Minimum wage by region
Looking at summary statistics of grouped data is a great way to learn about potential differences and similarities between the groups. Combine it with the @chain
macro and you have nicely readable code!
In this exercise, you'll revisit the US minimum wage trends. As the 50 states plus Washington, D.C., is too much, you will look at the four regions of the United States - Northeast, Midwest, South, and West. The dataset has been loaded as wages
.
Let's see how the different regions handle the minimum wage!
DataFrames
, Statistics
, Chain
, and Plots
packages have been loaded for you with the using
keyword.
This exercise is part of the course
Data Manipulation in Julia
Exercise instructions
- Create a chain macro on the
wages
DataFrame and save the result asregions_wage
. - Inside the macro, group by
region
andyear
columns. - Calculate the median of
effective_min_wage_2020_dollars
and name the columnmedian_effective_wage_2020
. - Unstack the result so that the rows correspond to years, columns to regions, and values to the median wage.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the chain macro
regions_wage = ____
# Group by region and year
groupby(____)
# Calculate the median wage per region per year
combine(____)
# Reshape the result
____
end
make_plot(regions_wage)