search for: foverlap

...ally the output should be 4/1/2013 0:00 4/1/2013 0:10 0.05 0 4/1/2013 0:10 4/1/2013 0:20 0.05 0 4/1/2013 0:20 4/1/2013 0:30 0.05 0 4/1/2013 0:30 4/1/2013 0:40 0.05 0 4/1/2013 0:40 4/1/2013 0:50 0.05 0.01 4/1/2013 0:50 4/1/2013 1:00 0.05 0.01 I tried setkey(dums,start,end) setkey(map,start,end) foverlaps(map,dums,type="within",nomatch=0L) I keep getting the error Error in foverlaps(map, dums, type = "within", nomatch = 0L) : All entries in column start should be <= corresponding entries in column end in data.table 'y' [[alternative HTML version deleted]]

...the department will be named "Final Name".? When I merge these two tables, I'd like it to pull the transactional data and match it up to department name "Modified Name" since that was the active department name at the time of that transaction.? I've read documentation on foverlaps, but I'm not sure if this problem is considered a range of dates or not. At the bottom of this post is a temporarily solution that is working but it runs for a long time due to the amount of data in my actual source. Here is some sample data to get started: library(data.table) dtDistribution...

Hi,? I have two big data set.? data _1 :? > dim(data_1) [1] 15820 5 > head(data_1) ? ?Chromosome ?????Start????????End????????Feature GroupA_3 1: ? ? ? ????????chr1 521369 ?750000 ????chr1-0001 ? ?????0.170 2: ? ? ? ????????chr1 750001 ?800000 ????chr1-0002 ? ????-0.086 3: ? ? ? ????????chr1 800001 ?850000 ????chr1-0003 ? ?????0.006 4: ? ? ? ????????chr1 850001 ?900000 ????chr1-0004 ?

...a 4. Repeat the same with some grouping criteria from DateTime or suitable index, example day of week or by truckid. From index 1 (00:00:00 to 00:10:00 ) to index 6(00:00:50 to 01:00:00), 60 miles and 6 gallons are equally distributed. So far, I have created the 10 min HH:MM ids and exploring the foverlaps in data.table, period.apply from xts, converting the time indices into numerics and explore ddply %>% mutate %>% group by options. I am trying to get a more streamlined version that would work as a time series object which would help me get the descriptive statistics (aggregate by grouping c...