In this chapter, we focus on visualizing proportions of a whole; we see that pie charts really aren't so bad, along with discussing the waffle chart and stacked bars for comparing multiple proportions.

Pengantar Grammar of Graphics

Mengenal data penyakit

Pemanasan pengolahan data

Diagram pai dan kawan-kawannya

P-I-E yang tersohor

Merapikan diagram pai

Bagaimana dengan waffle?

Kapan menggunakan batang

Batang bertumpuk dasar

Mengatur urutan tumpukan agar mudah dibaca

Sumbu x kategorikal

Proportions of a whole

We shift our focus now to single-observation or point data and go over when bar charts are appropriate and when they are not, what to use when they are not, and general perception-based enhancements for your charts.

Bars and dots: point data

Are bars appropriate?

Working with geom_col

Wrangling geom_bar

Point charts

Ordered point chart

Adding visual anchors

Faceting to show structure.

Tuning your charts

Let's flip some axes

Cleaning up the bars

Converting to point chart

Point data

We now move on to visualizing distributional data, we expose the fragility of histograms, discuss when it is better to shift to a kernel density plots, and how to make both plots work best for your data.

Importance of distributions

Orienting with the data

Looking at all data

Changing y-axis to density

Histogram nuances

Adjusting the bin numbers

More bars

Bin width by context

The kernel density estimator

Histogram to KDE

Putting a rug down

KDE with lots of data

Single distributions

Finishing off we take a look at comparing multiple distributions to each other. We see why the traditional box plots are very dangerous and how to easily improve them, along with investigating when you should use more advanced alternatives like the beeswarm plot and violin plots.

Intro to comparing distributions

A simple boxplot

Adding some jitter

Faceting to show all colors

Beeswarms and violins

Your first beeswarm

Fiddling with a violin plot

Violins with boxplots

Comparing lots of distributions

Comparing spatially-related distributions

A basic ridgeline plot

Cleaning up your ridgelines

Making it rain (data points)

Wrap-up

Comparing distributions

World Health Organization Disease Dataset

Kursus ini akan membantu Anda melampaui dasar-dasar visualisasi data dan mengasahnya menjadi bagian yang kuat dari perangkat data science Anda. Sepanjang pelajaran, kita akan menggunakan dua himpunan data terbuka yang menarik untuk membahas berbagai jenis data (proporsi, data titik, distribusi tunggal, dan beberapa distribusi) serta meninjau kelebihan dan kekurangan visualisasi yang paling umum. Selain itu, kita akan membahas beberapa alternatif visualisasi yang kurang umum untuk jenis data tersebut dan cara menyesuaikan pengaturan bawaan ggplot agar pesan Anda tersampaikan dengan paling efisien dan efektif.

Introduction to Data Visualization with ggplot2

Pelajari cara menyampaikan data dengan efektif menggunakan grafik umum, visualisasi alternatif, dan gaya.

Diagram pai dan kawan-kawannya

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