根据部分列名组合列答案

【问题标题】：Combining columns based on partial column name根据部分列名组合列
【发布时间】：2022-01-14 19:31:24
【问题描述】：

我希望有一种更简单/更快/更清洁的方式来做我想做的事，因为现在这非常复杂：

我的列名是：

"OTS_SM0_1","OTS_SM0_2","OTS_SM0_3","OTS_SM0_4","OTS_SM0_5","OTS_SM0_6",
"OTS_SM0_7","OTS_SM0_8","OTS_SM0_9",
"OTS_SM1_x1_4","OTS_SM1_x1_6","OTS_SM1_x1_7","OTS_SM1_x1_8",
"OTS_SM1_x2_4","OTS_SM1_x2_6","OTS_SM1_x2_7","OTS_SM1_x2_8",
"OTS_SM1_x3_4","OTS_SM1_x3_6","OTS_SM1_x3_7","OTS_SM1_x3_8",
"OTS_SM1_x4_4","OTS_SM1_x4_6","OTS_SM1_x4_7","OTS_SM1_x4_8",
"OTS_SM1_x5_4","OTS_SM1_x5_6","OTS_SM1_x5_7","OTS_SM1_x5_8",
"OTS_SM1_x6_4","OTS_SM1_x6_6","OTS_SM1_x6_7","OTS_SM1_x6_8",
"OTS_SM1_x7_4","OTS_SM1_x7_6","OTS_SM1_x7_7","OTS_SM1_x7_8",
"OTS_SM1_x8_4","OTS_SM1_x8_6","OTS_SM1_x8_7","OTS_SM1_x8_8",
"OTS_SM1_x9_4","OTS_SM1_x9_6","OTS_SM1_x9_7","OTS_SM1_x9_8",
"OTS_SM2_x1","OTS_SM2_x2","OTS_SM2_x3","OTS_SM2_x4","OTS_SM2_x5",
"OTS_SM2_x6","OTS_SM2_x7","OTS_SM2_x8","OTS_SM2_x9"

我需要根据名称将它们的条目连接到一列中。这些是要组合到的所需名称：

OTS_SM0 OTS_SM1_x1  OTS_SM1_x2  OTS_SM1_x3  OTS_SM1_x4  OTS_SM1_x5  OTS_SM1_x6  
OTS_SM1_x7  OTS_SM1_x8  OTS_SM1_x9  OTS_SM2_x1  OTS_SM2_x2  OTS_SM2_x3  OTS_SM2_x4  
OTS_SM2_x5  OTS_SM2_x6  OTS_SM2_x7  OTS_SM2_x8  OTS_SM2_x9

但问题是这些名称并不总是相同，只有 OTS_SM 部分保持不变，并且要组合的列数以及它们在数据框中的索引会发生变化。

我目前的解决方案是：

columnnames <- c("OTS_SM0_1","OTS_SM0_2","OTS_SM0_3","OTS_SM0_4","OTS_SM0_5","OTS_SM0_6","OTS_SM0_7","OTS_SM0_8","OTS_SM0_9",
"OTS_SM1_x1_4","OTS_SM1_x1_6","OTS_SM1_x1_7","OTS_SM1_x1_8","OTS_SM1_x2_4","OTS_SM1_x2_6","OTS_SM1_x2_7","OTS_SM1_x2_8","OTS_SM1_x3_4",
"OTS_SM1_x3_6","OTS_SM1_x3_7","OTS_SM1_x3_8","OTS_SM1_x4_4","OTS_SM1_x4_6","OTS_SM1_x4_7","OTS_SM1_x4_8","OTS_SM1_x5_4","OTS_SM1_x5_6",
"OTS_SM1_x5_7","OTS_SM1_x5_8","OTS_SM1_x6_4","OTS_SM1_x6_6","OTS_SM1_x6_7","OTS_SM1_x6_8","OTS_SM1_x7_4","OTS_SM1_x7_6","OTS_SM1_x7_7",
"OTS_SM1_x7_8","OTS_SM1_x8_4","OTS_SM1_x8_6","OTS_SM1_x8_7","OTS_SM1_x8_8","OTS_SM1_x9_4","OTS_SM1_x9_6","OTS_SM1_x9_7","OTS_SM1_x9_8",
"OTS_SM2_x1","OTS_SM2_x2","OTS_SM2_x3","OTS_SM2_x4","OTS_SM2_x5","OTS_SM2_x6","OTS_SM2_x7","OTS_SM2_x8","OTS_SM2_x9")

names1_index = grep('^(?!.*x).*OTS_SM', columnnames, perl=TRUE)
names1 = columnnames[names1_index]
names1 = substring(names1, 1, 7)
names2_index = grep("OTS_SM.*_x", columnnames)
names2 = columnnames[names2_index]
names2 = substring(names2, 1, 10)

给出这样的输出：

> names1
"OTS_SM0" "OTS_SM0" "OTS_SM0" "OTS_SM0" "OTS_SM0" 
"OTS_SM0" "OTS_SM0" "OTS_SM0" "OTS_SM0"
> names2
"OTS_SM1_x1" "OTS_SM1_x1" "OTS_SM1_x1" "OTS_SM1_x1" "OTS_SM1_x2" 
"OTS_SM1_x2" "OTS_SM1_x2" "OTS_SM1_x2" "OTS_SM1_x3" "OTS_SM1_x3" 
"OTS_SM1_x3" "OTS_SM1_x3" "OTS_SM1_x4" "OTS_SM1_x4" "OTS_SM1_x4" 
"OTS_SM1_x4" "OTS_SM1_x5" "OTS_SM1_x5" "OTS_SM1_x5" "OTS_SM1_x5" 
"OTS_SM1_x6" "OTS_SM1_x6" "OTS_SM1_x6" "OTS_SM1_x6" "OTS_SM1_x7" 
"OTS_SM1_x7" "OTS_SM1_x7" "OTS_SM1_x7" "OTS_SM1_x8" "OTS_SM1_x8"
"OTS_SM1_x8" "OTS_SM1_x8" "OTS_SM1_x9" "OTS_SM1_x9" "OTS_SM1_x9"
"OTS_SM1_x9" "OTS_SM2_x1" "OTS_SM2_x2" "OTS_SM2_x3" "OTS_SM2_x4" 
"OTS_SM2_x5" "OTS_SM2_x6" "OTS_SM2_x7" "OTS_SM2_x8" "OTS_SM2_x9"

例如对于数据帧 DF 中的 name1 变量：

   OTS_SM0_1 OTS_SM0_2 OTS_SM0_3 OTS_SM0_4 OTS_SM0_5 OTS_SM0_6 OTS_SM0_7 OTS_SM0_8 OTS_SM0_9        
   <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>     <chr>            
 1 0         0         0         0         0         0         0         0         None of the above
 2 Facebook  0         0         0         0         0         0         0         0                
 3 0         0         0         0         0         0         0         0         None of the above
 4 Facebook  Instagram Twitter   Snapchat  Pinterest 0         Tik Tok   0         0                
 5 0         0         0         0         0         LinkedIn  0         0         0                
 6 Facebook  0         0         0         Pinterest 0         0         0         0                
 7 Facebook  Instagram 0         0         0         0         0         0         0                
 8 Facebook  Instagram 0         0         Pinterest 0         Tik Tok   0         0                
 9 NA        NA        NA        NA        NA        NA        NA        NA        NA               
10 NA        NA        NA        NA        NA        NA        NA        NA        NA

然后我会合并同名列索引：

unique_names1 <- unique(names1)
for (i in 1:length(unique_names1)){
  combine1= DF[,grep(unique_names1[i],colnames(DF))]
  NewCol1 <- do.call(paste, c(combine1[], sep = ";"))
  NewCol1 <- str_remove_all(NewCol1,";NA")
  NewCol1 <- str_remove_all(NewCol1,"NA;")
  NewCol1 <- str_remove_all(NewCol1,";0")
  NewCol1 <- str_remove_all(NewCol1,"0;")
  DF <- cbind(DF,NewCol1)
}

NewCol1
   [1] "None of the above"        "Facebook"                                                             
   [3] "None of the above"        "Facebook;Instagram;Twitter;Snapchat;Pinterest;Tik Tok"
   [5] "LinkedIn"                 "Facebook;Pinterest"                                         
   [7] "Facebook;Instagram"       "Facebook;Instagram;Pinterest;Tik Tok"                        
   [9] "NA"                       "NA"

然后使用一些更有趣的索引显然将其重命名为“OTS_SM0”。以及删除原始列。

【问题讨论】：

如果您与dput 共享数据或将您的数据包装在head 中，然后在dput 中使用，则更容易帮助您。一般来说，我认为您可以使用dplyr::pivot_long -> dplyr::separate -> dplyr::pivot_wider 轻松解决这个问题

标签： r concatenation

【解决方案1】：

假设您的数据框 DF 看起来像这样

  OTS_SM0_1 OTS_SM0_2 OTS_SM0_3 OTS_SM0_4 OTS_SM0_5 OTS_SM0_6 OTS_SM0_7 OTS_SM0_8 OTS_SM0_9 OTS_SM1_x1_4 OTS_SM1_x1_6 OTS_SM1_x1_7 OTS_SM1_x1_8 OTS_SM1_x2_4 OTS_SM1_x2_6 OTS_SM1_x2_7 OTS_SM1_x2_8 OTS_SM1_x3_4 OTS_SM1_x3_6 OTS_SM1_x3_7 OTS_SM1_x3_8 OTS_SM1_x4_4 OTS_SM1_x4_6 OTS_SM1_x4_7 OTS_SM1_x4_8 OTS_SM1_x5_4 OTS_SM1_x5_6 OTS_SM1_x5_7 OTS_SM1_x5_8 OTS_SM1_x6_4 OTS_SM1_x6_6 OTS_SM1_x6_7 OTS_SM1_x6_8 OTS_SM1_x7_4 OTS_SM1_x7_6 OTS_SM1_x7_7 OTS_SM1_x7_8 OTS_SM1_x8_4 OTS_SM1_x8_6 OTS_SM1_x8_7 OTS_SM1_x8_8 OTS_SM1_x9_4 OTS_SM1_x9_6 OTS_SM1_x9_7 OTS_SM1_x9_8 OTS_SM2_x1 OTS_SM2_x2 OTS_SM2_x3 OTS_SM2_x4 OTS_SM2_x5 OTS_SM2_x6 OTS_SM2_x7 OTS_SM2_x8 OTS_SM2_x9
1         1         2         3         4         5         6         7         8         9           10           11           12           13           14           15           16           17           18           19           20           21           22           23           24           25           26           27           28           29           30           31           32           33           34           35           36           37           38           39           40           41           42           43           44           45         46         47         48         49         50         51         52         53         54
2         2         4         6         8        10        12        14        16        18           20           22           24           26           28           30           32           34           36           38           40           42           44           46           48           50           52           54           56           58           60           62           64           66           68           70           72           74           76           78           80           82           84           86           88           90         92         94         96         98        100        102        104        106        108

这是dplyr 方法。我们使用id 来保留行关系。在第一个主元之后，我们删除最后一个 "_" 后面的字符，前提是它们是数字。最后，我们将每组 id 和变量（在本例中为name）的行汇总到一个单元格中，并将数据帧从长转换为宽。

library(dplyr)
library(tidyr)

DF %>%
  mutate(id = row_number()) %>%
  pivot_longer(-id) %>%
  group_by(id, name = sub("(_\\d+)?$", "", name)) %>%
  summarize(value = paste0(value, collapse = ";"), .groups = "drop") %>%
  pivot_wider() %>%
  select(-id)

输出（我使用不同的打印方法向您显示所有列。默认的打印方法可能会在您的屏幕上呈现不同的内容，但底层对象是相同的。）

                 OTS_SM0  OTS_SM1_x1  OTS_SM1_x2  OTS_SM1_x3  OTS_SM1_x4  OTS_SM1_x5  OTS_SM1_x6  OTS_SM1_x7  OTS_SM1_x8  OTS_SM1_x9 OTS_SM2_x1 OTS_SM2_x2 OTS_SM2_x3 OTS_SM2_x4 OTS_SM2_x5 OTS_SM2_x6 OTS_SM2_x7 OTS_SM2_x8 OTS_SM2_x9
1      1;2;3;4;5;6;7;8;9 10;11;12;13 14;15;16;17 18;19;20;21 22;23;24;25 26;27;28;29 30;31;32;33 34;35;36;37 38;39;40;41 42;43;44;45         46         47         48         49         50         51         52         53         54
2 2;4;6;8;10;12;14;16;18 20;22;24;26 28;30;32;34 36;38;40;42 44;46;48;50 52;54;56;58 60;62;64;66 68;70;72;74 76;78;80;82 84;86;88;90         92         94         96         98        100        102        104        106        108

但是，如果您想对变量进行一些分析，将值嵌套在单个单元格中通常没有帮助。或许您还想考虑稍微不同的数据表示。

library(dplyr)
library(tidyr)

DF %>%
  rename_with(~sub("(_\\d+)?$", "`\\1", .)) %>%
  mutate(id = row_number()) %>%
  pivot_longer(-id, names_to = c(".value", NA), names_pattern = "(.+)`(_\\d+)?")

，它给出了

   id OTS_SM0 OTS_SM1_x1 OTS_SM1_x2 OTS_SM1_x3 OTS_SM1_x4 OTS_SM1_x5 OTS_SM1_x6 OTS_SM1_x7 OTS_SM1_x8 OTS_SM1_x9 OTS_SM2_x1 OTS_SM2_x2 OTS_SM2_x3 OTS_SM2_x4 OTS_SM2_x5 OTS_SM2_x6 OTS_SM2_x7 OTS_SM2_x8 OTS_SM2_x9
1   1       1         10         14         18         22         26         30         34         38         42         46         47         48         49         50         51         52         53         54
2   1       2         11         15         19         23         27         31         35         39         43         NA         NA         NA         NA         NA         NA         NA         NA         NA
3   1       3         12         16         20         24         28         32         36         40         44         NA         NA         NA         NA         NA         NA         NA         NA         NA
4   1       4         13         17         21         25         29         33         37         41         45         NA         NA         NA         NA         NA         NA         NA         NA         NA
5   1       5         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
6   1       6         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
7   1       7         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
8   1       8         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
9   1       9         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
10  2       2         20         28         36         44         52         60         68         76         84         92         94         96         98        100        102        104        106        108
11  2       4         22         30         38         46         54         62         70         78         86         NA         NA         NA         NA         NA         NA         NA         NA         NA
12  2       6         24         32         40         48         56         64         72         80         88         NA         NA         NA         NA         NA         NA         NA         NA         NA
13  2       8         26         34         42         50         58         66         74         82         90         NA         NA         NA         NA         NA         NA         NA         NA         NA
14  2      10         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
15  2      12         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
16  2      14         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
17  2      16         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA
18  2      18         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA         NA

数据

structure(list(OTS_SM0_1 = c(1, 2), OTS_SM0_2 = c(2, 4), OTS_SM0_3 = c(3, 
6), OTS_SM0_4 = c(4, 8), OTS_SM0_5 = c(5, 10), OTS_SM0_6 = c(6, 
12), OTS_SM0_7 = c(7, 14), OTS_SM0_8 = c(8, 16), OTS_SM0_9 = c(9, 
18), OTS_SM1_x1_4 = c(10, 20), OTS_SM1_x1_6 = c(11, 22), OTS_SM1_x1_7 = c(12, 
24), OTS_SM1_x1_8 = c(13, 26), OTS_SM1_x2_4 = c(14, 28), OTS_SM1_x2_6 = c(15, 
30), OTS_SM1_x2_7 = c(16, 32), OTS_SM1_x2_8 = c(17, 34), OTS_SM1_x3_4 = c(18, 
36), OTS_SM1_x3_6 = c(19, 38), OTS_SM1_x3_7 = c(20, 40), OTS_SM1_x3_8 = c(21, 
42), OTS_SM1_x4_4 = c(22, 44), OTS_SM1_x4_6 = c(23, 46), OTS_SM1_x4_7 = c(24, 
48), OTS_SM1_x4_8 = c(25, 50), OTS_SM1_x5_4 = c(26, 52), OTS_SM1_x5_6 = c(27, 
54), OTS_SM1_x5_7 = c(28, 56), OTS_SM1_x5_8 = c(29, 58), OTS_SM1_x6_4 = c(30, 
60), OTS_SM1_x6_6 = c(31, 62), OTS_SM1_x6_7 = c(32, 64), OTS_SM1_x6_8 = c(33, 
66), OTS_SM1_x7_4 = c(34, 68), OTS_SM1_x7_6 = c(35, 70), OTS_SM1_x7_7 = c(36, 
72), OTS_SM1_x7_8 = c(37, 74), OTS_SM1_x8_4 = c(38, 76), OTS_SM1_x8_6 = c(39, 
78), OTS_SM1_x8_7 = c(40, 80), OTS_SM1_x8_8 = c(41, 82), OTS_SM1_x9_4 = c(42, 
84), OTS_SM1_x9_6 = c(43, 86), OTS_SM1_x9_7 = c(44, 88), OTS_SM1_x9_8 = c(45, 
90), OTS_SM2_x1 = c(46, 92), OTS_SM2_x2 = c(47, 94), OTS_SM2_x3 = c(48, 
96), OTS_SM2_x4 = c(49, 98), OTS_SM2_x5 = c(50, 100), OTS_SM2_x6 = c(51, 
102), OTS_SM2_x7 = c(52, 104), OTS_SM2_x8 = c(53, 106), OTS_SM2_x9 = c(54, 
108)), row.names = c(NA, -2L), class = "data.frame")

【讨论】：

【解决方案2】：

iris 数据集很好地近似您的问题：

head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

在 tidyverse 中我们可以很容易地做到这一点。首先，我们转换为长格式，以便名称易于使用。然后，我们将名称截断为要用于组合列的核心。然后我们用新的、数量更有限的列将它重新展开为宽格式。

还需要另一个步骤：pivot_wider 确保每一行都是唯一的，因此新的“Sepal”和“Petal”列各自包含每个物种的值列表。由于您每人一行（您只是在连接），我们使用unnest 来获取这些列表列并将它们转换为多行：

library(tidyverse)
iris %>%
    pivot_longer(-Species) %>%
    mutate(name = gsub('\\..*', '', name)) %>%
    pivot_wider(names_from = 'name', values_from = 'value', values_fn = list) %>%
    unnest(cols = c('Sepal', 'Petal'))

# A tibble: 300 × 3
   Species Sepal Petal
   <fct>   <dbl> <dbl>
 1 setosa    5.1   1.4
 2 setosa    3.5   0.2
 3 setosa    4.9   1.4
 4 setosa    3     0.2
 5 setosa    4.7   1.3
 6 setosa    3.2   0.2
 7 setosa    4.6   1.5
 8 setosa    3.1   0.2
 9 setosa    5     1.4
10 setosa    3.6   0.2
# … with 290 more rows

如果您真的不想在unnest 中指定列名，您可以使用unnest(cols = colnames(.)) 将其应用于所有列名，或者直接省略cols 参数（尽管这会发出警告和将来可能会中断），

【讨论】：

我认为这可以工作，但截断名称的问题是 OTS_SM0_1 和 OTS_SM0_2 需要组合，但 OTS_SM1_x1 OTS_SM1_x2 不是。所以截断为 OTS_SM# 会合并错误的列。
@megmac 你只需要找到正确的方法来截断它们。似乎您只想砍掉最后一个下划线数字。所以正则表达式：_[0-9]$ 会这样做