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

##### 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

##### Atkinson index #####
"""
    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
 
    v = v/Statistics.mean(v)
    w = w/sum(w)
 

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

weighted_atkinson_jl <- function(x, weights, epsilon, na.rm = FALSE){
  
  JuliaCall::julia_call("weighted_atkinson", x, weights, epsilon, na.rm)
  
}

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)]

JosepER

#' Weighted quantiles
#'
#' Wrap over Julia's 'StatsBase.quantile()' function.
#'
#' @param x A numeric vector for which the quantiles should be computed.
#' @param weights A numeric vector with the weights.
#' @param probs A numeric vector with the quantiles to compute.
#' @param na.rm a logical value indicating whether NA values should be stripped before the computation proceeds.
#'
#' @return A numeric vector with the weighted quantiles.

JosepER

import re

def backward_string_by_word(text: str) -> str:

    spaces = re.findall(" +", text)

    words = text.split()


    if len(words) == 0:

JosepER

library(readr) 
library(dplyr)   
library(magrittr)   
library(purrr)   
library(ggplot2)   
 
all_lissyrtools_scripts <- fs::dir_ls("/media/user/lissyrtools/")   
 
invisible(purrr::map(all_lissyrtools_scripts, ~ source(.x)))   
 

JosepER

# Prepare session 
 
library(rlang) 
library(glue) 
library(fs) 
library(dplyr) 
library(magrittr) 
library(purrr) 
library(ggplot2) 
 

JosepER

#' Compute gini index.
#' 
#' Compute the gini index with (optional) weights.
#' 
#' @param x A numeric vector with the data.
#' @param weights A numeric vector with sample weights.
#' @return res An atomic vector with the gini index.
gini <- function(x, weights = NULL, na.rm = FALSE) {