{"title":"clickR:利用变化跟踪对杂乱数据进行半自动预处理,实现数据集整体清理","authors":"David Hervas , David Fuente","doi":"10.1016/j.softx.2024.101865","DOIUrl":null,"url":null,"abstract":"<div><p>In this contribution, we present <em>clickR</em>, an <strong>R</strong> package intended for data cleaning following a semi-automatic and supervised procedure. Few packages and commercial software with cleaning capacities are available. In all cases, their functionalities just cover part of the overall data pre-processing and do not follow an integral approach to cleaning up the data. In contrast, <em>clickR</em> brings together all functions needed for correcting the main structural, variable-assignment and typographical errors found in databases and allows researchers to have a strict control on the suggested changes. This is possible because the package creates a data frame that keeps track of all the implemented data modifications. To prove its capacity for detecting and fixing errors, we clean a messy database that exhibits multiple types of errors within date, numeric and factor variables.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"27 ","pages":"Article 101865"},"PeriodicalIF":2.4000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024002358/pdfft?md5=f478aa00c2254dab4baa13c22b076de0&pid=1-s2.0-S2352711024002358-main.pdf","citationCount":"0","resultStr":"{\"title\":\"clickR: Semi-automatic pre-processing of messy data with change tracking for integral dataset cleaning\",\"authors\":\"David Hervas , David Fuente\",\"doi\":\"10.1016/j.softx.2024.101865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this contribution, we present <em>clickR</em>, an <strong>R</strong> package intended for data cleaning following a semi-automatic and supervised procedure. Few packages and commercial software with cleaning capacities are available. In all cases, their functionalities just cover part of the overall data pre-processing and do not follow an integral approach to cleaning up the data. In contrast, <em>clickR</em> brings together all functions needed for correcting the main structural, variable-assignment and typographical errors found in databases and allows researchers to have a strict control on the suggested changes. This is possible because the package creates a data frame that keeps track of all the implemented data modifications. To prove its capacity for detecting and fixing errors, we clean a messy database that exhibits multiple types of errors within date, numeric and factor variables.</p></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"27 \",\"pages\":\"Article 101865\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352711024002358/pdfft?md5=f478aa00c2254dab4baa13c22b076de0&pid=1-s2.0-S2352711024002358-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711024002358\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024002358","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
摘要
在这篇论文中,我们介绍了 clickR,这是一个 R 软件包,用于按照半自动和监督程序进行数据清理。目前,具有数据清理功能的软件包和商业软件屈指可数。在所有情况下,它们的功能都只是涵盖了整体数据预处理的一部分,并没有采用整体方法来清理数据。相比之下,clickR 汇集了纠正数据库中主要结构、变量分配和排版错误所需的所有功能,并允许研究人员严格控制建议的更改。之所以能做到这一点,是因为该软件包创建了一个数据框架,可跟踪所有已实施的数据修改。为了证明该软件包检测和修复错误的能力,我们清理了一个凌乱的数据库,该数据库在日期、数字和因素变量方面存在多种类型的错误。
clickR: Semi-automatic pre-processing of messy data with change tracking for integral dataset cleaning
In this contribution, we present clickR, an R package intended for data cleaning following a semi-automatic and supervised procedure. Few packages and commercial software with cleaning capacities are available. In all cases, their functionalities just cover part of the overall data pre-processing and do not follow an integral approach to cleaning up the data. In contrast, clickR brings together all functions needed for correcting the main structural, variable-assignment and typographical errors found in databases and allows researchers to have a strict control on the suggested changes. This is possible because the package creates a data frame that keeps track of all the implemented data modifications. To prove its capacity for detecting and fixing errors, we clean a messy database that exhibits multiple types of errors within date, numeric and factor variables.
期刊介绍:
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.