Keşfet ve açıkla

Bu videoda veriyi keşfetmeye ve açıklamaya yönelik grafikler arasında ayrım yaptık. Aşağıdakilerden hangisi keşif amaçlı grafikler için tipik olarak DEĞİLDİR?

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ggplot2 ile Veri Görselleştirmeye Giriş

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ggplot2 ile Veri Görselleştirmeye Giriş

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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: Giriş Exercise 2: Keşfet ve açıkla

Geçerli Egzersiz

Exercise 3: İlk grafiğini çiz Exercise 4: Veri sütunu türleri grafik türlerini etkiler Exercise 5: Grafikler dilbilgisi Exercise 6: Veri sütunlarını estetiklere eşleme Exercise 7: Değişkenleri anlama Exercise 8: ggplot2 katmanları Exercise 9: Geometriler ekleme Exercise 10: Tek bir geom’u ya da tüm geom’ları değiştirmek Exercise 11: Grafikleri değişken olarak kaydetme

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: Scatter plots Exercise 2: Overplotting 1: large datasets Exercise 3: Overplotting 2: Aligned values Exercise 4: Overplotting 3: Low-precision data Exercise 5: Overplotting 4: Integer data Exercise 6: Histograms Exercise 7: Drawing histograms Exercise 8: Positions in histograms Exercise 9: Bar plots Exercise 10: Position in bar and col plots Exercise 11: Overlapping bar plots Exercise 12: Bar plots: sequential color palette Exercise 13: Line plots Exercise 14: Basic line plots Exercise 15: Multiple time series

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