JosepER

library(tidyverse)
library(magrittr)
library(bench)

julia_weighted_atkinson_function <- JuliaCall::julia_eval("function weighted_atkinson(v, w, ϵ::Real, skipmissing::Bool)
    if skipmissing
           w = w[findall(!ismissing, v)]
           v = v[findall(!ismissing, v)]
    end
    v = v/Statistics.mean(v)

JosepER

##### Atkinson index #####
"""
    wtd_atkinson(v, w, ϵ)
Computes the weighted Atkinson index given an inequality adversion parameter ϵ.
"""
function wtd_atkinson(v::Array{<:Real,1}, w::Array{<:Real,1}, ϵ::Real)::Float64
 
    norm_mean!(v)
    w = w/sum(w)
 

JosepER

# Benchmarking a call to 'Inequality.gini' without weights,
#   and with a vector of 1s as weights.

using BenchmarkTools
using Inequality

v = rand(1:10000, 300)
w = repeat([1], 300)

@benchmark gini($v) evals=15

JosepER

v <- c(8,5,1,3,5,6,7,6,3) # income
w <- seq(0.1, 0.9, 0.1) # weights


w <- w[order(v)]
v <- sort(v)


# compute πₖ

JosepER

import cppyy
import numpy as np

# 'translation' of the Python function below done by ChatGPT
cppyy.cppdef("""
#include <iostream>
#include <vector>
#include <algorithm>
#include <numeric>

JosepER

#' Download the OECD detailed National Accounts
#'
#' @description
#' Uses the API to the OECD tables to download the National Accounts.
#'
#' Downloads all variables for all years for the selected countries.
#'
#' @details
#' The OECD API takes countries with ISO3. The function uses the METIS 'country'
#'   table to convert ISO2 to ISO3.

JosepER

library(rlang)
library(dplyr)

it14ih <- data.frame(hid = 1:6,
                     hi11 = c(3601, 13190, 0,  11479, 0, 4713),
                     hi13 = c(973, 0, 0, 478, 815, 399),
                     hi12 = c(0, 0, 0,  0, 0, 842),
                     hicapital = c(40, 41, 0, 100, 0, 83),
                     hpopwgt = c(567.1, 5308.3, 666.7, 
                                 692.2, 254.2, 632.6)

JosepER

library(tidyverse)

national_accounts_df <-  tibble::tibble(country = c("ITA", "ITA", "ITA", "ITA", "ITA"), 
                                        variable = c("NFD41R", "NFD11P", "NFB3GR", "NFD42R", "NFD45R"), 
                                        sector = c("S14", "S14", "S14", "S14", "S14"), 
                                        year = c("2016", "2016", "2016", "2016", "2016"), 
                                        value = c(27593.2, 84325810, 227852.9, 119509.5, 1034.7))

estimates_from_microdata_df <- tibble::tibble(indicator = c("D11P", "B3GR+D41R+D42R+D45R"), 
                                              value = c(84328052, 894699.3))

JosepER

library(tidyverse)
library(haven)

#' Download the OECD detailed National Accounts
#'
#' @description
#' Uses the API to the OECD tables to download the National Accounts.
#'
#' Downloads all variables for all years for the selected countries.
#'

JosepER

# Summer workshop - Data preparation in R --------


# COPY TO LISSY: ----------------------------------------------------------

library(tidyverse)
library(magrittr)
# install.packages("Hmisc")