Bellabeat Case Study by Using R-Language

INTRODUCTION ABOUT Bellabeat:

Bellabeat is a health and wellness technology company that creates innovative products for women. They specialize in creating wearable technology devices and apps that track fitness, stress, sleep, and menstrual cycles. Their products include the Leaf Urban, a health tracker that can be worn as a necklace, bracelet, or clip, and the Bellabeat app, which allows users to track their fitness and health goals. Bellabeat is known for their focus on women's health and their use of stylish, high-quality materials in their products.

ASK?Process:

We can use?SMART?framework for Ask process to perform better analysis.

• S-Specific
• M-Measurable
• A-Action-Oriented
• R-Relevant
• T-Time-Bound

Questions???

1. What are some trends in smart device usage?
2. How could these trends apply to Bellabeat customers?
3. How could these trends help influence Bellabeat marketing strategy?

Business Task???

In the Bellabeat dataset, the business task is to analyze smart device usage data to gain insights into women's activity and sleep patterns, and to use these insights to provide personalized wellness recommendations to improve women's overall health and wellbeing.

Prepare?Process:

Data Source:

The data source for Bellabeat is a publicly available dataset called "FitBit Fitness Tracker Data" which includes various fitness and health-related metrics collected from Fitbit devices worn by 30 Fitbit users over a period of several months. The data includes information on physical activity, sleep patterns, heart rate, weight, and other health metrics. Bellabeat used this dataset to analyze patterns and trends in women's health and wellness, with the aim of developing new products and services to improve women's health.

Data Cleaning and Transformation?– R

# install & load packages for data manipulation and visualization

library(tidyverse)
library(ggplot2)
library(lubridate)
library(ggmap)
library(ggthemes)
library(janitor)
library(readr)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
? dplyr     1.1.0     ? readr     2.1.4
? forcats   1.0.0     ? stringr   1.5.0
? ggplot2   3.4.1     ? tibble    3.1.8
? lubridate 1.9.2     ? tidyr     1.3.0
? purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
? dplyr::filter() masks stats::filter()
? dplyr::lag()    masks stats::lag()
? Use the conflicted package (<https://conflicted.r-lib.org/>) to force all conflicts to become errors
? Google's Terms of Service: <https://mapsplatform.google.com>

? Please cite ggmap if you use it! Use `citation("ggmap")` for details.


Attaching package: ‘janitor’


The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test

        

Import Data to R-Studio

daily_ac <- read_csv("/kaggle/input/bellabeat/daily_ac.csv")
hourly_c <- read_csv("/kaggle/input/bellabeat/hourly_c.csv")
hourly_i <- read_csv("/kaggle/input/bellabeat/hourly_i.csv")
sleep_d  <- read_csv("/kaggle/input/bellabeat/sleep_d.csv")
w_log    <- read_csv("/kaggle/input/bellabeat/w_log.csv")

Rows: 940 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): ActivityDate
dbl (14): Id, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDi...

? Use `spec()` to retrieve the full column specification for this data.
? Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 22099 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): ActivityHour
dbl (2): Id, Calories

? Use `spec()` to retrieve the full column specification for this data.
? Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 22099 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): ActivityHour
dbl (3): Id, TotalIntensity, AverageIntensity

? Use `spec()` to retrieve the full column specification for this data.
? Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 413 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): SleepDay
dbl (4): Id, TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed

? Use `spec()` to retrieve the full column specification for this data.
? Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 67 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date
dbl (6): Id, WeightKg, WeightPounds, Fat, BMI, LogId
lgl (1): IsManualReport

? Use `spec()` to retrieve the full column specification for this data.
? Specify the column types or set `show_col_types = FALSE` to quiet this message.        

# To check imported files

head(daily_ac)
head(hourly_c)
head(hourly_i)
head(sleep_d)
head(w_log)        

#checking the discrepancy in column names because sometimes error occurs.
# we can use clear_name() function also to remove discrepancies from the column names.

names(daily_ac)
names(hourly_c)
names(hourly_i)
names(sleep_d)
names(w_log)        

1. 'Id''ActivityDate''TotalSteps''TotalDistance''TrackerDistance''LoggedActivitiesDistance''VeryActiveDistance''ModeratelyActiveDistance''LightActiveDistance''SedentaryActiveDistance''VeryActiveMinutes''FairlyActiveMinutes''LightlyActiveMinutes''SedentaryMinutes''Calories'
2. 'Id''ActivityHour''Calories'
3. 'Id''ActivityHour''TotalIntensity''AverageIntensity'
4. 'Id''SleepDay''TotalSleepRecords''TotalMinutesAsleep''TotalTimeInBed'
5. 'Id''Date''WeightKg''WeightPounds''Fat''BMI''IsManualReport''LogId'

Date Manipulation

# Date Manipulation in all data frames "Becuase date in char format".

#note tht im using here "Y" for century and "y" for just year without century.

# For Daily Activity 
names(daily_ac)
daily_ac$ActivityDate<-as.POSIXct(daily_ac$ActivityDate, format= "%m/%d/%Y", tz=Sys.timezone()) 
daily_ac$date<-format(daily_ac$ActivityDate, format="%m/%d/%y")

# For hourly Calories 
names(hourly_c)
hourly_c$ActivityHour<-as.POSIXct(hourly_c$ActivityHour, format="%m/%d/%Y %I:%M:%S %p",tz=Sys.timezone())
hourly_c$date<-format(hourly_c$ActivityHour, format="%m/%d/%y") 
hourly_c$time<-format(hourly_c$ActivityHour, format="%H:%M:%S") 
        

For hourly Intensities

names(hourly_i)
hourly_i$ActivityHour<-as.POSIXct(hourly_i$ActivityHour, format="%m/%d/%Y %I:%M:%S %p",tz=Sys.timezone())
hourly_i$date<-format(hourly_i$ActivityHour, format="%m/%d/%y" )
hourly_i$time<-format(hourly_i$ActivityHour, format="%H:%M:%S")

#For sleep_d 
names(sleep_d)
sleep_d$SleepDay<-as.POSIXct(sleep_d$SleepDay, format="%m/%d/%Y %I:%M:%S %p",tz=Sys.timezone())
sleep_d$date<-format(sleep_d$SleepDay, format="%m/%d/%y")
sleep_d$time<-format(sleep_d$SleepDay, format="%H:%M:%S")
        

For weightlog

names(w_log)
w_log$Date<-as.POSIXct(w_log$Date, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
w_log$date<-format(w_log$Date, format="%m/%d/%y")
w_log$time<-format(w_log$Date, format="%H:%M:%S")        

1. 'Id''ActivityDate''TotalSteps''TotalDistance''TrackerDistance''LoggedActivitiesDistance''VeryActiveDistance''ModeratelyActiveDistance''LightActiveDistance''SedentaryActiveDistance''VeryActiveMinutes''FairlyActiveMinutes''LightlyActiveMinutes''SedentaryMinutes''Calories'
2. 'Id''ActivityHour''Calories'
3. 'Id''ActivityHour''TotalIntensity''AverageIntensity'
4. 'Id''SleepDay''TotalSleepRecords''TotalMinutesAsleep''TotalTimeInBed'
5. 'Id''Date''WeightKg''WeightPounds''Fat''BMI''IsManualReport''LogId'

#To identify formats and structure.

glimpse(daily_ac)
glimpse(hourly_c)
glimpse(hourly_i)
glimpse(sleep_d)
glimpse(w_log)
Rows: 940
Columns: 16
$ Id                       <dbl> 1503960366, 1503960366, 1503960366, 150396036…
$ ActivityDate             <dttm> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-…
$ TotalSteps               <dbl> 13162, 10735, 10460, 9762, 12669, 9705, 13019…
$ TotalDistance            <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
$ TrackerDistance          <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.8…
$ LoggedActivitiesDistance <dbl> 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, 3.5…
$ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1.3…
$ LightActiveDistance      <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5.0…
$ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ VeryActiveMinutes        <dbl> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4…
$ FairlyActiveMinutes      <dbl> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21…
$ LightlyActiveMinutes     <dbl> 328, 217, 181, 209, 221, 164, 233, 264, 205, …
$ SedentaryMinutes         <dbl> 728, 776, 1218, 726, 773, 539, 1149, 775, 818…
$ Calories                 <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 203…
$ date                     <chr> "04/12/16", "04/13/16", "04/14/16", "04/15/16…
Rows: 22,099
Columns: 5
$ Id           <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396036…
$ ActivityHour <dttm> 2016-04-12 00:00:00, 2016-04-12 01:00:00, 2016-04-12 02:…
$ Calories     <dbl> 81, 61, 59, 47, 48, 48, 48, 47, 68, 141, 99, 76, 73, 66, …
$ date         <chr> "04/12/16", "04/12/16", "04/12/16", "04/12/16", "04/12/16…
$ time         <chr> "00:00:00", "01:00:00", "02:00:00", "03:00:00", "04:00:00…
Rows: 22,099
Columns: 6
$ Id               <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 15039…
$ ActivityHour     <dttm> 2016-04-12 00:00:00, 2016-04-12 01:00:00, 2016-04-12…
$ TotalIntensity   <dbl> 20, 8, 7, 0, 0, 0, 0, 0, 13, 30, 29, 12, 11, 6, 36, 5…
$ AverageIntensity <dbl> 0.333333, 0.133333, 0.116667, 0.000000, 0.000000, 0.0…
$ date             <chr> "04/12/16", "04/12/16", "04/12/16", "04/12/16", "04/1…
$ time             <chr> "00:00:00", "01:00:00", "02:00:00", "03:00:00", "04:0…
Rows: 413
Columns: 7
$ Id                 <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150…
$ SleepDay           <dttm> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-16, 20…
$ TotalSleepRecords  <dbl> 1, 2, 1, 2, 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, 2…
$ TotalTimeInBed     <dbl> 346, 407, 442, 367, 712, 320, 377, 364, 384, 449, 3…
$ date               <chr> "04/12/16", "04/13/16", "04/15/16", "04/16/16", "04…
$ time               <chr> "00:00:00", "00:00:00", "00:00:00", "00:00:00", "00…
Rows: 67
Columns: 10
$ Id             <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212…
$ Date           <dttm> 2016-05-02 23:59:59, 2016-05-03 23:59:59, 2016-04-13 0…
$ WeightKg       <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.3, …
$ WeightPounds   <dbl> 115.9631, 115.9631, 294.3171, 125.0021, 126.3249, 159.6…
$ Fat            <dbl> 22, NA, NA, NA, NA, 25, 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,…
$ IsManualReport <lgl> TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, …
$ LogId          <dbl> 1.462234e+12, 1.462320e+12, 1.460510e+12, 1.461283e+12,…
$ date           <chr> "05/02/16", "05/03/16", "04/13/16", "04/21/16", "05/12/…
$ time           <chr> "23:59:59", "23:59:59", "01:08:52", "23:59:59", "23:59:…        

ANALYZE:

Data has been cleaned and now we will move toward the heart of analysis process. There are four phases in the analysis process.

1. Organize Data.
2. Format and adjust data.
3. Perform calculations.
4. Identify trends and relationships.

#I'm using distinct function to see how many values e unique().

n_distinct(daily_ac)
n_distinct(hourly_c)
n_distinct(hourly_i)
n_distinct(sleep_d)
n_distinct(w_log)        

940

22099

22099

410

67

The above information is identify that how many member are available in each data set.

As we can identify.

• There is 940 participants in the "daily activity".
• 22099 in "hourly calories" and "hourly intensities" data sets.
• 410 in the "sleep day".
• 67 in the "weight log" data set.

Note: 67 participants in "weight log" is not significant to make any recommendations and conclusions based on this data.

For Summary

daily_ac %>%  
  select(TotalSteps,
         TotalDistance,
         SedentaryMinutes, Calories) %>%
  summary()
        

I'm checking here active minute foe every user

daily_ac %>%
  select(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes) %>% 
  summary()
# Average intensities
hourly_i%>% select(TotalIntensity) %>% summary()
        

Summary for calories

hourly_c %>% 
  select(Calories) %>% 
  summary()

#Summary for sleep 
sleep_d %>% select(TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed) %>% 
  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  
 VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes
 Min.   :  0.00    Min.   :  0.00      Min.   :  0.0       
 1st Qu.:  0.00    1st Qu.:  0.00      1st Qu.:127.0       
 Median :  4.00    Median :  6.00      Median :199.0       
 Mean   : 21.16    Mean   : 13.56      Mean   :192.8       
 3rd Qu.: 32.00    3rd Qu.: 19.00      3rd Qu.:264.0       
 Max.   :210.00    Max.   :143.00      Max.   :518.0       
 TotalIntensity  
 Min.   :  0.00  
 1st Qu.:  0.00  
 Median :  3.00  
 Mean   : 12.04  
 3rd Qu.: 16.00  
 Max.   :180.00  
    Calories     
 Min.   : 42.00  
 1st Qu.: 63.00  
 Median : 83.00  
 Mean   : 97.39  
 3rd Qu.:108.00  
 Max.   :948.00  
 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          

The data of weight is not enough,I'm doing my analysis with it, you can neglect it or can take any other decision.

w_log %>% select(WeightKg, BMI) %>% summary()

    WeightKg           BMI       
 Min.   : 52.60   Min.   :21.45  
 1st Qu.: 61.40   1st Qu.:23.96  
 Median : 62.50   Median :24.39  
 Mean   : 72.04   Mean   :25.19  
 3rd Qu.: 85.05   3rd Qu.:25.56  
 Max.   :133.50   Max.   :47.54          

These summaries making my understanding better for next step visualization

• A sedentary time of 991 minutes per day (which is equivalent to over 16 hours) is very high and not considered normal or healthy.
• It suggests that the individual is spending most of their day sitting or lying down with little physical activity.
• We can analyze that the most of the participants are lightly active.
• On the average, participants sleep 1 time for 7 hours.
• As we can analyze from the summary of daily activity -> average footsteps of participant is 7638, In terms of steps, a good goal for improving overall health and fitness is to aim for 7,000 to 10,000 steps per day, which is roughly equivalent to 30 minutes of moderate-intensity activity per day.

Data Merging

merged_data<-merge(sleep_d,daily_ac, by=c('Id', 'date'))        

Visualization :

ggplot(data=daily_ac,aes(x=TotalSteps,y=Calories))+geom_point(colour="Blue")+geom_line()+geom_smooth(colour="Red")+labs(title="Relation Between Calories and TotalSteps" )+ theme(plot.title = element_text(color = "Green"))        

• The plot i have created shows the relationship between the number of total steps taken and the number of calories burned. From the plot, we can see that as the number of total steps taken increases, the number of calories burned also tends to increase. This suggests that there is a positive relationship between these two variables.
• One possible explanation for this relationship is that as people take more steps, they are engaging in more physical activity, which requires more energy and therefore burns more calories. This is consistent with the idea that increasing physical activity is an effective way to burn calories and maintain a healthy weight.
• Overall, the plot suggests that increasing the number of steps taken may be a useful strategy for burning more calories and promoting physical activity. However, it's important to consider other factors that may influence this relationship and to consult with a healthcare professional before making significant changes to your physical activity or diet.

Checking relatrion betwen sleep time and bed time

ggplot(data=sleep_d, aes(x=TotalMinutesAsleep, y=TotalTimeInBed ))+geom_point(colour="Blue")+geom_line()+geom_smooth(colour="Red")+labs(title="Corelation Between Sleep Time in Minute and Total Time in Bed")+theme(plot.title = element_text(color = "Green"))        

As we can see the relationship between Total Minutes Asleep and Total Time in Bed looks linear, it suggests that there is a linear association between these two variables.

Relation between total Intensities and Time(Hours).

hourly_i %>% group_by(time) %>% drop_na() %>%
  summarize(mean_total_intensities=mean(TotalIntensity)) %>% 
  ggplot(aes(x=time,y=mean_total_intensities))+
  geom_col(width=0.4,position=position_dodge(width=0.5),colour="white",fill="lightgreen")+
  labs(title="Relation B/W Total Intensities and Hourly Time",x="Time",y="Average Total Intensities")+
  theme(axis.text.x =element_text(angle = 90))+
  theme(plot.title=element_text(colour="black"))+
  theme(plot.title=element_text(face="bold"))+
  theme(panel.background =element_rect(fill = "black"))+
  theme(axis.text.x =element_text(face="bold",colour = "black" ))+
  theme(axis.text.y =element_text(face="bold",colour = "black" ))        

• Upon conducting an analysis of the hourly distribution of Total Intensity, it was observed that individuals tend to exhibit higher levels of physical activity between the hours of 5 AM and 10 PM.
• There could be several reasons for the increased activity levels observed between 5 PM and 7 PM. One possible explanation is that this time period coincides with the end of the workday for many people, which may allow them more free time for physical activity. Additionally, this time period may be conducive to outdoor activities due to favorable weather conditions, such as longer daylight hours in the summer months. Other factors, such as social activities and group exercise classes that tend to occur during this time period, may also contribute to the observed trend. Further analysis and data collection may be necessary to determine the specific causes underlying the increased activity levels during this particular time period.
• Based on the observed trend of increased activity levels between 5 PM and 7 PM, Bellabeat could potentially utilize this information to develop targeted interventions aimed at encouraging physical activity during this time period. For example, the Bellabeat app could send reminders or notifications to users encouraging them to engage in physical activity during this window of time. Additionally, Bellabeat could consider partnering with local gyms or fitness studios to offer group exercise classes or other activities specifically targeted to this time period. By leveraging this trend in physical activity, Bellabeat can potentially improve user engagement and contribute to overall improvements in health and wellness.

Relationship B/W Total Minutes Asleep and Sedentary Minutes

ggplot(data=merged_data, aes(x=TotalMinutesAsleep,y=SedentaryMinutes))+
  geom_point(colour="green")+
  geom_smooth(colour="white")+
  geom_line(colour="white")+
  theme(plot.background = element_rect(fill="grey"),
        panel.background = element_rect(fill="black"),
        axis.text.x=element_text(face="bold",colour="black"),
        axis.text.y=element_text(face="bold",colour="black"),
        plot.title = element_text(face="bold",colour="black"))+
  labs(title="Correlation B/W TotalMinutesAsleep and SedentaryMinutes",x="Total Minutes Asleep",y="Sedentary Minutes")        

• Through my analysis, a clear negative correlation was observed between Sedentary Minutes and Sleep time.
• Based on the observed negative correlation between Sedentary Minutes and Sleep time, Bellabeat could potentially provide personalized recommendations to users to reduce their sedentary behavior in order to improve their sleep quality.

SHARE:

This phase will be done by presentation but you can view my Kaggle Notebook ??Here

Key tasks

• Determine the best way to share your findings.
• Create effective data visualizations.
• Present your findings.
• Ensure your work is accessible.

ACT:

This phase will be carried out by the executive team, Director of Marketing (Bellabeat) and the Marketing Analytics team based on my analysis which is called Data-driven decision making.

Data-Driven decisions:

My recommendations based on my analysis.

• Increase engagement with the app: Bellabeat can offer personalized recommendations to users based on their fitness and health goals. This can increase user engagement and lead to better outcomes.
• Improve sleep patterns: Bellabeat can provide users with insights on their sleep patterns and suggest ways to improve sleep quality, such as reducing sedentary time and increasing physical activity.
• Increase physical activity: Bellabeat can motivate users to increase their physical activity by providing personalized goals and challenges. This can be done through the app, wearable devices, or both.
• Provide more personalized content: Bellabeat can offer personalized content to users based on their interests and preferences. This can include articles, videos, and tips on fitness, nutrition, and wellness.
• Improve user experience: Bellabeat can improve the user experience by making the app more user-friendly and intuitive. This can include simplifying navigation, improving the design, and offering more features and functionalities.

These are just some ideas for improving the Bellabeat data set. It's important to analyze the data further and conduct user research to determine the most effective strategies for improving user engagement, health outcomes, and overall satisfaction with the app.

Resources

• R-Documentation
• R-Studio and?Kaggle?community.