Question

For a data frame populated from a SQL query which looks like this:

Company        Month         Total_Count
ABC            2012-03         10
ABC            2009-01         1
DEF            2011-01         29
GHI            2001-09         10
GHI            2008-01         9
GHI            2010-09         100

I would like to come up with a summary report that would output this:

Company          Total         Months
ABC              11            10         1
DEF              29            29
GHI              119           10      9    100

where column Total would be the sum across all the months
and column Months would be the individual counts for each month encountered for that org. I would love to see if it is possible to somehow squeeze in the actual Month also. Something like:

Company          Total         Months
                               2009-01    2012-03
ABC              11            10      1
                               2011-01
DEF              29            29
                               2001-09   2008-01   2010-09
GHI              119           10        9         100

So far, I have only been able to get the first 2 columns of the desired result above by doing:

df1 <- ddply(resultSet, .(company_name), summarize, sum(as.numeric(total_count)))

But I am not sure how I can append information for individual months

Answer 1

library(reshape2)
res <- ddply(resultSet, .(Company), summarize, Total = sum(Total_Count))
# if value.var is not specified, dcast uses the last column. see ?guess_value
months <- dcast(resultSet, Company ~ Month, value.var = "Total_Count", sum)
join(res, months, by = "Company")
  Company Total 2001-09 2008-01 2009-01 2010-09 2011-01 2012-03
1     ABC    11      NA      NA       1      NA      NA      10
2     DEF    29      NA      NA      NA      NA      29      NA
3     GHI   119      10       9      NA     100      NA      NA

it was important in context of the question to have the aggregrate function.