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dplyr:需要帮助返回每行中第一个非NA值的列索引

[英]dplyr: Need help returning column index of first non-NA value in every row

I've recently started trying to do all of my code in the tidyverse. 我最近开始尝试在tidyverse中完成所有代码。 This has sometimes lead me to difficulties. 这有时会让我遇到困难。 Here is a simple task that I haven't been able to complete in the tidyverse: I need a column in a dataframe that returns the position index of the first non-na value from the left. 这是一个我在tidyverse中无法完成的简单任务:我需要一个数据帧中的列,它返回左边第一个非na值的位置索引。 Does anyone know how to achieve this in dplyr using mutate? 有谁知道如何使用mutate在dplyr中实现这一目标?

Here is the desired output. 这是所需的输出。

data.frame(                           
"X1"=c(100,rep(NA,8)),
"X2"=c(NA,10,rep(NA,7)),
"X3"=c(NA,NA,1000,1000,rep(NA,5)),
"X4"=c(rep(NA,4),25,50,10,40,50),
"FirstNonNaPosition"=c(1,2,3,3,4,4,4,4,4)
)

An easier and efficient base R option would be max.col after replacing the NA elements to 0 NA元素替换为0后,更简单有效的base R选项是max.col

max.col(replace(df2[1:4], is.na(df2[1:4]), 0), 'first')

Or even 甚至

df2$FirstNonNaPosition <- max.col(!is.na(df2[1:4]), "first")
df2$FirstNonNaPosition
#[1] 1 2 3 3 4 4 4 4 4

With tidyverse , a possible option is pmap 使用tidyverse ,可能的选项是pmap

df2 %>% 
  mutate(FirstNonNaPosition = pmap_int(.[-5], ~ 
                          which.max(!is.na(c(...)))))

Or wrap the max.col 或者包装max.col

df2 %>% 
   mutate(FirstNonNaPosition = max.col(!is.na(.[-5]), 'first'))

data 数据

df2 <- structure(list(X1 = c(100, NA, NA, NA, NA, NA, NA, NA, NA), X2 = c(NA, 
10, NA, NA, NA, NA, NA, NA, NA), X3 = c(NA, NA, 1000, 1000, NA, 
NA, NA, NA, NA), X4 = c(NA, NA, NA, NA, 25, 50, 10, 40, 50), 
    FirstNonNaPosition = c(1, 2, 3, 3, 4, 4, 4, 4, 4)), 
    class = "data.frame", row.names = c(NA, 
-9L))

Also a base R possibility: 也是base R可能性:

apply(df, 1, which.max)

[1] 1 2 3 3 4 4 4 4 4

The same with dplyr : dplyr相同:

df %>%
 mutate(FirstNonNaPosition = apply(., 1, which.max))

A modification for a scenario mentioned by @Andrew: 对@Andrew提到的场景的修改:

apply(df, 1, function(x) which.max(!is.na(x)))

The same with dplyr : dplyr相同:

df %>%
 mutate(FirstNonNaPosition = apply(., 1, function(x) which.max(!is.na(x))))

You could use apply as well: 您也可以使用apply

data.frame(                           
"X1"=c(100,rep(NA,8)),
"X2"=c(NA,10,rep(NA,7)),
"X3"=c(NA,NA,1000,1000,rep(NA,5)),
"X4"=c(rep(NA,4),25,50,10,40,50),
"FirstNonNaPosition"=c(1,2,3,3,4,4,4,4,4)
) %>%
  mutate(First_Non_NA_Pos = apply(., 1, function(x) which(!is.na(x))[1]))

   X1 X2   X3 X4 FirstNonNaPosition First_Non_NA_Pos
1 100 NA   NA NA                  1                1
2  NA 10   NA NA                  2                2
3  NA NA 1000 NA                  3                3
4  NA NA 1000 NA                  3                3
5  NA NA   NA 25                  4                4
6  NA NA   NA 50                  4                4
7  NA NA   NA 10                  4                4
8  NA NA   NA 40                  4                4
9  NA NA   NA 50                  4                4

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