如何创建一个messy_impute()函数，将NA值用平均值或中值计算在杂乱的数据中？

Question

我有一个有家庭作业和考试成绩的学生的数据框架。

> student1
         UID Homework_1 Homework_2 Homework_3 Homework_4 Homework_5 Homework_6 Homework_7 Homework_8
10 582493224         59         99          88         10         66         90         50         80
   Homework_9 Homework_10 Exam_1 Exam_2 Exam_3 Section
10         16          NA     41     61     11       A

Homework_10评分缺失了，我需要创建一个函数来计算NA值的平均值或中位数。

函数messy_impute应该有以下参数：

数据:数据帧或数据提示。

中心:是用平均值还是中间值来计算。

边距:是使用行还是列输入值(1-使用第2行-使用列)。

例如,

messy_impute(student1,mean,1) should print out 

> student1
         UID Homework_1 Homework_2 Homework_3 Homework_4 Homework_5 Homework_6 Homework_7 Homework_8
10 582493224         59         99          88         10         66         90         50         80
   Homework_9 Homework_10 Exam_1 Exam_2 Exam_3 Section
10         16          **62**    41     61     11       A

since the mean of the rest of the homework is 62.

如果A节中作业10的专栏(其他学生)的平均值是50，那么

messy_impute(student1,mean,2) should print out 

> student1
         UID Homework_1 Homework_2 Homework_3 Homework_4 Homework_5 Homework_6 Homework_7 Homework_8
10 582493224         59         99          88         10         66         90         50         80
   Homework_9 Homework_10 Exam_1 Exam_2 Exam_3 Section
10         16          **50**    41     61     11       A

由于A节中各栏的平均数为50。

请注意，如果边距为2，则计算应在同一区段进行。

我真的被困在这个定义函数上了。

Answer 1

R基解决方案：

  # Define function to Impute a row-wise mean (assumes one observation per student):
row_wise_mean_impute <- function(df){
  grade_df <- df[,names(df) != "studid"]
  return(cbind(df[,c("studid"), drop = FALSE], 
         replace(grade_df, is.na(grade_df), apply(grade_df, 1, mean, na.rm = TRUE))))
}


# Apply function: 
row_wise_mean_impute(student1)

数据：

x <- c(rnorm(85, 50, 3), rnorm(15, 50, 15))
student1 <- cbind(studid = 1010101, data.frame(t(x)))
student1[, 10] <- NA_real_