在R中数据清理期间，Qualtrics继承随机子集合并列？

Question

当我在Qualtrics中创建我的调查时，我使用了进位随机化，让来自度量的问题子集出现在第一页上，然后在下一页显示一个尚未看到的问题的随机子集，以此类推。最终的结果是长时间的测量被分解成5页，问题的顺序是完全随机的。效果很好！但是，当作为CSV输出时，数据会变得非常混乱。下面是一个可重复的例子，其中只有两个块导出的数据。

reprex.data1 <- matrix(c("124", "Completely", "Completely", NA, "973", NA, "Moderately", NA, "592", NA, NA, "Completely"), nrow=3, byrow=TRUE)
reprex.data2 <- matrix(c("124", NA, NA, NA, "973", "Moderately", NA, "Completely", "592", NA, "Completely", NA), nrow=3, byrow=TRUE)
reprex1.cols <- c("id", "scale1_1", "scale2_1", "scale3_1")
reprex2.cols <- c("id", "scale1_2", "scale2_2", "scale3_2")
colnames(reprex.data1) <- reprex1.cols
colnames(reprex.data2) <- reprex2.cols

> reprex.data1
     id    scale1_1     scale2_1     scale3_1    
[1,] "124" "Completely" "Completely" NA          
[2,] "973" NA           "Moderately" NA          
[3,] "592" NA           NA           "Completely"
> reprex.data2
     id    scale1_2     scale2_2     scale3_2    
[1,] "124" NA           NA           NA          
[2,] "973" "Moderately" NA           "Completely"
[3,] "592" NA           "Completely" NA   

我想把这些问题组合起来，这样五个街区的调查结果就会被分解成一个街区。这看起来应该是相对简单的，但是我无法用我习惯使用的不同的合并/连接函数来解决它。有小费吗？下面是我希望这些数据最终看起来是什么样子：

reprex.final <- matrix(c("124", "Completely", "Completely", NA, "973", "Moderately", "Moderately", "Completely", "592", NA, "Completely", "Completely"), nrow=3, byrow=TRUE)
reprex.cols <- c("id", "scale1", "scale2", "scale3")
colnames(reprex.final) <- reprex.cols

> reprex.final
     id    scale1       scale2       scale3      
[1,] "124" "Completely" "Completely" NA          
[2,] "973" "Moderately" "Moderately" "Completely"
[3,] "592" NA           "Completely" "Completely"

Answer 1

您可以像这样使用来自tidyr的tidyr实用程序/还可以使用dplyr/stringr，所以下面我已经加载了tidyverse

library(tidyverse)

bind_rows(
  pivot_longer(as.data.frame(reprex.data1), cols=-id),
  pivot_longer(setNames(as.data.frame(reprex.data2), colnames(reprex.data1)), cols=-id)
) %>% 
  filter(!is.na(value)) %>% 
  mutate(name = str_remove(name,"_1")) %>% 
  pivot_wider(id_cols = id, names_from = name, values_from=value)

输出：

  id    scale1     scale2     scale3    
  <chr> <chr>      <chr>      <chr>     
1 124   Completely Completely NA        
2 973   Moderately Moderately Completely
3 592   NA         Completely Completely