{"title":"使用正则表达式和nc包进行从宽到高的数据重塑","authors":"T. Hocking","doi":"10.32614/rj-2021-029","DOIUrl":null,"url":null,"abstract":"Regular expressions are powerful tools for extracting tables from non-tabular text data. Capturing regular expressions that describe the information to extract from column names can be especially useful when reshaping a data table from wide (few rows with many regularly named columns) to tall (fewer columns with more rows). We present the R package nc (short for named capture), which provides functions for wide-to-tall data reshaping using regular expressions. We describe the main new ideas of nc, and provide detailed comparisons with related R packages (stats, utils, data.table, tidyr, tidyfast, tidyfst, reshape2, cdata).","PeriodicalId":20974,"journal":{"name":"R J.","volume":"66 1","pages":"69"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Wide-to-tall Data Reshaping Using Regular Expressions and the nc Package\",\"authors\":\"T. Hocking\",\"doi\":\"10.32614/rj-2021-029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Regular expressions are powerful tools for extracting tables from non-tabular text data. Capturing regular expressions that describe the information to extract from column names can be especially useful when reshaping a data table from wide (few rows with many regularly named columns) to tall (fewer columns with more rows). We present the R package nc (short for named capture), which provides functions for wide-to-tall data reshaping using regular expressions. We describe the main new ideas of nc, and provide detailed comparisons with related R packages (stats, utils, data.table, tidyr, tidyfast, tidyfst, reshape2, cdata).\",\"PeriodicalId\":20974,\"journal\":{\"name\":\"R J.\",\"volume\":\"66 1\",\"pages\":\"69\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R J.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2021-029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R J.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2021-029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wide-to-tall Data Reshaping Using Regular Expressions and the nc Package
Regular expressions are powerful tools for extracting tables from non-tabular text data. Capturing regular expressions that describe the information to extract from column names can be especially useful when reshaping a data table from wide (few rows with many regularly named columns) to tall (fewer columns with more rows). We present the R package nc (short for named capture), which provides functions for wide-to-tall data reshaping using regular expressions. We describe the main new ideas of nc, and provide detailed comparisons with related R packages (stats, utils, data.table, tidyr, tidyfast, tidyfst, reshape2, cdata).