Perluas dan potong

Fungsi layer coord_*() menyediakan dua argumen berguna yang saling melengkapi: expand dan clip.

expand mengatur jarak tepi (buffer) di sekitar plot agar data dan sumbu tidak saling tumpang tindih. Mengatur expand ke 0 akan menggambar sumbu tepat pada batas himpunan data.
clip menentukan apakah elemen plot yang berada di luar panel plot akan ditampilkan atau diabaikan ("dipotong").

Jika digunakan dengan tepat, ini dapat menghasilkan efek visual yang sangat baik! Kita akan menggunakan theme_classic() dan memodifikasi garis sumbu pada contoh ini.

Latihan ini adalah bagian dari kursus

Visualisasi Data Tingkat Menengah dengan ggplot2

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

ggplot(mtcars, aes(wt, mpg)) +
  geom_point(size = 2) +
  # Add Cartesian coordinates with zero expansion
  ___ +
  theme_classic()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Visualisasi Data Tingkat Menengah dengan ggplot2

SkillTag.level.intermediateSkillTag.label

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Mulai Kursus Gratis

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: Koordinat Exercise 2: Memperbesar Tampilan Exercise 3: Rasio aspek I: rasio 1:1 Exercise 4: Rasio aspek II: mengatur rasio Exercise 5: Perluas dan potong

Latihan Saat Ini

Exercise 6: Koordinat vs. skala Exercise 7: Transformasi skala log Exercise 8: Menambahkan statistik ke skala yang ditransformasikan Exercise 9: Sumbu ganda dan terbalik Exercise 10: Sumbu ganda yang berguna Exercise 11: Membalik sumbu I Exercise 12: Membalik sumbu II Exercise 13: Koordinat polar Exercise 14: Diagram pai Exercise 15: Plot wind rose

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