在R中编写相关代码。我有一个 Dataframe （称为realData），具有10个名为“RealAttribute_X”的变量，其中X是1到10之间的数字（包括）。每列包含以下十个属性之一：“智慧”、“吸引力”、“魅力”、“野心”、“懒惰”、“慷慨”、“快乐”、“友好”、“傲慢”“冷静”。属性被随机分配给10个“RealAttribute_X”中的1个。

dt<-structure(list(session_id = c("17472631", "17472632", "17472633", 
"17472635", "17472636", "17472638"), RealAttribute_1 = c("Moderately ugly", 
"Very dull", "Very distant", "Very joyful", "Moderately joyful", 
"Very distant"), RealAttribute_2 = c("Very nervous", "Very gloomy", 
"Very generous", "Moderately charismatic", "Moderately hard working", 
"Moderately modest"), RealAttribute_3 = c("Slightly generous", 
"Moderately ugly", "Moderately arrogant", "Moderately calm", 
"Moderately charismatic", "Moderately charismatic"), RealAttribute_4 = c("Moderately arrogant", 
"Slightly generous", "Very dull", "Moderately distant", "Slightly distant", 
"Slightly ambitious"), RealAttribute_5 = c("Slightly unambitious", 
"Moderately calm", "Moderately unambitious", "Moderately lazy", 
"Very modest", "Moderately intelligent"), RealAttribute_6 = c("Slightly dull", 
"Slightly ambitious", "Very calm", "Moderately ambitious", "Moderately generous", 
"Moderately generous"), RealAttribute_7 = c("Very intelligent", 
"Slightly distant", "Very intelligent", "Slightly ugly", "Very good-looking", 
"Very lazy"), RealAttribute_8 = c("Very joyful", "Slightly modest", 
"Slightly joyful", "Very arrogant", "Very ambitious", "Slightly good-looking"
), RealAttribute_9 = c("Very distant", "Very lazy", "Slightly good-looking", 
"Very generous", "Moderately intelligent", "Moderately gloomy"
), RealAttribute_10 = c("Slightly lazy", "Moderately intelligent", 
"Slightly hard working", "Moderately intelligent", "Very calm", 
"Moderately nervous")), class = "data.frame", row.names = c(NA, 
-6L))

head(dt)

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每个属性都可以有六个值之一。属性及其可选值为：智力=“非常不聪明”、“中等不聪明”、“稍微不聪明”、“稍微聪明”、“中等聪明”、“非常聪明”吸引力=“非常丑”、“中等丑”、“稍微丑”、“稍微好看”、“中等好看”，“非常好看”懒惰=“非常懒惰”、“中等懒惰”、“稍微懒惰”、“稍微努力工作”、“中等努力工作”、“非常努力工作”友好=“非常疏远”、“中等疏远”、“稍微疏远”、“稍微友好”、“中等友好”、“非常友好”魅力=“非常迟钝”，“适度迟钝”、“稍微迟钝”、“稍微有魅力”、“稍微有魅力”、“非常有魅力”冷静=“非常紧张”、“适度紧张”、“稍微紧张”、“稍微冷静”、“适度冷静”、“非常冷静”慷慨=“非常吝啬”、“适度吝啬”、“稍微吝啬”、“稍微慷慨”、“适度慷慨”，“非常慷慨”的快乐=“非常沮丧”，“适度沮丧”，“稍微沮丧”，“稍微快乐”，“适度快乐”，“非常快乐”的傲慢=“非常傲慢”，“适度傲慢”，“稍微傲慢”，“稍微谦虚”，“适度谦虚”，“非常谦虚”的雄心=“非常没有雄心”，“适度没有雄心”，“稍微没有雄心”，“稍微雄心勃勃”、“中等雄心勃勃”、“非常雄心勃勃”
我尝试用R编写一个代码，它将创建10个新列，每个属性一个列（“智慧”、“吸引力”、“魅力”、“野心”、“懒惰”、“慷慨”、“快乐”、“友好”、“傲慢”、“冷静”），在包含相关属性的现有 Dataframe 中查找正确的变量，并将该属性的值分配给正确的新变量变量
例如，这是数据框的前四行：RealAttribute_1：“适度丑陋”、“非常沉闷”、“非常疏远”、“非常快乐”<br RealAttribute_2：“非常紧张”、“非常阴郁”、“非常慷慨”、“适度魅力”<br RealAttribute_3：“稍微慷慨”、“适度丑陋”、“适度傲慢”、“适度平静”<br RealAttribute_4：“适度傲慢”、“稍微大方”、“非常迟钝”、“适度疏远”<br RealAttribute_5：“稍微没有野心”、“适度冷静”、“适度没有野心”、“适度懒惰”<br RealAttribute_6：“稍微迟钝”、“稍微有野心”、“非常冷静”、“适度有野心”<br RealAttribute_7：“非常聪明”、“有点疏远”、“非常聪明”、“有点丑”<br RealAttribute_8：“非常快乐”、“有点谦虚”、“有点快乐”、“非常傲慢”<br RealAttribute_9：“非常疏远”、“非常懒惰”、“有点好看”、“非常大方”<br RealAttribute_10：“稍懒”、“中智”、“稍努力”、“中智”
因此，前四行的最终结果应该是这样的：智力：“非常聪明”、“中等聪明”、“非常聪明”、“中等聪明”<br吸引力：“中等丑”、“中等丑”、“稍微好看”、“稍微丑”<br懒惰：“稍微懒惰”、“非常懒惰”、“稍微勤奋”、“稍微懒惰”<br友好：“非常疏远”、“稍微疏远”、“非常疏远“、“中等疏远”“很高兴”、“很郁闷”、“有点高兴”、“很高兴”
我定义了所有属性值

attribute_values <- list(
  intelligence = c("Very unintelligent", "Moderately unintelligent", "Slightly unintelligent", 
                   "Slightly intelligent", "Moderately intelligent", "Very intelligent"),
  attractiveness = c("Very ugly", "Moderately ugly", "Slightly ugly", 
                     "Slightly good-looking", "Moderately good-looking", "Very good-looking"),
  laziness = c("Very lazy", "Moderately lazy", "Slightly lazy", 
               "Slightly hard working", "Moderately hard working", "Very hard working"),
  friendliness = c("Very distant", "Moderately distant", "Slightly distant", 
                   "Slightly friendly", "Moderately friendly", "Very friendly"),
  charisma = c("Very dull", "Moderately dull", "Slightly dull", 
               "Slightly charismatic", "Moderately charismatic", "Very charismatic"),
  calmness = c("Very nervous", "Moderately nervous", "Slightly nervous", 
               "Slightly calm", "Moderately calm", "Very calm"),
  generosity = c("Very stingy", "Moderately stingy", "Slightly stingy", 
                 "Slightly generous", "Moderately generous", "Very generous"),
  joyfullness = c("Very gloomy", "Moderately gloomy", "Slightly gloomy", 
                  "Slightly joyful", "Moderately joyful", "Very joyful"),
  arrogance = c("Very arrogant", "Moderately arrogant", "Slightly arrogant", 
                "Slightly modest", "Moderately modest", "Very modest"),
  ambition = c("Very unambitious", "Moderately unambitious", "Slightly unambitious", 
               "Slightly ambitious", "Moderately ambitious", "Very ambitious")
)

型
但开始检查我的代码中的第一个变量（intelligence）：

intelligence_values <- c("Very unintelligent", "Moderately unintelligent", "Slightly unintelligent", 
                         "Slightly intelligent", "Moderately intelligent", "Very intelligent")

realData$Intelligence <- apply(dt[, grep("RealAttribute_", colnames(realData), value = TRUE)], 1, function(row) {
  match_value <- which(row %in% intelligence_values)[1]
  if (is.na(match_value)) {
    return(NA)
  } else {
    return(intelligence_values[match_value])
  }
})

型
这段代码只向“intelligence”列返回NA。

realData <- realData %>%
  rowwise() %>%
  mutate(Intelligence = intelligence_values[match(c_across(starts_with("RealAttribute")), intelligence_values)])

型
但得到以下错误：c_across()中的错误：！只能在数据掩码 predicate （如mutate()、filter()和group_by()）内部使用。
并且在运行rlang：：last_trace（）：<error/rlang_error> c_across()中出错：！只能在数据掩码 predicate （如mutate()、filter()和group_by()）内部使用。**- Backtrace：x
1.+-realData %>%按行方式（）%>%...
1.-plyr：：突变（...）
1.-base：：eval（列列，.数据，父帧（））
1.

\-base::eval(cols[[col]], .data, parent.frame())

型
1.

+-base::match(c_across(starts_with("RealAttribute")), intelligence_values)

型
1.

\-dplyr::c_across(starts_with("RealAttribute"))

型
运行rlang：：last_trace（drop = FALSE）查看4个隐藏帧。

有什么想法出了问题或我应该怎么写？提前感谢！