 5: Expand and clip Exercise 6: Coordinates vs. scales Exercise 7: Log-transforming scales Exercise 8: Adding stats to transformed scales Exercise 9: Double and flipped axes Exercise 10: Useful double axes Exercise 11: Flipping axes I Exercise 12: Flipping axes II Exercise 13: Polar coordinates Exercise 14: Pie charts Exercise 15: Wind rose plots

Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.

Exercise 1: The facets layer Exercise 2: Facet layer basics Exercise 3: Many variables Exercise 4: Formula notation Exercise 5: Facet labels and order Exercise 6: Labeling facets Exercise 7: Setting order Exercise 8: Facet plotting spaces Exercise 9: Variable plotting spaces I: continuous variables Exercise 10: Variable plotting spaces II: categorical variables Exercise 11: Facet wrap & margins Exercise 12: Wrapping for many levels Exercise 13: Margin plots

Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

Exercise 1: Best practices: bar plots Exercise 2: Bar plots: dynamite plots Exercise 3: Bar plots: position dodging Exercise 4: Bar plots: Using aggregated data Exercise 5: Heatmaps use case scenario Exercise 6: Heat maps Exercise 7: Useful heat maps Exercise 8: Heat map alternatives Exercise 9: When good data makes bad plots Exercise 10: Suppression of the origin Exercise 11: Color blindness Exercise 12: Typical problems