Visualizing Female Proportion Borrowing

The return type of functions in the bigtabulate and biganalytics packages are base R types that can be used just like you would with any analysis. This means that we can visualize results using ggplot2.

In this exercise, you will visualize the female proportion borrowing for urban and rural areas across all years.

Diese Übung ist Teil des Kurses

Scalable Data Processing in R

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Anleitung zur Übung

The matrix prop_female from the previous exercise is available in your workspace.

Load the tidyr and ggplot2 packages.
Convert prop_female to a data frame using as.data.frame().
Add a new column, Year. Set it to the row.names() of prop_female_df.
Call pivot_longer() on the columns of prop_female_df to convert it into a long format.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Load the tidyr and ggplot2 packages
___
___

# Convert prop_female to a data frame
prop_female_df <- ___

# Add a new column Year
prop_female_df$Year <- ___

# Call pivot_longer on prop_female_df
prop_female_long <- ___(prop_female_df, -Year, names_to = "Region", values_to = "Prop")

# Create a line plot
ggplot(prop_female_long, aes(x = Year, y = Prop, group = Region, color = Region)) + 
    geom_line()

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