在R中交叉加入dplyr [英] Cross Join in dplyr in R

问题描述

library(dplyr)
cust_time<-data.frame(cid=c("c1","c2","c3","c4","c5"),ts=c(2,7,11,13,17))
#I want to do a cross join on self, preferable in dplyr else base package is Ok
#But w/o renaming header names
#Currently I have to create a duplicate cust_time to do this.
cust_time.1<-rename(cust_time,cid1=cid,ts1=ts)
merge(cust_time,cust_time.1,by=NULL)

#Later I will want to do cross join within the grouped region
cust_time <-mutate(cust_time,ts.bucket=ts%/%10)
#If using duplicate tables, not sure, how to do the below
#group_by(cust_time,ts.bucket) %>%
#do cross join within this bucket

基本上，我想在表上进行交叉自连接，但是由于我无法使用dplyr解决方案，因此我使用了基本包。但这需要我重命名所有列。但是，我后来希望能够在分组级别进行交叉联接，这就是我绊脚石的地方。

任何帮助表示赞赏。

Basically, I want to do a cross self-join on a table but since I cant a dplyr solution, I used the base package. But it requires me to rename all the columns. However, I later want to be able do a cross-join at a grouped level and this is where I am stumbling.
Any help appreciated.

推荐答案

您只需要一个虚拟列即可加入：

You just need a dummy column to join on:

cust_time$k <- 1
cust_time %>% 
  inner_join(cust_time, by='k') %>%
  select(-k)

或者如果您不想修改原始数据框：

Or if you don't want to modify your original dataframe:

cust_time %>%
  mutate(k = 1) %>%
  replicate(2, ., simplify=FALSE) %>%
  Reduce(function(a, b) inner_join(a, b, by='k'), .) %>%
  select(-k)

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