英文:
adding new variables to a existing dataframe using for loop
问题
我有一个包括以下变量的数据集：
ada_27、ada_28、ada_29、ada_30，...，ada_38
每个变量的值可能是0、1或NA。
我想使用以下代码创建新变量ada_27_p、ada_28_p，...，ada_38_p（指示变量，表示每个变量是否包含值0或NA）：
dat$ada_@@_p <- ifelse((dat$ada_@@==0 | is.na(dat$ada_@@)),1,0)
# @@ 是数字27到38
我需要帮助将这个过程放入for循环函数中，这样我就不需要在R中编写重复的代码。谢谢！
我在将上述函数放入for循环中时遇到了问题，因为我不知道如何在循环中将新创建的变量添加到数据框中。
英文:
I have a dataset which include the following variables:
ada_27, ada_28, ada_29, ada_30,....., ada_38
Each variable may have a value of 0, 1, or NA.
I want to create new variables ada_27_p, ada_28_p,..., ada_38_p (indicator variable whether each variable contains value of 0 or NA) using the following code:
dat$ada_@@_p <- ifelse((dat$ada_@@==0 | is.na(dat$ada_@@)),1,0)
# @@ is numbers 27 to 38
I need help with puttin this procedure in for loop function so that I do not need to write repetitive codes in R. Thanks!
I have trouble putting the above function in for loop as I do not know how to add newly created variables to the dataframe in the loop.
答案1
得分: 0
请提供下次的一些数据。我创建了你的数据的一个简单版本。
你要求一个for循环。横跨列的for循环可能会有问题。由于你试图横跨列进行操作，使用across()更容易执行所需的操作。
library(tidyverse)
# 你的数据框的简化版本
df <- data.frame(ada_1 = sample(v, 10, T), ada_2 = sample(v, 10, T), ada_3 = sample(v , 10, T), ada_4 = sample(v, 10, T))
# 它是什么样子
df
ada_1 ada_2 ada_3 ada_4
1 1 0 1 0
2 NA 1 0 NA
3 0 NA NA 1
4 0 0 NA 1
5 1 NA 0 1
6 1 NA 0 1
7 1 1 1 0
8 1 NA 1 NA
9 NA 1 NA NA
10 NA NA NA NA
# mutate() 在数据框上执行操作。across在列之间工作。
# 阅读文档以获取更多细节。
df <- df %>% mutate(across( ada_1:ada_4, ~ ifelse(. == 0 | is.na(.), 1, 0), .names="{.col}_p") )
df
ada_1 ada_2 ada_3 ada_4 ada_1_p ada_2_p ada_3_p ada_4_p
1 1 0 1 0 0 1 0 1
2 NA 1 0 NA 1 0 1 1
3 0 NA NA 1 1 1 1 0
4 0 0 NA 1 1 1 1 0
5 1 NA 0 1 0 1 1 0
6 1 NA 0 1 0 1 1 0
7 1 1 1 0 0 0 0 1
8 1 NA 1 NA 0 1 0 1
9 NA 1 NA NA 1 0 1 1
10 NA NA NA NA 1 1 1 1
这是你提供的代码的翻译。
英文:
Please provide some data next time. I created a simple version of your data.
You asked for a for loop. For loops across columns can be problematic. Since you're trying to work across columns, it's even easier to perform your desired operations across the columns with across().
library(tidyverse)
# Small version of your dataframe
df <- data.frame(ada_1 = sample(v, 10, T), ada_2 = sample(v, 10, T), ada_3 = sample(v , 10, T), ada_4 = sample(v, 10, T))
# What it looks like
df
ada_1 ada_2 ada_3 ada_4
1 1 0 1 0
2 NA 1 0 NA
3 0 NA NA 1
4 0 0 NA 1
5 1 NA 0 1
6 1 NA 0 1
7 1 1 1 0
8 1 NA 1 NA
9 NA 1 NA NA
10 NA NA NA NA
# mutate() performs operations on data frame. across works across columns.
# Read the documentation for more details.
df <- df %>% mutate(across( ada_1:ada_4, ~ ifelse(. == 0 | is.na(.), 1, 0), .names="{.col}_p") )
df
ada_1 ada_2 ada_3 ada_4 ada_1_p ada_2_p ada_3_p ada_4_p
1 1 0 1 0 0 1 0 1
2 NA 1 0 NA 1 0 1 1
3 0 NA NA 1 1 1 1 0
4 0 0 NA 1 1 1 1 0
5 1 NA 0 1 0 1 1 0
6 1 NA 0 1 0 1 1 0
7 1 1 1 0 0 0 0 1
8 1 NA 1 NA 0 1 0 1
9 NA 1 NA NA 1 0 1 1
10 NA NA NA NA 1 1 1 1
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