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.

Este exercício faz parte do curso

Scalable Data Processing in R

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Instruções do exercício

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.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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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