R 如何使用向量将数据帧缩减为单行

我有这个DF

  email       date      user_ipaddress       other data    
1 x@bla.com 2020-03-24  177.95.75.230         xxxx
2 x@bla.com 2020-04-02  177.139.49.93         yyyy
3 x@bla.com 2020-04-02  177.139.49.93         zzzz

我想把这些数据转换成它要存储的形状

整个问题将是一个包含不同电子邮件的大数据框架，我想将每封电子邮件的所有数据减少到一行，就像这样

  email       date      user_ipaddress                       other data    
1 x@bla.com 2020-04-02  c('177.95.75.230','177.139.49.93')   c('xxxx','yyyy','zzzz') 

事实上，如果有人能在只有一个电子邮件地址的情况下帮我的话，这会救我的命，但请放心帮我解决整个问题

使用

ipadreessVec<-Reduce(append,x =df$network_userid) 

我明白了

Error in `$<-.data.frame`(`*tmp*`, network_userid, value = c("20562206-f557-48a3-861b-5d1e18524bbb",  : 
  replacement has 3 rows, data has 1

---

“$中的

错误我们可以创建一个
列表
列，按“电子邮件”、“日期”分组

library(dplyr)
DF %>% 
    group_by(email, date) %>%
    summarise_all(list)
# A tibble: 2 x 4
# Groups:   email [1]
#  email     date       user_ipaddress otherdata
#  <chr>     <chr>      <list>         <list>   
#1 x@bla.com 2020-03-24 <chr [1]>      <chr [1]>
#2 x@bla.com 2020-04-02 <chr [2]>      <chr [2]>

数据
DF我可能误解了你，你更可能想要@akrun shows这样的节目，但从字面上解释，你可能想要使用
dput
：

as.data.frame（lappy（df，function（x）capture.output（dput（unique（x '））））
#>电子邮件日期用户\u IP地址
#> 1 "x@bla.com“c”（“2020-03-24”、“2020-04-02”）c（“177.95.75.230”、“177.139.49.93”）
#>其他
#>1c（“xxxx”、“yyyy”、“zzzz”）

通过电子邮件和日期：

setDT(df)[, .(user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email, date)]

#       email       date              user_ipaddress     other
# 1: x@bla.com 2020-03-24               177.95.75.230      xxxx
# 2: x@bla.com 2020-04-02 177.139.49.93,177.139.49.93 yyyy,zzzz

setDT(df)[, .(date = paste0(date, collapse = ","),
              user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email)]

#        email                             date                            user_ipaddress          other
# 1: x@bla.com 2020-03-24,2020-04-02,2020-04-02 177.95.75.230,177.139.49.93,177.139.49.93 xxxx,yyyy,zzzz

df <- read.table(text='email       date      user_ipaddress       other_data    
1 x@bla.com 2020-03-24  177.95.75.230         xxxx
                 2 x@bla.com 2020-04-02  177.139.49.93         yyyy
                 3 x@bla.com 2020-04-02  177.139.49.93         zzzz', header = TRUE, stringsAsFactors = FALSE)

仅通过电子邮件发送：

setDT(df)[, .(user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email, date)]

#       email       date              user_ipaddress     other
# 1: x@bla.com 2020-03-24               177.95.75.230      xxxx
# 2: x@bla.com 2020-04-02 177.139.49.93,177.139.49.93 yyyy,zzzz

setDT(df)[, .(date = paste0(date, collapse = ","),
              user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email)]

#        email                             date                            user_ipaddress          other
# 1: x@bla.com 2020-03-24,2020-04-02,2020-04-02 177.95.75.230,177.139.49.93,177.139.49.93 xxxx,yyyy,zzzz

df <- read.table(text='email       date      user_ipaddress       other_data    
1 x@bla.com 2020-03-24  177.95.75.230         xxxx
                 2 x@bla.com 2020-04-02  177.139.49.93         yyyy
                 3 x@bla.com 2020-04-02  177.139.49.93         zzzz', header = TRUE, stringsAsFactors = FALSE)

数据：

setDT(df)[, .(user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email, date)]

#       email       date              user_ipaddress     other
# 1: x@bla.com 2020-03-24               177.95.75.230      xxxx
# 2: x@bla.com 2020-04-02 177.139.49.93,177.139.49.93 yyyy,zzzz

setDT(df)[, .(date = paste0(date, collapse = ","),
              user_ipaddress = paste0(user_ipaddress, collapse = ","),
              other = paste0(other_data, collapse = ",")), by = .(email)]

#        email                             date                            user_ipaddress          other
# 1: x@bla.com 2020-03-24,2020-04-02,2020-04-02 177.95.75.230,177.139.49.93,177.139.49.93 xxxx,yyyy,zzzz

df <- read.table(text='email       date      user_ipaddress       other_data    
1 x@bla.com 2020-03-24  177.95.75.230         xxxx
                 2 x@bla.com 2020-04-02  177.139.49.93         yyyy
                 3 x@bla.com 2020-04-02  177.139.49.93         zzzz', header = TRUE, stringsAsFactors = FALSE)

df也许你可以在基本R中尝试
aggregate
：

dfout <- aggregate(.~email,df,FUN = function(x) list(unique(levels(x))))

数据

df <-  structure(list(email = c("x@bla.com", "x@bla.com", "x@bla.com"
), date = c("2020-03-24", "2020-04-02", "2020-04-02"), user_ipaddress = c("177.95.75.230", 
"177.139.49.93", "177.139.49.93"), `other data` = c("xxxx", "yyyy", 
"zzzz")), class = "data.frame", row.names = c(NA, -3L))

df>df%%>%+分组依据（电子邮件，日期）%%>%+摘要（跨越（所有内容（），列表））在跨越（所有内容（），列表）中出错：未能找到函数“跨越”>df%%>%+分组依据（电子邮件，日期）%%>%+摘要依据（列表）错误：应为单面公式、函数或函数名。调用
rlang:：last_error（）
查看backtrace@filscapo它在德维尔versoin@filscapo这是一个完美的问题，我不确定这不是一个很难的问题，因为我在不到一小时的时间里得到了4个精彩的答案！。。或者，也许是以前患过此病的人遭受了太多的痛苦，他们将永远记住如何解决这一问题