使用`car`跨列重新编码

本文介绍了使用`car`跨列重新编码的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我一直在互联网上闲逛，而且不知道如何应用 car 来重新编码一系列列的值。

I've been poking about on the internet, and can't figure out how to apply car to recode values for a range of columns.

要重新编码单个列的值，我将运行以下命令：

To recode values for a single column, I'd run a command such as:

df$dv_r <- recode(df$dv, "2=1;1=0;0=NA")

然后，如果我想对整个data.frame执行此操作，则可以运行：

And then if I wanted to do this for the whole data.frame, I could run:

df_2 <- lapply(df, FUN = function(x) recode(x, "2=1;1=0;0=NA"))

但是，我不确定如何对一定范围的列执行此操作，例如，在假设的 data.table 称为 df ，我该如何重新编码 20:40 范围内的列的值？

However, I'm not sure how to do this for a range of columns -- for example, in a hypothetical data.table called df, how would I recode values for columns ranging from 20:40?

谢谢！当然，这对于R专家来说超级简单。

Thanks! Sure this is super easy for R experts.

推荐答案

也许还有更多的 data.table 的方法，但这是一种可能性：

Perhaps there is a more data.table way to do this, but here is one possibility:

library(data.table) library(car) ## Here is some sample data set.seed(1) dt <- data.table(A = sample(0:2, 10, replace = TRUE), B = sample(0:2, 10, replace = TRUE), C = sample(0:2, 10, replace = TRUE), D = rnorm(10), E = rnorm(10), ID = 1:10) dt # A B C D E ID # 1: 0 0 2 -0.04493361 -0.05612874 1 # 2: 1 0 0 -0.01619026 -0.15579551 2 # 3: 1 2 1 0.94383621 -1.47075238 3 # 4: 2 1 0 0.82122120 -0.47815006 4 # 5: 0 2 0 0.59390132 0.41794156 5 # 6: 2 1 1 0.91897737 1.35867955 6 # 7: 2 2 0 0.78213630 -0.10278773 7 # 8: 1 2 1 0.07456498 0.38767161 8 # 9: 1 1 2 -1.98935170 -0.05380504 9 # 10: 0 2 1 0.61982575 -1.37705956 10

使用 .SDcols 定义要将函数应用到哪些列。

Use .SDcols to define which columns you want to apply the function to.

dt[, 1:3 := lapply(.SD, recode, "2=1;1=0;0=NA"), .SDcols = 1:3] dt # A B C D E ID # 1: NA NA 1 -0.04493361 -0.05612874 1 # 2: 0 NA NA -0.01619026 -0.15579551 2 # 3: 0 1 0 0.94383621 -1.47075238 3 # 4: 1 0 NA 0.82122120 -0.47815006 4 # 5: NA 1 NA 0.59390132 0.41794156 5 # 6: 1 0 0 0.91897737 1.35867955 6 # 7: 1 1 NA 0.78213630 -0.10278773 7 # 8: 0 1 0 0.07456498 0.38767161 8 # 9: 0 0 1 -1.98935170 -0.05380504 9 # 10: NA 1 0 0.61982575 -1.37705956 10