r - merge data frames to eliminate missing observations

Question

I have two data frames. One (df1) contains all columns and rows of interest, but includes missing observations. The other (df2) includes values to be used in place of missing observations, and only includes columns and rows for which at least one NA was present in df1. I would like to merge the two data sets somehow to obtain the desired.result.

This seems like a very simple problem to solve, but I am drawing a blank. I cannot get merge to work. Maybe I could write nested for-loops, but have not done so yet. I also tried aggregate a few time. I am a little afraid to post this question, fearing my R card might be revoked. Sorry if this is a duplicate. I did search here and with Google fairly intensively. Thank you for any advice. A solution in base R is preferable.

df1 = read.table(text = "
  county year1 year2 year3
    aa     10    20   30
    bb      1    NA    3
    cc      5    10   NA
    dd    100    NA  200
", sep = "", header = TRUE)

df2 = read.table(text = "
  county year2 year3
    bb      2   NA
    cc     NA   15
    dd    150   NA
", sep = "", header = TRUE)

desired.result = read.table(text = "
  county year1 year2 year3
    aa     10    20   30
    bb      1     2    3
    cc      5    10   15
    dd    100   150  200
", sep = "", header = TRUE)

Answer 1

aggregate can do this:

aggregate(. ~ county,
          data=merge(df1, df2, all=TRUE), # Merged data, including NAs
          na.action=na.pass,              # Aggregate rows with missing values...
          FUN=sum, na.rm=TRUE)            # ...but instruct "sum" to ignore them.
##   county year2 year3 year1
## 1     aa    20    30    10
## 2     bb     2     3     1
## 3     cc    10    15     5
## 4     dd   150   200   100