在R中的不同列中拆分值 数据

如何解决在R中的不同列中拆分值 数据

数据集中的一列包含类似值

utm_source=google&utm_medium=cpc&utm_campaign=1234567&utm_term=brand%20&utm_content=Brand&gclid=ERtyuiipotf_YTj

如何将其拆分为R中的值？

utm_source utm_medium  utm_campaign utm_brand  utm_content
  google      cpc          1234567   brand%20     Brand

dput(column)给出以下输出

structure(list("null","gclid=ertyyhglkdl-kjkY","utm_source=google&utm_medium=cpc&utm_campaign=1234556&utm_term=brand%20shirts&utm_content=Brand&gclid=jhajsgjdgd_ajs","utm_source=google&utm_medium=cpc&utm_campaign=1674814043&utm_term=brand%20shirts&utm_content=Brand&gclid=KvgMsEAAYASAAEgLq6vD_BwE","null","utm_source=fb&utm_medium=ctw&utm_campaign=Shirt_rem&utm_content=CasciaShirt"),class = c("extracted","list"))

解决方法

以OP的更新示例为list，我们遍历list，if元素不是"null"，然后创建tibble，使用&将separate_rows处的列拆分，然后将该列拆分为多列（separate），并使用{{1}从命名向量（deframe）中创建小标题}）

as_tibble_row

-输出

library(dplyr)
library(tidyr)
library(tibble)
library(purrr)
map_dfr(lst1,~ if(.x != "null") tibble(col1 = .x) %>% 
             separate_rows(col1,sep="&") %>% 
             separate(col1,into = c('col1','col2'),sep="\\=") %>%
             deframe %>% 
             as_tibble_row())

---

或者，我们可以将# A tibble: 4 x 6 # gclid utm_source utm_medium utm_campaign utm_term utm_content # <chr> <chr> <chr> <chr> <chr> <chr> #1 ertyyhglkdl-kjkY <NA> <NA> <NA> <NA> <NA> #2 jhajsgjdgd_ajs google cpc 1234556 brand%20shirts Brand #3 KvgMsEAAYASAAEgLq6vD_BwE google cpc 1674814043 brand%20shirts Brand #4 <NA> fb ctw Shirt_rem <NA> CasciaShirt 转换为list中的一列，而不必循环执行，只需执行一次并转换为宽格式即可。

data.frame

数据

library(data.table)
keep(lst1,~ .x != "null") %>%
     flatten_chr %>% 
     tibble(col1 = .) %>%
     mutate(rn = row_number()) %>% 
     separate_rows(col1,sep='&') %>% 
     separate(col1,sep="\\=") %>%
     pivot_wider(names_from = col1,values_from = col2) %>% 
     select(-rn)
# A tibble: 4 x 6
#  gclid                    utm_source utm_medium utm_campaign utm_term       utm_content
#  <chr>                    <chr>      <chr>      <chr>        <chr>          <chr>      
#1 ertyyhglkdl-kjkY         <NA>       <NA>       <NA>         <NA>           <NA>       
#2 jhajsgjdgd_ajs           google     cpc        1234556      brand%20shirts Brand      
#3 KvgMsEAAYASAAEgLq6vD_BwE google     cpc        1674814043   brand%20shirts Brand      
#4 <NA>                     fb         ctw        Shirt_rem    <NA>           CasciaShirt

,

我不确定这是否是预期的输出。以下可能是您目标的基本R选项

Reduce(
  function(...) merge(...,all = TRUE),lapply(
    column,function(x) {
      u <- unlist(strsplit(x,"&"))
      setNames(data.frame(as.list(gsub(".*=","",u))),gsub("=.*",u))
    }
  )
)

给出

  utm_source utm_medium utm_campaign utm_content null                    gclid
1         fb        ctw    Shirt_rem CasciaShirt <NA>                     <NA>
2     google        cpc      1234556       Brand <NA>           jhajsgjdgd_ajs
3     google        cpc   1674814043       Brand <NA> KvgMsEAAYASAAEgLq6vD_BwE
4       <NA>       <NA>         <NA>        <NA> null         ertyyhglkdl-kjkY
        utm_term
1           <NA>
2 brand%20shirts
3 brand%20shirts
4           <NA>

---

更新

如果您想保留所有数据，即使它们是null，也可以尝试下面的代码

Reduce(
  function(x,y) {
    if (all(is.na(x)) | all(is.na(y))) {
      return(rbind(x,y))
    }
    dplyr::full_join(x,y)
  },function(x) {
      if (x == "null") {
        return(NA)
      }
      u <- unlist(strsplit(x,u))
    }
  )
)

给出

                      gclid utm_source utm_medium utm_campaign       utm_term
1                      <NA>       <NA>       <NA>         <NA>           <NA>
2          ertyyhglkdl-kjkY       <NA>       <NA>         <NA>           <NA>
3            jhajsgjdgd_ajs     google        cpc      1234556 brand%20shirts
4  KvgMsEAAYASAAEgLq6vD_BwE     google        cpc   1674814043 brand%20shirts
5                      <NA>       <NA>       <NA>         <NA>           <NA>
6                      <NA>       <NA>       <NA>         <NA>           <NA>
7                      <NA>       <NA>       <NA>         <NA>           <NA>
8                      <NA>       <NA>       <NA>         <NA>           <NA>
9                      <NA>       <NA>       <NA>         <NA>           <NA>
10                     <NA>       <NA>       <NA>         <NA>           <NA>
11                     <NA>       <NA>       <NA>         <NA>           <NA>
12                     <NA>       <NA>       <NA>         <NA>           <NA>
13                     <NA>       <NA>       <NA>         <NA>           <NA>
14                     <NA>       <NA>       <NA>         <NA>           <NA>
15                     <NA>         fb        ctw    Shirt_rem           <NA>
   utm_content
1         <NA>
2         <NA>
3        Brand
4        Brand
5         <NA>
6         <NA>
7         <NA>
8         <NA>
9         <NA>
10        <NA>
11        <NA>
12        <NA>
13        <NA>
14        <NA>
15 CasciaShirt