#Here's a look at the data(cpt1). All the dates follow the following format "%Y-%m-01"
     Cash         Date           isic
1   373165     2014-06-01         K
2   373165     2014-12-01         K
3   373165     2017-09-01         K
4   NA             <NA>           K
5   4789       2015-05-01         K
6   982121     2013-07-01         K
                 .
                 .
                 .
#I was able to group to group them by sector and sum them
cpt_by_sector=cpt1 %>% mutate(sector=recode_factor(isic,
     'A'='Agriculture','B'='Industry','C'='Industry','D'='Industry',
     'E'='Industry','F'='Industry')) %>%
  group_by(sector) %>% summarise_if(is.numeric, sum, na.rm=T)
#here's the result
   sector         `Cash`
    <fct>         <dbl>
1 Agriculture   2094393819.
2 Industry     53699068183.
3 Services     223995196357.

#Below is what I would like to get. I would like to take into account the fiscal year i.e. from july to june.
Sector      `2009/10` `2010/11` `2011/12` `2012/13` `2013/14` `2014/15` `2015/16` `2016/17`
<chr>        <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
1 Agriculture 2.02     3.62      3.65      6.26      7.04      8.36      11.7      11.6
2 Industry   87.8      117.      170.      163.      185.      211.       240.      252.
3 Services   271.      343.      479.      495.      584.      664.       738.      821.
4 Total      361.      464.      653.      664.      776.      883.       990.     1085.