R:如何使用dplyr计算具有缺失值的每一行的均值 [英] R: How to calculate mean for each row with missing values using dplyr

问题描述

我想为数据框中包含缺失值的每一行计算几列的均值，然后将结果放置在名为均值"的新列中.这是我的数据框:

I want to calculate means over several columns for each row in my dataframe containing missing values, and place results in a new column called 'means.' Here's my dataframe:

df <- data.frame(A=c(3,4,5),B=c(0,6,8),C=c(9,NA,1))
  A B  C
1 3 0  9
2 4 6 NA
3 5 8  1

如果列没有缺失值(例如列A和B)，则下面的代码成功完成任务.

The code below successfully accomplishes the task if columns have no missing values, such as columns A and B.

 library(dplyr)
 df %>%
 rowwise() %>%
 mutate(means=mean(A:B, na.rm=T))

     A     B     C   means
  <dbl> <dbl> <dbl> <dbl>
1     3     0     9   1.5
2     4     6    NA   5.0
3     5     8     1   6.5

但是，如果一列缺少值(例如C)，则会出现错误:

However, if a column has missing values, such as C, then I get an error:

> df %>% rowwise() %>% mutate(means=mean(A:C, na.rm=T))
Error: NA/NaN argument

理想情况下，我想用dplyr实现它.

Ideally, I'd like to implement it with dplyr.

推荐答案

df %>% 
  mutate(means=rowMeans(., na.rm=TRUE))

.是一个代词"，它引用通过管道传输到mutate的数据帧df.

The . is a "pronoun" that references the data frame df that was piped into mutate.

  A B  C    means
1 3 0  9 4.000000
2 4 6 NA 5.000000
3 5 8  1 4.666667

您还可以使用所有常用方法(列名，索引，grep等)仅选择要包括的特定列.

You can also select only specific columns to include, using all the usual methods (column names, indices, grep, etc.).

df %>% 
  mutate(means=rowMeans(.[ , c("A","C")], na.rm=TRUE))

  A B  C means
1 3 0  9     6
2 4 6 NA     4
3 5 8  1     3

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