R dplyr: row-based conditions split/apply/combine

Question

This is the dplyr version of this question

I have the following data.table

initial.date <- as.POSIXct(\'2018-10-27 10:00:00\',tz=\'GM         


        

Answer 1

Solution using dplyr package and lapply function:

result_df <- do.call(rbind, lapply(1:nrow(res), function(row_id) {

             temp <-   res %>% filter(InstrumentSymbol == res$InstrumentSymbol[row_id]) %>% 
                       mutate(time_diff = abs(difftime(res$datetime[row_id], datetime, units = "secs")),
                              diff_price = abs(TradePrice - res$TradePrice[row_id])) %>% 
                       filter(id != res$id[row_id], time_diff <= 60) %>% 
                       filter(diff_price == min(diff_price)) %>% select(TradePrice, id) %>% 
                       rename(minpricewithin60 = TradePrice, index.minpricewithin60 = id)

             if(nrow(temp) == 0) temp[1,] <- c(NA, NA)

             return(bind_cols(res %>% slice(rep(row_id, nrow(temp))), temp))
                                                                  }))

head(result_df)

     PriorityDateTime TradePrice InstrumentSymbol id            datetime minpricewithin60 index.minpricewithin60
1 2018-10-27 10:00:00          1           asset1  1 2018-10-27 10:00:00                3                      3
2 2018-10-27 10:00:30          2           asset2  2 2018-10-27 10:00:30                4                      4
3 2018-10-27 10:01:00          3           asset1  3 2018-10-27 10:01:00                1                      1
4 2018-10-27 10:01:00          3           asset1  3 2018-10-27 10:01:00                5                      5
5 2018-10-27 10:01:30          4           asset2  4 2018-10-27 10:01:30                2                      2
6 2018-10-27 10:01:30          4           asset2  4 2018-10-27 10:01:30                6                      6