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.

Statistik dengan geom

Pemulusan

Variabel pengelompokan

Memodifikasi stat_smooth

Memodifikasi stat_smooth (2)

Statistik: jumlah dan kuantil

Kuantil

Menggunakan stat_sum

Statistik di luar geom

Persiapan

Menggunakan objek posisi

Variasi pemetaan

Statistics

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.

Coordinates

Zooming In

Aspect ratio I: 1:1 ratios

Aspect ratio II: setting ratios

Expand and clip

Coordinates vs. scales

Log-transforming scales

Adding stats to transformed scales

Double and flipped axes

Useful double axes

Flipping axes I

Flipping axes II

Polar coordinates

Pie charts

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.

The facets layer

Facet layer basics

Many variables

Formula notation

Facet labels and order

Labeling facets

Setting order

Facet plotting spaces

Variable plotting spaces I: continuous variables

Variable plotting spaces II: categorical variables

Facet wrap & margins

Wrapping for many levels

Margin plots

Facets

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.

Best practices: bar plots

Bar plots: dynamite plots

Bar plots: position dodging

Bar plots: Using aggregated data

Heatmaps use case scenario

Heat maps

Useful heat maps

Heat map alternatives

When good data makes bad plots

Suppression of the origin

Color blindness

Typical problems

Best Practices

Kursus ggplot2 ini membangun pengetahuan Anda dari kursus pengantar untuk menghasilkan plot penjelas yang bermakna. Statistik akan dihitung secara langsung dan Anda akan melihat bagaimana Coordinates dan Facets membantu komunikasi. Anda juga akan mengeksplorasi praktik terbaik visualisasi data dengan ggplot2 untuk memastikan Anda memahami dengan baik apa yang bekerja dan alasannya. Di akhir kursus, Anda akan memiliki semua alat yang diperlukan untuk membuat fungsi plotting kustom guna mengeksplorasi himpunan data yang besar, menggabungkan statistik dan visual yang sangat baik.

Introduction to Data Visualization with ggplot2

Pelajari penggunaan faset, sistem koordinat, dan statistik di ggplot2 untuk membuat plot penjelasan.

Visualisasi Data Tingkat Menengah dengan ggplot2

Pelajari cara menggunakan facet, sistem koordinat, dan statistik dalam ggplot2 untuk membuat grafik penjelasan yang bermakna.

Statistik dengan geom

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