Invertendo eixos I

Inverter eixos significa trocar as variáveis mapeadas nas estéticas x e y. Podemos simplesmente alterar os mapeamentos em aes(), mas também podemos usar a função de camada coord_flip().

Há dois motivos para usar essa função:

Queremos que um geom vertical fique horizontal, ou
Finalizamos uma longa sequência de funções de plotagem e queremos inverter sem precisar reescrever todos os comandos.

Este exercicio faz parte do curso

Visualização de Dados Intermediária com ggplot2

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

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

Editar e Executar Código

Este exercicio faz parte do curso

Visualização de Dados Intermediária com ggplot2

IntermediárioNível de habilidade

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: Coordenadas Exercise 2: Dando Zoom Exercise 3: Proporção de aspecto I: razões 1:1 Exercise 4: Proporção de aspecto II: definindo proporções Exercise 5: Expandir e cortar Exercise 6: Coordenadas vs. escalas Exercise 7: Transformando escalas com log Exercise 8: Adicionando estatísticas a escalas transformadas Exercise 9: Eixos duplos e invertidos Exercise 10: Eixos duplos úteis Exercise 11: Invertendo eixos I

Exercicio Atual

Exercise 12: Invertendo eixos II Exercise 13: Coordenadas polares Exercise 14: Gráficos de pizza Exercise 15: Gráficos de rosa dos ventos

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