Figures
Description
The relationship between the number of calories burned and the total number of steps taken on a daily basis is shown below. The data is presented visually, with each point color-coded based on the level of activity associated with it.
#function for adjusting size of plot
fig <- function(width, heigth) {
options(repr.plot.width = width, repr.plot.height = heigth, scales = "free")
}
#plot1
ggplot(data = daily_activity,aes(x = TotalSteps, y = Calories, color = VeryActiveMinutes), fig(25,30)) +
geom_point() +
geom_smooth() +
labs(title = "Calories vs. Total Daily Steps") +
scale_colour_gradient(low="yellow", high="blue")
#plot2
ggplot(data = daily_activity,aes(x = TotalSteps, y = Calories, color = FairlyActiveMinutes), fig(25,30)) +
geom_point() +
geom_smooth() +
labs(title = "Calories vs. Total Daily Steps") +
scale_colour_gradient(low="pink", high="grey")
#plot2
ggplot(data = daily_activity,aes(x = TotalSteps, y = Calories, color = LightlyActiveMinutes), fig(25,30)) +
geom_point() +
geom_smooth() +
labs(title = "Calories vs. Total Daily Steps") +
scale_colour_gradient(low="orange", high="purple")
#plot3
ggplot(data = daily_activity,aes(x = TotalSteps, y = Calories, color = SedentaryMinutes), fig(25,30)) +
geom_point() +
geom_smooth() +
labs(title = "Calories vs. Total Daily Steps") +
scale_colour_gradient(low="red", high="turquoise") 
Implications
Analyzing these graphs reveals that the majority of data points fall below 15,000 daily steps and 3,000 calories burned. The last two graphs support this conclusion, as they show that most users participate in light physical activity and sedentary behaviors. Bellabeat can use this information to align its marketing strategy with its typical market audience, which may consist of people who work in office settings and have limited opportunities for vigorous exercise. However, it’s important to note that there is a small minority of users who occasionally engage in very active exercise.
Description
The plots below depict the average number of steps taken per hour by survey participants, separated by day of the week.
ggplot() +
stat_identity(data = hourly_steps, aes(hour,total_steps,fill = day_of_week),geom = "bar") +
facet_wrap(~factor(day_of_week,c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")),scales="free_x") +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Hourly Steps Taken By Week Day")
Use Horizontal Scroll To View Other Graphs
Implications
The charts show data on the walking and running habits of Fitbit users, which can inform Bellabeat’s marketing efforts. For example, the data illustrates a significant decrease in step count after 7:00 PM every day of the week. Additionally, it highlights that the peak time for step taking is between 1:00 PM and 2:00 PM on Saturdays, followed by the period between 5:00 PM and 7:00 PM on Wednesdays. This information can be used to determine the most effective times to run advertising and to inform the content used in those ads.
Description
Seen previously, the relationship between physical activity and calorie burn is well-established. To further explore this connection, the visualization below plots the total active minutes against the total number of steps taken on a daily basis.
Use Horizontal Scroll To View Other Grapahsggplot(data = activity_v_steps) +
geom_point(mapping = aes(x = StepTotal, y = TotalIntensity, color = TotalIntensity)) +
labs(title = "Steps vs. Intensity") +
scale_colour_gradient(low="red", high="turquoise") 
Implications
The graph shows a strong correlation between daily exercise levels and physical activities like walking or running. This indicates that people who are likely to use Bellabeat products are likely to achieve their fitness goals through these activities. Therefore, it would be beneficial for Bellabeat’s marketing efforts to focus on this segment of activity, specifically running and walking, to better target these potential users.