#MakeOverMonday Week 23 2019
From MakeOverMonday I got data about sleep times per day, age and sex. With the question to work on the graph and remake it. This week I first took the data into Jupyter Notebook to analyse it with Python, after that I went to Power BI for the visualization.
Exploring the data
The data is having 945 rows with 8 columns
There is no missing data
It is information about 15 years (2003-2017)
The people are put in 7 age groups and there is a group with all the information together. They are also divided in 3 sex groups (both, men and women)
There are 2 different types of day’s (“Nonholiday weekdays” and “Weekend days and holidays”) and there is a group were all information has been put together.
In Python I left the data like it was, I did not alter it.
I made several histograms, to see what they were telling me. The histogram of Average hours per day sleeping gave me a nice right skewed distribution (0.4661909713080754)
The mean of this one is 8.069 and the median is 8.81
The correlation between the year and the Average hours per day sleeping is 0.15, this is small
I want to know if there is a relation between ‘Average hours per day sleeping’ and the categorical variables
I found something as can been seen in the two boxplots, it looks like 15 to 24 year old people sleep more hours
During the Weekend days and holidays, people sleep more hours
This is the graph how it was made to remake
Visualize the data in Power BI
I did not find it easy to pick out a topic to visualize, there was not really something that got my attention during explorating the date, beside that I found it a lot of hours that people spend sleeping. I think the title should be, hours spend in bed.
I decided to visualize the average of hours spend in bed by age group over the years, with the possibility to filter on man of women.