BellaBeat Case Study

Introduction

You have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights you discover will then help guide marketing strategy for the company. You will present your analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat’s marketing strategy.

ASK

# What are some trends in smart device usage?

# How could these trends apply to Bellabeat customers?

# How could these trends help influence Bellabeat marketing strategy

PREPARE

#FitBit Fitness Tracker Data (Dataset made available through Mobius on Kaggle).

#Thirty eligible Fitbit users consented to the submission of personal tracker data.

#About this data: It contains a total of 18 datasets, with various records on

participants’ activity and fitness data.

#I will be using R for this project.

Does your data ROCCC ?

#Reliability: Not reliable due to the small sample size of 30.

#Original: Yes, data is from FitBit.

#Comprehensive: Based on the initial screening of the dataset, it is quite comprehensive to the user's activity.

#Current: Dataset dates back to 2016 which makes it not current.

#Cited: No information was provided.

# Importing Dataset

# Import datasets to view and analyze the data.

Activity <- read.csv("dailyActivity_merged.csv"")

daily_calories <- read_csv("dailyCalories_merged.csv")

daily_Intensities <- read_csv("dailyIntensities_merged.csv")

daily_Steps <- read_csv("dailySteps_merged.csv")

heart_ratesec <- read_csv("heartrate_seconds_merged.csv")

minute_MET <- read_csv("minuteMETsNarrow_merged.csv")

daily_sleep <- read_csv("sleepDay_merged.csv")

weight_log <- read_csv("weightLogInfo_merged.csv")

glimpse(daily_activity)

# Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 150396036…

# ActivityDate <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/2016", "4/16/2016", "4/17/2016", "4/…

# TotalSteps <dbl> 13162, 10735, 10460, 9762, 12669, 9705, 13019, 15506, 10544, 9819, 12764, 14371, …

# TotalDistance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.88, 6.68, 6.34, 8.13, 9.04, 6.41, 9.8…

# TrackerDistance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.88, 6.68, 6.34, 8.13, 9.04, 6.41, 9.8…

# LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …

# VeryActiveDistance <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, 3.53, 1.96, 1.34, 4.76, 2.81, 2.92, 5.2…

# ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1.32, 0.48, 0.35, 1.12, 0.87, 0.21, 0.5…

# LightActiveDistance <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5.03, 4.24, 4.65, 2.24, 5.36, 3.28, 3.9…

# SedentaryActiveDistance <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…

# VeryActiveMinutes <dbl> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 41, 39, 73, 31, 78, 48, 16, 52, 33, 4…

# FairlyActiveMinutes <dbl> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21, 5, 14, 23, 11, 28, 12, 34, 35, 15,…

# LightlyActiveMinutes <dbl> 328, 217, 181, 209, 221, 164, 233, 264, 205, 211, 130, 262, 238, 216, 279, 243, 1…

# SedentaryMinutes <dbl> 728, 776, 1218, 726, 773, 539, 1149, 775, 818, 838, 1217, 732, 709, 814, 833, 110…

# Calories <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 1775, 1827, 1949, 1788, 201…

#Daily_activity contains most of the important tracker data including total steps,

calories, and distance information.

glimpse(heart_ratesec)

Rows: 2,483,658

Columns: 3

$ Id <dbl> 2022484408, 2022484408, 2022484408, 2022484408, 2022484408, 2022484408, 2022484408, 2022484408, 2022…

$ Time <chr> "4/12/2016 7:21:00 AM", "4/12/2016 7:21:05 AM", "4/12/2016 7:21:10 AM", "4/12/2016 7:21:20 AM", "4/1…

$ Value <dbl> 97, 102, 105, 103, 101, 95, 91, 93, 94, 93, 92, 89, 83, 61, 60, 61, 61, 57, 54, 55, 58, 60, 59, 57, …

glimpse(daily_sleep)

Rows: 413

Columns: 5

$ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 150…

$ 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 1…

$ TotalSleepRecords <dbl> 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …

$ TotalMinutesAsleep <dbl> 327, 384, 412, 340, 700, 304, 360, 325, 361, 430, 277, 245, 366, 341, 404, 369, 277, 27…

$ TotalTimeInBed <dbl> 346, 407, 442, 367, 712, 320, 377, 364, 384, 449, 323, 274, 393, 354, 425, 396, 309, 29…

glimpse(weight_log)

Rows: 67

Columns: 8

$ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212765, 4319703577, 4319703577, 4558609…

$ Date <chr> "5/2/2016 11:59:59 PM", "5/3/2016 11:59:59 PM", "4/13/2016 1:08:52 AM", "4/21/2016 11:59:59…

$ WeightKg <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.3, 69.9, 69.2, 69.1, 90.7, 62.5, 62.1, …

$ WeightPounds <dbl> 115.9631, 115.9631, 294.3171, 125.0021, 126.3249, 159.6147, 159.3942, 153.6622, 154.9850, 1…

$ Fat <dbl> 22, NA, NA, NA, NA, 25, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

$ BMI <dbl> 22.65, 22.65, 47.54, 21.45, 21.69, 27.45, 27.38, 27.25, 27.46, 27.32, 27.04, 27.00, 28.00, …

$ IsManualReport <lgl> TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE,…

$ LogId <dbl> 1.462234e+12, 1.462320e+12, 1.460510e+12, 1.461283e+12, 1.463098e+12, 1.460938e+12, 1.46240…

# Check distinct Id

n_distinct(daily_activity)

[1] 940

n_distinct(daily_activity$Id)

[1] 33

n_distinct(heart_ratesec)

[1] 2483658

n_distinct(heart_ratesec$Id)

[1] 14

n_distinct(daily_sleep)

[1] 410

n_distinct(daily_sleep$Id)

[1] 24

n_distinct(weight_log)

[1] 67

n_distinct(weight_log$Id)

[1] 8

#Daily Activity data contains 940 records of 33 different participants regarding their daily activities.

#Heart Rate data contains 2,483,658 records of 14 different participants on their heart rate.

#Daily Sleep data contains 410 records of 24 different participants on their daily sleep information.

#Weight Log data contains 67 records of 8 different participants on their daily weight record.

#A big problem about this data are the low entries on the tracker measurements. 14 and 8 participants

are significantly low numbers to make recommendations.

Process and Analyze

# ACTIVITY

daily_activity %>%

select(TotalSteps,

TotalDistance,

SedentaryMinutes, Calories) %>%

summary()

TotalSteps TotalDistance SedentaryMinutes Calories

Min. : 0 Min. : 0.000 Min. : 0.0 Min. : 0

1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8 1st Qu.:1828

Median : 7406 Median : 5.245 Median :1057.5 Median :2134

Mean : 7638 Mean : 5.490 Mean : 991.2 Mean :2304

3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5 3rd Qu.:2793

Max. :36019 Max. :28.030 Max. :1440.0 Max. :4900

# INTENSITIES

daily_Intensities %>%

select(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes, SedentaryMinutes) %>%

summary()

VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes

Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.0

1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:127.0 1st Qu.: 729.8

Median : 4.00 Median : 6.00 Median :199.0 Median :1057.5

Mean : 21.16 Mean : 13.56 Mean :192.8 Mean : 991.2

3rd Qu.: 32.00 3rd Qu.: 19.00 3rd Qu.:264.0 3rd Qu.:1229.5

Max. :210.00 Max. :143.00 Max. :518.0 Max. :1440.0

# CALORIES

daily_calories %>%

select(Calories) %>%

summary()

Calories

Min. : 0

1st Qu.:1828

Median :2134

Mean :2304

3rd Qu.:2793

Max. :4900

# SLEEP

daily_sleep %>%

select(TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed) %>%

summary()

TotalSleepRecords TotalMinutesAsleep TotalTimeInBed

Min. :1.000 Min. : 58.0 Min. : 61.0

1st Qu.:1.000 1st Qu.:361.0 1st Qu.:403.0

Median :1.000 Median :433.0 Median :463.0

Mean :1.119 Mean :419.5 Mean :458.6

3rd Qu.:1.000 3rd Qu.:490.0 3rd Qu.:526.0

Max. :3.000 Max. :796.0 Max. :961.0

# WEIGHT

weight_log %>%

select(WeightKg, Fat) %>%

summary()

WeightKg Fat

Min. : 52.60 Min. :22.00

1st Qu.: 61.40 1st Qu.:22.75

Median : 62.50 Median :23.50

Mean : 72.04 Mean :23.50

3rd Qu.: 85.05 3rd Qu.:24.25

Max. :133.50 Max. :25.00

NA's :65

# FINDINGS FROM THIS ANALYSIS:

#The sedimentary time from the participants is too high. A good marketing

strategy could reduce this.

#Average total steps per day is 7,638. According to CDC, it is recommended to

reach 10,000 steps per day. A 2020 study found that participants who took 8,000

steps per day had 51% lower risk of dying by any cause.

#Average sleep time of participants is 7 hours.

Share and Act Phase

# Visualizations

# Relationship between Steps and Sedentary Time.

ggplot(data=daily_activity, aes(x=TotalSteps, y= SedentaryMinutes))+

geom_point()+geom_smooth()+labs(title="Total Steps vs Sedentary Minutes")

#There is a negative correlation between the two. The more sedimentary time you have, the less steps you're taking during the day.

#This data can be used for the next marketing strategy so that more

users will be more active and have less sedimentary time.

# Relationship between Minutes Asleep and Time in Bed.

ggplot(data=daily_sleep, aes(x=TotalMinutesAsleep, y=TotalTimeInBed))+

geom_point()+geom_smooth()+labs(title= "Minutes Asleep vs Time In Bed")

#As we can see on the plot. Its almost completely a linear trend. To help users

improve their sleep, Bellabeat should add a notification feature that lets users

know its time for bed.

# Relationship between Steps and Calories.

ggplot(data=daily_activity, aes(x= TotalSteps y= Calories))+

geom_point()+geom_smooth()+labs(title= "Total Steps vs Calories")

#The more steps we take and how active we are will result in more burned calories.

Key Findings

#It dates back to 2016. Technology has evolved and habits of people might have changed.

#Relatively small sample size.

#Based on limited data, participants were most likely to complete activity tracker data but not all participants record their progress.

Recommendations

#Since the sedimentary time is high, Bellabeat should add a vibrate function if

the user has been sitting or idle for too long. Signaling the user to get active.

#Obtain more data for an accurate analysis.

#Bellabeat should consider doing a point award incentive system where hitting

daily fitness goals can earn users points for products or membership.

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