Eksenleri çevirmek I

Eksenleri çevirmek, x ve y görsel özniteliklerine eşlenen değişkenleri tersine çevirmek demektir. Bunu aes() içindeki eşlemeleri değiştirerek yapabiliriz, ama coord_flip() katman fonksiyonunu da kullanabiliriz.

Bu fonksiyonu kullanmak için iki neden vardır:

Dikey bir geomu yatay yapmak istiyoruz veya
Uzun bir çizim fonksiyonları serisini tamamladık ve tüm komutları yeniden yazmadan grafiği çevirmek istiyoruz.

Bu egzersiz

ggplot2 ile Orta Düzey Veri Görselleştirme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

ggplot2 ile Orta Düzey Veri Görselleştirme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Koordinatlar Exercise 2: Yakınlaştırma Exercise 3: En-boy oranı I: 1:1 oranları Exercise 4: En-boy oranı II: oranları ayarlama Exercise 5: Genişlet ve kırp Exercise 6: Koordinatlar ve ölçekler Exercise 7: Log-dönüşümlü ölçekler Exercise 8: Dönüştürülmüş ölçeklere istatistik ekleme Exercise 9: Çift ve çevrilmiş eksenler Exercise 10: Faydalı çift eksenler Exercise 11: Eksenleri çevirmek I

Geçerli Egzersiz

Exercise 12: Eksenleri çevirmek II Exercise 13: Kutup koordinatları Exercise 14: Pasta grafikleri Exercise 15: Rüzgâr gülü grafikler

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