如何基于data.table中的其他列创建新列? [英] How to create a new column based on other columns in a data.table?

问题描述

DT0 = data.table(x=rep(c(NA,NA,NA)), y=c(0,1,NA), v=c(0, 0, NA), l=c(1,1,1))
DT0
#    x  y  v l
#1: NA  0  0 1
#2: NA  1  0 1
#3: NA NA NA 1

基于前三个列 x ， y 和 v ，我想添加一个新的列，并带有以下输出

Based on the first three cols x, y and v I want to add a new col with following output

#1:  No
#2: Yes
#3:  NA

如果所有行均为 NA ，则为

NA .是(如果其中任何一个为 1 ，否则为 0 ).我目前的方法是

NA if all rows are NA. Yes if any of them is 1 else 0. My current approach is

relevant_cols <- c('x', 'y', 'v')
new <- data.table(apply(DT0[, relevant_cols, with=F], 1, function(val) { ifelse(all(is.na(val)), NA_character_, ifelse(any(val == TRUE, na.rm = TRUE), 'Yes', 'No')) }))
DT0[, new:= new]
DT0
#    x  y  v l new
#1: NA  0  0 1  No
#2: NA  1  0 1 Yes
#3: NA NA NA 1  NA

但是，由于实际的 data.table 很大，有没有更好的方法呢?

However, as the actual data.table is large, is there a better way to do this?

修改:data.table条目通常是非数字的，因此如果我能提供比使用 pmax 例如，

Often the data.table entries are non-numeric hence it would be quite helpful if I can have a more general solution than using pmax e.g.,

DT = data.table(x=rep(c(NA,NA,NA)), y=c('No','Yes',NA), v=c('No', 'No', NA), l=c(1,1,1))
DT
#    x  y     v l
#1: NA  No   No 1
#2: NA  Yes  No 1
#3: NA NA    NA 1

推荐答案

这里是一个选择:

DT[, new := ifelse(rowSums(.SD == "Yes", na.rm = T) > 0,
                   'Yes',
                   ifelse(rowSums(is.na(.SD)) != ncol(.SD), "No", NA))
   , .SDcols = x:v]
#    x   y  v l new
#1: NA  No No 1  No
#2: NA Yes No 1 Yes
#3: NA  NA NA 1  NA

这篇关于如何基于data.table中的其他列创建新列?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！