Bellabeat Products Analysis

The problem statement

“Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat wants to expand their business for one of their products so they wants to analyse the usage of one of their products in order to gain insight into how people are already using their smart devices.Then, using this information,she(Urška Sršen, cofounder and Chief Creative Officer of Bellabeat) would like high-level recommendations for how these trends can inform Bellabeat marketing strategy”

Key stakeholders:-

Urška Sršen: Bellabeat’s co-founder and Chief Creative Officer Sando Mur: Mathematician and Bellabeat’s co-founder Bellabeat marketing analytics team

Introduction:-

This is a capstone project for my Google Data Analytics Capstone. In this case study, I will be analyzing one of Bellabeat’s smart device data to gain insight into how consumers are using their smart devices, Bellabeat is a high-tech manufacturer of health focused products for women. The insights I discover will then help guide to decide or enhance marketing strategy for the company

1) Ask:-

Key Objectives:

What are some daily habit trends in smart devise usage?
How could these daily habit trends apply to Bellabeat customers?
How could these daily habit trends help influence the Bellabeat marketing strategy?

2) Prepare:-

Data preparation:

 Source, Licensing, Privacy

Source: FitBit Fitness Tracker Data - (Dataset made available through Mobius)

License: CC0: Public Domain

Privacy: This Kaggle data set contains personal fitness tracker from 30
Fitbit users who consented to the submission of personal tracker data,including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

3) Process:

importing necessory packages

 if (!require('tidyverse'))
 {
  install.packages('tidyverse');
   library(tidyverse);
 }

## Loading required package: tidyverse

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

if(!require('tidyverse'))
{
  install.packages('here');
  library(here);
}

if(!require('skimr'))
{
  install.packages('skimr');
  library(skimr);
}

## Loading required package: skimr

if(!require('janitor'))
{
  install.packages('janitor');
  library(janitor)
}

## Loading required package: janitor

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

if(!require('lubridate'))
{
install.packages('lubridate');
  library(lubridate)
}

## Loading required package: lubridate

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

if(!require('dplyr'))
{
install.packages('dplyr');
  library(dplyr);
}

if(!require('ggplot2'))
{
install.packages('ggplot2');
  library(ggplot2);
}

if(!require('tidyr'))
{
  install.packages('tidyr');
  library(tidyr);
}

if(!require('corrplot'))
{
  install.packages('corrplot');
  library(corrplot);
}

## Loading required package: corrplot

## corrplot 0.90 loaded

if(!require('ggpubr'))
{
install.packages('ggpubr');
  library(ggpubr);
}

## Loading required package: ggpubr

  if(!require('chron'))
{
  install.packages('chron');
  library(chron);
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## Loading required package: chron

## 
## Attaching package: 'chron'

## The following objects are masked from 'package:lubridate':
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if(!require('hms'))
{
  install.packages('hms');
  library(hms);
}

## Loading required package: hms

## 
## Attaching package: 'hms'

## The following object is masked from 'package:lubridate':
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if(!require('kableExtra'))
{
  install.packages(kableExtra);
  library(kableExtra);
}

## Loading required package: kableExtra

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## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
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if(!require('magrittr'))
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  install.packages('magrittr');
  library(magrittr);
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## Loading required package: magrittr

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if(!require('scales'))
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  install.packages('scales');
  library(scales);
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## Loading required package: scales

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if(!require('devtools'))
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install.packages('devtools');
  library(devtools)
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## Loading required package: devtools

## Loading required package: usethis

if(!require('readxl'))
{
  install.packages('readxl');
  library(readxl);
}

## Loading required package: readxl

if(!require('tinytex'))
{
  install.packages('tinytex');
  library(tinytex)
}

## Loading required package: tinytex

 As I explore through all the files present I found that much files's columns are already present in the dailyActivity_merged file so we are here taking only those files which we have to analyze so those files are:
 dailyActivity_merged
 hourlyIntensities_merged
 heartrate_seconds_merged
 sleepDay_merged
 weightLogInfo_merged

###- importing files:

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

Hourly_Calories <- read.csv("hourlyCalories_merged.csv")

Hourly_Intensities <- read.csv("hourlyIntensities_merged.csv")

 Hourly_Steps <- read.csv("hourlySteps_merged.csv")

Heartrate_Seconds <- read.csv("heartrate_seconds_merged.csv")

Sleep_Day <- read.csv("sleepDay_merged.csv")

WeightLogInfo <- read.csv("weightLogInfo_merged.csv")

Exploring files-

head(Activity)

head(Hourly_Calories)

head(Hourly_Intensities)

head(Hourly_Steps)

head(Heartrate_Seconds)

head(Sleep_Day)

head(WeightLogInfo)

Checking the length of Id column

   length(unique(Activity$Id))

## [1] 33

   length(unique(Hourly_Intensities$Id))

## [1] 33

   length(unique(Heartrate_Seconds$Id))

## [1] 7

   length(unique(Sleep_Day$Id))

## [1] 24

   length(unique(WeightLogInfo$Id))

## [1] 8

   length(unique(Hourly_Calories$Id))

## [1] 33

   length(unique(Hourly_Steps$Id))

## [1] 33

Check for duplicates

any(duplicated(Activity))

## [1] FALSE

any(duplicated(Hourly_Calories))

## [1] FALSE

any(duplicated(Hourly_Intensities))

## [1] FALSE

any(duplicated(Hourly_Steps))

## [1] FALSE

any(duplicated(Heartrate_Seconds))

## [1] TRUE

any(duplicated(Sleep_Day))

## [1] TRUE

any(duplicated(WeightLogInfo))

## [1] FALSE

remove duplicate from Sleep_Day

Sleep_Day <- distinct(Sleep_Day)
any(duplicated(Sleep_Day))

## [1] FALSE

here in most of files we have unnecessory columns are present which we don’t have to use so we are going to delete them first

Activity <- subset(Activity,select=-c(TrackerDistance,LoggedActivitiesDistance,SedentaryActiveDistance))

head(Activity)

WeightLogInfo <- subset(WeightLogInfo,select=-c(WeightPounds,Id))
head(WeightLogInfo)

1. change the data type from char to date of ActivityDate and Adding Day_of_Week column to Activity :

Activity <- Activity %>%
mutate(ActivityDate = mdy(ActivityDate), Day_of_Week = weekdays(ActivityDate))

let’s check the added column in Activity dataframe

head(Activity)

add new columnSleep_Amount to Sleep_Day dataframe

Sleep_Day = Sleep_Day %>% 
mutate(Sleep_Amount = case_when(TotalMinutesAsleep/60>=6.0 & TotalMinutesAsleep/60<=9.0   ~ "Good Sleep",
    TotalMinutesAsleep/60<6.0 ~ "Under Sleep", 
    TotalMinutesAsleep/60>9.0 ~ "Over sleep"))

let’s see the data

head(Sleep_Day)

we are going to delete some columns from Sleep_Day dataframe because we do’t need it

Sleep_Day <- subset(Sleep_Day,select=-c(SleepDay,TotalSleepRecords,TotalTimeInBed))

Let’s check the dataframe

head(Sleep_Day)

since Id is in dbl data type we have to convert it into character type of all dataframes

Activity <- mutate(Activity, Id=as.character('Id'))
Hourly_Intensities <-mutate(Hourly_Intensities,Id=as.character('Id'))
Hourly_Calories <- mutate(Hourly_Calories,Id=as.character('Id'))
Hourly_Steps <- mutate(Hourly_Steps,Id=as.character('Id'))
Heartrate_Seconds <- mutate(Heartrate_Seconds,Id=as.character('Id'))
Sleep_Day <- mutate(Sleep_Day,Id=as.character('Id'))
WeightLogInfo <-mutate(WeightLogInfo,Id=as.character('Id'))

check it

summary(Activity)

##       Id             ActivityDate          TotalSteps    TotalDistance   
##  Length:940         Min.   :2016-04-12   Min.   :    0   Min.   : 0.000  
##  Class :character   1st Qu.:2016-04-19   1st Qu.: 3790   1st Qu.: 2.620  
##  Mode  :character   Median :2016-04-26   Median : 7406   Median : 5.245  
##                     Mean   :2016-04-26   Mean   : 7638   Mean   : 5.490  
##                     3rd Qu.:2016-05-04   3rd Qu.:10727   3rd Qu.: 7.713  
##                     Max.   :2016-05-12   Max.   :36019   Max.   :28.030  
##  VeryActiveDistance ModeratelyActiveDistance LightActiveDistance
##  Min.   : 0.000     Min.   :0.0000           Min.   : 0.000     
##  1st Qu.: 0.000     1st Qu.:0.0000           1st Qu.: 1.945     
##  Median : 0.210     Median :0.2400           Median : 3.365     
##  Mean   : 1.503     Mean   :0.5675           Mean   : 3.341     
##  3rd Qu.: 2.053     3rd Qu.:0.8000           3rd Qu.: 4.782     
##  Max.   :21.920     Max.   :6.4800           Max.   :10.710     
##  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    Day_of_Week       
##  Min.   :   0   Length:940        
##  1st Qu.:1828   Class :character  
##  Median :2134   Mode  :character  
##  Mean   :2304                     
##  3rd Qu.:2793                     
##  Max.   :4900

changing data type of date column

#Hourly_Calories
Hourly_Calories$ActivityHour=as.POSIXct(Hourly_Calories$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
Hourly_Calories$ActivityHour <- format(Hourly_Calories$ActivityHour, format = "%m/%d/%y")

#Heartrate_Seconds
Heartrate_Seconds$Time=as.POSIXct(Heartrate_Seconds$Time, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
Heartrate_Seconds$Time <- format(Heartrate_Seconds$Time, format = "%m/%d/%y")

# WeightLogInfo
WeightLogInfo$Date=as.POSIXct(WeightLogInfo$Date, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
WeightLogInfo$Date <- format(WeightLogInfo$Date, format = "%m/%d/%y")

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

WeightLogInfo$Date <- as.Date(WeightLogInfo$Date)

head(WeightLogInfo)

Heartrate_Seconds$Time <-as.Date(Heartrate_Seconds$Time)

Hourly_Calories$ActivityHour <-as.Date(Hourly_Calories$ActivityHour)

head(Hourly_Calories)

ggplot(data=Activity,aes(x=TotalSteps,y=Calories,color=Calories))+geom_point()+ ggtitle("Calories Burned depending on the Steps taken")

Observation

we can say smart devices works very good on total steps taken and calories burned

Activity <- merge(Activity,Sleep_Day,by="Id")

colnames(Activity)

##  [1] "Id"                       "ActivityDate"            
##  [3] "TotalSteps"               "TotalDistance"           
##  [5] "VeryActiveDistance"       "ModeratelyActiveDistance"
##  [7] "LightActiveDistance"      "VeryActiveMinutes"       
##  [9] "FairlyActiveMinutes"      "LightlyActiveMinutes"    
## [11] "SedentaryMinutes"         "Calories"                
## [13] "Day_of_Week"              "TotalMinutesAsleep"      
## [15] "Sleep_Amount"

Activity <- Activity %>%
  mutate(DistanceCategory= case_when(
    Activity$TotalDistance < 4500 ~ "LowActive",
    Activity$TotalDistance >= 5000 & .$TotalDistance < 6000 ~ "AverageActive",
    Activity$TotalDistance >= 6500 & .$TotalDistance < 7000 ~ "MorethanAverageActive",
    Activity$TotalDistance > 7500 ~ "VeryActive"
  ))

ggplot(data = Activity)+
  geom_col(mapping = aes(Sleep_Amount, y= Calories, fill =Sleep_Amount))+
  facet_grid(~DistanceCategory)+
  labs(title = "How much Calories burns affecting by Sleep Quality By Distance Categories")+
  theme(axis.text = element_text(angle = 45))

Observations

we can see the smart device works very well on sleep data and calories burned

ggplot(data = Activity)+
  geom_col(mapping = aes(Day_of_Week, y=TotalSteps, fill =Day_of_Week))+
  facet_grid(~DistanceCategory)+
  labs(title = "How much Steps taken on which day of week")+
  theme(axis.text = element_text(angle = 45))

Observations_ by data viz we can say on sunday there are less data for totalsteps taken due to holiday on tuesday again back to routine work so good steps are taken and on other days average steps are taken

Recommendations:

The app which is made must have to update to track all the data which is needed from the smart devices.

So I would like to suggest the company must have to work on the app to get these information consistently and accurately.

Because the smart devices are rooted to the mobile to track the information from the consumers through the smart devices.

The age of the consumer not tracked so I would like to add the age data to this app to get accurate results.

2. Add new column TotalActiveMinutes to Activity which is the sum of all three ActiveMinutes columns:

Activity <- Activity %>%
  rowwise() %>%
  mutate(TotalActiveMinutes = sum(c(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes)))

Next, I created new columns with categories in datasets

1. Sleep_Day

Sleep_Day = Sleep_Day %>% mutate(Sleep_Amount = case_when(TotalMinutesAsleep/60>=6.0 & TotalMinutesAsleep/60<=9.0   ~ "Good Sleep",
    TotalMinutesAsleep/60<6.0 ~ "Under Sleep", 
    TotalMinutesAsleep/60>9.0 ~ "Over sleep"))
    
   New_Weight <- WeightLogInfo %>%
     select(Id,Date,WeightKg,BMI)

since Id is in dbl data type we have to convert it into character type of all dataframes

Activity <- mutate(Activity, Id=as.character('Id'))
Hourly_Intensities <-mutate(Hourly_Intensities,Id=as.character('Id'))
Hourly_Calories <- mutate(Hourly_Calories,Id=as.character('Id'))
Hourly_Steps <- mutate(Hourly_Steps,Id=as.character('Id'))
Heartrate_Seconds <- mutate(Heartrate_Seconds,Id=as.character('Id'))
Sleep_Day <- mutate(Sleep_Day,Id=as.character('Id'))
WeightLogInfo <-mutate(WeightLogInfo,Id=as.character('Id'))

check it

summary(Activity)

##       Id             ActivityDate          TotalSteps    TotalDistance   
##  Length:385400      Min.   :2016-04-12   Min.   :    0   Min.   : 0.000  
##  Class :character   1st Qu.:2016-04-19   1st Qu.: 3790   1st Qu.: 2.620  
##  Mode  :character   Median :2016-04-26   Median : 7406   Median : 5.245  
##                     Mean   :2016-04-26   Mean   : 7638   Mean   : 5.490  
##                     3rd Qu.:2016-05-04   3rd Qu.:10727   3rd Qu.: 7.713  
##                     Max.   :2016-05-12   Max.   :36019   Max.   :28.030  
##  VeryActiveDistance ModeratelyActiveDistance LightActiveDistance
##  Min.   : 0.000     Min.   :0.0000           Min.   : 0.000     
##  1st Qu.: 0.000     1st Qu.:0.0000           1st Qu.: 1.945     
##  Median : 0.210     Median :0.2400           Median : 3.365     
##  Mean   : 1.503     Mean   :0.5675           Mean   : 3.341     
##  3rd Qu.: 2.053     3rd Qu.:0.8000           3rd Qu.: 4.782     
##  Max.   :21.920     Max.   :6.4800           Max.   :10.710     
##  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    Day_of_Week        TotalMinutesAsleep Sleep_Amount      
##  Min.   :   0   Length:385400      Min.   : 58.0      Length:385400     
##  1st Qu.:1828   Class :character   1st Qu.:361.0      Class :character  
##  Median :2134   Mode  :character   Median :432.5      Mode  :character  
##  Mean   :2304                      Mean   :419.2                        
##  3rd Qu.:2793                      3rd Qu.:490.0                        
##  Max.   :4900                      Max.   :796.0                        
##  DistanceCategory   TotalActiveMinutes
##  Length:385400      Min.   :  0.0     
##  Class :character   1st Qu.:146.8     
##  Mode  :character   Median :247.0     
##                     Mean   :227.5     
##                     3rd Qu.:317.2     
##                     Max.   :552.0

  Activity = Activity %>% 
  mutate(Steps_Amount = case_when(TotalSteps<=4500 ~ "Less Walker",
    TotalSteps>4000 & TotalSteps <=9000 ~ "Good Walker", TotalSteps>9000 & TotalSteps<=12000 ~ "Better Walker",
    TotalSteps>12000 ~ "Best Walker"))

New_Weight = New_Weight %>% 
mutate(Weight_Amount = case_when(BMI <= 18.5 ~ "UnderWeight", BMI >= 18.6 & BMI <= 24.9 ~ "NormalWeight", BMI >= 25 & BMI <= 29.9 ~ "OverWeight", BMI >= 30 ~ "Obesity"))

Heartrate_Seconds = Heartrate_Seconds %>%
mutate(Heartrate_Amount = case_when(Value <= 80 ~ "Normal B.P.", 
Value >= 81 ~ "High B.P."))

Activity = Activity %>% mutate(Burned_Calories = case_when(Calories<=1800 ~ "Low", Calories>1800 & Calories<=2200 ~ "Medium",
    Calories>2200 & Calories<=2600 ~ "High", Calories>2600 ~ "Very High"))

4. Sedentary Time

Activity = Activity %>% 
  mutate(Sedentary_Time = case_when(SedentaryMinutes  > 626 ~ "Good",
    SedentaryMinutes < 627 & SedentaryMinutes  ~ "Good", SedentaryMinutes/60>10 & SedentaryMinutes/60<=12 ~ "Bad",
    SedentaryMinutes/60>12 ~ "Very Bad"))

changing data type of date column

Hourly_Calories

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

Heartrate_Seconds

Heartrate_Seconds$Time=as.POSIXct(Heartrate_Seconds$Time, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
Heartrate_Seconds$Time <- format(Heartrate_Seconds$Time, format = "%m/%d/%y")

WeightLogInfo

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

hourly_Intensities

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

Recommendations:

The app which is made must have to update to track all the data which is needed from the smart devices.

So I would like to suggest the company must have to work on the app to get these information consistently and accurately.

Because the smart devices are rooted to the mobile to track the information from the consumers through the smart devices.

The age of the consumer not tracked so I would like to add the age data to this app to get accurate results.

Bellabeat Product Analysis

Pritee Kadam

9/28/2021

Bellabeat Products Analysis

The problem statement

Key stakeholders:-

Introduction:-

1) Ask:-

2) Prepare:-

3) Process:

importing necessory packages

Exploring files-

Checking the length of Id column

Check for duplicates

remove duplicate from Sleep_Day

1. change the data type from char to date of ActivityDate and Adding Day_of_Week column to Activity :

let’s check the added column in Activity dataframe

add new columnSleep_Amount to Sleep_Day dataframe

let’s see the data

we are going to delete some columns from Sleep_Day dataframe because we do’t need it

Let’s check the dataframe

since Id is in dbl data type we have to convert it into character type of all dataframes

check it

changing data type of date column

Observation

Observations

Recommendations:

2. Add new column TotalActiveMinutes to Activity which is the sum of all three ActiveMinutes columns:

Next, I created new columns with categories in datasets

1. Sleep_Day

since Id is in dbl data type we have to convert it into character type of all dataframes

check it

4. Sedentary Time

changing data type of date column

Hourly_Calories

Heartrate_Seconds

WeightLogInfo

hourly_Intensities

Recommendations:

Bellabeat Product Analysis

Pritee Kadam

9/28/2021

Bellabeat Products Analysis

The problem statement

Key stakeholders:-

Introduction:-

1) Ask:-

2) Prepare:-

3) Process:

importing necessory packages

Exploring files-

Checking the length of Id column

Check for duplicates

remove duplicate from Sleep_Day

1. change the data type from char to date of ActivityDate and Adding Day_of_Week column to Activity :

let’s check the added column in Activity dataframe

add new columnSleep_Amount to Sleep_Day dataframe

let’s see the data

we are going to delete some columns from Sleep_Day dataframe because we do’t need it

Let’s check the dataframe

since Id is in dbl data type we have to convert it into character type of all dataframes

check it

changing data type of date column

Share

Observation

Observations

Recommendations:

2. Add new column TotalActiveMinutes to Activity which is the sum of all three ActiveMinutes columns:

Next, I created new columns with categories in datasets

1. Sleep_Day

since Id is in dbl data type we have to convert it into character type of all dataframes

check it

4. Sedentary Time

changing data type of date column

Hourly_Calories

Heartrate_Seconds

WeightLogInfo

hourly_Intensities

Recommendations: