合并改良的Likert类别

Question

我目前正在使用Likert包，它要求我的Likert矩阵表采用不同的格式。为此，我使用了以下代码：

LikertQ9_1 <- SurveyClean2 |> 
  dplyr::select(Q9_1) |> 
  mutate(Question = "Grazing or Forage Production") |> 
  group_by(Question, Q9_1) |> 
  count() |> 
  ungroup() |> 
  pivot_wider(names_from = Q9_1, values_from = n)

然而，我也有Q9_2通过Q9_10，这也需要重新格式化。有什么办法可以散装地做这件事，还是在最后把它们结合起来？我尝试过合并并离开它们，但它破坏了格式。

原始格式如下所示，仅供参考：

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新格式如下所示，仅供参考。它只适用于Q9_1，我需要为2-10添加其他行：

​

​

Dataframe样本：

SurveyClean2 <- dplyr::tibble(
      Q9_1 = c("Not Interested", "Not Interested", "NA", "Slightly Interested", "NA"),
      Q9_2 = c("Extremely Interested", "Not Interested", "Somewhat Interested", "NA", "NA"),
      Q9_3 = c("Not Interested", "Extremely Interested", "Slightly Interested", "Somewhat Interested", "Not Interested"),
      Q9_4 = c("Not Interested", "Extremely Interested", "Slightly Interested", "Somewhat Interested", "Not Interested"), 
      Q9_5 = c("Slightly Interested", "Extremely Interested", "Slightly Interested", "Somewhat Interested", "Not Interested"),
      Q9_6 = c("Not Interested", "Extremely Interested", "Slightly Interested", "Somewhat Interested", "NA"),
      Q9_7 = c("Not Interested", "Extremely Interested", "Slightly Interested", "Extremely Interested", "NA"),
      Q9_8 = c("Not Interested", "Extremely Interested", "Somewhat Interested", "Somewhat Interested", "NA"),
      Q9_9 = c("NA", "Extremely Interested", "Slightly Interested", "Somewhat Interested", "NA"),
      Q9_10 = c("Not Interested", "Slightly Interested", "Slightly Interested", "Somewhat Interested", "NA"))

Answer 1

这里有一个使用tidyverse的解决方案。我对您的数据做了一些可能不正确的假设--如果您能够提供一些样本数据来显示您的调查数据是如何构造的，这将是有帮助的：

library(dplyr)
library(tidyr)
library(purrr)

# your survey data
SurveyClean2 <- dplyr::tibble(
  Q9_1 = c("Agree", "Disagree", "Neutral", "Agree"),
  Q9_2 = rep("Agree", 4),
  Q9_3 = c("Disagree", "Disagree", "Neutral", "Agree")
)

# function to clean data
clean_survey <- function(data, column, question) {
  data %>% 
    dplyr::select(all_of({{column}})) %>% 
    dplyr::mutate(Question = question) %>% 
    dplyr::group_by(Question, across(1)) %>% 
    dplyr::count() %>% 
    dplyr::ungroup() %>% 
    tidyr::pivot_wider(names_from = 2, values_from = n)
}

# table that contains survey questions/columns and the question name
survey_table <- dplyr::tibble(
  column = c("Q9_1", "Q9_2", "Q9_3"),
  question = c("Grazing or Forage Production", "Coffee or Tea", "Something else")
)


# loop through your data and clean it, then bind as dataframe
purrr::map2_df(survey_table$column, survey_table$question, function(x, y){
  clean_survey(SurveyClean2, x, y)
})
#> # A tibble: 3 × 4
#>   Question                     Agree Disagree Neutral
#>   <chr>                        <int>    <int>   <int>
#> 1 Grazing or Forage Production     2        1       1
#> 2 Coffee or Tea                    4       NA      NA
#> 3 Something else                   1        2       1

如果您想用0替换NA：

purrr::map2_df(survey_table$column, survey_table$question, function(x, y){
  clean_survey(SurveyClean2, x, y)
}) %>% 
  mutate(across(everything(), ~ifelse(is.na(.), 0, .)))