Keep before and after date of an external list

Question

Having this dataframe:

dframe1 <- structure(list(id = c(1L, 1L, 1L, 2L, 2L), name = c(\"Google\", 
\"Yahoo\", \"Amazon\", \"Amazon\", \"Google\"), date =          


        

Answer 1

If I understand correctly, the OP wants to find matching entries on id, name and the day before or the day after. Therefore, a non-equi join will not help as it will include matches on the day itself.

I suggest to perform two inner joins, one for the day before and a second for the day after using lapply(). Subsequently, the results are combined with rbindlist() which also adds a new column matching_day as requested by the OP:

library(data.table)
library(magrittr)
setDT(dframe1)[, date := as.Date(date)]
setDT(dframe2)[, date := as.Date(date)]

lapply(
  c(-1, +1), 
  function(x) dframe2[dframe1[, .(id, name, date = date + x)], on = .(id, name, date), nomatch = 0L]
) %>%
  set_names(c("before", "after")) %>% 
  rbindlist(idcol = "matching_day") %>% 
  .[order(id)]

    matching_day id       date   name     text_sth
 1:       before  1 2008-10-31 Google another text
 2:       before  1 2008-10-31  Yahoo        other
 3:        after  1 2008-11-02 Google         test
 4:        after  1 2008-11-02 Google another text
 5:        after  1 2008-11-02  Yahoo     text_sth
 6:        after  1 2008-11-05 Amazon    text here
 7:       before  2 2008-10-31 Amazon          etc
 8:       before  2 2008-11-01 Google         test
 9:        after  2 2008-11-02 Amazon another text
10:        after  2 2008-11-03 Google    text here

Answer 2

A base R way could be to transform dframe1 into a data frame dframe1a that already consists of the desired dates and merge() with dframe2.

dframe1a <- do.call(rbind, lapply(1:nrow(dframe1), function(m) 
  cbind(dframe1[m, -3], date=as.matrix(dframe1[m, "date"] + c(-1, 1)), row.names=NULL)))
dframe1a$date <- as.Date(as.numeric(as.character(dframe1a$date)), origin="1970-01-01")
merge(dframe2, dframe1a)
#    id       date   name     text_sth
# 1   1 2008-10-31 Google another text
# 2   1 2008-10-31  Yahoo        other
# 3   1 2008-11-02 Google another text
# 4   1 2008-11-02 Google         test
# 5   1 2008-11-02  Yahoo     text_sth
# 6   1 2008-11-05 Amazon    text here
# 7   2 2008-10-31 Amazon          etc
# 8   2 2008-11-01 Google         test
# 9   2 2008-11-02 Amazon another text
# 10  2 2008-11-03 Google    text here

Note: Of course your origin dates need to be formatted as such, e.g. dframe1$date <- as.Date(dframe1$date).

Answer 3

One approach could be to expand dframe1 dataset and include rows with has +1 and -1 date for each id and name. We remove the original rows of dframe1 and do an inner_join with dframe2.

library(dplyr)

dframe1 %>%
  mutate(date = as.Date(date), date1 = date) %>%
  group_by(id, name) %>%
  tidyr::complete(date1 = seq(date1 - 1, date1 + 1, by = "1 day")) %>%
  filter(date1 != date | is.na(date)) %>%
  select(-date) %>%
  rename(date = 3) %>%
  inner_join(dframe2 %>% mutate(date = as.Date(date)))

#Joining, by = c("id", "name", "date")
# A tibble: 10 x 4
# Groups:   id, name [5]
#      id name   date       text_sth    
#   <int> <chr>  <date>     <chr>       
# 1     1 Amazon 2008-11-05 text here   
# 2     1 Google 2008-10-31 another text
# 3     1 Google 2008-11-02 test        
# 4     1 Google 2008-11-02 another text
# 5     1 Yahoo  2008-10-31 other       
# 6     1 Yahoo  2008-11-02 text_sth    
# 7     2 Amazon 2008-10-31 etc         
# 8     2 Amazon 2008-11-02 another text
# 9     2 Google 2008-11-01 test        
#10     2 Google 2008-11-03 text here 

To add a new columns we can add another mutate statement.

dframe1 %>%
   mutate(date = as.Date(date), date1 = date) %>%
   group_by(id, name) %>%
   tidyr::complete(date1 = seq(date1 - 1, date1 + 1, by = "1 day")) %>%
   filter(date1 != date | is.na(date)) %>%
   select(-date) %>%
   mutate(col = c("before", "after")) %>%
   rename(date = 3) %>%
   inner_join(dframe2 %>% mutate(date = as.Date(date)))