I have a data frame with 900,000 rows and 11 columns in R. The column names and types are as follows:
column name: date / mcode / mname / ycode / yname / yissue
OK. Assuming your data are in a data frame named foo
:
> head(foo)
date mcode mname ycode yname yissue bsent breturn tsent
417572 2010/07/28 45740 ENDPOINT A 5772 XMAG 20100800 7 0 7
417573 2010/07/31 45740 ENDPOINT A 5772 XMAG 20100800 0 0 0
417574 2010/08/04 45740 ENDPOINT A 5772 XMAG 20100800 0 0 0
417575 2010/08/14 45740 ENDPOINT A 5772 XMAG 20100800 0 0 0
417576 2010/08/26 45740 ENDPOINT A 5772 XMAG 20100800 0 4 0
417577 2010/07/28 45741 ENDPOINT L 5772 XMAG 20100800 2 0 2
treturn csales
417572 0 0
417573 0 1
417574 0 1
417575 0 1
417576 0 0
417577 0 0
Then this will do the aggregation of the numeric columns in your data:
> aggregate(cbind(bsent, breturn, tsent, treturn, csales) ~ yname, data = foo,
+ FUN = sum)
yname bsent breturn tsent treturn csales
1 XMAG 14 8 14 0 6
2 YMAG 11 6 11 6 5
That was using the snippet of data you included in your Q. I used the formula interface to aggregate()
, which is a bit nicer in this instance because you don't need all the foo$
bits on the variable names you wish the aggregate. If you have missing data (NA
)in your full data set, then you'll need add an extra argument na.rm = TRUE
which will get passed to sum()
, like so:
> aggregate(cbind(bsent, breturn, tsent, treturn, csales) ~ yname, data = foo,
+ FUN = sum, na.rm = TRUE)