合并行并删除NA [英] Combine rows and remove NA's

问题描述

我有一个像这样的数据框:

I have a data frame like this:

idx  type  val1 val2 val3 val4 val5 val6
1    a     0.2   NA   NA   NA   NA   NA
2    a     0.3   NA   NA   NA   NA   NA 
3    a     0.2   NA   NA   NA   NA   NA
4    a     NA    0.3  NA   NA   NA   NA 
5    a     NA    0.5  NA   NA   NA   NA
6    a     NA    0.2  NA   NA   NA   NA
7    a     NA    NA   0.2  NA   NA   NA
8    a     NA    NA   0.5  NA   NA   NA
9    a     NA    NA   0.4  NA   NA   NA
10   a     NA    NA   NA   0.4  NA   NA
11   a     NA    NA   NA   0.6  NA   NA
12   a     NA    NA   NA   0.6  NA   NA
.
. 
.
34   b     NA    NA   NA   NA   NA   0.6
35   b     NA    NA   NA   NA   NA   0.4
36   b     NA    NA   NA   NA   NA   0.3

我想合并行并删除NA.所以这就是我要实现的:

I want to combine the rows and remove the NA's. So this is what I want to achieve:

idx  type  val1 val2 val3 val4 val5 val6
1    a     0.2  0.3  0.2  0.4  0.3  0.2
2    a     0.3  0.5  0.5  0.6  0.4  0.5
3    a     0.2  0.2  0.4  0.6  0.5  0.6
4    b     0.4  0.2  0.2  0.5  0.4  0.6
5    b     0.3  0.5  0.6  0.3  0.6  0.4
6    b     0.3  0.4  0.3  0.6  0.5  0.3

推荐答案

您可能只想对每个感兴趣的列应用一个函数，然后将其绑定到已过滤的类别列:

You might just want to apply a function to each of the columns of interest and then cbind it to the category column filtered:

test.df = data.frame(cat = rep(c('a','b'), each=3),x1=c(1,NA,NA,5.5,NA,NA),
                     x2=c(NA,2,NA,NA,4.5,NA),
                     x3=c(NA,NA,3.,NA,NA,3.5))

collapse_column <- function(data, col){
  data[!is.na(data[,col]),col]
}

main_vals = sapply(2:4,collapse_column, data=test.df)

cat_vals = test.df[!is.na(test.df[,2]),'cat']

new_df = as.data.frame(cbind(cat_vals, main_vals))
names(new_df) = names(test.df)

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