Gráficos de barras: uso de datos agregados

Si es apropiado usar gráficos de barras (¡consulta el vídeo!), entonces es buena idea dar una idea del número de valores en cada grupo.

stat_summary() no guarda el recuento. stat_sum() sí lo hace (ese es su objetivo), pero es difícil de acceder. Es más sencillo calcular por nuestra cuenta exactamente lo que queremos representar.

Aquí hemos creado un data frame de resumen llamado mtcars_by_cyl que contiene la media (mean_wt), las desviaciones estándar (sd_wt) y el recuento (n_wt) de los pesos de los coches para cada grupo de cilindros, cyl. También contiene la proporción (prop) de cada número de cilindros representado en todo el conjunto de datos. Usa la consola para familiarizarte con el data frame mtcars_by_cyl.

Este ejercicio forma parte del curso

Visualización de datos intermedia con ggplot2

Ejercicio interactivo práctico

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# Using mtcars_cyl, plot mean_wt vs. cyl
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  # Add a bar layer with identity stat, filled skyblue
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Este ejercicio forma parte del curso

Visualización de datos intermedia con ggplot2

IntermedioNivel de habilidad

4.8+

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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: 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: Mejores prácticas: gráficos de barras Exercise 2: Gráficos de barras: dynamite plots Exercise 3: Gráficos de barras: posición con dodge Exercise 4: Gráficos de barras: uso de datos agregados

Ejercicio actual

Exercise 5: Caso de uso de los mapas de calor Exercise 6: Mapas de calor Exercise 7: Mapas de calor útiles Exercise 8: Alternativas al mapa de calor Exercise 9: Cuando buenos datos generan malos gráficos Exercise 10: Supresión del origen Exercise 11: Daltonismo Exercise 12: Problemas típicos