如何根据我在 DF2 中获得的重要变量对 DF1 中的列变量进行子集化? [英] How do I subset column variables in DF1 based on the important variables I got in DF2?

问题描述

我有 2 个这样的 df

I have 2 df's like this

ID = c('x1','x2','x5')
df1 <- data.frame(ID)

x1 = c(1,2,3,4,5)
x2 = c(11,12,13,14,15)
x3 = c(21,22,23,24,25)
x4 = c(31,32,33,34,35)
x5 = c(41,42,43,44,45)
df2 <- data.frame(x1,x2,x3,x4,x5)

期望的输出

  x1 x2 x5
1  1 11 41
2  2 12 42
3  3 13 43
4  4 14 44
5  5 15 45

我希望我的新数据集仅包含那些在 df1 中标识为重要的变量(即:x1、x2、x5)以及来自 df2 的值.

I would like my new dataset to contain only those variables that are identified in df1 as important (i.e: x1,x2,x5) with the values from df2.

在这个简单的数据集中，我知道我可以做到这一点，但只是删除 df2 中的 x3、x4，但理想情况下，我想将其应用于一个更大的数据集，其中我有 100 多个变量，因此想以编程方式进行.

In this simple dataset, I know I could do this but just removing x3,x4 in df2 but ideally I would like to apply it to a larger data set where I have more than 100 variables and hence would like to do it programatically.

推荐答案

我找不到一个骗子，所以这里是 - 简单地将 as.character(df1$ID) 的值作为子集作为在

I can't find a dupe so here goes- simply subset by the values of as.character(df1$ID) as in

df2[as.character(df1$ID)] ## Or just `df2[df1$ID]` if its already a character
#   x1 x2 x5
# 1  1 11 41
# 2  2 12 42
# 3  3 13 43
# 4  4 14 44
# 5  5 15 45

as.character 的原因是为了避免通过df1$ID 底层存储模式(整数)而不是它的级别进行子设置

The reason for as.character is in order to avoid sub-setting by df1$ID underlying storage mode (integer) rather by it's levels

虽然这个问题被标记为data.table，所以我们也可以通过引用来做到这一点(如果我们有一个data.table)——不需要转换为字符

Though this question is tagged with data.table, so we could also do this by reference (if we have a data.table)- no need to convert to character

setDT(df2)[, setdiff(names(df2), df1$ID) := NULL]
df2
#    x1 x2 x5
# 1:  1 11 41
# 2:  2 12 42
# 3:  3 13 43
# 4:  4 14 44
# 5:  5 15 45

这篇关于如何根据我在 DF2 中获得的重要变量对 DF1 中的列变量进行子集化?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！