Tag cloud
還沒有人認領這個 Issue。
- 主要語言
- F#
- 星號
- 860
- 分支
- 99
- 平均合併
- 22 分鐘
- 30 天內合併 PR
- 1
描述
Description
A tag cloud is a visual representation for text data, typically used to depict keyword metadata (tags) on websites, to visualize free form text or to analyses speeches( e.g. election’s campaign). Tags are usually single words, and the importance of each tag is shown with font size or color. This format is useful for quickly perceiving the most prominent terms and for locating a term alphabetically to determine its relative prominence [1].
Depending on the level of sophistication, calculating positioning in a given space can become quite complex [2, 3]. If you need external dependencies, make sure to move this issue to the FsLab repository
Example
References
- [1] https://datavizproject.com/data-type/tag-cloud/
- [2] https://en.wikipedia.org/wiki/Tag_cloud#Collocate_clouds
- [3] https://i11www.iti.kit.edu/extra/publications/bfklnopsuw-swcrh-14.pdf
Pointers
- Ideally, you start prototyping in a scripting or notebook environment where you can iterate fast. installation instructions can be found here: https://plotly.net/#Installation
- Charts like this that are using baseline trace types to create a new chart type should only be implemented in the top-level Chart API. An example where this is already done is the Range chart that combines a set of differently styled line charts.
- Ideally prevent text processing and just focus the plot on creating the tag cloud based on occurrence in a collection of strings. Preprocessing should be done in the pipeline before applying the visualization technique.
Hints (click to expand if you need additional pointers)
- you can position text on a scatterplot and hide the markers to only show text
- in a more sophisticated manner, you can also draw shapes on a plot that are either boxes containing text or even svg paths representing the text.
貢獻指南
這個儲存庫沒有索引到貢獻指南
從這裡開始
- 先讀完整個 Issue,再讀專案的貢獻指南。
- 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
- Fork 儲存庫,在一個分支上完成修改。
- 送出 Pull Request,並在描述裡引用這個 Issue 編號。
研究方向
先在 issue 中描述的指令碼或 notebook 環境中製作原型,接著閱讀 src/Plotly.NET/ChartAPI/Chart2D.fs 中頂層的 Chart API 和 Range chart 範例。探索使用隱藏 marker 的散佈圖,並將前處理放在視覺化之外。完成的標準是 Chart API 能夠根據字串集合中的出現次數資料建立標籤雲。
由索引模型根據 Issue 內容生成。
評估
- 技術堆疊
- fsharp
- 領域
- data-visualization
- Issue 類型
- 功能
- 難度
- 5/5
- 預估耗時
- 一週以上
- 活躍度
- 停滯
- 描述清晰度
- 基本清楚
- 新手友好度
- 35/100