As society progresses, being busy is a norm. People incur in more responsibilities and keep growing their to-do lists while time remains fixed. Books written on “time-management” and maintaining “work-life balance” gain popularity as people explore how to best maximize the limited 24 hours they have each day.
The amount of time we spend within 24-hour affects our mental, physical, and emotional health. We track how we use time to assess how well we are spending our days for future improvement and to make the most out of the time we have each day.
Time-use data can reveal interesting insights on how different demographic groups spend their time and possibly enable in-depth discovery of evidence supporting or opposing popular conceptions regarding gender inequality, poverty, and other social problems exist in the community.
Companies and businesses can also utilize this knowledge of time use to devise optimal strategies to advertise their products by gaining insights into their customers’ behaviors.
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Understanding a nation’s time use allows policy makers, economists, sociologists, demographers, and individuals to better serve the nation and better understand people’s behaviors.
Our data is derived from American Time Use Survey (ATUS) database, sponsored by the Bureau of Labor Statistics and conducted by the US Census Bureau, tracking the amount of time people spend doing various activities such as sleeping, volunteering, travelling, working, playing sports.
The data is huge, consisting of data from 2003 to 2014. To avoid the burden of analyzing such a huge database, we decide to pick data from 2014. The survey was conducted by randomly selecting individuals from a subset of households. Subjects are interviewed only once about how they spent their time on the previous day. Demographic information on the subjects (age, sex, race, zip code of location, gender, etc…) was also collected.
We use R extensively to clean and format the data. Major steps for cleaning the data include: Reformat data; Draw histograms to see overall distributions of various categories of time spent by people of different demographic groups; Eliminate rows with NIU values for employment status or school/college enrollment; Subgroup data into various demographic groups based on gender, races, employment status, family income, and various other factors to determine interesting variables to be featured in the data visualizations; Create dataframes and new csv files with narrowed list of relevant variables of interests such as states, sex, age, race, working time, educational time, leisure time, personal care time.
More information can be found at: http://www.bls.gov/tus/atususersguide.pdf
This category includes time spent working in one’s job, engaging in income-earning activities (not as part of one’s job), interviewing, and looking for jobs. Work-related activities involve those that are not obviously work but are parts of one’s job, such as attending meetings with business partners, getting dinner with potential clients, playing golf with customers, etc.
This category includes self-care, showering, grooming, and sleeping.
This category includes housework, yard care, pet care, vehicle and home maintenance and repair, home decorations and renovation, cooking, and other activities that revolve around household management (checking mail, filling out paperwork, planning party).
This category includes socializing, relaxing, engaging in leisure activities, doing sports, exercising, and engaging in recreational activities.
This category includes taking classes, doing any educational activities, including homework and conducting research, doing administrative tasks, and engaging in extracurricular activities except sports.
We are Team Triple Treat|
Yuqi Hou, '15-16
Tuongvan Le, '17
Maria Lai, '17
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