欲在R语言中安装第三方包,包的名字是“plyr”,则安装语句是A.install.packages(“plyr”)B.install.package(“plyr”)C.install.packagesD.install(“plyr”)搜索 题目 欲在R语言中安装第三方包,包的名字是“plyr”,则安装语句是 A.install.packages(“plyr”)B.install.package(“plyr”)C.install.packagesD...
🚀 R package: doFuture - Use Foreach to Parallelize via Future Framework packagecranrhpcparalleldistributed-computingforeachplyrhpc-clustersbatchjobsbatchtoolsbiocparallel UpdatedApr 5, 2025 R betahut/movie-app Star57 Code Issues Pull requests ...
It's already possible to do this with base R functions (like split and the apply family of functions), but plyr makes it all a bit easier with: totally consistent names, arguments and outputs convenient parallelisation through the foreach package ...
问plyr::ddply错误消息:类中出错(output[[var]]) <- class(value)：尝试将属性设置为NULLEN这在不...
crplyr包商品说明书 Package‘crplyr’March21,2023 Type Package Title A'dplyr'Interface for Crunch Description In order to facilitate analysis of datasets hosted on the Crunch data platform<https://crunch.io/>,the'crplyr'package implements'dplyr' methods on top of the Crunch backend.The usual ...
更多例子见vignette("introduction", package = "dplyr") 另: 一些汇总时的小函数 n(): 计算个数 n_distinct(): 计算 x 中唯一值的个数. (原文为 count_distinct(x), 测试无用) first(x), last(x) 和 nth(x, n): 返回对应秩的值, 类似于自带函数 x[1], x[length(x)], 和 x[n] ...
不过R用户一般不建议使用for循环,数据量一般庞大后,for循环运行会比较慢。 00 示例数据集 示例采用breedRPlus包的数据集dfm2。 ## data sets data(dfm2,package='breedRPlus') # Only keep aim factors and traits for analysis dat<-dfm2[,c(4:5,10:14)] # 将需要的分析性状(h1,h2,...)整合到...
Results: To address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with ...
Hadley Wickham has just released updates to his data-manipulation packages for R, plyr and reshape (now called reshape2), that are much faster and more memory-efficient than the previous incarnations. The reshape2 package lets you flexibly restructure an
It's already possible to do this with base R functions (like split and the apply family of functions), but plyr makes it all a bit easier with: * totally consistent names, arguments and outputs * convenient parallelisation through the foreach package ...