cp ../homework-repos/06-data-manip/gapminder_data.csv .Homework 6: Data Manipulation
HW
Week06
Week07
Note: This assignment must be submitted in github classroom.
co2_sum <- data.frame() # This is here to be a blank data frame so that the
# lines below work before you've filled your code in.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 CO2. 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.
There are 0 countries with at least 30 years of CO2 data.
Energy
Answer in Python
Income
GDP
rm ./gapminder_data.csv