Etiquetar facetas

Si los niveles de tus factores no están claros, las etiquetas de las facetas pueden resultar confusas. Puedes asignar etiquetas adecuadas en tus datos originales antes de graficar (verás cómo en el siguiente ejercicio) o usar el argumento labeller en la capa de facetas.

El valor predeterminado es

label_value: Predeterminado, muestra solo el valor

Alternativas habituales:

label_both: Muestra el valor y el nombre de la variable
label_context: Muestra solo los valores o muestra valores y variables según si se facetan varios factores

Este ejercicio forma parte del curso

Visualización de datos intermedia con ggplot2

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Prueba este ejercicio y completa el código de muestra.

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

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Este ejercicio forma parte del curso

Visualización de datos intermedia con ggplot2

IntermedioNivel de habilidad

4.8+

Comienza el curso gratis

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: La capa de facetas Exercise 2: Conceptos básicos de la capa de facetas Exercise 3: Muchas variables Exercise 4: Notación con fórmulas Exercise 5: Etiquetas y orden de facetas Exercise 6: Etiquetar facetas

Ejercicio actual

Exercise 7: Definir el orden Exercise 8: Espacios de trazado en facetas Exercise 9: Espacios de trazado variables I: variables continuas Exercise 10: Espacios variables para el trazado II: variables categóricas Exercise 11: Facet wrap y márgenes Exercise 12: Envolver para muchos niveles Exercise 13: Gráficos con márgenes

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