合并多个包含空NA值的列

Question

我有一个创建多列的数据集，但是数据是相同的（boxID）。 我想统一这些列，以便仅拥有boxID（字母数字代码：两个字母的状态缩写和2个数字）而不是NA值，这就是现在当我从以下位置使用unite（）函数时发生的情况dplyr。 有类似的功能可以做到这一点，还是我需要基于与stringr的模式匹配来提取boxID？

dat <- structure(list(boxId = c("CA04", "CA04", "CA01", "CA02", "CA04", 
"CA02", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), boxId__1 = c(NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "NM01", "NM14", "NM15", 
"NM16", "NM17", "NM18", "NM19", "NM20", "NM02", "NM03", "NM04", 
"NM05", "NM06", "NM07", "NM08", "NM09", "NM10", "NM11", "NM12", 
"NM13"), boxId__2 = c(NA, NA, NA, NA, NA, NA, "FL01", "FL02", 
"FL03", "FL09", "FL08", "FL07", "FL04", "FL05", "FL06", "FL10", 
"FL11", "FL13", "FL12", "FL20", "FL19", "FL18", "FL17", "FL16", 
"FL14", "FL15", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), boxID = c(NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), boxID__1 = c(NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    boxID__2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA), boxID__3 = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, "IN05", NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
    ), boxID__4 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA), boxID__5 = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    boxID__6 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA), boxID__7 = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    boxID__8 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA), boxID__9 = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, "WA11", NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
    )), row.names = c(NA, -48L), class = c("tbl_df", "tbl", "data.frame"
))

数据如下所示：

# A tibble: 48 x 13
   boxId boxId__1 boxId__2 boxID boxID__1 boxID__2 boxID__3 boxID__4 boxID__5 boxID__6
   <chr> <chr>    <chr>    <lgl> <lgl>    <lgl>    <chr>    <lgl>    <lgl>    <lgl>   
 1 CA04  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 2 CA04  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 3 CA01  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 4 CA02  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 5 CA04  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 6 CA02  NA       NA       NA    NA       NA       NA       NA       NA       NA      
 7 NA    NA       FL01     NA    NA       NA       NA       NA       NA       NA      
 8 NA    NA       FL02     NA    NA       NA       NA       NA       NA       NA      
 9 NA    NA       FL03     NA    NA       NA       NA       NA       NA       NA      
10 NA    NA       FL09     NA    NA       NA       NA       NA       NA       NA      
# … with 38 more rows, and 3 more variables: boxID__7 <lgl>, boxID__8 <lgl>, boxID__9 <chr>

当我使用unite（）时，它看起来像这样：

dat %>%
  unite('newID')

我坚持使用这些NA值：

# A tibble: 48 x 1
   newID                                   
   <chr>                                   
 1 CA04_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 2 CA04_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 3 CA01_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 4 CA02_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 5 CA04_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 6 CA02_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 7 NA_NA_FL01_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA
 8 NA_NA_FL02_NA_NA_NA_NA_NA_NA_NA_NA_NA_NA

Answer 1

您知道每行只有一个非NA吗？ 如果是，那么您可以透视表并删除所有NA。 您将获得一张表，该表的行数与原始表相同。

library("tidyverse")

dat %>%
  # Adding row ID for clarity
  mutate(row = row_number()) %>%
  gather(box, name, - row) %>%
  drop_na()
#> # A tibble: 48 x 3
#>      row box      name 
#>    <int> <chr>    <chr>
#>  1     1 boxId    CA04 
#>  2     2 boxId    CA04 
#>  3     3 boxId    CA01 
#>  4     4 boxId    CA02 
#>  5     5 boxId    CA04 
#>  6     6 boxId    CA02 
#>  7    29 boxId__1 NM01 
#>  8    30 boxId__1 NM14 
#>  9    31 boxId__1 NM15 
#> 10    32 boxId__1 NM16 
#> # ... with 38 more rows

^{由reprex软件包 （v0.2.1）创建于2019-03-11}

Answer 2

一种基本的R方法是从数据unlist所有值，并仅选择非NA值以创建一列的新数据框。

x <- unlist(dat)
data.frame(new_id = x[!is.na(x)])

#           new_id
#boxId1       CA04
#boxId2       CA04
#boxId3       CA01
#boxId4       CA02
#boxId5       CA04
#boxId6       CA02
#boxId__129   NM01
#boxId__130   NM14
#boxId__131   NM15
#......

Answer 3

随着coalesce ：

dat %>% 
  mutate_all(as.character) %>% 
  transmute(newID = coalesce(!!! syms(names(.))))

# # A tibble: 48 x 1
#    newID
#    <chr>
#  1 CA04 
#  2 CA04 
#  3 CA01 
#  4 CA02 
#  5 CA04 
#  6 CA02 
#  7 FL01 
#  8 FL02 
#  9 FL03 
# 10 FL09 
# # … with 38 more rows