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)
This will do:
m <- merge(df1, df2, by="county", all=TRUE)
dotx <- m[,grepl("\\.x",names(m))]
doty <- m[,grepl("\\.y",names(m))]
dotx[is.na(dotx)] <- doty[is.na(dotx)]
names(dotx) <- sapply(strsplit(names(dotx),"\\."), `[`, 1)
result <- cbind(m[,!grepl("\\.x",names(m)) & !grepl("\\.y",names(m))], dotx)
Checking:
> result
county year1 year2 year3
1 aa 10 20 30
2 bb 1 2 3
3 cc 5 10 15
4 dd 100 150 200
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
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