Diagram batang: posisi dodging

Pada latihan sebelumnya, kita menggunakan himpunan data mtcars untuk menggambar dynamite plot tentang berat mobil per jenis silinder.

Pada latihan ini, kita akan menambahkan pembedaan berdasarkan jenis transmisi, fam, untuk dynamite plot dan mengeksplorasi position dodging (di mana batang disusun berdampingan).

Latihan ini merupakan bagian dari kursus

Visualisasi Data Tingkat Menengah dengan ggplot2

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Update the aesthetics to color and fill by fam
ggplot(mtcars, aes(x = fcyl, y = wt)) +
  stat_summary(fun = mean, geom = "bar") +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", width = 0.1)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Visualisasi Data Tingkat Menengah dengan ggplot2

SkillTag.level.intermediateSkillTag.label

4.8+

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: 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: Praktik terbaik: plot batang Exercise 2: Plot batang: dynamite plots Exercise 3: Diagram batang: posisi dodging

Latihan Saat Ini

Exercise 4: Plot batang: Menggunakan data teragregasi Exercise 5: Skenario penggunaan heatmap Exercise 6: Heat map Exercise 7: Heat map yang bermanfaat Exercise 8: Alternatif heat map Exercise 9: Ketika data yang baik menghasilkan plot yang buruk Exercise 10: Penekanan titik asal (origin)Exercise 11: Buta warna Exercise 12: Masalah yang umum