Dasar-dasar layer facet

Faceting membagi himpunan data ke dalam kelompok berdasarkan variabel kategorikal, lalu memplot setiap kelompok pada panelnya masing-masing. Untuk membagi data menurut satu atau dua variabel kategorikal, facet_grid() adalah pilihan terbaik.

Diberikan variabel kategorikal A dan B, pola kodenya adalah

plot +
  facet_grid(rows = vars(A), cols = vars(B))

Ini menggambar sebuah panel untuk setiap kombinasi pasangan nilai A dan B.

Di sini, kita akan menggunakan himpunan data mtcars untuk berlatih. Meskipun cyl dan am tidak dikodekan sebagai variabel faktor di himpunan data tersebut, ggplot2 akan mengonversi variabel menjadi faktor saat digunakan dalam facets.

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() +
  # Facet rows by am
  ___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Visualisasi Data Tingkat Menengah dengan ggplot2

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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: Coordinates Exercise 2: Zooming In Exercise 3: Aspect ratio I: 1:1 ratios Exercise 4: Aspect ratio II: setting ratios Exercise 5: Expand and clip Exercise 6: Coordinates vs. scales Exercise 7: Log-transforming scales Exercise 8: Adding stats to transformed scales Exercise 9: Double and flipped axes Exercise 10: Useful double axes Exercise 11: Flipping axes I Exercise 12: Flipping axes II Exercise 13: Polar coordinates Exercise 14: Pie charts Exercise 15: 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.

Exercise 1: Lapisan facet Exercise 2: Dasar-dasar layer facet

Latihan Saat Ini

Exercise 3: Banyak variabel Exercise 4: Notasi formula Exercise 5: Label dan urutan facet Exercise 6: Memberi label pada facet Exercise 7: Mengatur urutan Exercise 8: Ruang plotting facet Exercise 9: Ruang plotting variabel I: variabel kontinu Exercise 10: Ruang plotting variabel II: variabel kategorikal Exercise 11: Facet wrap & margin Exercise 12: Membungkus untuk banyak level Exercise 13: Plot margin

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