Homework 6: Data Manipulation

Note: This assignment must be submitted in github classroom.

cp ../homework-repos/06-data-manip/gapminder_data.csv .

The Gapminder project’s mission is to fight devastating ignorance with a fact-based worldview everyone can understand. To do this, they assemble reliable data about a variety of global variables to help educate the general public.

I have collected data from Gapminder about several variables:

• Population
• GDP Per Capita (inflation-adjusted US dollars)
• Mean Household Income
• CO2 emissions (tonnes per capita)
• Energy Use (Kg of Oil-equivalent per capita)

Use the data manipulation tools you’ve learned about to answer each of the following questions, which I’ve grouped into several general topics. Some questions may specify a specific language to use; if no language is specified, you may choose whether to use R or python to answer the question.

Read In the Data

Read in the data in R and Python. In both languages, store the table in the variable gapminder.

Data Exploration

Missingness

Gapminder puts a lot of effort into curating certain variables; other variables are less frequently used (or are harder to assemble from reliable sources).

CO2

Create a table of all countries with at least 30 observations of the CO2 variable. Your table should be called co2_sum and should have columns country and n_obs. Do not print out the table; instead, if you have done everything correctly, when your document is compiled the correct number of countries will be filled in in the sentence below the code chunk.

co2_sum <- data.frame() # This is a blank data frame so that the lines below 
                        # work before you've filled your code in.
                        # You can write your code below this line, or remove 
                        # these lines, whichever you prefer.

There are 0 countries with at least 30 years of CO2 data. On average, countries have NA years of data.

Energy

For each country with defined energy data for at least 10 years, summarize the data by computing the following:

• year of minimum energy consumption per capita
• year of maximum energy consumption per capita
• average rate of energy consumption change from min to max.
  A rough estimate might be something like (max consumption - min consumption)/(max year - min year). This may not include the most recent data; that is ok.

Your summary data frame, energy_sum should have one row for each country, with columns yearmin, yearmax, and avgincrease.

Answer in Python

Income

Locate the country which has had the biggest growth in income between 1980 and 2019. Create a simple line graph or scatter plot showing the income in this country (and only this country) over time.

GDP

Locate the country which had the biggest decrease (or smallest increase, if there are no decreases) in GDP between 1990 and 2022. Include only countries with at least 5 years of reported data between 1990 and 2022. Create a simple line graph or scatter plot showing the GDP in this country for all years which this information is provided.

rm ./gapminder_data.csv