Etichettare i facet

Se i livelli del tuo fattore non sono chiari, le etichette dei facet possono risultare confuse. Puoi assegnare etichette corrette ai tuoi dati originali PRIMA di tracciare (vedi il prossimo esercizio) oppure puoi usare l'argomento labeller nel layer dei facet.

Il valore predefinito è

label_value: Predefinito, mostra solo il valore

Alternative comuni sono:

label_both: Mostra sia il valore sia il nome della variabile
label_context: Mostra solo i valori oppure sia i valori sia le variabili a seconda che vengano usati più fattori nei facet

Questo esercizio fa parte del corso

Visualizzazione dei dati intermedia con ggplot2

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Plot wt by mpg
ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  # The default is label_value
  ___(___ = ___(___))

Modifica ed esegui il codice

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: Stats with geoms Exercise 2: Smoothing Exercise 3: Grouping variables Exercise 4: Modifying stat_smooth Exercise 5: Modifying stat_smooth (2)Exercise 6: Stats: sum and quantile Exercise 7: Quantiles Exercise 8: Using stat_sum Exercise 9: Stats outside geoms Exercise 10: Preparations Exercise 11: Using position objects Exercise 12: Plotting variations

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: Il layer dei facet Exercise 2: Nozioni di base sul livello di facet Exercise 3: Molte variabili Exercise 4: Notazione con formula Exercise 5: Etichette e ordine dei facet Exercise 6: Etichettare i facet

Esercizio in corso

Exercise 7: Impostare l'ordine Exercise 8: Spazi di tracciamento delle facet Exercise 9: Spazi di tracciamento variabili I: variabili continue Exercise 10: Spazi di tracciamento variabili II: variabili categoriche Exercise 11: Facet wrap e margini Exercise 12: Wrappare con molti livelli Exercise 13: Grafici con margini

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