Merge rows in a dataframe where the rows are disjoint and contain NAs

I have a dataframe that has two rows:

| code | name  | v1 | v2 | v3 | v4 |
|------|-------|----|----|----|----|
| 345  | Yemen | NA | 2  | 3  | NA |
| 346  | Yemen | 4  | NA | NA | 5  |

Is there an easy way to merge these two rows? What if I rename "345" in "346", would that make things easier?

You can use aggregate. Assuming that you want to merge rows with identical values in column name:

aggregate(x=DF[c("v1","v2","v3","v4")], by=list(name=DF$name), min, na.rm = TRUE)
   name v1 v2 v3 v4
1 Yemen  4  2  3  5

This is like the SQL SELECT name, min(v1) GROUP BY name. The min function is arbitrary, you could also use max or mean, all of them return the non-NA value from an NA and a non-NA value if na.rm = TRUE. (An SQL-like coalesce() function would sound better if existed in R.)

However, you should check first if all non-NA values for a given name is identical. For example, run the aggregate both with min and max and compare, or run it with range.

Finally, if you have many more variables than just v1-4, you could use DF[,!(names(DF) %in% c("code","name"))] to define the columns.

Adding dplyr & data.table solutions for completeness

Using dplyr::coalesce()

library(dplyr)

sum_NA <- function(x) {if (all(is.na(x))) x[NA_integer_] else sum(x, na.rm = TRUE)}

df %>% 
  group_by(name) %>% 
  summarise_all(sum_NA)
#> # A tibble: 1 x 6
#>   name   code    v1    v2    v3    v4
#>   <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Yemen   691     4     2     3     5

# Ref: https://stackoverflow.com/a/45515491
# Supply lists by splicing them into dots:
coalesce_by_column <- function(df) {
  return(dplyr::coalesce(!!! as.list(df)))
}

df %>% 
  group_by(name) %>% 
  summarise_all(coalesce_by_column)
#> # A tibble: 1 x 6
#>   name   code    v1    v2    v3    v4
#>   <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Yemen   345     4     2     3     5

Using data.table

# Ref: https://stackoverflow.com/q/28036294/
library(data.table)
setDT(df)[, lapply(.SD, na.omit), by = name]
#>     name code v1 v2 v3 v4
#> 1: Yemen  345  4  2  3  5
#> 2: Yemen  346  4  2  3  5

setDT(df)[, code := NULL][, lapply(.SD, na.omit), by = name]    
#>     name v1 v2 v3 v4
#> 1: Yemen  4  2  3  5

setDT(df)[, code := NULL][, lapply(.SD, sum_NA), by = name]
#>     name v1 v2 v3 v4
#> 1: Yemen  4  2  3  5