Visualizing many variables
As you begin to consider more variables, plotting them all at the same time becomes increasingly difficult. In addition to using x and y scales for two numeric variables, you can use color for a third numeric variable, and you can use faceting for categorical variables. And that's about your limit before the plots become to difficult to interpret. There are some specialist plot types like correlation heatmaps and parallel coordinates plots that will handle more variables, but they give you much less information about each variable, and they aren't great for visualizing model predictions.
Here you'll push the limits of the scatter plot by showing the house price, the distance to the MRT station, the number of nearby convenience stores, and the house age, all together in one plot.
taiwan_real_estate
is available; ggplot2
is loaded.
This exercise is part of the course
Intermediate Regression in R
Exercise instructions
- Using the
taiwan_real_estate
dataset, draw a scatter plot ofn_convenience
versus the square root ofdist_to_mrt_m
, colored byprice_twd_msq
. - Use the continuous viridis plasma color scale.
- Facet the plot, wrapping by
house_age_years
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Using taiwan_real_estate, no. of conv. stores vs. sqrt of dist. to MRT, colored by plot house price
___ +
# Make it a scatter plot
___ +
# Use the continuous viridis plasma color scale
___ +
# Facet, wrapped by house age
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