Combine column to remove NA's

A dplyr::coalesce based solution could be as:

data %>% mutate(mycol = coalesce(x,y,z)) %>%
         select(a, mycol)
#   a mycol
# 1 A     1
# 2 B     2
# 3 C     3
# 4 D     4
# 5 E     5 

Data

data <- data.frame('a' = c('A','B','C','D','E'),
                 'x' = c(1,2,NA,NA,NA),
                 'y' = c(NA,NA,3,NA,NA),
                 'z' = c(NA,NA,NA,4,5))

You can use unlist to turn the columns into one vector. Afterwards, na.omit can be used to remove the NAs.

cbind(data[1], mycol = na.omit(unlist(data[-1])))

   a mycol
x1 A     1
x2 B     2
y3 C     3
z4 D     4
z5 E     5

Here's a more general (but even simpler) solution which extends to all column types (factors, characters etc.) with non-ordered NA's. The strategy is simply to merge the non-NA values of other columns into your merged column using is.na for indexing:

data$m = data$x  # your new merged column start with x
data$m[!is.na(data$y)] = data$y[!is.na(data$y)]  # merge with y
data$m[!is.na(data$z)] = data$z[!is.na(data$z)]  # merge with z

> data
  a  x  y  z m
1 A  1 NA NA 1
2 B  2 NA NA 2
3 C NA  3 NA 3
4 D NA NA  4 4
5 E NA NA  5 5

Note that this will overwrite existing values in m if there are several non-NA values in the same row. If you have a lot of columns you could automate this by looping over colnames(data).