Visualizing the Adjusted Demographic Trends

Let's compare changes in borrowing across demographics over time. The data frame rydf you created in the last exercise is available in your workspace.

Note: We removed the row corresponding to "Not Avail".

This exercise is part of the course

Scalable Data Processing in R

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Exercise instructions

tidyr and ggplot2 are loaded in your workspace.

Print the rydf and pop_proportion objects.
Convert rydf to a long-formatted data frame by gathering all columns except Race.
Create a line chart with Year and Adjusted_Count on the x and y axes, respectively.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# View rydf
___ 

# View pop_proportion
___

# Gather on all variables except Race
rydfl <- ___(rydf, ___, names_to = "Year", values_to = "Count")

# Create a new adjusted count variable
rydfl$Adjusted_Count <- rydfl$Count / pop_proportion[rydfl$Race]

# Plot
ggplot(rydfl, aes(x = ___, y = ___, group = Race, color = Race)) + 
    geom_line()

Edit and Run Code