更改R中列的类别以匹配其他数据集的类别的函数 [英] Function to change class of columns in R to match the class of an other dataset

问题描述

我试图编写一个函数来更改数据集中变量的类，以匹配另一个数据集的列的类.

I am trying to write a function that changes the class of the variables in a dataset in order to match the class of the column of an other dataset.

例如，如果我有一个数据集1，其中有一个名为A的列作为类因子，而一个数据集2的有一个名为A的列具有类字符，我想在数据集中更改列A的类2为因数.

By example, if I have a dataset 1 with a column named A which as a class factor, and a dataset 2 with a column named A which has a class character, I want to change the class of the column A in dataset 2 to factor.

我有一个代码，但我不明白为什么，它无法更改类别.你有什么主意吗?

I have a code, but I don't understand why, it fails to change the classe. Do you have any idea?

change_class2=function(predict_set,train_set){
  col_drop=c()
  for(column in colnames(predict_set)){
    if(!column %in% colnames(train_set))
    {col_drop=c(col_drop,column)}

v=grep(column, colnames(predict_set))
w=grep(column, colnames(train_set))

if((class(predict_set[,v])!=class(train_set[,w]))*(is.factor(train_set[,w]))==1){
  predict_set[,v]=factor(predict_set[,v])}
else if((class(predict_set[,v])!=class(train_set[,w]))*(is.character(train_set[,w]))==1){
  predict_set[,v]=as.character(predict_set[,v])}
else if((class(predict_set[,v])!=class(train_set[,w]))*(is.numeric(train_set[,w]))==1){
  predict_set[,v]=as.numeric(predict_set[,v])}
else if((class(predict_set[,v])!=class(train_set[,w]))*(is.integer(train_set[,w]))==1){
  predict_set[,v]=as.integer(predict_set[,v])}
else{
  predict_set[,v]=predict_set[,v]}

}}

推荐答案

您可以使用 class 函数将一个类分配给对象:

You can use the class function to assign a class to an object:

class(predict_set[, v]) <- class(train_set[, w])

更进一步，您可以将列称为字符串而不是其索引，这样您就可以取出 v 和 w grep 语句，只需使用列:

Taking it a step further, you can refer to columns as strings instead of their indices so you can take out your v and w grep statements and just use column:

class(predict_set[, column]) <- class(train_set[, column])

所以在一起:

change_class2 <- function(predict_set, train_set) {
  for (column in colnames(predict_set) {
    class(predict_set[, column]) <- class(train_set[, column])
  }
}

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