Dataset: Gapminder data

Description: Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country.

Variables:

$($i$)$ Country

$($ii$)$ Continent

$($iii$)$year: ranges from $1952$ to $2007$ in increments of $5$ years

$($iv$)$ lifeExp: life expectancy at birth, in years

$($v$)$ pop: population

$($vi$)$ gdpPercap: GDP per capita $($US$$,$ inflation$-$adjusted$)$

$1.$ Using the 'Gapminder' dataset, filter the data for the year $2007$ and create a new variable $\text{'}$gdp$\_$category' based on GDP per capita. Classify countries into three categories: 'Low,$\text{'}$ 'Medium,$\text{'}$ and 'High' based on the following criteria:

$$ 'Low' if GDP per capita is less than $10000.$

$$ 'Medium' if GDP per capita is between $10000($inclusive$)$ and $20000($exclusive$).$

$$ 'High' if GDP per capita is $20000$ or more.

Write a script to perform these operations and provide a brief explanation of your approach.

$2.$ Using the filtered 'gapminder' dataset for the year $2007$ and the previously created $\text{'}$gdp$\_$category' variable, conduct a detailed comparative analysis of life expectancy across different GDP categories. Hint: Only use appropriate graphics and$/$or descriptive statistics to help you achieve this.

$3.$ Generate an appropriate plot to explore the evolution of life expectancy across any five selected countries, with each country representing one continent. Utilize the 'gapminder' dataset for this analysis. USING R STUDIO