006_inter_opport.qmd

---
title: "Analyzing international opportunities"
author: "Luis Francisco Gomez Lopez"
date: 2023-07-11
format:
  beamer:
    colortheme: dolphin
    fonttheme: structurebold
    theme: AnnArbor
    link-citations: true
    linkcolor: blue
    include-in-header:
      - text: |
          \usepackage{booktabs}
          \usepackage{longtable}
          \usepackage{array}
          \usepackage{multirow}
          \usepackage{wrapfig}
          \usepackage{float}
          \usepackage{colortbl}
          \usepackage{pdflscape}
          \usepackage{tabu}
          \usepackage{threeparttable}
          \usepackage{threeparttablex}
          \usepackage[normalem]{ulem}
          \usepackage{makecell}
          \usepackage{xcolor}
          \usepackage{fontawesome5}
bibliography: econ_glob_faedis.bib
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo      = FALSE, 
                      warning   = FALSE, 
                      message   = FALSE,
                      fig.align = "center")
```

```{r libraries}

library(tidyverse)
library(tidyquant)
library(wbstats)
library(DiagrammeR)
```

# Contents

- Please Read Me
- Purpose
- National business environment
- Market-potential analysis
- Secondary market research
- Acknowledgments
- References

# Please Read Me

- Check the message __Welcome greeting__ published in the News Bulletin Board.

- Dear student please edit your profile uploading a photo where your face is clearly visible.

- The purpose of the virtual meetings is to answer questions and not to make a summary of the study material.

- This presentation is based on [@wild_international_2020, Chapter 12]

# Purpose

Understand the importance of analyzing the characteristics of the national economies to select a market and point out how to do it using data

# National business environment

\definecolor{tq_red}{RGB}{227, 26, 28}

- Data

    + Doing Business - World Bank: [https://www.doingbusiness.org/](https://www.doingbusiness.org/)
    
    + Doing Business 2020: [@world_bank_doing_2020]
    
- Methodology

    + https://www.doingbusiness.org/ > Methodology
    
    + Knowing your data
    
        + __Doesn't__ measure all aspects of the business environment that matter to companies or investors
        
        + Only 10 topics with the specific aim on measuring business regulations are cover
        
- **\textcolor{tq_red}{Warning:} Investigation of Data Irregularities in  
Doing Business 2018 and Doing Business 2020** [@c_machen_investigation_2021]
        
# National business environment

\definecolor{tq_blue}{RGB}{44, 62, 80}
\definecolor{tq_steel_blue}{RGB}{166, 206, 227}
\definecolor{tq_navy_blue}{RGB}{31, 120, 180}

- What is measure in _Doing Business_ 2020? [@world_bank_doing_2020, p 3] 

    + Opening a business

        + \textcolor{tq_blue}{\faIcon{store-alt}} Starting a business
    
    + Getting a location

        + \textcolor{tq_steel_blue}{\faIcon{tools}} Dealing with construction permits
        + \textcolor{tq_steel_blue}{\faIcon{bolt}} Getting electricity
        + \textcolor{tq_steel_blue}{\faIcon{city}} Registering property
    
    + Accessing finance

        + \textcolor{tq_navy_blue}{\faIcon[regular]{credit-card}} Getting credit
        + \textcolor{tq_navy_blue}{\faIcon{user-shield}} Protecting minority investors
        
# National business environment

\definecolor{tq_light_green}{RGB}{178, 223, 138}
\definecolor{tq_orange}{RGB}{255, 127, 0}

- What is measure in _Doing Business_ 2020? [@world_bank_doing_2020, p 3] 

    + Dealing withday-to-day operations

        + \textcolor{tq_light_green}{\faIcon{file-invoice-dollar}} Paying taxes
        + \textcolor{tq_light_green}{\faIcon{truck-moving}} Trading across borders

    + Operating in a secure business environment

        + \textcolor{tq_orange}{\faIcon{file-signature}} Enforcing contracts
        + \textcolor{tq_orange}{\faIcon{gavel}} Resolving insolvency
        
# National business environment

- Some data about _Doing Business_ 2020 which corresponds to year 2019

```{r, out.width="75%"}

# By regions
# More information:
# - How does the World Bank classify countries?
# - World Bank Country and Lending Groups
wb_by_region_iso3c <- wb_regions() %>%
                        drop_na() %>%
                        pull(iso3c)

# Data Doing Business 2020: year 2019
doing_business_score_2019 <- wb_data(indicator  = c("IC.BUS.DFRN.XQ", "IC.BUS.EASE.XQ"),
                                     country    = "all",
                                     start_date = 2019,
                                     end_date   = 2019)

# Data World Bank Regions
region_doing_business_score_2019 <- doing_business_score_2019 %>%
    filter(iso3c %in% wb_by_region_iso3c) %>%
    mutate(text_score = as.character(IC.BUS.DFRN.XQ %>% round(digits = 2)))

    # Plot World Bank Regions
    region_doing_business_score_2019 %>%
    ggplot(aes(x = IC.BUS.DFRN.XQ, y = fct_reorder(country, IC.BUS.DFRN.XQ))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_score),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, 90) +
    labs(x        = "",
         y        = "",
         title    = "Ease of doing business score by World Bank regions: year 2019",
         subtitle = str_glue("0 = lowest performance to 100 = best performance
                              Doing Business code: IC.BUS.DFRN.XQ"),
                             # You need to update it manually
         caption  = str_glue("Source: Doing Business - World Bank
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# National business environment

- Some data about _Doing Business_ 2020 which corresponds to year 2019

```{r, out.width="75%"}

# Data World Bank countries
top_country_doing_business_score_2019 <- doing_business_score_2019 %>%
                                        drop_na() %>%
                                        arrange(desc(IC.BUS.DFRN.XQ)) %>%
                                        slice_max(IC.BUS.DFRN.XQ,
                                                  n         = 20,
                                                  with_ties = FALSE) %>%
                                     mutate(text_score = as.character(IC.BUS.DFRN.XQ %>%
                                                                          round(digits = 2)))

# Plot World Bank Regions
top_country_doing_business_score_2019 %>%
    ggplot(aes(x = IC.BUS.DFRN.XQ, y = fct_reorder(country, IC.BUS.DFRN.XQ))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_score),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, 100) +
    labs(x        = "",
         y        = "",
         title    = str_glue("Top {nrow(top_country_doing_business_score_2019)} ease of doing business score by countries: year 2019"),
         subtitle = str_glue("0 = lowest performance to 100 = best performance
                              Doing Business code: IC.BUS.DFRN.XQ"),
                             # You need to update it manually
         caption  = str_glue("Source: Doing Business - World Bank
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# National business environment

- Some data about _Doing Business_ 2020 which corresponds to year 2019

```{r, out.width="75%"}

# Data World Bank countries
low_country_doing_business_score_2019 <- doing_business_score_2019 %>%
                                        drop_na() %>%
                                        arrange(desc(IC.BUS.DFRN.XQ)) %>%
                                        slice_min(IC.BUS.DFRN.XQ,
                                                  n         = 20,
                                                  with_ties = FALSE) %>%
                                     mutate(text_score = as.character(IC.BUS.DFRN.XQ %>%
                                                                          round(digits = 2)))

# Plot World Bank Regions
low_country_doing_business_score_2019 %>%
    ggplot(aes(x = IC.BUS.DFRN.XQ, y = fct_reorder(country, IC.BUS.DFRN.XQ))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_score),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, 50) +
    labs(x        = "",
         y        = "",
         title    = str_glue("Lower {nrow(low_country_doing_business_score_2019)} ease of doing business score by countries: year 2019"),
         subtitle = str_glue("0 = lowest performance to 100 = best performance
                              Doing Business code: IC.BUS.DFRN.XQ"),
                             # You need to update it manually
         caption  = str_glue("Source: Doing Business - World Bank
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# Market-potential analysis

- Main topics commonly included and possible variables

```{r, out.width="95%", dev='png'}

grViz("
digraph boxes_and_circles {

  graph [rankdir = LR]

  node [shape = circle, color = '#2C3E50']
  'Main variables';

  node [shape = oval, color = '#E31A1C']

  'Market size';
  'Market growth rate';
  'Market intensity';
  'Market consumption\ncapacity';
  'Infrastructure';
  'Market receptivity'
  
  node [shape = oval, color = '#18BC9C']
  
  'Population\nElectric power consumption\nEnergy consumption';
  'GDP per capita growth';
  'GDP based on\npurchasing power parity (PPP)';
  'Labor force\nwith advanced education';
  'Fixed broadband subscriptions\nIndividuals using the internet\nMobile cellular subscriptions';
  'Sum of exports and imports\nof goods and services\nas a percentage of GDP';

  'Main variables'-> 'Market size';
  'Main variables'-> 'Market growth rate';
  'Main variables' -> 'Market intensity';
  'Main variables' -> 'Market consumption\ncapacity';
  'Main variables' -> 'Infrastructure';
  'Main variables' -> 'Market receptivity';
  
  'Market size' -> 'Population\nElectric power consumption\nEnergy consumption';
  'Market growth rate' -> 'GDP per capita growth';
  'Market intensity' -> 'GDP based on\npurchasing power parity (PPP)';
  'Market consumption\ncapacity' -> 'Labor force\nwith advanced education';
  'Infrastructure' -> 'Fixed broadband subscriptions\nIndividuals using the internet\nMobile cellular subscriptions';
  'Market receptivity' -> 'Sum of exports and imports\nof goods and services\nas a percentage of GDP'; 
  
}
")
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r}

iso3c_countries_selected <- c("ARG", "BRA", "CHL", "COL", "PER")

# Variables
## EG.USE.ELEC.KH.PC: Electric power consumption (kWh per capita)
## NY.GDP.PCAP.KD.ZG: GDP per capita growth (annual %)
## NY.GDP.MKTP.PP.KD: GDP, PPP (constant 2017 international $)
## SL.TLF.ADVN.ZS: Labor force with advanced education (% of total working-age population with advanced education)
## IT.NET.BBND.P2: Fixed broadband subscriptions (per 100 people)
## E.TRD.GNFS.ZS: Trade (% of GDP)
variable_codes <- c("EG.USE.ELEC.KH.PC", "NY.GDP.PCAP.KD.ZG",
                    "NY.GDP.MKTP.PP.KD", "SL.TLF.ADVN.ZS",
                    "IT.NET.BBND.P2", "NE.TRD.GNFS.ZS")

countries_selected_data <- wb_data(indicator = variable_codes,
                                   country   = iso3c_countries_selected) %>%
  select(-iso2c)
```

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[1]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[1],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[1],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    scale_color_tq() +
    scale_fill_tq() +
    xlim(c(1970, 2015)) +
    labs(x        = "Year",
         y        = "kWh per capita",
         fill     = "",
         title    = "Electric power consumption",
         subtitle = str_glue("Variables units: Kilowatt-hour per capita
                              Variable WDI code : {variable_codes[1]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[2]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[2],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[2],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    geom_hline(yintercept = 0) +
    scale_color_tq() +
    scale_fill_tq() +
    scale_y_continuous(labels = scales::number_format(suffix = "%", accuracy = 1)) +
    labs(x        = "Year",
         y        = "Percent",
         fill     = "",
         title    = "Annual GDP per capita growth",
         subtitle = str_glue("Variables units: percent
                              Variable WDI code : {variable_codes[2]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[3]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[3],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[3],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    xlim(c(1990, 2020)) +
    scale_color_tq() +
    scale_fill_tq() +
    scale_y_continuous(labels = scales::number_format(scale    = 1e-9,
                                                      suffix   = "B",
                                                      accuracy = 1)) +
    labs(x        = "Year",
         y        = "Billions (Short scale)",
         fill     = "",
         title    = "GDP purchase power parity (PPP)",
         subtitle = str_glue("Variables units: International constant USD (PPP) (Base year = 2017)
                              Variable WDI code : {variable_codes[3]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[4]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[4],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[4],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    xlim(c(1996, 2020)) +
    scale_color_tq() +
    scale_fill_tq() +
    scale_y_continuous(labels = scales::number_format(suffix = "%", accuracy = 1)) +
    labs(x        = "Year",
         y        = "Percent",
         fill     = "",
         title    = "Labor force with advanced education",
         subtitle = str_glue("Variables units: percent of total working-age population with advanced education
                              Variable WDI code : {variable_codes[4]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[5]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[5],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[5],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    xlim(c(1998, 2018)) +
    scale_color_tq() +
    scale_fill_tq() +
    labs(x        = "Year",
         y        = "Subscriptions per 100 people",
         fill     = "",
         title    = "Fixed broadband subscriptions",
         subtitle = str_glue("Variables units: number of subscriptions per 100 people
                              Variable WDI code : {variable_codes[5]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Market-potential analysis

- Some data for the case of selected countries in South America

```{r, out.width="80%"}

countries_selected_data %>%
    select(iso3c:date, variable_codes[6]) %>%
    ggplot() +
    geom_point(aes_string(x     = "date",
                          y     = variable_codes[6],
                          fill = "country"),
               color = "black",
               shape = 21) +
    geom_line(aes_string(x     = "date",
                         y     = variable_codes[6],
                         group = "country",
                         color = "country"),
              show.legend = FALSE) +
    scale_color_tq() +
    scale_fill_tq() +
    scale_y_continuous(labels = scales::number_format(suffix = "%", accuracy = 1)) +
    labs(x        = "Year",
         y        = "Percent",
         fill     = "",
         title    = "Trade as percent of GDP",
         subtitle = str_glue("Variables units: sum of exports and imports as percent of GDP
                              Variable WDI code : {variable_codes[6]}"),
                             # You need to update it manually
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: 2023−06−29")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          legend.position   = "bottom",
          axis.text         = element_text(face = "bold"))
```

# Secondary market research

- __Market research companies__

    + Outside USA 
    
        + Euromonitor International: [https://www.euromonitor.com/](https://www.euromonitor.com/)
        
    + Inside or with a subsidiary in USA [@greenbook_2019_2019, p 12-13]
        
        + Nielsen Holdings PLC: [https://www.nielsen.com/us/en/](https://www.nielsen.com/us/en/)
        + Gartner, Inc: [https://www.gartner.com/en](https://www.gartner.com/en)
        + IQVIA: [https://www.iqvia.com/](https://www.iqvia.com/)
        + Kantar: [https://www.kantar.com/](https://www.kantar.com/)
        + Information Resources, Inc: [https://www.iriworldwide.com/en-us](https://www.iriworldwide.com/en-us)
        
- __Government agencies__
    
    + National statistical offices: [https://unstats.un.org/home/nso_sites/](https://unstats.un.org/home/nso_sites/)
    + Data DANE[^1] (Colombia): [https://www.dane.gov.co/files/anda/](https://www.dane.gov.co/files/anda/)

[^1]: Departamento Administrativo Nacional de Estadística

# Secondary market research

\definecolor{tq_navy_blue}{RGB}{31, 120, 180}
\definecolor{python_green}{RGB}{10,103,27}

- Software for Data Analysis

    + \textcolor{tq_navy_blue}{\faIcon{r-project}} is a free software environment for statistical computing and graphics
    
        + [https://www.r-project.org/](https://www.r-project.org/) 
        
    + \textcolor{python_green}{\faIcon{python}} is an interpreted, high-level, general-purpose programming language
    
        + [https://www.python.org/about/](https://www.python.org/about/)
        
- Tools to use with \textcolor{tq_navy_blue}{\faIcon{r-project}} and \textcolor{python_green}{\faIcon{python}}  

    + __Rstudio IDE__ is an integrated development environment (IDE) for R
    
        + [https://posit.co/download/rstudio-desktop/](https://posit.co/download/rstudio-desktop/)
        
    + __Anaconda__ is a python distribution, with installation and package management tools 

        + [https://www.anaconda.com/download/](https://www.anaconda.com/download/)
        
# Secondary market research

\definecolor{tq_navy_blue}{RGB}{31, 120, 180}
\definecolor{python_green}{RGB}{10,103,27}

- Learning Data Analysis

    + \textcolor{tq_navy_blue}{\faIcon{r-project}}

        + __Statistical Inference via Data Science__: [https://moderndive.com/](https://moderndive.com/)

        + __R for Data Science__: [https://r4ds.hadley.nz/](https://r4ds.hadley.nz/)
        
    + \textcolor{python_green}{\faIcon{python}}
    
        + __Python Data Science Handbook__: [https://jakevdp.github.io/PythonDataScienceHandbook/](https://jakevdp.github.io/PythonDataScienceHandbook/)
        + __Python for Data Analysis__: [https://wesmckinney.com/book/](https://wesmckinney.com/book/)
        
# Acknowledgments

\definecolor{tq_navy_blue}{RGB}{31, 120, 180}
\definecolor{tq_orange}{RGB}{255,127,0}
        
- To my family that supports me

- To the taxpayers of Colombia and the __[UMNG students](https://www.umng.edu.co/estudiante)__ who pay my salary

- To the __[Business Science](https://www.business-science.io/)__ and __[R4DS Online Learning](https://www.rfordatasci.com/)__ communities where I learn __[R](https://www.r-project.org/about.html)__ 

- To the __[R Core Team](https://www.r-project.org/contributors.html)__, the creators of __[RStudio IDE](https://rstudio.com/products/rstudio/)__ and the authors and maintainers of the packages __[tidyverse](https://CRAN.R-project.org/package=tidyverse)__, __[tidyquant](https://CRAN.R-project.org/package=tidyquant)__, __[wbstats](https://CRAN.R-project.org/package=wbstats)__, __[DiagrammeR](https://CRAN.R-project.org/package=DiagrammeR)__ and __[tinytex](https://CRAN.R-project.org/package=tinytex)__ for allowing me to access these tools without paying for a license

- To the __[Linux kernel community](https://www.kernel.org/category/about.html)__ for allowing me the possibility to use some __[Linux distributions](https://static.lwn.net/Distributions/)__ as my main __[OS](https://en.wikipedia.org/wiki/Operating_system)__ without paying for a license
        
# References