如何在引用 R 中其他列的一列的值上创建多个计算列？

Question

我正在尝试创建一个时间序列，以显示特定列在特定时间的值。我目前只能访问一个记录所有更改、列的当前值、日期和被更改的列的名称的表。我想创建一个新列来跟踪该列的先前值在更改之前是什么。 “Column_name”中引用的更改日志中有超过 63 个不同的列

这是我目前拥有的

________________________________________________
Name |  date    |A  | B  |C  |NEW | Column_name|
bob  |  12302019|2  | 23 |153|2   | a          | 
bob  |  12102019|2  | 23 |153|362 | a          |
bob  |  10242019|2  | 23 |153|7   | a          |
john |  10062017|684| 452|1  |254 | c          |
john |  11052018|684| 452|1  |1   | c          |
________________________________________________

这就是我想要的帮助创建

_________________________________________________________________________________
Name |  date    |A  | B  |C  |NEW | Column_name| a_ at Date| b_ at Date | c_ at Date |
bob  |  12302019|2  | 23 |153|2   | a          |2          | 23         | 153        |
bob  |  12102019|2  | 23 |153|362 | a          |362        | 23         | 153        |
bob  |  10242019|2  | 23 |153|7   | a          |7          | 23         | 153        |
john |  10062017|684| 452|1  |254 | c          |684        | 452        | 254        | 
john |  11052018|684| 452|1  |1   | c          |684        | 452        | 1          | 
______________________________________________________________________________________     
I have tested the solution on the following test Data frame, where there is only one column Name "A" and it has several factors 
'data.frame':   755 obs. of  5 variables:
 $ name       : int  606765182 83595892 538663788 779873188 957405600 522796409 41212559 145402647 304688204 83595892 ...
 $ date       : POSIXct, format: "2019-11-01" "2019-11-01" "2019-10-21" ...
 $ A          : Factor
 $ B          : Factor
 $ C          : Factor

 $ Column_name: Factor w/ 1
 $ NEW        : Factor w/ 8 

Answer 1

基础 R
这是一个基本的 R 解决方案。它使用sapply/ifelse 来创建一个包含新值的矩阵，然后cbind 使用输入数据框df1。

cols_to_change <- c("A", "B", "C")

tmp <- sapply(cols_to_change, function(x){
  x2 <- tolower(x)
  y <- tolower(df1[["Column_name"]])
  ifelse(x2 == y, df1[["NEW"]], df1[[x]])
})
colnames(tmp) <- paste0(colnames(tmp), "_new")
df2 <- cbind(df1, tmp)

rm(tmp)    # final cleanup

dplyr 解决方案。

newcol <- function(x, DF){
  x <- deparse(substitute(x))
  x2 <- tolower(x)
  y <- tolower(DF[["Column_name"]])
  ifelse(x2 == y, DF[["NEW"]], DF[[x]])
}

df1 %>%
  mutate_at(vars(cols_to_change), 
            .funs = funs(new=newcol(., df1)))

数据。

df1 <- 
structure(list(Name = c("bob", "bob", "bob", "john", "john"), 
date = c(12302019L, 12102019L, 10242019L, 10062017L, 11052018L),
A = c(2, 2, 2, 684, 684), B = c(23, 23, 23, 452, 452), 
C = c(153, 153, 153, 1, 1), NEW = c(2, 362, 7, 254, 1), 
Column_name = c("a", "a", "a", "c", "c")), 
row.names = c(NA, -5L), class = "data.frame")