Histogramlarda konumlar

Burada, histogramlara konumları uygulamanın çeşitli yollarını inceleyeceğiz. geom_histogram(), geom_bar()'ın özel bir durumudur ve aşağıdaki değerleri alabilen bir position argümanına sahiptir:

stack (varsayılan): Farklı grupların çubukları üst üste yığılır.
dodge: Farklı grupların çubukları yan yana yerleştirilir.
fill: Farklı grupların çubukları oransal olarak gösterilir.
identity: Değerleri veri kümesinde göründüğü gibi çizer.

Bu egzersiz

ggplot2 ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Update the aesthetics so the fill color is by fam
ggplot(mtcars, aes(mpg)) +
  geom_histogram(binwidth = 1)

Kodu Düzenle ve Çalıştır

Bu egzersiz

ggplot2 ile Veri Görselleştirmeye Giriş

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.

Exercise 1: Introduction Exercise 2: Explore and explain Exercise 3: Drawing your first plot Exercise 4: Data columns types affect plot types Exercise 5: The grammar of graphics Exercise 6: Mapping data columns to aesthetics Exercise 7: Understanding variables Exercise 8: ggplot2 layers Exercise 9: Adding geometries Exercise 10: Changing one geom or every geom Exercise 11: Saving plots as variables

Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens - converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter you’ll understand how to choose the best aesthetic mappings for your data.

Exercise 1: Visible aesthetics Exercise 2: All about aesthetics: color, shape and size Exercise 3: All about aesthetics: color vs. fill Exercise 4: All about aesthetics: comparing aesthetics Exercise 5: Aesthetics for categorical & continuous variables Exercise 6: Using attributes Exercise 7: All about attributes: color, shape, size and alpha Exercise 8: All about attributes: conflicts with aesthetics Exercise 9: Going all out Exercise 10: Modifying aesthetics Exercise 11: Updating aesthetic labels Exercise 12: Setting a dummy aesthetic Exercise 13: Aesthetics best practices Exercise 14: Appropriate mappings

A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter - scatter plots, bar charts and line plots. We’ll look at a variety of different ways to construct these plots.

Exercise 1: Saçılım grafikleri Exercise 2: Aşırı çizim 1: büyük veri kümeleri Exercise 3: Aşırı bindirme 2: Hizalanmış değerler Exercise 4: Aşırı Çakışma 3: Düşük hassasiyetli veriler Exercise 5: Aşırı Çakışma 4: Tamsayı veriler Exercise 6: Histogramlar Exercise 7: Histogram çizmek Exercise 8: Histogramlarda konumlar

Geçerli Egzersiz

Exercise 9: Çubuk grafikleri Exercise 10: Bar ve sütun grafiklerinde konum (position)Exercise 11: Örtüşen çubuk grafikleri Exercise 12: Çubuk grafikler: sıralı renk paleti Exercise 13: Çizgi grafikleri Exercise 14: Temel çizgi grafikleri Exercise 15: Birden çok zaman serisi

In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.

Exercise 1: Themes from scratch Exercise 2: Moving the legend Exercise 3: Modifying theme elements Exercise 4: Modifying whitespace Exercise 5: Theme flexibility Exercise 6: Built-in themes Exercise 7: Exploring ggthemes Exercise 8: Setting themes Exercise 9: Publication-quality plots Exercise 10: Effective explanatory plots Exercise 11: Using geoms for explanatory plots Exercise 12: Using annotate() for embellishments