在r/dplyr中，如何根据现有变量批量添加新变量

发布于 2025-02-12 13:51:48 字数 744 浏览 1 评论 0原文

有RAW_DATA，如何在附加图像中添加新变量为黄色区域？

library(tidyverse)
raw_data <- data.frame(
  Category=c("A","B","C","D","E","F","G"),
 Amount=c(19,14,19,11,2,11,13),
 Ad=c(0.73,0.78,0.37,0.32,0.62,0.76,0.25),
 ST=c(0.54,0.74,0.86,0.57,0.98,0.58,0.35),
 WT=c(0.3,0.2,0.94,0.9,0.75,0.39,0.23),
 MM=c(0.79,0.35,0.87,0.76,0.74,0.55,0.72))

下面的代码可以正确返回计算结果，但是要替换当前变量，并且变量名称不是我想要的。我知道我们可以分别添加新变量（逐步），对此有任何方便的方法吗？谢谢！

raw_data %>% mutate(Amount=as.character(Amount)) %>% 
  mutate(across(where(is.numeric),  ~ . * as.numeric(Amount)))

原文

There is raw_data, how to add new variables as yellow area in attached image ?

library(tidyverse)
raw_data <- data.frame(
  Category=c("A","B","C","D","E","F","G"),
 Amount=c(19,14,19,11,2,11,13),
 Ad=c(0.73,0.78,0.37,0.32,0.62,0.76,0.25),
 ST=c(0.54,0.74,0.86,0.57,0.98,0.58,0.35),
 WT=c(0.3,0.2,0.94,0.9,0.75,0.39,0.23),
 MM=c(0.79,0.35,0.87,0.76,0.74,0.55,0.72))

Below code can return the calculation result correctly, but the current variable be replaced and the variables name isn't what I want.
I know that we can add the new variables separately (step by step ),is there any convenient method for this ? Thanks!

raw_data %>% mutate(Amount=as.character(Amount)) %>% 
  mutate(across(where(is.numeric),  ~ . * as.numeric(Amount)))

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假装爱人 2025-02-19 13:51:48

我想您只需要跨函数的名称参数：

raw_data %>% 
    mutate(Amount=as.character(Amount)) %>% 
    mutate(across(where(is.numeric),  ~ . * as.numeric(Amount), .names = "{col}_AMOUNT"))

  Category Amount   Ad   ST   WT   MM Ad_AMOUNT ST_AMOUNT WT_AMOUNT MM_AMOUNT
1        A     19 0.73 0.54 0.30 0.79     13.87     10.26      5.70     15.01
2        B     14 0.78 0.74 0.20 0.35     10.92     10.36      2.80      4.90
3        C     19 0.37 0.86 0.94 0.87      7.03     16.34     17.86     16.53
4        D     11 0.32 0.57 0.90 0.76      3.52      6.27      9.90      8.36
5        E      2 0.62 0.98 0.75 0.74      1.24      1.96      1.50      1.48
6        F     11 0.76 0.58 0.39 0.55      8.36      6.38      4.29      6.05
7        G     13 0.25 0.35 0.23 0.72      3.25      4.55      2.99      9.36

I guess all you need is the names argument of the across function:

raw_data %>% 
    mutate(Amount=as.character(Amount)) %>% 
    mutate(across(where(is.numeric),  ~ . * as.numeric(Amount), .names = "{col}_AMOUNT"))

  Category Amount   Ad   ST   WT   MM Ad_AMOUNT ST_AMOUNT WT_AMOUNT MM_AMOUNT
1        A     19 0.73 0.54 0.30 0.79     13.87     10.26      5.70     15.01
2        B     14 0.78 0.74 0.20 0.35     10.92     10.36      2.80      4.90
3        C     19 0.37 0.86 0.94 0.87      7.03     16.34     17.86     16.53
4        D     11 0.32 0.57 0.90 0.76      3.52      6.27      9.90      8.36
5        E      2 0.62 0.98 0.75 0.74      1.24      1.96      1.50      1.48
6        F     11 0.76 0.58 0.39 0.55      8.36      6.38      4.29      6.05
7        G     13 0.25 0.35 0.23 0.72      3.25      4.55      2.99      9.36

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