r-将多个列变成一行:按行加宽数据透视

Question

我一直在尝试获得包含25个单词的每一个组合的单行，但我需要一些帮助才能按正确的顺序获得这些单词。

到目前为止，我有一个这样的表，其中每个单词在Var1中连续重复25次，Var2将Var1与其他24个单词匹配。

Var1     Var2     id
laugh    area     1
laugh    game     2      
laugh    river    3
...
area     laugh    25
area     game     26
area     river    27
...
game     laugh    49
game     area     50
game     river    51
...

我需要将其转换为一行，值从Var1和Var2交替(因此按行组合(，如下所示：

V1     V2    V3     V4    V5     V6         V25   V26    V27   V28   V29   V30
laugh  area  laugh  game  laugh  river  ... area  laugh  area  game  area  river  ...

我尝试过使用：

words %>% pivot_wider(names_from = id, values_from = c(Var1, Var2)

然而，它按列而不是按行给我单词，即

Var2_1 Var2_2 Var2_3     Var2_25  Var2_26 Var2_27     Var1_1 Var1_2 Var1_3
laugh  laugh  laugh  ... area     area    area    ... area   game   river ...

我尝试将names_vary = "slowest"添加到pivot_wider()，但得到错误：

Error: 1 components of `...` were not used.
We detected these problematic arguments:
* `names_vary`
Did you misspecify an argument?
Backtrace:
9. tidyr::pivot_wider(...)
11. ellipsis:::action_dots(...)

Answer 1

library(tidyr)
library(dplyr)
billboard %>% # Replace with words
select(wk1:wk2) %>% # Replace with Var1:Var2
pivot_longer(everything()) %>% 
mutate(
id = paste0('V', 1:n())
) %>% 
select(-name) %>% 
pivot_wider(values_from = value, names_from = id)
# A tibble: 1 x 634
V1    V2    V3    V4    V5    V6    V7    V8    V9   V10   V11   V12   V13   V14   V15   V16   V17   V18   V19   V20   V21   V22   V23   V24   V25   V26
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1    87    82    91    87    81    70    76    76    57    34    51    39    97    97    84    62    59    53    76    76    84    84    57    47    50    39
# ... with 608 more variables: V27 <dbl>, V28 <dbl>, V29 <dbl>, V30 <dbl>, V31 <dbl>, V32 <dbl>, V33 <dbl>, V34 <dbl>, V35 <dbl>, V36 <dbl>, V37 <dbl>,
#   V38 <dbl>, V39 <dbl>, V40 <dbl>, V41 <dbl>, V42 <dbl>, V43 <dbl>, V44 <dbl>, V45 <dbl>, V46 <dbl>, V47 <dbl>, V48 <dbl>, V49 <dbl>, V50 <dbl>, V51 <dbl>,
#   V52 <dbl>, V53 <dbl>, V54 <dbl>, V55 <dbl>, V56 <dbl>, V57 <dbl>, V58 <dbl>, V59 <dbl>, V60 <dbl>, V61 <dbl>, V62 <dbl>, V63 <dbl>, V64 <dbl>, V65 <dbl>,
#   V66 <dbl>, V67 <dbl>, V68 <dbl>, V69 <dbl>, V70 <dbl>, V71 <dbl>, V72 <dbl>, V73 <dbl>, V74 <dbl>, V75 <dbl>, V76 <dbl>, V77 <dbl>, V78 <dbl>, V79 <dbl>,
#   V80 <dbl>, V81 <dbl>, V82 <dbl>, V83 <dbl>, V84 <dbl>, V85 <dbl>, V86 <dbl>, V87 <dbl>, V88 <dbl>, V89 <dbl>, V90 <dbl>, V91 <dbl>, V92 <dbl>, V93 <dbl>,
#   V94 <dbl>, V95 <dbl>, V96 <dbl>, V97 <dbl>, V98 <dbl>, V99 <dbl>, V100 <dbl>, V101 <dbl>, V102 <dbl>, V103 <dbl>, V104 <dbl>, V105 <dbl>, V106 <dbl>,
#   V107 <dbl>, V108 <dbl>, V109 <dbl>, V110 <dbl>, V111 <dbl>, V112 <dbl>, V113 <dbl>, V114 <dbl>, V115 <dbl>, V116 <dbl>, V117 <dbl>, V118 <dbl>, ...