Introduction to R for Social Scientists: A Tidy Programming Approach
暫譯: 社會科學家入門 R：整潔編程方法
Kennedy, Ryan, Waggoner, Philip D.
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商品描述
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.
Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.
商品描述(中文翻譯)
《社會科學家使用 R 的入門：整潔編程方法》介紹了在社會科學研究中使用 R 的整潔編程方法，幫助定量研究者發展現代技術工具箱。整潔方法圍繞一致的語法、通用的文法和堆疊代碼構建，這些都促進了清晰且高效的編程。作者提供了數百行代碼，以展示開發和調試高效社會科學研究工作流程的一系列技術。為了加深對於在 R 中進行社會科學研究時教授整潔最佳實踐的承諾，作者包含了許多使用真實世界數據的範例，包括美國國家選舉研究和世界指標數據。雖然不假設讀者有 R 的先前經驗，但期望讀者對常見的社會科學研究設計和術語有所了解。
無論是作為參考手冊還是從頭到尾閱讀，讀者將對 R 和 Tidyverse 有更深入的理解，並獲得如何最佳利用這些強大工具來編寫整潔、高效代碼以解決問題的框架。為此，作者提供了許多額外閱讀和工具的建議，以擴展所涵蓋的概念。他們在自己的學術和實踐工作中使用了所有涵蓋的技術。
作者簡介
Ryan Kennedy is an associate professor of political science at the University of Houston and a research associate for the Hobby Center for Public Policy. His work has appeared in top journals including Science, the American Political Science Review, and Journal of Politics. These articles have won several awards, including best paper in the American Political Science Review, and have been cited over 1,700 times. They have also drawn attention from media outlets like Time, the New York Times, and Smithsonian Magazine.
Philip Waggoner is an assistant instructional professor of computational social science at the University of Chicago and a visiting research scholar at ISERP at Columbia University. He is an Associate Editor at the Journal of Mathematical Sociology and the Journal of Open Research Software, and author of the forthcoming book, Unsupervised Machine Learning for Clustering in Political and Social Research (Cambridge University Press). His work has appeared or is forthcoming in many journals including the Journal of Politics, Journal of Mathematical Sociology, and Journal of Statistical Theory and Practice.
作者簡介(中文翻譯)
瑞安·肯尼迪是休士頓大學的政治學副教授，也是霍比公共政策中心的研究助理。他的研究成果發表在多個頂尖期刊上，包括科學、美國政治科學評論和政治期刊。這些文章獲得了多項獎項，包括美國政治科學評論的最佳論文，並且被引用超過1,700次。這些研究也引起了媒體的關注，如時代雜誌、紐約時報和史密森學會雜誌。
菲利普·瓦格納是芝加哥大學計算社會科學的助理教學教授，也是哥倫比亞大學ISERP的訪問研究學者。他是數學社會學期刊和開放研究軟體期刊的副編輯，並且是即將出版的書籍政治與社會研究中的無監督機器學習聚類（劍橋大學出版社）的作者。他的研究成果已發表或即將發表在多個期刊上，包括政治期刊、數學社會學期刊和統計理論與實踐期刊。
