combine rows in data frame containing NA to make complete row

I know this is a duplicate Q but I can't seem to find the post again

Using the following data

df <- data.frame(A=c(1,1,2,2),B=c(NA,2,NA,4),C=c(3,NA,NA,5),D=c(NA,2,3,NA),E=c(5,NA,NA,4))

  A  B  C  D  E
  1 NA  3 NA  5
  1  2 NA  2 NA
  2 NA NA  3 NA
  2  4  5 NA  4

Grouping by A, I'd like the following output using a tidyverse solution

  A  B  C  D  E
  1  2  3  2  5
  2  4  5  3  4

I have many groups in A. I think I saw an answer using coalesce but am unsure how to get it work. I'd like a solution that works with characters as well. Thanks!

Not tidyverse but here's one base R solution

df <- data.frame(A=c(1,1),B=c(NA,2),C=c(3,NA),D=c(NA,2),E=c(5,NA))
sapply(df, function(x) x[!is.na(x)][1])
#A B C D E 
#1 2 3 2 5

With updated data

do.call(rbind, lapply(split(df, df$A), function(a) sapply(a, function(x) x[!is.na(x)][1])))
#  A B C D E
#1 1 2 3 2 5
#2 2 4 5 3 4

Oriol Mirosa

I haven't figured out how to put the coalesce_by_column function inside the dplyr pipeline, but this works:

coalesce_by_column <- function(df) {
  return(coalesce(df[1], df[2]))
}

df %>%
  group_by(A) %>%
  summarise_all(coalesce_by_column)

##       A     B     C     D     E
##   <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     1     2     3     2     5
## 2     2     4     5     3     4

Edit: include @Jon Harmon's solution for more than 2 members of a group

# Supply lists by splicing them into dots:
coalesce_by_column <- function(df) {
  return(dplyr::coalesce(!!! as.list(df)))
}

df %>%
  group_by(A) %>%
  summarise_all(coalesce_by_column)

#> # A tibble: 2 x 5
#>       A     B     C     D     E
#>   <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     1     2     3     2     5
#> 2     2     4     5     3     4

Here is an even more general solution (using unique, na.omit to sort of create coalesce), which can handle more than two rows with overlapping information. Super simply and forward.

> df <- data.frame(A=c(1,1,2,2,2),B=c(NA,2,NA,4,4),C=c(3,NA,NA,5,NA),D=c(NA,2,3,NA,NA),E=c(5,NA,NA,4,4))

> df
  A  B  C  D  E
1 1 NA  3 NA  5
2 1  2 NA  2 NA
3 2 NA NA  3 NA
4 2  4  5 NA  4
5 2  4 NA NA  4

> df %>% group_by(A) %>% summarise_all(funs( na.omit(unique(.)) ))
# A tibble: 2 x 5
      A     B     C     D     E
  <dbl> <dbl> <dbl> <dbl> <dbl>
1     1     2     3     2     5
2     2     4     5     3     4

We can use fill to fill all the missing values. And then filter just one row for each group.

library(dplyr)
library(tidyr)

df2 <- df %>%
  group_by(A) %>%
  fill(everything(), .direction = "down") %>%
  fill(everything(), .direction = "up") %>%
  slice(1)

A different tidyverse possibility could be:

df %>%
 gather(var, val, -A, na.rm = TRUE) %>%
 group_by(A, var) %>%
 distinct(val) %>%
 spread(var, val)

      A     B     C     D     E
  <dbl> <dbl> <dbl> <dbl> <dbl>
1     1     2     3     2     5
2     2     4     5     3     4

Here it, first, performs a wide-to-long data-transformation, excluding the "A" column and removing the missing values. Second, it groups by "A" column and the variable names. Third, it removes the duplicate values. Finally, it returns the data to its original wide format.

来源：https://stackoverflow.com/questions/45515218/combine-rows-in-data-frame-containing-na-to-make-complete-row

标签

coalesce

tidyverse