Invertire gli assi I

Invertire (flipping) gli assi significa scambiare le variabili mappate sulle estetiche x e y. Possiamo semplicemente cambiare le mappature in aes(), ma possiamo anche usare la funzione di livello coord_flip().

Ci sono due motivi per usare questa funzione:

Vogliamo che un geom verticale diventi orizzontale, oppure
Abbiamo completato una lunga serie di funzioni di grafico e vogliamo invertirlo senza dover riscrivere tutti i comandi.

Questo esercizio fa parte del corso

Visualizzazione dei dati intermedia con ggplot2

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Prova a risolvere questo esercizio completando il codice di esempio.

# Plot fcyl bars, filled by fam
ggplot(mtcars, aes(___, fill = ___)) +
  # Place bars side by side
  geom_bar(position = ___)

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: Coordinate Exercise 2: Zoom avanti Exercise 3: Rapporto d'aspetto I: rapporti 1:1 Exercise 4: Rapporto d'aspetto II: impostare i rapporti Exercise 5: Espansione e ritaglio Exercise 6: Coordinate vs. scale Exercise 7: Trasformare le scale in log Exercise 8: Aggiungere statistiche a scale trasformate Exercise 9: Assi doppi e ribaltati Exercise 10: Doppi assi utili Exercise 11: Invertire gli assi I

Esercizio in corso

Exercise 12: Invertire gli assi II Exercise 13: Coordinate polari Exercise 14: Grafici a torta Exercise 15: Grafici a rosa dei venti

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