Bilingual data visualization studio · 双语数据可视化课程
用 ggplot2 与 Plotly 把数据讲清楚。
Learn publication-ready static graphics with ggplot2 and browser-based interaction with Plotly—from the first mapping or trace to polished, reproducible figures ready for analysis, teaching, and sharing. 从第一张映射图或 trace 开始，逐步掌握出版级静态图形与可分享的互动 HTML，做出适合分析、教学和沟通的可重复图表。
Move from understanding the data to choosing an honest visual form, then refine the chart for its audience. 从理解数据开始，选择诚实清晰的视觉表达，再针对读者完善图表。
Confirm the unit of analysis, variable types, missingness, and the comparison the reader must make.
Start with the grammarMatch relationships, distributions, counts, time series, and grouped comparisons to suitable geoms.
Explore common geomsUse scales, facets, coordinates, and annotations to reveal the intended pattern without distortion.
Refine the mappingWrap recurring plots in functions, apply a consistent theme, and export at a deliberate size.
Prepare the final figureChoose one of two bilingual courses. The ggplot2 editions contain 15 sections and 20 rendered charts; the Plotly editions contain 18 sections and 23 interactive figures. Each English–Chinese pair follows the same examples and learning sequence. 选择两门双语课程之一：ggplot2 中英文版各含 15 章、20 幅实绘图表；Plotly 中英文版各含 18 章、23 个互动图表。每组中英文教程均保持相同示例与学习顺序。
English edition
A runnable guide to mappings, geoms, statistical transformations, positions, scales, facets, annotations, reusable themes, plotting functions, and export.
Grammar of graphics · distributions · trends · scales · facets · themes · export
中文实践教程
从映射、几何对象与统计变换开始，系统学习位置、标度、分面、注释、可复用主题、绘图函数和高质量导出。
图形语法 · 分布 · 趋势 · 标度 · 分面 · 主题 · 导出
English interactive edition
A runnable guide to traces, mappings, hover, layout, subplots, Crosstalk, animation, maps, Shiny events, HTML publishing, and WebGL performance.
plot_ly · ggplotly · hover · subplot · Crosstalk · animation · maps · export
中文互动教程
系统学习 trace、映射、悬停、布局、子图、Crosstalk、动画、地图、Shiny 事件、HTML 发布与 WebGL 性能。
plot_ly · ggplotly · 悬停 · 子图 · Crosstalk · 动画 · 地图 · 导出
Use the library by topic when you need a quick answer, or follow the full tutorial for a connected understanding. 需要快速查用法时可按主题进入；希望系统掌握时请按完整教程学习。
Scatterplots, overplotting, grouping, smooths, and regression lines.
Histograms, densities, boxplots, violins, and meaningful bin widths.
Counts versus summaries, ordering, stacking, dodging, and proportions.
Dates, time-series grouping, reference lines, and readable axis labels.
Turn familiar ggplot2 figures into browser widgets with deliberate tooltips and controls.
Build native traces, linked subplots, animations, maps, and shareable HTML.
The tutorials connect every ggplot2 layer and Plotly trace to a comparison task, explain what the chart computes, and highlight choices that can change the reader's interpretation. 课程把每个 ggplot2 图层与 Plotly trace 放回实际比较任务中，说明图表计算了什么，并指出哪些选择会改变读者的理解。