R嵌套多个if循环以生成新向量

Question

我有 20 名工人，每人执行 100 项任务。我已经为每个任务生成了真正的答案，这是 5 个答案中的 1 个

answers <- c("liver", "blood", "lung", "brain", "heart")
truth <- sample(answers, no.tasks, replace = TRUE, prob = c(0.2, 0.2, 0.2, 0.2, 0.2))

我的数据集包含列 workerID、taskID、truth。现在我需要生成另一个向量，我在其中模拟工人将根据特定概率回答的内容。例如，如果我对任务 1 的真值，工人 1 是“肝脏”，我希望工人 1 以高概率回答任务 1 的“肝脏”。同样，对于所有 2000 个任务的五个答案中的每一个，我都想要工人的答案。为此，我使用以下 for 和 if 循环。

for (i in nrow(dataSet)){
if (dataSet$truth[i] == "liver")
{
df <- (rep(sample(answers, no.tasks, prob = c(0.9, 0.02, 0.02, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "blood")
{ 
df <-  (rep(sample(answers, no.tasks, prob = c(0.02, 0.9, 0.02, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "lung")
{
df <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.9, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "brain")
{
df <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.9, 0.02), no.workers)))
} else if (dataSet$truth[i] == "heart")
{
df <-  (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.02, 0.9), no.workers)))
} else {
df <- (rep(sample(answers, no.tasks, prob = c(0.2, 0.2, 0.2, 0.2, 0.2), no.workers)))
}
}

但是，由于我的任务 1 的真相是大脑，所以输出向量 df 有很多答案是“大脑”。有人可以提示这里出了什么问题吗？

Answer 1

考虑使用包含 1,000 个元素的基础字符向量的列表进行初始化。

df <- vector("list", 2000) 

for (i in 1:nrow(dataSet)){
if (dataSet$truth[i] == "liver")
{
df[[i]] <-(rep(sample(answers, no.tasks, prob = c(0.9, 0.02, 0.02, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "blood")
{ 
df[[i]] <-(rep(sample(answers, no.tasks, prob = c(0.02, 0.9, 0.02, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "lung")
{
df[[i]] <-(rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.9, 0.02, 0.02), no.workers)))
} else if (dataSet$truth[i] == "brain")
{
df[[i]] <-(rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.9, 0.02), no.workers)))
} else if (dataSet$truth[i] == "heart")
{
df[[i]] <-(rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.02, 0.9), no.workers)))
} 
}

或者，您可以使用lapply()，它将输出与输入相同长度的列表向量（即dataSet的行），而不需要初始化：

df2 <- lapply(seq_len(nrow(dataSet)), function(i){
  if (dataSet$truth[i] == "liver")
  {
  temp <- (rep(sample(answers, no.tasks, prob = c(0.9, 0.02, 0.02, 0.02, 0.02), no.workers)))
  } else if (dataSet$truth[i] == "blood")
  { 
  temp <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.9, 0.02, 0.02, 0.02), no.workers)))
  } else if (dataSet$truth[i] == "lung")
  {
  temp <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.9, 0.02, 0.02), no.workers)))
  } else if (dataSet$truth[i] == "brain")
  {
  temp <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.9, 0.02), no.workers)))
  } else if (dataSet$truth[i] == "heart")
  {
  temp <- (rep(sample(answers, no.tasks, prob = c(0.02, 0.02, 0.02, 0.02, 0.9), no.workers)))
  } 
  return(temp)
})

更好的是，您可以通过匹配 answers 向量中的当前 dataSet$truth 来修剪嵌套的 if 语句，然后将概率向量中的相应索引替换为 0.9:

df3 <- lapply(seq_len(nrow(dataSet)), function(i){
  probs <- c(0.02, 0.02, 0.02, 0.02, 0.2)      
  probs[match(dataSet$truth[i], answers)] <- 0.9

  temp <- (rep(sample(answers, no.tasks, prob = probs, no.workers)))
})