Merge nearest date, and related variables from a another dataframe by group

We can also do this by one-liner with dplyr.

library(dplyr)

left_join(df1, df2, by = "ID") %>%
  mutate(dateDiff = abs(dateTarget.x - dateTarget.y)) %>%
  group_by(ID, dateTarget.x) %>%
  filter(dateDiff == min(dateDiff))

Here is the solution based on the base package:

z <- lapply(intersect(df1$ID,df2$ID),function(id) {
   d1 <- subset(df1,ID==id)
   d2 <- subset(df2,ID==id)

   d1$indices <- sapply(d1$dateTarget,function(d) which.min(abs(d2$dateTarget - d)))
   d2$indices <- 1:nrow(d2)

   merge(d1,d2,by=c('ID','indices'))
  })

z2 <- do.call(rbind,z)
z2$indices <- NULL

print(z2)

#    ID dateTarget.x Value dateTarget.y ValueMatch
# 1   3   2015-11-14    47   2015-07-06         48
# 2   3   2015-12-08    98   2015-07-06         48
# 3   3   2015-02-22    52   2015-03-09         94
# 4   3   2014-11-17    68   2014-12-15         95
# 5   3   2013-05-30    91   2013-04-01         85
# 6   1   2013-11-04    70   2014-02-21         35
# 7   1   2014-12-29    18   2014-12-06         88
# 8   2   2013-01-14    52   2013-04-08         77
# 9   2   2015-07-29    97   2015-08-01         68
# 10  2   2015-06-15    98   2015-08-01         68

Using data.table, simple and elegant solution:

library(data.table)

setDT(df1)
setDT(df2)

setkey(df2, ID, dateTarget)[, dateMatch:=dateTarget]
df2[df1, roll='nearest']

    ID dateTarget ValueMatch  dateMatch Value
 1:  3 2015-11-14         48 2015-07-06    47
 2:  3 2015-02-22         94 2015-03-09    52
 3:  1 2014-12-29         88 2014-12-06    18
 4:  3 2015-12-08         48 2015-07-06    98
 5:  2 2013-01-14         77 2013-04-08    52
 6:  2 2015-07-29         68 2015-08-01    97
 7:  3 2013-05-30         85 2013-04-01    91
 8:  1 2013-11-04         35 2014-02-21    70
 9:  2 2015-06-15         68 2015-08-01    98
10:  3 2014-11-17         95 2014-12-15    68

Here's my take using dplyr, based on the accepted answer. I wanted to have a bit more freedom on the grouping column.

match_by_group_date <- function(df1, df2, grp, datecol) {
  
  grp1 <- df1 %>% pull({{grp}}) %>% unique()
  grp2 <- df2 %>% pull({{grp}}) %>% unique()
  
  li <-
  lapply(intersect(grp1, grp2), function(tt) {
    d1 <- filter(df1, {{grp}}== tt)
    d2 <- filter(df2, {{grp}}==tt) %>% mutate(indices = 1:n())
    d2_date <- d2 %>% pull({{datecol}}) %>% as.POSIXct()
    print(d2_date)
    d1 <- mutate(d1, indices = map_dbl({{datecol}}, function(d) which.min(abs(d2_date - as.POSIXct(d)))))
    
    left_join(d1,d2, by=c(quo_name(enquo(grp)), "indices"))
  })
  
  # bind rows
  return(bind_rows(li))
}

Update

As of 2022, there is a join_by() in the works. See dplyr dev docs here

https://dplyr.tidyverse.org/dev/reference/join_by.html

For now I will continue using this method, or data.table. But join_by() will probably get stable enough that is fast and preferred.