---
title: "Introduction to Quarto and Zotero"
from: markdown+emoji
author: "<Put your name here>"
format: 
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        self-contained: true
jupyter: python3
---

```{python}
#| echo: FALSE
#| warning : false

# import the libraries you need here
```

```{python}
#| echo: FALSE
#| warning : false

# load the GDP data here
```

Understanding missing data mechanisms

```{python}
#create a function that will allow you to calculate average GDP per year
```

There are various reasons for data to be missing: according to Donald Rubin, data values can be Missing Completely at Random (MCAR) i.e the missingness of these values follows a totally random pattern and there isn’t anything in the data driving it that you need to be further concerned with (this is the best case scenario and can be dealt with with rather simplistic methods) data values can also follow the Missing at Random (MAR) mechanism. In this case, the missingness depends on other variables observed in the data you you collected but not on unseen data and not on the variable that is missing data itself. Finally, if the missing values depend on unobserved data and/or the variable with missing values itself, then we have a case of Not Missing At Random (MNAR) Mechanism. 

Data Analysis Case Study: GDP (OECD data)

Trends in GDP per capita, 1972 to 2022

In 1972, GDP per capita was ... on average across the globe. By 2007, that number had increased to ... . In 2022 (latest year available), the GDP per capita was ... .

GDP table for selected countries


```{python}
#| eval : FALSE
#| echo : FALSE 


# create some code to get the GDP of the UK, India, China, Spain, the USA, Italy and France between 2028 and 2020 and present the GDP per country per year as a table
```


GDP barplot for selected countries

```{python}
#| eval : FALSE
#| echo : FALSE 

# create a plot that show the GDP of the UK, India, China, Spain, the USA, Italy and France in 2020 (remember to label the plot appropriately!)
# Use either one of seaborn, lets_plot or plotly to create the plot 
```

Criticism of the GDP measure

GDP is heavily criticized as measure of economic welfare. It is not an appropriate or good measure of human well-being and fails to capture the distribution of income across society. It is overly focused on measuring production outputs in economies where services play an increasingly larger role. Unpaid work, though clearly contributing to economies in myriads of ways, is not measured by it. Nor does it concern itself with the environmental cost or externalities of the measured economic outputs. 



