# Visualization with Tableau

## 臺灣收容所動物收容現況分析
### 利用Tableau製作一份臺灣收容所動物收容現況的視覺化組圖，能從中看出收容所動物性質（縣市分佈、品種、性別）及近十年收容量變化趨勢
1. 儀表板中的主圖為臺灣各縣市收容所動物數量，由顏色深淺可看出動物數量多寡，將游標暫留於縣市上會以工具提示顯示所在縣市、所在縣市之收容所、動物數量，並搭配下方臺灣各縣市收容所之動物品種分組長條圖醒目提示。
2. 新北市有9間收容所，動物收容總數為全國最多；臺北市雖然僅有1間收容所，但自2013年起開始實施無限期收容動物的政策，因此動物收容量高，收容總數為全國第二。
3. 右方三組圖表顯示全國收容之動物之中，狗、貓、其它動物的亞種數量：收容所內的貓狗皆以「米克斯」（混種狗、貓）最多，其它動物的數量（全國僅收容6隻非犬貓動物）及種類則較少。性別分佈則相當平均。
4. 在全國動物收容數量逐年變化趨勢的折線圖上標示流浪動物相關重要事件：2015年1月《動物保護法》修正案三讀通過，廢除「十二夜條款」，並於2017年起全面實施，由圖可見全國動物收容總數大致呈逐年攀升，而2020年至2022年期間，數量急遽攀升，推測可能與疫情期間的生活模式相關（疫情期間全球各國因防疫隔離限制，飼養寵物的人數增加，但在防疫規定放寬後出現疫後棄養潮），但臺灣的防疫模式與其它國家較不同，目前尚無足夠的資料解釋臺灣的疫情生活模式如何影響流浪動物收容量，僅推測具關聯性。

## Exploring Educational Dataset
### Side-by-side bar chart
#### Compare the number of Master’s/ Bachelor’s degrees awarded by US regions
### Lollipop chart
### Geographical layout
#### Find out US universities with the larger enrollment (>20k) and display data points on the map.

[Data source: National Center for Education Statistics](https://nces.ed.gov/ipeds/datacenter/DataFiles.aspx)

## Exploring The Trend of Programming Languages
### Dashboard
#### Aboutness
* This dataset contains information on over 4000 programming languages, including facts about the year it was created, its rank, and other parameters (type, fact count, book count, last activity...).
* The filter criteria are as follows: the language must have been created between 1940 and 2023 and must have a ranking between 1 and 50.
* The captions of the four quadrants are: “New and Popular”, “New but Unpopular”, “Old but Popular”, and “Old and Unpopular”.
#### How to view it:
* This plot is divided into four quadrants based on their average values for the year of creation and ranking. The quadrants are labeled as "New and Popular," "New but Unpopular," "Old but Popular," and "Old and Unpopular."
* The histograms around the scatter plot provide additional information about the distribution of languages across the different years and rankings.
* The color of each data point represents the abundancy of the language. By applying the filter criteria, we have limited the data to programming languages (applications, queryLanguage, textMarkup... not included) created between 1940 and 2023 and ranked between 1 and 50.
* This approach helps to focus on the most relevant languages in the field and enables readers to observe trends and patterns in the evolution of programming languages.