Roll countries up to region / income / continent

Aggregate a country-level value to a coarser grouping, optionally with population-weighted means.

Usage

aggregate_regions(data, value, by = "region", fun = "sum", weight = NULL)

Arguments

data

A country-level data frame.

value

The value column to aggregate (unquoted).

by

Grouping column(s) (character), default "region". Combine with "year" for panel roll-ups.

fun

Aggregation: "sum" (default), "mean", "median", "min", "max" or "weighted_mean".

weight

Optional weight column (unquoted) for "weighted_mean".

Value

A tibble of by plus the aggregated value.

Groups with no data

Missing values are dropped before aggregating, so a group is summarised from whatever it does have. A group with no non-missing value returns NA rather than a figure: sum() would otherwise report 0, mean() NaN and min()/max() -Inf/Inf, each of which reads as a real total for a region we simply have no data for. Use audit_coverage() to see where those gaps are.

Examples

df <- data.frame(iso3c = c("USA", "CAN", "BRA"),
                 region = c("North America", "North America", "Latin America"),
                 gdp = c(21, 1.7, 1.4))
aggregate_regions(df, gdp, fun = "sum")
#> # A tibble: 2 × 2
#>   region          gdp
#>   <chr>         <dbl>
#> 1 Latin America   1.4
#> 2 North America  22.7