Quantified Self

Introduction

The Quantified Self (QS) is a movement motivated to leverage the synergy of wearables, analytics, and “Big Data”. This movement exploits the ease and convenience of data acquisition through the internet of things (IoT) to feed the growing obsession of personal informatics and quotidian data. The value of the QS for our class is that its core mandate is to visualize and generate questions and insights about a topic that is of immense importance to most people - themselves. It also produces a wealth of data in a variety of forms. Therefore, designing this project around the QS movement makes perfect sense because it offers you the opportunity to be both the data and question provider, the data analyst, the vis designer, and the end user. This means you will be in the unique position of being capable of providing feedback and direction at all points along the data visualization/analysis life cycle.

In this project, I generated 5 questions related to my health activities in this April. The variables I collected from my apple health including calories burned, steps, food calories and flights climbed. These 5 questions will be answered by data visualization. Finally, I will get the answers according to my plots.

Questions:

1.How many daily calories burned? 2.How many steps do I walk everyday? 3.What’s the relationship between steps and calories burned? 4.How many dietary calories do I eat per day? 5.What’s the range of flight climbed covered by day of week?

Question 1: How many daily calories burned?

The largest amount of calories burned was almost 3500 on April 20 and April 18. On most other days, the calories I burned are around 2000 to 2500. During the weekdays, the calories I burned are quite similar because the route of my life is simple: from home to work.

  ggplot(QS_Data) + 
  aes(Dates, Calories.Burned) +
  geom_bar(stat = "identity", color = 'Red') +
  theme(axis.text.x=element_text(angle=60, hjust=1))

Question 2: How many steps do I walk everyday?

The largest amount of steps I walked was almost 15000 on April 20 because on that day, I went to visit Washington D.C. for one exhibition. On most other days, the steps are around 4500 to 5500 because of the simple route of my life.

ggplot(QS_Data) +
  aes(Dates, Steps) +
  geom_bar(stat = "identity", color = 'Red')

Question 3: What’s the relationship between steps and calories burned?

We can clearly see from the plot the linear regression line in the plot. The steps I walked every day are positive correlated with the calories I burned every day.

ggplot(QS_Data) +
  aes(Steps, Calories.Burned) +
  geom_point(color = 'Red') +
  geom_smooth(method = "lm",color = 'Blue') +
  theme(axis.text.x=element_text(angle=60, hjust=1))

Question 4:How many dietary calories do I eat per day?

We can see from the bar chart I drew with R, the calories I took in every day are around 750 to 1750. Some of the days the calories I took in was less than 1000, that’s because on that day I tries to go on diet and eat healthily in this April.

ggplot(QS_Data) +
  aes(Dates, Food.Calories) +
  geom_bar(stat = "identity", color = 'Green')

Question 5: What’s the range of flight climbed covered by day of week?

We can see that the least amount of fights I climbed was on Sundays. Thursdays, Wednesdays and Fridays have the highest amount of flights I climbed.

day_of_week <- QS_Data %>% mutate(Weekday = weekdays(Dates))

ggplot(day_of_week) + aes(Weekday, Flights.Climbed) + geom_boxplot() + theme_economist()

Conclusion

Based on the visualization analysis, we have the following conclusions: The calories I burned are around 2000 to 2500, which is more than the calories I took in every day, which is around 750 to 1750. This is a good diet pattern to make me not gain unnecessary weight and keep me fit. My physical activity is more during weekends than during weekdays. The calories I burned every day is positive correlated with the steps I walked every day. To conclude, in this April, I keep a healthy diet pattern and keep right amount of physical exercise.