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我有这个数据集

study_ID title                  experiment question_ID participant_ID estimate_level estimate correct_answer question                      type   category   age gender
      <dbl> <chr>                       <dbl> <chr>                <int> <chr>             <dbl>          <dbl> <chr>                         <chr>  <chr>    <int> <chr> 
 1       11 Dallacker_Parents'_co…          1 1                        1 individual          3             10   How many sugar cubes does or… unlim… nutriti…    32 Female
 2       11 Dallacker_Parents'_co…          1 2                        1 individual         10             11.5 How many sugar cubes does a … unlim… nutriti…    32 Female
 3       11 Dallacker_Parents'_co…          1 3                        1 individual          7              6.5 How many sugar cubes does a … unlim… nutriti…    32 Female
 4       11 Dallacker_Parents'_co…          1 4                        1 individual          1             16.5 How many sugar cubes does a … unlim… nutriti…    32 Female
 5       11 Dallacker_Parents'_co…          1 5                        1 individual          7             11   How many sugar cubes does a … unlim… nutriti…    32 Female
 6       11 Dallacker_Parents'_co…          1 6                        1 individual          5              2.5 How many sugar cubes does a … unlim… nutriti…    32 Female
 7       11 Dallacker_Parents'_co…          1 1                        2 individual          2             10   How many sugar cubes does or… unlim… nutriti…    29 Female
 8       11 Dallacker_Parents'_co…          1 2                        2 individual         10             11.5 How many sugar cubes does a … unlim… nutriti…    29 Female
 9       11 Dallacker_Parents'_co…          1 3                        2 individual          1.5            6.5 How many sugar cubes does a … unlim… nutriti…    29 Female
10       11 Dallacker_Parents'_co…          1 4                        2 individual          2             16.5 How many sugar cubes does a … unlim… nutriti…    29 Female

这个数据集中有6道题，每道题都有一correct_answer栏，一estimate栏。我正在尝试计算每个问题的量级，以便获得低估或高估以及正确估计的人的百分比。

例如，对于 6 个问题中的每一个，它都会返回如下内容：80% 被低估，10% 被高估，10% 回答正确。

我怎样才能做到这一点？我难住了。提前致谢！

这是输入

dput(head(DF, 10))
structure(list(study_ID = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5), title = c("5_Jayles_Debiasing_The_Crowd", 
"5_Jayles_Debiasing_The_Crowd", "5_Jayles_Debiasing_The_Crowd", 
"5_Jayles_Debiasing_The_Crowd", "5_Jayles_Debiasing_The_Crowd", 
"5_Jayles_Debiasing_The_Crowd", "5_Jayles_Debiasing_The_Crowd", 
"5_Jayles_Debiasing_The_Crowd", "5_Jayles_Debiasing_The_Crowd", 
"5_Jayles_Debiasing_The_Crowd"), experiment = c(1, 1, 1, 1, 1, 
1, 1, 1, 1, 1), question_ID = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 
    participant_ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), estimate_level = c("individual", 
    "individual", "individual", "individual", "individual", "individual", 
    "individual", "individual", "individual", "individual"), 
    estimate = c(2e+07, 4500000, 21075541, 2e+07, 1e+06, 1.1e+07, 
    2.5e+07, 8e+06, 1.6e+07, 9800000), correct = c(3.8e+07, 3.8e+07, 
    3.8e+07, 3.8e+07, 3.8e+07, 3.8e+07, 3.8e+07, 3.8e+07, 3.8e+07, 
    3.8e+07), question = c("What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?", 
    "What is the population of Tokyo and its agglomeration?"), 
    type = c("unlimited", "unlimited", "unlimited", "unlimited", 
    "unlimited", "unlimited", "unlimited", "unlimited", "unlimited", 
    "unlimited"), category = c("demographics", "demographics", 
    "demographics", "demographics", "demographics", "demographics", 
    "demographics", "demographics", "demographics", "demographics"
    ), age = c("NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", 
    "NA", "NA"), gender = c("NA", "NA", "NA", "NA", "NA", "NA", 
    "NA", "NA", "NA", "NA")), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))
4

2 回答 2

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这是一种dplyr方法：

library(dplyr)
df %>%
  group_by(question_ID) %>%
  summarize(prop_over = mean(estimate > correct),
            prop_under = mean(estimate < correct),
            prop_correct = mean(estimate == correct)
  )
# `summarise()` ungrouping output (override with `.groups` argument)
# # A tibble: 1 x 4
#   question_ID prop_over prop_under prop_correct
#         <dbl>     <dbl>      <dbl>        <dbl>
# 1           1         0          1            0
于 2020-10-01T14:30:31.307 回答
0 
list1 <- lapply(split(DF, DF$question_ID), function (x) {
  overestimated <- 100 * length(which(x$estimate > x$correct)) / length(x$estimate)
  underestimated <- 100 * length(which(x$estimate < x$correct)) / length(x$estimate)
  correct <- 100 * length(which(x$estimate == x$correct)) / length(x$estimate)
  data.frame(overestimated, underestimated, correct)
})
list2 <- mapply(function (x, y) {
  x$question_ID <- y
  return (x)
}, x = list1, y = names(list1), SIMPLIFY = F)
Percent_Data <- do.call("rbind", list2)
Percent_Data <- Percent_Data[, c(which(colnames(Percent_Data) == "question_ID"), which(colnames(Percent_Data) != "question_ID"))]
Percent_Data
#   question_ID overestimated underestimated correct
# 1           1             0            100       0
于 2020-10-01T14:38:34.433 回答