Country_v_World.qmd

---
title: "2022 Index of Economic Freedom"
subtitle: ""
author: "Felipe Valencia"
format:
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    toc: true
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    number-sections: false
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---

## Background

_Background or Elevator Pitch goes here_

## Data Wrangling

_Data wrangling process goes here_

```{python}
#| label: libraries
#| include: false
# Import libraries
import pandas as pd
import altair as alt
import numpy as np
import sqlite3

from IPython.display import Markdown
from IPython.display import display
from tabulate import tabulate

# Handle Limit size
alt.data_transformers.disable_max_rows()
```

```{python}
#| label: Read CSV file
#| code-summary: Read and format data

# Loading in data:

url = "https://raw.githubusercontent.com/felipevalenciaclavijo/EconomicFreedomIndex/main/index2022_data.csv"

data = pd.read_csv(url, encoding_errors='ignore')
```


```{python}
#| label: Data wrangling
#| code-summary: Data wrangling

# Filter to compare between observations: Countries, Regions, and World
# Input:
Observation_Name1 = 'Mexico'
Observation_Name2 = 'World'

# fix a blank space on column 'Country Name' 
data = data.rename(columns = {'Country Name':'Country_Name'})

comparison_data = data.query('Country_Name == @Observation_Name1 | Country_Name == @Observation_Name2')

# Simplified data frame containing what I need
comparison_data_scores = comparison_data[['Country_Name', 'Property Rights', 'Judical Effectiveness', 'Government Integrity', 'Tax Burden', "Gov't Spending", 'Fiscal Health', 'Business Freedom', 'Labor Freedom', 'Monetary Freedom', 'Trade Freedom', 'Investment Freedom ', 'Financial Freedom']].reset_index()
# fix a blank space at the end of 'Investment Freedom ' 
comparison_data_scores = comparison_data_scores.rename(columns = {'Investment Freedom ':'Investment Freedom'})
# Transpose dataframe to desired way
comparison_data_scores = comparison_data_scores.transpose()
# Rename columns as desired
comparison_data_scores.columns = np.append(comparison_data_scores.iloc[1, :2], comparison_data_scores.columns[2:])
comparison_data_scores = comparison_data_scores.reset_index()
comparison_data_scores = comparison_data_scores.rename(columns = {'index':'Subcategories'})
# Filter to remove unnecessary rows
comparison_data_scores = comparison_data_scores.query('Subcategories != "index" & Subcategories != "Country_Name"')
# Tidy dataframe for data visualization purposes
comparison_data_scores = comparison_data_scores.melt(id_vars="Subcategories", 
        var_name="Country", 
        value_name="Score")
```

## Data Visualization

_Data Visualizations go here_

```{python}
#| label: Name for each label (has to be different)
#| code-summary: Summary goes here

# Determine colors
domain = [Observation_Name1, Observation_Name2]
range_ = ['#ff7f0e', 'steelblue']

# Plot grouped bar chart
comparison_with_world = (alt.Chart(comparison_data_scores).mark_bar()
    .encode(
    x=alt.X('Country:N', title='', axis=None),
    y=alt.Y('Score:Q', scale=alt.Scale(domain=[0, 100])),
    color=alt.Color('Country:N',title=None,
        scale=alt.Scale(domain=domain, range=range_)))
    .properties(
    width=45,
    height=200)
)

text = comparison_with_world.mark_text(
    align='center',
    baseline='middle',
    dy=-7  # Nudges text to right so it doesn't appear on top of the bar
).encode(
    text='Score:Q'
)

grouped_bar_chart= (alt.layer(comparison_with_world, text)
    .transform_calculate(
        Subcategories="split(datum.Subcategories, ' ')") # This wraps text
    .facet(
        column='Subcategories:N')
        .properties(
            title = {
            "text":  [f"2022 Index of Economic Freedom: {Observation_Name1} vs {Observation_Name2}"],
            "subtitle": ["The Index of Economic Freedom is a score divided into 12 subcategories used to determine the degree of Economic Freedom of Nations", ""]
            })
            .configure_title(
                fontSize = 18,
                anchor = "start", 
                subtitleFontSize = 12)
            .configure(font='arial')
)
grouped_bar_chart

# Save plot as a PNG
grouped_bar_chart.save(f'2022_EFI_{Observation_Name1}_vs_{Observation_Name2}.svg')
```

## Conclusions

_Conclusions go here_

## APPENDIX A (Additional Code)

_Aditional stuff goes here_