Aggregate an epi_df object

Aggregates an epi_df object by the specified group columns, summing the value column, and returning an epi_df. If aggregating over geo_value, the resulting epi_df will have geo_value set to "total".

Usage

sum_groups_epi_df(.x, sum_cols, group_cols = "time_value")

Arguments

.x

an epi_df

sum_cols

<tidy-select> An unquoted column name (e.g., cases), multiple column names (e.g., c(cases, deaths)), other tidy-select expression, or a vector of characters (e.g. c("cases", "deaths")). Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

group_cols

character vector of column names to group by. "time_value" is included by default.

Value

an epi_df object

Examples

# This data has other_keys age_group and edu_qual:
grad_employ_subset
#> An `epi_df` object, 1,445 x 7 with metadata:
#> * geo_type  = custom
#> * time_type = integer
#> * other_keys = age_group, edu_qual
#> * as_of     = 2024-09-18
#> 
#> # A tibble: 1,445 × 7
#>    geo_value           age_group edu_qual time_value num_graduates med_income_2y
#>  * <chr>               <fct>     <fct>         <int>         <dbl>         <dbl>
#>  1 Newfoundland and L… 15 to 34… Career,…       2010           430         48800
#>  2 Newfoundland and L… 35 to 64… Career,…       2010           140         38100
#>  3 Newfoundland and L… 15 to 34… Career,…       2010           630         49500
#>  4 Newfoundland and L… 35 to 64… Career,…       2010           140         48400
#>  5 Newfoundland and L… 15 to 34… Other c…       2010            60         32700
#>  6 Newfoundland and L… 35 to 64… Other c…       2010            40         30500
#>  7 Newfoundland and L… 15 to 34… Undergr…       2010            20         55400
#>  8 Newfoundland and L… 35 to 64… Undergr…       2010            30         70600
#>  9 Newfoundland and L… 15 to 34… Undergr…       2010          1050         63600
#> 10 Newfoundland and L… 35 to 64… Undergr…       2010           130         85700
#> # ℹ 1,435 more rows
#> # ℹ 1 more variable: med_income_5y <dbl>

# Aggregate num_graduates within each geo_value (and time_value):
grad_employ_subset %>%
  sum_groups_epi_df(num_graduates, group_cols = "geo_value")
#> An `epi_df` object, 86 x 3 with metadata:
#> * geo_type  = custom
#> * time_type = integer
#> * as_of     = 2024-09-18
#> 
#> # A tibble: 86 × 3
#>    geo_value        time_value num_graduates
#>    <chr>                 <int>         <dbl>
#>  1 Alberta                2010         19920
#>  2 Alberta                2011         22290
#>  3 Alberta                2012         23710
#>  4 Alberta                2013         25200
#>  5 Alberta                2014         25790
#>  6 Alberta                2015         26590
#>  7 Alberta                2016         26350
#>  8 Alberta                2017         27230
#>  9 British Columbia       2010         27190
#> 10 British Columbia       2011         29410
#> # ℹ 76 more rows