Replace multiple columns in a dataframe with a new column that indicates if the original columns contained any non-missing data

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英文:

Replace multiple columns in a dataframe with a new column that indicates if the original columns contained any non-missing data

问题

我有一个类似下面简化版的数据框。我希望用一个新列（new_column）替换A:C列，对于有数据的行提供1，对于没有数据的行提供NA。

  1. A  B  C
  2. NA NA NA
  3. 1  0  0
  4. 0  1  0
  5. 0  0  1

结果应该类似这样：

  1. new_column
  2. NA
  3. 1
  4. 1
  5. 1

我尝试使用dplyr中的mutate命令：

  1. library(dplyr)
  3. df %>%
  4.   mutate(new_column = apply(is.na(df[, c("A","B","C")]), 1, all),
  5.          .keep = "unused",
  6.          .before = "D" ) # 其中D是数据框中的下一列
英文:

I have a dataframe that resembles the simplified one below. I am hoping to replace columns A:C with a new column (new_column) that provides a 1 for a row with data and an NA for a row without data.

  1. A  B  C
  2. NA NA NA
  3. 1  0  0
  4. 0  1  0
  5. 0  0  1

The outcome would look something like this:

  1. new_column
  2. NA
  3. 1
  4. 1
  5. 1

I tried using the mutate command in dplyr

  1. library(dplyr)
  3. df %>%
  4.   mutate(new_column = apply(is.na(df[, c("A","B","C")]), 1, all),
  5.          .keep = "unused",
  6.          .before = "D" ) # where D is the next column in the data frame

答案1

得分: 1

请尝试以下代码：

  1. library(tidyverse)
  3. data %>% mutate(new=ifelse(!is.na(rowSums(across(c(A:C)))),1,NA))

基于R的方法：

  1. data$new_column <- ifelse(rowSums(is.na(data)) == ncol(data), NA, 1)
英文:

Please try the below code

  1. library(tidyverse)
  3. data %&gt;% mutate(new=ifelse(!is.na(rowSums(across(c(A:C)))),1,NA))
  6.    A  B  C new
  7. 1 NA NA NA  NA
  8. 2  1  0  0   1
  9. 3  0  1  0   1
  10. 4  0  0  1   1
  11. 5  0  0  0   1

base r approach

  1. data$new_column &lt;- ifelse(rowSums(is.na(data)) == ncol(data), NA, 1)

答案2

得分: 0

你可以使用 if_all() + is.na 来进行操作：

  1. library(dplyr)
  3. df %>%
  4.   mutate(new_column = ifelse(if_all(A:C, is.na), NA, 1),
  5.          .keep = "unused")
  7. #   new_column
  8. # 1         NA
  9. # 2          1
  10. # 3          1
  11. # 4          1
数据
  1. df <- structure(list(A = c(NA, 1, 0, 0), B = c(NA, 0, 1, 0), C = c(NA, 
  2. 0, 0, 1)), class = "data.frame", row.names = c(NA, -4L))
英文:

You can use if_all() + is.na:

  1. library(dplyr)
  3. df %&gt;%
  4.   mutate(new_column = ifelse(if_all(A:C, is.na), NA, 1),
  5.          .keep = &quot;unused&quot;)
  7. #   new_column
  8. # 1         NA
  9. # 2          1
  10. # 3          1
  11. # 4          1
Data
  1. df &lt;- structure(list(A = c(NA, 1, 0, 0), B = c(NA, 0, 1, 0), C = c(NA, 
  2. 0, 0, 1)), class = &quot;data.frame&quot;, row.names = c(NA, -4L))

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  • 本文由 发表于 2023年8月11日 02:04:45
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  • dataframe
  • dplyr
  • na
  • r
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