课程

使用 ggplot2 进行数据可视化入门

基础技能水平

更新时间 2025年12月

通过理解图形语法，学习用 ggplot2 制作有意义且美观的数据可视化。

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RData Visualization

4小时

14 视频

52 道练习

4,300 XP

180K+

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课程描述

能够制作有意义且美观的数据可视化，是数据科学家必备的核心技能。本课程是该系列中的首个 R 数据可视化教程，将向您介绍优质可视化的基本原则，以及在 ggplot2 包中实现的图形语法绘图概念。ggplot2 已成为在 R 中创建灵活且专业图表的首选工具。在这里，我们将讲解绘图所必需的前三个图层——数据（Data）、美学映射（Aesthetics）和几何对象（Geometries）。完成本课程后，您将能够制作复杂的探索性图表。视频包含实时字幕，您可以点击视频左下方的 "Show transcript" 展开查看。课程词汇表位于右侧的资源部分。如需获得 CPE 学分，您需要完成课程并在合格测评中达到 70% 的得分。您可以点击右侧的 CPE 学分提示跳转到测评页面。

先决条件

Introduction to the Tidyverse

1

Introduction

In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.

Introduction

Explore and explain

Drawing your first plot

Data columns types affect plot types

The grammar of graphics

Mapping data columns to aesthetics

Understanding variables

ggplot2 layers

Adding geometries

Changing one geom or every geom

Saving plots as variables

2

Aesthetics

Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens - converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter you’ll understand how to choose the best aesthetic mappings for your data.

Visible aesthetics

All about aesthetics: color, shape and size

All about aesthetics: color vs. fill

All about aesthetics: comparing aesthetics

Aesthetics for categorical & continuous variables

Using attributes

All about attributes: color, shape, size and alpha

All about attributes: conflicts with aesthetics

Going all out

Modifying aesthetics

Updating aesthetic labels

Setting a dummy aesthetic

Aesthetics best practices

Appropriate mappings

3

Geometries

A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter - scatter plots, bar charts and line plots. We’ll look at a variety of different ways to construct these plots.

Scatter plots

Overplotting 1: large datasets

Overplotting 2: Aligned values

Overplotting 3: Low-precision data

Overplotting 4: Integer data

Drawing histograms

Positions in histograms

Position in bar and col plots

Overlapping bar plots

Bar plots: sequential color palette

Basic line plots

Multiple time series

4

Themes

In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.

Themes from scratch

Moving the legend

Modifying theme elements

Modifying whitespace

Theme flexibility

Built-in themes

Exploring ggthemes

Setting themes

Publication-quality plots

Effective explanatory plots

Using geoms for explanatory plots

Using annotate() for embellishments

使用 ggplot2 进行数据可视化入门

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