cumsum in grouped data with dplyr

Question

I have a data frame df (which can be downloaded here) referred to a register of companies that looks something like this:

    Provider.ID        Lo         


        

Answer 1

When you group by local.Authority & year it takes unique values and print the result as 1,-1,1 so better group by only local.Authority where cumsum works based on total values and result 1,0,1

 df <- df %>%
      group_by(Local.Authority) %>%
      mutate(cum.to = cumsum(total))

    > df
    Source: local data frame [3 x 8]
    Groups: Local.Authority [1]

      Provider.ID Local.Authority month  year entry  exit total cum.to
            <chr>           <chr> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
    1 1-102642676            Kent    10  2010     1     0     1      1
    2 1-102642676            Kent     9  2011     0     1    -1      0
    3 1-102642676            Kent    10  2014     1     0     1      1

Answer 2

I got the solution to my problem. I restarted my session and I got my result grouping just by Local Authority and then arranging:

> df.1 = df %>% group_by(Local.Authority) %>%
+ mutate(cum.total = cumsum(total)) %>%
+ arrange(year, month, Local.Authority)
> df.1
Source: local data frame [41 x 8]
Groups: Local.Authority [36]

   Provider.ID  Local.Authority month  year entry  exit total cum.total
        <fctr>           <fctr> <int> <int> <int> <int> <int>     <int>
1  1-102642676           Bexley    10  2010     1     0     1         1
2  1-102642676            Brent    10  2010     1     0     1         1
3  1-102642676 Bristol, City of    10  2010     1     0     1         1
4  1-102642676             Bury    10  2010     1     0     1         1
5  1-102642676   Cambridgeshire    10  2010     1     0     1         1
6  1-102642676    Cheshire East    10  2010     2     0     2         2
7  1-102642676      East Sussex    10  2010     5     0     5         5
8  1-102642676          Enfield    10  2010     1     0     1         1
9  1-102642676            Essex    10  2010     1     0     1         1
10 1-102642676        Hampshire    10  2010     1     0     1         1

Checking "Kent" now it yields the expected result:

> check = df.1 %>% filter(Local.Authority == "Kent")
> check
Source: local data frame [3 x 8]
Groups: Local.Authority [1]

  Provider.ID Local.Authority month  year entry  exit total cum.total
       <fctr>          <fctr> <int> <int> <int> <int> <int>     <int>
1 1-102642676            Kent    10  2010     1     0     1         1
2 1-102642676            Kent     9  2011     0     1    -1         0
3 1-102642676            Kent    10  2014     1     0     1         1