Compute Weighted Mean Across a List of Data Frames (with optional grouping)

Usage

run_weighted_mean(
  data_list,
  var_name,
  wgt_name = NULL,
  na.rm = TRUE,
  by = NULL
)

Arguments

data_list: A named list of data frames, (e.g., across countries or years).
var_name: A string specifying the variable name (e.g., "dhi", "pilabour") to compute the mean on.
wgt_name: An optional string specifying the weight variable to be used. If NULL, equal weights are assumed.
na.rm: Logical. If TRUE, missing values in var_name or wgt_name are removed.
by: Optional string giving the name of a categorical variable to split the data within each data frame before computing the mean.

Value

A named list.

If by is NULL: each list element is named by country and contains a named numeric vector, where the names are years and the values are the computed statistics.
If by is not NULL: each list element is named by ccyy (country-year) identifiers and contains a named numeric vector, where the names represent the by-categories (e.g., gender, region) and the values are the corresponding statistics.

Examples

if (FALSE) { # \dontrun{
library(lissyrtools)
library(purrr)

data <- lissyrtools::lissyuse(data = c("de", "es", "uk"), vars = c("dhi", "age", "pi11", "region_c", "area_c", "educ", "emp"), from = 2016)

data %>% 
purrr::map(~ .x %>% filter(relation == 1000)) %>%
run_weighted_mean("dhi", "hpopwgt")

data %>% 
purrr::map(~ .x %>% filter(age > 25 & age <65)) %>%
run_weighted_mean("pi11", "ppopwgt", by = "educ")

} # }