Project 2

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University of California, Irvine *

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Course

182

Subject

Mechanical Engineering

Date

Dec 6, 2023

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pdf

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2

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Introduction to Machine Learning Data Analysis: Dabdub’s Email ME 182 - Winter 2022 Each entry in the data is a Unix time stamp indicating the date and time when an email was received in one of my email accounts. This project is the analysis of such data. Read all the data files into R and create ONE data frame that looks exactly as shown below. You might have to first convert to .csv dow month date hour minute second tz year dd.hour dd.dow 1 Wed Jan 1 02 37 15 PST 2014 2.620833 Wed 2 Wed Jan 1 02 58 37 PST 2014 2.976944 Wed 3 Wed Jan 1 05 09 40 PST 2014 5.161111 Wed 4 Wed Jan 1 05 10 28 PST 2014 5.174444 Wed 5 Wed Jan 1 06 51 12 PST 2014 6.853333 Wed 6 Wed Jan 1 08 30 35 PST 2014 8.509722 Wed ... ... Verify that the structure of the data frame is: ’data.frame’: [some integer number here] obs. of 10 variables: $ dow : chr "Wed" "Wed" "Wed" "Wed" ... $ month : chr "Jan" "Jan" "Jan" "Jan" ... $ date : chr "1" "1" "1" "1" ... $ hour : chr "02" "02" "05" "05" ... $ minute : chr "37" "58" "09" "10" ... $ second : chr "15" "37" "40" "28" ... $ tz : chr "PST" "PST" "PST" "PST" ... $ year : chr "2014" "2014" "2014" "2014" ... $ dd.hour: num 2.62 2.98 5.16 5.17 6.85 ... $ dd.dow : Factor w/ 7 levels "Mon","Tue","Wed",..: 3 3 3 3 3 3 3 3 3 3 ... Note that in order to complete the task, you will need to use functions that were not discussed in class. Expect that. In part, the purpose of the project is to test your ability to find answers in a completely independent approach. For the rest of the assignment you might need to make changes (e.g., factor, perform time conver- sions, etc) to the data frame. Investigate the effect of the day of the week on the amount of email received. Create a plot that shows the total emails received per day of the week. Investigate the time of the day that emails are received each day of the week. Create a box plot that shows distribution of hour for each day of the week. Investigate the time when people send email. Create a plot that shows the number of emails received at a given hour for a given year. Use the facet feature to show data for the last 6 years.
Impact of Covid. Create a plot that shows the number of emails received in a given month for a given year. Use the facet feature to show data for the last 6 years. Investigate the month in more detail. Create a bar plot that shows the total number of emails for a given month for the entire data. You can color the bar using the hour of the day that the email was received. Get creative. Is there another kind of plot that would be insightful? Excellent Work! That is it. You are done.
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