比较几个表并创建一个新表，显示哪些变量使用 R 匹配

Question

我是 R 新手。为了练习，我试图创建一个表，在比较大约 50 个表后显示哪些变量匹配。如果列匹配，我希望在单元格中看到“是”。否则为“否”。我将不胜感激有关如何解决此问题的任何提示。

我的输入数据如下所示：

Tables	Variables
tabla_1	A
tabla_1	Z
tabla_1	Y
tabla_1	V
tabla_1	B
tabla_2	H
tabla_2	B
tabla_2	A
tabla_2	U
tabla_3	U
tabla_3	S
tabla_3	M
tabla_4	U
tabla_4	A
tabla_4	B
tabla_4	V
tabla_4	Q
tabla_4	O
tabla_4	F

我想得到这个：

Variables	tabla_1	tabla_2	tabla_3	tabla_4
A	Yes	Yes	No	Yes
Z	Yes	No	No	No
Y	Yes	No	No	No
V	Yes	No	No	Yes
B	No	Yes	No	Yes
H	No	Yes	No	No
U	No	Yes	Yes	Yes
S	No	Yes	Yes	No
M	No	No	Yes	No
Q	No	No	No	Yes
O	No	No	No	Yes
F	No	No	No	Yes

感谢您的帮助。

Answer 1

您可以使用table，它将返回 1/0 值而不是“是”/“否”。

table(rev(df))

#   Tables
#Variables tabla_1 tabla_2 tabla_3 tabla_4
#        A       1       1       0       1
#        B       1       1       0       1
#        F       0       0       0       1
#        H       0       1       0       0
#        M       0       0       1       0
#        O       0       0       0       1
#        Q       0       0       0       1
#        S       0       0       1       0
#        U       0       1       1       1
#        V       1       0       0       1
#        Y       1       0       0       0
#        Z       1       0       0       0

要获得“是”/“否”值，您可以这样做 -

tab <- table(rev(df))
tab <- ifelse(tab == 1, 'Yes', 'No')

Answer 2

我们可以创建一个“是”列并使用pivot_wider。然后，在values_fill 中，指定“否”值（默认为NA）

library(dplyr)
library(tidyr)
df1 %>%
    mutate(new = 'Yes') %>%
    pivot_wider(names_from = Tables, values_from = new, values_fill = 'No')

-输出

# A tibble: 12 x 5
   Variables tabla_1 tabla_2 tabla_3 tabla_4
   <chr>     <chr>   <chr>   <chr>   <chr>  
 1 A         Yes     Yes     No      Yes    
 2 Z         Yes     No      No      No     
 3 Y         Yes     No      No      No     
 4 V         Yes     No      No      Yes    
 5 B         Yes     Yes     No      Yes    
 6 H         No      Yes     No      No     
 7 U         No      Yes     Yes     Yes    
 8 S         No      No      Yes     No     
 9 M         No      No      Yes     No     
10 Q         No      No      No      Yes    
11 O         No      No      No      Yes    
12 F         No      No      No      Yes    

数据

df1 <- structure(list(Tables = c("tabla_1", "tabla_1", "tabla_1", "tabla_1", 
"tabla_1", "tabla_2", "tabla_2", "tabla_2", "tabla_2", "tabla_3", 
"tabla_3", "tabla_3", "tabla_4", "tabla_4", "tabla_4", "tabla_4", 
"tabla_4", "tabla_4", "tabla_4"), Variables = c("A", "Z", "Y", 
"V", "B", "H", "B", "A", "U", "U", "S", "M", "U", "A", "B", "V", 
"Q", "O", "F")), class = "data.frame", row.names = c(NA, -19L
))

Answer 3

distinct() 和 pivor_wider()

df %>%
  distinct(Variables, Tables) %>%
  mutate(n = "Yes") %>%
  pivot_wider(names_from = Tables, values_from = n, values_fill = list(n = "No"))

   Variables tabla_1 tabla_2 tabla_3 tabla_4
   <chr>       <dbl>   <dbl>   <dbl>   <dbl>
 1 A               1       1       0       1
 2 Z               1       0       0       0
 3 Y               1       0       0       0
 4 V               1       0       0       1
 5 B               1       1       0       1
 6 H               0       1       0       0
 7 U               0       1       1       1
 8 S               0       0       1       0
 9 M               0       0       1       0
10 Q               0       0       0       1
11 O               0       0       0       1
12 F               0       0       0       1