Variasi pemetaan

Persiapan sudah selesai; sekarang mari jelajahi stat_summary().

Statistik ringkas mengacu pada kombinasi letak (rata-rata atau median) dan sebaran (simpangan baku atau interval kepercayaan).

Metrik ini dihitung dalam stat_summary() dengan meneruskan sebuah fungsi ke argumen fun.data. mean_sdl(), menghitung kelipatan simpangan baku dan mean_cl_normal() menghitung IK 95% yang dikoreksi t.

Argumen untuk fungsi data diteruskan ke argumen fun.args milik stat_summary() dalam bentuk list.

Objek posisi, posn_d, dan plot dengan titik jitter, p_wt_vs_fcyl_by_fam_jit, tersedia.

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.

p_wt_vs_fcyl_by_fam_jit +
  # Add a summary stat of std deviation limits
  ___

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: Statistik dengan geom Exercise 2: Pemulusan Exercise 3: Variabel pengelompokan Exercise 4: Memodifikasi stat_smooth Exercise 5: Memodifikasi stat_smooth (2)Exercise 6: Statistik: jumlah dan kuantil Exercise 7: Kuantil Exercise 8: Menggunakan stat_sum Exercise 9: Statistik di luar geom Exercise 10: Persiapan Exercise 11: Menggunakan objek posisi Exercise 12: Variasi pemetaan

Latihan Saat Ini

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: 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