Replacing certain values in data.frame in R

Question

I am trying to replace the NAs in "test" with the forecast values in "forecast". I am trying to use match, but I can't figure it out. keep in mind the id and time create a two-part unique id. Any suggestions? ( keep in mind my data set is much larger than this example (rows=32000))

test = data.frame(id =c(1,1,1,2,2,2), time=c(89,99,109,89,99,109), data=c(3,4,NA,5,2,NA))
forecast = data.frame(id =c(1,2), time=c(109,109), data=c(5,1))

Desired output

out = data.frame(id =c(1,1,1,2,2,2), time=c(89,99,109,89,99,109), data=c(3,4,5,5,2,1))

Answer 1

Here is the data.table solution

test_dt <- data.table(test, key = c('id', 'time'))
forecast_dt <- data.table(test, key = c('id', 'time'))
forecast[test][,data := ifelse(is.na(data), data.1, data)]

EDIT. Benchmarking Tests: Data Table is ~ 3x faster even for a small dataset.

library(rbenchmark)

f_merge <- function(){
  out2 <- merge(test, forecast, by = c("id", "time"), all.x = TRUE)
  out2 <- transform(out2, 
   newdata = ifelse(is.na(data.x), data.y, data.x), data.x = NULL, data.y = NULL)
  return(out2)
}

f_dtable <- function(){
  test <- data.table(test, key = c('id', 'time'))
  forecast <- data.table(forecast, key = c('id', 'time'))
  test <- forecast[test][,data := ifelse(is.na(data), data.1, data)]
  test$data.1 <- NULL
  return(test)
}

benchmark(f_merge(), f_dtable(), order = 'relative', 
  columns = c('test', 'elapsed', 'relative'))

        test elapsed relative
2 f_dtable()    0.86     1.00
1  f_merge()    2.26     2.63

Answer 2

I would use merge to join the data together and then compute your new column in two steps:

out2 <- merge(test, forecast, by = c("id", "time"), all.x = TRUE)
> out2
  id time data.x data.y
1  1   89      3     NA
2  1   99      4     NA
3  1  109     NA      5
4  2   89      5     NA
5  2   99      2     NA
6  2  109     NA      1

#Compute new variable and clean up old ones:

out2 <- transform(out2, newdata = ifelse(is.na(data.x), data.y, data.x), data.x = NULL, data.y = NULL)
> out2
  id time newdata
1  1   89       3
2  1   99       4
3  1  109       5
4  2   89       5
5  2   99       2
6  2  109       1

Answer 3

尝试这个：

test$data[is.na(test$data)] <- forecast[((forecast$id %in% test$id) & (forecast$time %in% test$time)),]$data