有没有一种简单的方法可以使用循环简化此代码? [英] Is there an easy way to simplify this code using a loop?

问题描述

有没有一种使用循环来简化此代码的方法?

Is there a way to simplify this code using a loop?

set.seed(100) 

AL_INDEX <- sample(1:nrow(AL_DF), 0.7*nrow(AL_DF))
AL_TRAIN <- AL_DF[AL_INDEX,]
AL_TEST <- AL_DF[-AL_INDEX,]  

AR_INDEX <- sample(1:nrow(AR_DF), 0.7*nrow(AR_DF))
AR_TRAIN <- AR_DF[AR_INDEX,]
AR_TEST <- AR_DF[-AR_INDEX,]  

AZ_INDEX <- sample(1:nrow(AZ_DF), 0.7*nrow(AZ_DF))
AZ_TRAIN <- AZ_DF[AZ_INDEX,]
AZ_TEST <- AZ_DF[-AZ_INDEX,]  

AL_DF，AR_DF&AZ_DF是具有相同字段结构但记录数量不同的数据帧.

AL_DF, AR_DF & AZ_DF are data frames that have the same field structure, but different number of records.

推荐答案

查找模式以捕获所有数据框名称.在示例共享的所有文件中，所有文件均以"_ DF" 结尾，使用 mget 将其放入列表.将测试中的数据划分并训练，然后将它们 unlist 一级.

Find a pattern to capture all the dataframe names. In the example shared all of them end with "_DF", use mget to get them in list. Divide the data in test and train and unlist them one level.

data <- unlist(lapply(mget(ls(pattern = '_DF$')), function(df) {
            index <- sample(1:nrow(df), 0.7*nrow(df))
            list(train = df[index,], test = df[-index,])  
         }), recursive = FALSE)

现在使用 list2env 将它们分成单独的数据框.

Now get them into individual dataframes using list2env.

list2env(data, .GlobalEnv)

这篇关于有没有一种简单的方法可以使用循环简化此代码?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！