Author : Karan Arora

Date : 1 August 2024

The case study consists of six steps of the data analysis process:

❓ Ask

💻 Prepare

🛠 Process

📊 Analyze

📋 Share

🧗 Act

Scenario

Founded in 2014, Bellabeat is the company that developed one of the first wearables specifically designed for women and has since gone on to create a portfolio of digital products for tracking and improving the health of women.

Focusing on creating innovative health and wellness products for women, their mission is to empower women to take control of their health by providing them with technology-driven solutions that blend design and function.

1. Ask

💡 BUSINESS TASK : Analyze Fitbit data to gain insight and help guide marketing strategy for Bellabeat to grow as a global player.

Primary stakeholders: Urška Sršen and Sando Mur, executive team members.

Secondary stakeholders: Bellabeat marketing analytics team.

2. Prepare

🗄️ DATA SOURCE : 30 participants FitBit Fitness Tracker Data from Mobius: https://www.kaggle.com/arashnic/fitbit

The dataset has 18 CSV. The data also follow a ROCCC approach:

  • Reliability: The data is from 30 FitBit users who consented to the submission of personal tracker data and generated by from a distributed survey via Amazon Mechanical Turk.
  • Original: The data is from 30 FitBit users who consented to the submission of personal tracker data via Amazon Mechanical Turk.
  • Comprehensive: Data minute-level output for physical activity, heart rate, and sleep monitoring. While the data tracks many factors in the user activity and sleep, but the sample size is small and most data is recorded during certain days of the week.
  • Current: Data is from March 2016 to May 2016. Data is not current so the users habit may be different now.
  • Cited: Unknown.

⚠️ The data set has limitations:

  • Only 30 user data is available. The central limit theorem general rule of n≥30 applies and we can use the t test for statstic reference. However, a larger sample size is preferred for the analysis.
  • Upon further investigation with n_distinct() to check for unique user Id, the set has 33 user data from daily activity, 24 from sleep and only 8 from weight. There are 3 extra users and some users did not record their data for tracking daily activity and sleep.
  • For the 8 user data for weight, 5 users manually entered their weight and 3 recorded via a connected wifi device (eg: wifi scale).
  • Most data is recorded from Tuesday to Thursday, which may not be comprehensive enough to form an accurate analysis.

Importing the 3 CSVs : daily_activity, sleep, weight into R using read.csv.

daily_activity <- read.csv("/Users/karanarora/Documents/Learning/Projects/Bellabeat/dailyActivity_merged.csv")
sleep_day <- read.csv("/Users/karanarora/Documents/Learning/Projects/Bellabeat/sleepDay_merged.csv")
weight <-read.csv("/Users/karanarora/Documents/Learning/Projects/Bellabeat/weightLogInfo_merged.csv")

3. Process

Examining the three tables (daily_activity,sleep,weight) for any duplicates or null values :

sum(is.na(daily_activity))
## [1] 0
sum(is.na(sleep_day))
## [1] 0
sum(is.na(weight))
## [1] 65
sum(duplicated(daily_activity))
## [1] 0
sum(duplicated(sleep_day))
## [1] 3
sum(duplicated(weight))
## [1] 0

Removing the 3 duplicates from sleep_day :

sleep_day <- sleep_day[!duplicated(sleep_day), ]
sum(duplicated(sleep_day))
## [1] 0

Convert ActivityDate into date format and add column for the weekday :

daily_activity <- daily_activity %>% 
  mutate( Weekday = weekdays(as.Date(ActivityDate,"%m/%d/%Y")))

Checking to see if we have 30 users using n_distinct. The data set has 33 users from daily_activity, 24 from sleep and only 8 from weight. If there is a discrepency such as in the weight table, checking to see how the data are recorded. The way the user record the data may give us insight on why there is missing data.

weight %>% 
  filter(IsManualReport == "TRUE") %>% 
  group_by(Id) %>% 
  summarise("Manual Weight Report" = n()) %>% 
  distinct()
## # A tibble: 0 × 2
## # ℹ 2 variables: Id <dbl>, Manual Weight Report <int>

Merging the 3 data tables :

merged1 <- merge(daily_activity,sleep_day,by = c("Id"), all=TRUE)
merged_data <- merge(merged1,weight,by = c("Id"), all = TRUE)

Checking how often users record their data. We can see most of the users record their data from Tuesday to Thursday using ggplot. We need to ask why the users are not recording their data on Mondays or Fridays?

ggplot(data=merged_data, aes(x= Weekday)) + geom_bar(fill = "blue") +
  labs(title = "Data Recording During the Week")

⛔ From weekday’s total asleep minutes, we can see the graph look almost same as the graph above! We can confirmed that most sleep data is also recorded during Tuesday to Thursday. This raised a question “how comprehensive are the data to form an accurate analysis?”

ggplot(data = merged_data,aes(x = Weekday,y = TotalMinutesAsleep, fill = Weekday))+
  geom_bar(stat="identity",fill="blue")+
  labs(title = "Total Minutes Asleep During the Week", y = "Total Minutes Asleep")

4. Analyze

Summary

Check min, max, mean, median and any outliers. Avg weight is 135 pounds with BMI of 24 and burn 2050 calories. Avg steps is 10200, max is almost triple that 36000 steps. Users spend on avg 12 hours a day in sedentary minutes, 4 hours lightly active, only half hour in fairly+very active! Users also gets about 7 hour of sleep.

merged_data %>% 
  select(TotalSteps,
         TotalDistance,
         VeryActiveMinutes,
         FairlyActiveMinutes,
         LightlyActiveMinutes,
         SedentaryMinutes,
         Calories,
         TotalSleepRecords,
         TotalMinutesAsleep,
         TotalTimeInBed,
         WeightPounds,
         BMI) %>% 
  summary()
##    TotalSteps    TotalDistance    VeryActiveMinutes FairlyActiveMinutes
##  Min.   :    0   Min.   : 0.000   Min.   :  0.00    Min.   :  0.00     
##  1st Qu.: 5832   1st Qu.: 3.910   1st Qu.:  0.00    1st Qu.:  3.00     
##  Median :10199   Median : 6.820   Median : 15.00    Median : 14.00     
##  Mean   : 9373   Mean   : 6.415   Mean   : 23.57    Mean   : 17.82     
##  3rd Qu.:12109   3rd Qu.: 8.350   3rd Qu.: 38.00    3rd Qu.: 31.00     
##  Max.   :36019   Max.   :28.030   Max.   :210.00    Max.   :143.00     
##                                                                        
##  LightlyActiveMinutes SedentaryMinutes    Calories    TotalSleepRecords
##  Min.   :  0.0        Min.   :   0.0   Min.   :   0   Min.   :1.000    
##  1st Qu.:194.0        1st Qu.: 637.0   1st Qu.:1850   1st Qu.:1.000    
##  Median :238.0        Median : 697.0   Median :2046   Median :1.000    
##  Mean   :232.2        Mean   : 722.6   Mean   :2103   Mean   :1.101    
##  3rd Qu.:288.0        3rd Qu.: 745.0   3rd Qu.:2182   3rd Qu.:1.000    
##  Max.   :518.0        Max.   :1440.0   Max.   :4900   Max.   :3.000    
##                                                       NA's   :971      
##  TotalMinutesAsleep TotalTimeInBed   WeightPounds        BMI       
##  Min.   : 58.0      Min.   : 61.0   Min.   :116.0   Min.   :21.45  
##  1st Qu.:400.0      1st Qu.:421.0   1st Qu.:134.9   1st Qu.:23.89  
##  Median :442.0      Median :457.0   Median :135.6   Median :24.00  
##  Mean   :433.8      Mean   :458.2   Mean   :139.6   Mean   :24.42  
##  3rd Qu.:477.0      3rd Qu.:510.0   3rd Qu.:136.7   3rd Qu.:24.21  
##  Max.   :796.0      Max.   :961.0   Max.   :294.3   Max.   :47.54  
##  NA's   :971        NA's   :971     NA's   :8881    NA's   :8881

Active minutes

total_minutes <- sum(daily_activity$SedentaryMinutes, daily_activity$VeryActiveMinutes,
                     daily_activity$FairlyActiveMinutes, daily_activity$LightlyActiveMinutes)
sedentary_percentage <- sum(daily_activity$SedentaryMinutes)/total_minutes*100
lightly_percentage <- sum(daily_activity$LightlyActiveMinutes)/total_minutes*100
fairly_percentage <- sum(daily_activity$FairlyActiveMinutes)/total_minutes*100
active_percentage <- sum(daily_activity$VeryActiveMinutes)/total_minutes*100

percentage <- data.frame(
  level = c("Sedentary", "Lightly", "Fairly", "Active"),
  minutes = c(sedentary_percentage,lightly_percentage,fairly_percentage,active_percentage)
)

plot_ly(percentage, labels = ~level, values = ~minutes, type = 'pie',textposition = 'outside',textinfo = 'label+percent') %>%
  layout(title = 'Activity Level Minutes',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

The American Heart Association and World Health Organization recommend at least 150 minutes of moderate-intensity activity or 75 minutes of vigorous activity, or a combination of both, each week. That means it needs an daily goal of 21.4 minutes of FairlyActiveMinutes or 10.7 minutes of VeryActiveMinutes.

In our dataset, 30 users met fairly active minutes or very active minutes.

active_users <- daily_activity %>% 
  filter(FairlyActiveMinutes >= 21.4 | VeryActiveMinutes>= 10.7) %>% 
  group_by(Id) %>% 
  count(Id)

active_users
## # A tibble: 30 × 2
## # Groups:   Id [30]
##            Id     n
##         <dbl> <int>
##  1 1503960366    30
##  2 1624580081     6
##  3 1644430081    16
##  4 1927972279     2
##  5 2022484408    28
##  6 2320127002     2
##  7 2347167796    12
##  8 2873212765    11
##  9 3372868164    11
## 10 3977333714    27
## # ℹ 20 more rows

Noticeable day

The bar graph shows that there is a jump on Tuesday: user spent LESS time in sedentary minutes and take MORE steps. Users are out and about on Tuesday.

ggplot(data = merged_data,aes(x = Weekday,y = TotalSteps, fill = Weekday))+
  geom_bar(stat="identity")+
  labs(title = "More Steps on Tuesday", y = "Total Steps")

Interesting finds

The more active that you’re, the more steps you take, and the more calories you will burn. This is an obvious fact, but we can still look into the data to find any interesting. Here we see that some users who are sedentary, take minimal steps, but still able to burn over 1500 to 2500 calories compare to users who are more active, take more steps, but still burn similar calories.

Sleep

According to article: Fitbit Sleep Study, 55 minutes are spent awake in bed before going to sleep. We have 13 users in our dataset spend 55 minutes awake before alseep.

sleep_day_in_hour <-sleep_day
sleep_day_in_hour$TotalMinutesAsleep <- sleep_day_in_hour$TotalMinutesAsleep/60
sleep_day_in_hour$TotalTimeInBed <- sleep_day_in_hour$TotalTimeInBed/60
head(sleep_day_in_hour)
##           Id              SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM                 1           5.450000
## 2 1503960366 4/13/2016 12:00:00 AM                 2           6.400000
## 3 1503960366 4/15/2016 12:00:00 AM                 1           6.866667
## 4 1503960366 4/16/2016 12:00:00 AM                 2           5.666667
## 5 1503960366 4/17/2016 12:00:00 AM                 1          11.666667
## 6 1503960366 4/19/2016 12:00:00 AM                 1           5.066667
##   TotalTimeInBed
## 1       5.766667
## 2       6.783333
## 3       7.366667
## 4       6.116667
## 5      11.866667
## 6       5.333333

13 users spend more than 55 minutes in bed before falling asleep :

awake_in_bed <- mutate(sleep_day, AwakeTime = TotalTimeInBed - TotalMinutesAsleep)
awake_in_bed <- awake_in_bed %>% 
  filter(AwakeTime >= 55) %>% 
  group_by(Id) %>% 
  arrange(AwakeTime, desc=TRUE) 

n_distinct(awake_in_bed$Id) 
## [1] 13

Majority of the users sleep between 5 to 10 hours burns around 1500 to 4500 calories a day.

7. Act

Conclusion based on our analysis:

Sedentary make up a significant portion, 81% of users daily active minutes. Users spend on avg 12 hours a day in sedentary minutes, 4 hours lightly active, and only half-hour in fairly+very active! We see the most change on Saturday: users take more steps, burn more calories, and spend less time sedentary. Sunday is the most “lazy” day for users. 54% of the users who recorded their sleep data spent 55 minutes awake in bed before falling asleep. Users takes the most steps from 5 PM to 7 PM Users who are sedentary take minimal steps and burn 1500 to 2500 calories compared to users who are more active, take more steps, but still burn similar calories. Marketing recommendations to expand globally:

🔢 Obtain more data for an accurate analysis, encouraging users to use a wifi-connected scale instead of manual weight entries.

🚲 Educational healthy style campaign encourages users to have short active exercises during the week, longer during the weekends, especially on Sunday where we see the lowest steps and most sedentary minutes.

🎁 Educational healthy style campaign can pair with a point-award incentive system. Users completing the whole week’s exercise will receive Bellabeat points on products/memberships.

🏃‍♂️ The product, such as Leaf wellness tracker, can beat or vibrate after a prolonged period of sedentary minutes, signaling the user it’s time to get active! Similarly, it can also remind the user it’s time to sleep after sensing a prolonged awake time in bed.