FitBit Fitness Capstone
Pratish Nagvekar
2023-03-01
About the company
Bellabeat, a high-tech company that manufactures health-focused smart products. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits.
Phase 1: Ask:
- Create a business objective that aligns with Stakeholders desired outcomes and expectations
Business Task
Analyze smart device usage data in order to gain insight into how consumers use smart devices.
Phase 2: Prepare:
- Exploratory data analysis to check basic characteristics of dataset.
- Check dataset for ROCCC.
- Check data licensing, privacy, security, accessibility and integrity.
- What are problems with data and how will data help answer the question.
Loading Packages
library(tidyverse)
library(ggpubr)
library(lubridate)Uploading Datasets
activity <- read.csv("r/files/dailyActivity_merged.csv")
sleep <- read.csv("r/files/sleepDay_merged.csv")
calories <- read.csv("r/files/hourlyCalories_merged.csv")
intensities <- read.csv("r/files/hourlyIntensities_merged.csv")
steps <- read.csv("r/files/hourlySteps_merged.csv")Exploratory Data Analysis of dataset
str(activity)## 'data.frame': 940 obs. of 15 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDate : chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ TotalSteps : int 13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...
## $ TotalDistance : num 8.5 6.97 6.74 6.28 8.16 ...
## $ TrackerDistance : num 8.5 6.97 6.74 6.28 8.16 ...
## $ LoggedActivitiesDistance: num 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...head(activity)## Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366 4/12/2016 13162 8.50 8.50
## 2 1503960366 4/13/2016 10735 6.97 6.97
## 3 1503960366 4/14/2016 10460 6.74 6.74
## 4 1503960366 4/15/2016 9762 6.28 6.28
## 5 1503960366 4/16/2016 12669 8.16 8.16
## 6 1503960366 4/17/2016 9705 6.48 6.48
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 1.88 0.55
## 2 0 1.57 0.69
## 3 0 2.44 0.40
## 4 0 2.14 1.26
## 5 0 2.71 0.41
## 6 0 3.19 0.78
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 6.06 0 25
## 2 4.71 0 21
## 3 3.91 0 30
## 4 2.83 0 29
## 5 5.04 0 36
## 6 2.51 0 38
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 13 328 728 1985
## 2 19 217 776 1797
## 3 11 181 1218 1776
## 4 34 209 726 1745
## 5 10 221 773 1863
## 6 20 164 539 1728str(sleep)## 'data.frame': 413 obs. of 5 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ SleepDay : chr "4/12/2016 12:00:00 AM" "4/13/2016 12:00:00 AM" "4/15/2016 12:00:00 AM" "4/16/2016 12:00:00 AM" ...
## $ TotalSleepRecords : int 1 2 1 2 1 1 1 1 1 1 ...
## $ TotalMinutesAsleep: int 327 384 412 340 700 304 360 325 361 430 ...
## $ TotalTimeInBed : int 346 407 442 367 712 320 377 364 384 449 ...head(sleep)## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM 1 327
## 2 1503960366 4/13/2016 12:00:00 AM 2 384
## 3 1503960366 4/15/2016 12:00:00 AM 1 412
## 4 1503960366 4/16/2016 12:00:00 AM 2 340
## 5 1503960366 4/17/2016 12:00:00 AM 1 700
## 6 1503960366 4/19/2016 12:00:00 AM 1 304
## TotalTimeInBed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320str(calories)## 'data.frame': 22099 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour: chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ Calories : int 81 61 59 47 48 48 48 47 68 141 ...head(calories)## Id ActivityHour Calories
## 1 1503960366 4/12/2016 12:00:00 AM 81
## 2 1503960366 4/12/2016 1:00:00 AM 61
## 3 1503960366 4/12/2016 2:00:00 AM 59
## 4 1503960366 4/12/2016 3:00:00 AM 47
## 5 1503960366 4/12/2016 4:00:00 AM 48
## 6 1503960366 4/12/2016 5:00:00 AM 48str(intensities)## 'data.frame': 22099 obs. of 4 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour : chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ TotalIntensity : int 20 8 7 0 0 0 0 0 13 30 ...
## $ AverageIntensity: num 0.333 0.133 0.117 0 0 ...head(intensities)## Id ActivityHour TotalIntensity AverageIntensity
## 1 1503960366 4/12/2016 12:00:00 AM 20 0.333333
## 2 1503960366 4/12/2016 1:00:00 AM 8 0.133333
## 3 1503960366 4/12/2016 2:00:00 AM 7 0.116667
## 4 1503960366 4/12/2016 3:00:00 AM 0 0.000000
## 5 1503960366 4/12/2016 4:00:00 AM 0 0.000000
## 6 1503960366 4/12/2016 5:00:00 AM 0 0.000000str(steps)## 'data.frame': 22099 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour: chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ StepTotal : int 373 160 151 0 0 0 0 0 250 1864 ...head(steps)## Id ActivityHour StepTotal
## 1 1503960366 4/12/2016 12:00:00 AM 373
## 2 1503960366 4/12/2016 1:00:00 AM 160
## 3 1503960366 4/12/2016 2:00:00 AM 151
## 4 1503960366 4/12/2016 3:00:00 AM 0
## 5 1503960366 4/12/2016 4:00:00 AM 0
## 6 1503960366 4/12/2016 5:00:00 AM 0activity dataset has 16 variables and 940 observations. sleep dataset has 5 variables and 410 observations. calories dataset has 3 variables and 22099 observations. intensities dataset has 5 variables and 22099 obervations and steps dataset has 3 variables and 22099 observations.
Upon checking it is identified that there are blanks and null values in the observations hence datasets can be determined as not reliable. This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016 hence it is from a 2nd party data source and 6 years old data. This dataset has been provided for public domain via License and clears privacy, security and accessibility issues.
Dataset is not complete or accurate. There are issues in the datasets those needs to be cleaned before analyzing.
Phase 3: Process:
- Data Cleaning
- Data transformation
Data Cleaning
Eliminating Duplicates
Fixing Null values
sum(is.null(activity))## [1] 0sum(is.null(sleep))## [1] 0sum(is.null(intensities))## [1] 0sum(is.null(calories))## [1] 0sum(is.null(steps))## [1] 0Fixing date time data type
activity$ActivityDate <- mdy(activity$ActivityDate)
sleep$SleepDay <- mdy_hms(sleep$SleepDay)
calories$ActivityHour <- mdy_hms(calories$ActivityHour)
intensities$ActivityHour <- mdy_hms(intensities$ActivityHour)
steps$ActivityHour <- mdy_hms(steps$ActivityHour)Data Transformation
Standardizing Column names
activity <- rename_with(activity, tolower)
sleep <- rename_with(sleep, tolower)
intensities <- rename_with(intensities, tolower)
calories <- rename_with(calories, tolower)
steps <- rename_with(steps, tolower)Adding weekday column to activity dataset
activity <- activity %>%
mutate(weekday = weekdays(activitydate))Phase 4: Analyze and Share
Analyzing steps taken per weekday
activity_steps <- activity %>%
group_by(weekday) %>%
summarise(avgstepsperday = mean(totalsteps))
activity_steps$weekday <- factor(activity_steps$weekday, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
ggplot(activity_steps)+
geom_line(mapping = aes(x = weekday, y = avgstepsperday), color = "darkred", size = 0.8, group = 0)+
geom_point(mapping = aes(x = weekday, y = avgstepsperday), color = "darkred")+
labs(x = "Weekday", y = "Steps per day", title = "Average steps per day")
Above line graph demonstrate variations in average steps taken per day by users.
Analyzing Averag steps taken by users
activity_steps2 <- activity %>%
group_by(id) %>%
summarise(Avg_daily_steps = mean(totalsteps)) %>%
filter(Avg_daily_steps<= 12000)
ggplot(data = activity_steps2)+ geom_point(mapping = aes(x=id, y=Avg_daily_steps, color= Avg_daily_steps))+
labs(title = "Average steps taken", y = "Average steps taken ", x="User Id ", color = "Average steps")+
theme(axis.text.x.bottom = element_text (angle = 90))+
scale_color_gradient(low = "red", high = "green")+
geom_hline(yintercept = 8000, linetype = "dashed")
As per NIH
report which states that those who took 8,000 steps a day had a 50%
lower risk of dying from any cause during follow-up. Above scatterplot
shows average steps taken by the users. Above graph indicates that users
with less than average 8000 daily steps are at higher health related
risks.
Analyzing steps taken and calories burned
ggplot(data=activity, mapping = aes(x = totalsteps, y = calories, color = totalsteps))+
geom_jitter()+ geom_point()+ geom_smooth()+
labs(title = "Total steps v/s Calories burned per day", x = "Total steps taken", y = "Calories burned", color= "Total steps taken")+
theme_light()
Above scatter plot clearly demonstrates that higher the number of steps taken results in higher number of calories burned.
Analyzing average hourly intensities of the users
intensities <- intensities %>%
separate(activityhour, into = c('date','hour'), sep = " ") %>%
mutate(date = ymd(date))
intensities_hourly <- intensities %>%
group_by(hour) %>%
summarise(avg_intensity = mean(totalintensity))
ggplot(data = intensities_hourly, mapping = aes(x = hour, y = avg_intensity, fill=avg_intensity))+
geom_col()+ labs(title = "Intensity per hour", x="Hour", y="Average Intensity", fill="Average intensities")+
scale_fill_gradient(low = "red", high = "darkblue")+
theme(axis.text.x = element_text(angle = 90))
Above line graph demonstrates variations in physical intensities of the users throughout the day. Peak in intensities from 5 PM to 7 PM is easily noticeable as it is the time most people prefer to exercise.
Phase 5 : Action:
Key Recommendations
- Higher percentage of users has less than 8000 average daily steps which could be reminded to the users by using alerts in the devices
- Alerts to remind users to avoid prolonged idle time could be added to the devices.
- A feature can be added to calculate total amount of calories consumed to help users.