Bins — get_bins • wbpip

Calculate bins. TO BE DOCUMENTED.

Usage

get_bins(
  .data,
  welfare,
  weight,
  distribution_type = c("micro", "group", "aggregate", "imputed"),
  nbins = 100,
  output = "simple"
)

Arguments

.data: Household survey data frame with at least a welfare variable.
welfare: numeric: A vector of income or consumption values.
weight: numeric: A vector of weights.
distribution_type: character: Type of distribution, either micro, group, aggregate or imputed.
nbins: numeric: Number of bins.
output: character: It has two varieties. 1) it could be a vector of variables to retain after calculations (variables available are "welfare", "weight", "cum_pop", "cum_prop_pop", and "bins"). 2) It could be a one of two key words, "simple" or "full". output = "simple" is equivalent to output = "bins" (which is the default). output = "full" if equivalent to a vector with all the variables available, output = c("welfare", "weight", "cum_pop", "cum_prop_pop","bins")

Value

data.frame

Examples

data("md_ABC_2000_income")
df <- md_ABC_2000_income

bins <- get_bins(df, welfare, weight)
#> ℹ Data has been sorted by variable "welfare"
str(bins)
#>  num [1:2000] 1 1 1 1 1 1 1 1 1 1 ...
#>  - attr(*, "label")= chr "Quantiles"

bins <- get_bins(df, welfare, weight, output = "full")
str(bins)
#> Classes ‘data.table’ and 'data.frame':	2000 obs. of  5 variables:
#>  $ welfare     : num  0 64547 81078 83195 89137 ...
#>  $ weight      : num  66 279 180 105 37 ...
#>  $ cum_pop     : num  66 345 525 630 667 ...
#>   ..- attr(*, "label")= chr "cumulative population"
#>  $ cum_prop_pop: num  0.00012 0.000625 0.000951 0.001141 0.001208 ...
#>   ..- attr(*, "label")= chr "cumulative proportion of population"
#>  $ bins        : num  1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, "label")= chr "Quantiles"
#>  - attr(*, ".internal.selfref")=<externalptr>