The datasets provided nyt1.csv, nyt2.csv, and nyt3.csv represents three (simulated) days of ads shown and clicks recorded on the New York Times homepage. Each row represents a single user. There are 5 columns: age, gender (0=female, 1=male), number impressions, number clicks, and logged-in. Use R to handle this data. Perform some exploratory data analysis:

make a new variable, age_group, that categorizes users as "<20", "20-29", "30-39", "40-49", "50-59", "60-69", and "70+".

For each day: o Plot the distribution of number of impressions and click-through-rate (CTR = #clicks / #impressions) for these age categories o Define a new variable to segment or categorize users based on their click behavior. o Explore the data and make visual and quantitative comparisons across user segments/demographics (<20-year-old males versus <20-year-old females or logged-in versus not, for example).

Extend your analysis across days. Visualize some metrics and distributions over time.