An R package for automatically generating candidate correspondence tables between classifications

Q3 Decision Sciences
Martin Karlberg, Vasilis Chasiotis, Photis Stavropoulos, Christine Laaboudi, Mátyás Mészárosa, Despoina-Avgerini Nasiopoulou
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引用次数: 0

Abstract

Many statistical classifications exist in a statistical ecosystem, where they are interlinked with other classifications. When statistics on the same topic are compiled using different classifications, they need to be transformed in order to become comparable by means of a correspondence table – but sometimes, no correspondence table between the two classifications involved exists. This paper presents the newly developed ‘correspondenceTables’ R package, available on CRAN, which automates much of the ‘mechanical’ work required for developing a correspondence table (thus allowing statistical classification experts to focus on tasks with higher value added). Moreover, the paper presents lessons learned along the way, including unforeseen quality issues with the input data (that required considerable efforts to be successfully tackled), and outlines areas for future improvement.
一个R包,用于自动生成分类之间的候选对应表
许多统计分类存在于统计生态系统中,它们与其他分类相互联系。当使用不同的分类编制关于同一主题的统计数据时,需要对它们进行转换,以便通过对应表进行比较——但有时,所涉及的两种分类之间不存在对应表。本文介绍了新开发的“对应表”R包,可在CRAN上获得,它自动化了开发对应表所需的大部分“机械”工作(从而允许统计分类专家专注于具有更高附加值的任务)。此外,本文还介绍了在此过程中获得的经验教训,包括输入数据中不可预见的质量问题(需要相当大的努力才能成功解决),并概述了未来改进的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistical Journal of the IAOS
Statistical Journal of the IAOS Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
1.30
自引率
0.00%
发文量
116
期刊介绍: This is the flagship journal of the International Association for Official Statistics and is expected to be widely circulated and subscribed to by individuals and institutions in all parts of the world. The main aim of the Journal is to support the IAOS mission by publishing articles to promote the understanding and advancement of official statistics and to foster the development of effective and efficient official statistical services on a global basis. Papers are expected to be of wide interest to readers. Such papers may or may not contain strictly original material. All papers are refereed.
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