本課程主要目標為使用 R 軟體與真實社會科學調查的資料，透過資料視覺化分析來展現資料中的資訊。課程前半學期會先介紹圖像理論的概念，再透過實際操作與具體設計並畫出統計圖像、詮釋統計圖像的經驗，有效率地理解探索性資料視覺化。課程後半學期，會介紹如何將大多數研究者感興趣、卻無法實際量測到的概念或態度，使用視覺化的模型進行分析。完成課程後，學生將能夠使用 R 語言繪製圖表，並進行潛在變項分析與解釋分析結果。完成課程後，學生將能夠使用 R 語言繪製統計圖表，並進行潛在變項分析與解釋分析結果。

The main purpose of this course is to use R software and real social science survey data, and to display information in the data through visual analysis of data. In the first half of the course, we will first introduce the concept of image theory, and then use actual operations and specific designs and experiences of statistical images and essays to effectively understand exploratory data visualization. In the second half of the course, we will introduce how to analyze concepts or attitudes that most researchers are interested in and cannot be measured using visual models. After completing the course, students will be able to use R language to draw charts and perform potential change analysis and explanation analysis results. After completing the course, students will be able to use R language to draw consolidation charts and conduct potential change analysis and explanation analysis results.

(1) Kieran Healy, 2019, Data Visualization: A Practical Introduction, Princeton University Press: Princeton and Oxford.

(2) Antony Unwin, 2015, Graphical Data Analysis with R, CRC Press.

(3) W. Holmes Finch, Brian F. French, 2015, Latent Variable Modeling with R, Routledge.

(4) A. Alexander Beaujean, 2014, Latent Variable Modeling Using R: A Step-by-Step Guide, Routledge.

(1) Kieran Healy, 2019, Data Visualization: A Practical Introduction, Princeton University Press: Princeton and Oxford.

(2) Antony Unwin, 2015, Graphical Data Analysis with R, CRC Press.

(3) W. Holmes Finch, Brian F. French, 2015, Latent Variable Modeling with R, Routledge.

(4) A. Alexander Beaujean, 2014, Latent Variable Modeling Using R: A Step-by-Step Guide, Routledge.