Google Data Analytics |Case Study| Bellabeat
Nisha P
2024-07-4
Ask
Business task
The business task was to analyse smart device usage data in order to gain insights into how consumers use non-Bellabeat smart devices. Then selecting one Bellabeat product to apply these insights to in the presentation. The business questions are:
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?
Data Source
This is a public data that explores smart device users’ daily habits.
● FitBit Fitness Tracker Data clickMobius (CC0: Public Domain, dataset made available through Mobius) contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users 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.
ROCCC analysis
Reliable:low — the data collected from users without demographic information
Originality: low — the data was collected from third-party Amazon Mechanical Turk
Comprehensive: high — the data contained personal health data which allowed me to answer business questions
Current: low — the respondents were generated during 04.12.2016–05.12.2016.
Cited: high — the data source was well-documented.
Stakeholders
Urška Sršen:Bellabeat’s co-founder and Chief Creative Officer.
Sando Mur:Mathematician and Bellabeat’s co-founder.
Bellabeat marketing analytics team.
Prepare
To start, let’s set up the environment by downloading and opening the necessary libraries for the analysis.
Load Packages
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(ggplot2)
library(lubridate)
library(readr)
library(highcharter)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental useRead
Here we use Three Dataset from April 4.12.16 to May 5.12.16.
- Daily Activity
data1 <- read.csv("Raw datsets for Analysis/bellabelt case study dataset/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv")- Sleep Day
data2<- read.csv("Raw datsets for Analysis/bellabelt case study dataset/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv")- Weight Log
data3<- read.csv("Raw datsets for Analysis/bellabelt case study dataset/mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv")Process
Data Cleaning
str(data1)## '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 ...data1 <- data1 %>%
distinct() %>%
mutate(Date = lubridate::mdy(ActivityDate)) %>%
fill(everything(), .direction = "down") %>%
select(-ActivityDate)
glimpse(data1)## Rows: 940
## Columns: 15
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ TotalSteps <int> 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 <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4…
## $ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21…
## $ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, …
## $ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818…
## $ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 203…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-…# Check for missing values
sum(is.na(data1))## [1] 0# finding distinct and unique value
unique(data1$Id)## [1] 1503960366 1624580081 1644430081 1844505072 1927972279 2022484408
## [7] 2026352035 2320127002 2347167796 2873212765 3372868164 3977333714
## [13] 4020332650 4057192912 4319703577 4388161847 4445114986 4558609924
## [19] 4702921684 5553957443 5577150313 6117666160 6290855005 6775888955
## [25] 6962181067 7007744171 7086361926 8053475328 8253242879 8378563200
## [31] 8583815059 8792009665 8877689391unique(data1$Date)## [1] "2016-04-12" "2016-04-13" "2016-04-14" "2016-04-15" "2016-04-16"
## [6] "2016-04-17" "2016-04-18" "2016-04-19" "2016-04-20" "2016-04-21"
## [11] "2016-04-22" "2016-04-23" "2016-04-24" "2016-04-25" "2016-04-26"
## [16] "2016-04-27" "2016-04-28" "2016-04-29" "2016-04-30" "2016-05-01"
## [21] "2016-05-02" "2016-05-03" "2016-05-04" "2016-05-05" "2016-05-06"
## [26] "2016-05-07" "2016-05-08" "2016-05-09" "2016-05-10" "2016-05-11"
## [31] "2016-05-12"str(data2)## '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 ...## Mutate Date from given ActivityDate
data2 <- data2 %>%
distinct() %>%
mutate(Date = lubridate::mdy_hms(SleepDay)) %>%
fill(everything(), .direction = "down") %>%
select(-SleepDay)
glimpse(data2)## Rows: 410
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150…
## $ TotalSleepRecords <int> 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ TotalMinutesAsleep <int> 327, 384, 412, 340, 700, 304, 360, 325, 361, 430, 2…
## $ TotalTimeInBed <int> 346, 407, 442, 367, 712, 320, 377, 364, 384, 449, 3…
## $ Date <dttm> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-16, 20…# Check for missing values
sum(is.na(data2))## [1] 0unique(data2$Id)## [1] 1503960366 1644430081 1844505072 1927972279 2026352035 2320127002
## [7] 2347167796 3977333714 4020332650 4319703577 4388161847 4445114986
## [13] 4558609924 4702921684 5553957443 5577150313 6117666160 6775888955
## [19] 6962181067 7007744171 7086361926 8053475328 8378563200 8792009665unique(data2$Date)## [1] "2016-04-12 UTC" "2016-04-13 UTC" "2016-04-15 UTC" "2016-04-16 UTC"
## [5] "2016-04-17 UTC" "2016-04-19 UTC" "2016-04-20 UTC" "2016-04-21 UTC"
## [9] "2016-04-23 UTC" "2016-04-24 UTC" "2016-04-25 UTC" "2016-04-26 UTC"
## [13] "2016-04-28 UTC" "2016-04-29 UTC" "2016-04-30 UTC" "2016-05-01 UTC"
## [17] "2016-05-02 UTC" "2016-05-03 UTC" "2016-05-05 UTC" "2016-05-06 UTC"
## [21] "2016-05-07 UTC" "2016-05-08 UTC" "2016-05-09 UTC" "2016-05-10 UTC"
## [25] "2016-05-11 UTC" "2016-04-14 UTC" "2016-04-22 UTC" "2016-04-27 UTC"
## [29] "2016-05-04 UTC" "2016-05-12 UTC" "2016-04-18 UTC"str(data3)## 'data.frame': 67 obs. of 8 variables:
## $ Id : num 1.50e+09 1.50e+09 1.93e+09 2.87e+09 2.87e+09 ...
## $ 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 PM" ...
## $ WeightKg : num 52.6 52.6 133.5 56.7 57.3 ...
## $ WeightPounds : num 116 116 294 125 126 ...
## $ Fat : int 22 NA NA NA NA 25 NA NA NA NA ...
## $ BMI : num 22.6 22.6 47.5 21.5 21.7 ...
## $ IsManualReport: chr "True" "True" "False" "True" ...
## $ LogId : num 1.46e+12 1.46e+12 1.46e+12 1.46e+12 1.46e+12 ...sum(is.na(data3))## [1] 65data3$datetime <- mdy_hms(data3$Date)data3 <- data3 %>%
select(-Fat,-Date,-LogId,-WeightPounds,-IsManualReport)
glimpse(data3)## Rows: 67
## Columns: 4
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212765, 4…
## $ WeightKg <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.3, 69.9, …
## $ BMI <dbl> 22.65, 22.65, 47.54, 21.45, 21.69, 27.45, 27.38, 27.25, 27.46…
## $ datetime <dttm> 2016-05-02 23:59:59, 2016-05-03 23:59:59, 2016-04-13 01:08:5…data3$Date <- as.Date(data3$datetime)
data3$Time <- format(data3$datetime, "%H:%M:%S")
glimpse(data3)## Rows: 67
## Columns: 6
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212765, 4…
## $ WeightKg <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.3, 69.9, …
## $ BMI <dbl> 22.65, 22.65, 47.54, 21.45, 21.69, 27.45, 27.38, 27.25, 27.46…
## $ datetime <dttm> 2016-05-02 23:59:59, 2016-05-03 23:59:59, 2016-04-13 01:08:5…
## $ Date <date> 2016-05-02, 2016-05-03, 2016-04-13, 2016-04-21, 2016-05-12, …
## $ Time <chr> "23:59:59", "23:59:59", "01:08:52", "23:59:59", "23:59:59", "…data3 <- data3 %>%
arrange( Date) %>%
select(- datetime)
glimpse(data3)## Rows: 67
## Columns: 5
## $ Id <dbl> 6962181067, 8877689391, 1927972279, 6962181067, 8877689391, 6…
## $ WeightKg <dbl> 62.5, 85.8, 133.5, 62.1, 84.9, 61.7, 84.5, 61.5, 62.0, 85.5, …
## $ BMI <dbl> 24.39, 25.68, 47.54, 24.24, 25.41, 24.10, 25.31, 24.00, 24.21…
## $ Date <date> 2016-04-12, 2016-04-12, 2016-04-13, 2016-04-13, 2016-04-13, …
## $ Time <chr> "23:59:59", "06:47:11", "01:08:52", "23:59:59", "06:55:00", "…sum(is.na(data3))## [1] 0unique(data3$Id)## [1] 6962181067 8877689391 1927972279 4319703577 5577150313 4558609924 2873212765
## [8] 1503960366unique(data3$Date)## [1] "2016-04-12" "2016-04-13" "2016-04-14" "2016-04-15" "2016-04-16"
## [6] "2016-04-17" "2016-04-18" "2016-04-19" "2016-04-20" "2016-04-21"
## [11] "2016-04-22" "2016-04-23" "2016-04-24" "2016-04-25" "2016-04-26"
## [16] "2016-04-27" "2016-04-28" "2016-04-29" "2016-04-30" "2016-05-01"
## [21] "2016-05-02" "2016-05-03" "2016-05-04" "2016-05-05" "2016-05-06"
## [26] "2016-05-07" "2016-05-08" "2016-05-09" "2016-05-10" "2016-05-11"
## [31] "2016-05-12"#Analyze
- Merging the Dataset
final_df <- merge(merge(data1, data2,by = c('Id', 'Date'), all = TRUE), data3,by = c('Id', 'Date'), all = TRUE)glimpse(final_df)## Rows: 940
## Columns: 21
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-…
## $ TotalSteps <int> 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 <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4…
## $ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21…
## $ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, …
## $ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818…
## $ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 203…
## $ TotalSleepRecords <int> 1, 2, NA, 1, 2, 1, NA, 1, 1, 1, NA, 1, 1, 1, …
## $ TotalMinutesAsleep <int> 327, 384, NA, 412, 340, 700, NA, 304, 360, 32…
## $ TotalTimeInBed <int> 346, 407, NA, 442, 367, 712, NA, 320, 377, 36…
## $ WeightKg <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ BMI <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Time <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…View(final_df)summary(final_df)## Id Date TotalSteps TotalDistance
## Min. :1.504e+09 Min. :2016-04-12 Min. : 0 Min. : 0.000
## 1st Qu.:2.320e+09 1st Qu.:2016-04-19 1st Qu.: 3790 1st Qu.: 2.620
## Median :4.445e+09 Median :2016-04-26 Median : 7406 Median : 5.245
## Mean :4.855e+09 Mean :2016-04-26 Mean : 7638 Mean : 5.490
## 3rd Qu.:6.962e+09 3rd Qu.:2016-05-04 3rd Qu.:10727 3rd Qu.: 7.713
## Max. :8.878e+09 Max. :2016-05-12 Max. :36019 Max. :28.030
##
## TrackerDistance LoggedActivitiesDistance VeryActiveDistance
## Min. : 0.000 Min. :0.0000 Min. : 0.000
## 1st Qu.: 2.620 1st Qu.:0.0000 1st Qu.: 0.000
## Median : 5.245 Median :0.0000 Median : 0.210
## Mean : 5.475 Mean :0.1082 Mean : 1.503
## 3rd Qu.: 7.710 3rd Qu.:0.0000 3rd Qu.: 2.053
## Max. :28.030 Max. :4.9421 Max. :21.920
##
## ModeratelyActiveDistance LightActiveDistance SedentaryActiveDistance
## Min. :0.0000 Min. : 0.000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.: 1.945 1st Qu.:0.000000
## Median :0.2400 Median : 3.365 Median :0.000000
## Mean :0.5675 Mean : 3.341 Mean :0.001606
## 3rd Qu.:0.8000 3rd Qu.: 4.782 3rd Qu.:0.000000
## Max. :6.4800 Max. :10.710 Max. :0.110000
##
## 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 TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## Min. : 0 Min. :1.000 Min. : 58.0 Min. : 61.0
## 1st Qu.:1828 1st Qu.:1.000 1st Qu.:361.0 1st Qu.:403.8
## Median :2134 Median :1.000 Median :432.5 Median :463.0
## Mean :2304 Mean :1.119 Mean :419.2 Mean :458.5
## 3rd Qu.:2793 3rd Qu.:1.000 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :4900 Max. :3.000 Max. :796.0 Max. :961.0
## NA's :530 NA's :530 NA's :530
## WeightKg BMI Time
## Min. : 52.60 Min. :21.45 Length:940
## 1st Qu.: 61.40 1st Qu.:23.96 Class :character
## Median : 62.50 Median :24.39 Mode :character
## Mean : 72.04 Mean :25.19
## 3rd Qu.: 85.05 3rd Qu.:25.56
## Max. :133.50 Max. :47.54
## NA's :873 NA's :873- Summarize The Data
bella_daily <- final_df %>% ##eliminating na from the dataset
select(Id,Date,TrackerDistance,TotalSteps,Calories) %>%
filter(complete.cases(.))
glimpse(bella_daily)## Rows: 940
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-15, 2016-…
## $ TrackerDistance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.88, 6.68, …
## $ TotalSteps <int> 13162, 10735, 10460, 9762, 12669, 9705, 13019, 15506, …
## $ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, …summary(bella_daily)## Id Date TrackerDistance TotalSteps
## Min. :1.504e+09 Min. :2016-04-12 Min. : 0.000 Min. : 0
## 1st Qu.:2.320e+09 1st Qu.:2016-04-19 1st Qu.: 2.620 1st Qu.: 3790
## Median :4.445e+09 Median :2016-04-26 Median : 5.245 Median : 7406
## Mean :4.855e+09 Mean :2016-04-26 Mean : 5.475 Mean : 7638
## 3rd Qu.:6.962e+09 3rd Qu.:2016-05-04 3rd Qu.: 7.710 3rd Qu.:10727
## Max. :8.878e+09 Max. :2016-05-12 Max. :28.030 Max. :36019
## Calories
## Min. : 0
## 1st Qu.:1828
## Median :2134
## Mean :2304
## 3rd Qu.:2793
## Max. :4900bella_daily <- bella_daily %>%
distinct(Id,Date, .keep_all = TRUE) %>%
group_by(Id,Date) %>%
summarize(Total_Distance = sum(TrackerDistance),
Total_Steps =sum(TotalSteps),
Total_calories= sum(Calories)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument. glimpse(bella_daily)## Rows: 940
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-15, 2016-0…
## $ Total_Distance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.88, 6.68, 6…
## $ Total_Steps <int> 13162, 10735, 10460, 9762, 12669, 9705, 13019, 15506, 1…
## $ Total_calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 1…- Mutate Week in the bella daily activity.
bella_daily<- bella_daily %>%
mutate(Week = weekdays(Date))
glimpse(bella_daily)## Rows: 940
## Columns: 6
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-15, 2016-0…
## $ Total_Distance <dbl> 8.50, 6.97, 6.74, 6.28, 8.16, 6.48, 8.59, 9.88, 6.68, 6…
## $ Total_Steps <int> 13162, 10735, 10460, 9762, 12669, 9705, 13019, 15506, 1…
## $ Total_calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 1…
## $ Week <chr> "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday…bella_daily_week <- bella_daily%>%
distinct(Id,Date, .keep_all = TRUE) %>%
group_by(Id,Week) %>%
summarize( average_calories= mean(Total_calories),
average_steps = mean (Total_Steps),
average_distance= mean(Total_Distance)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(bella_daily_week)## Rows: 228
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 15039…
## $ Week <chr> "Friday", "Monday", "Saturday", "Sunday", "Thursday",…
## $ average_calories <dbl> 1826.25, 1939.25, 1895.00, 1769.00, 1481.60, 1967.80,…
## $ average_steps <dbl> 11466.50, 13780.75, 13426.25, 10101.50, 9500.60, 1394…
## $ average_distance <dbl> 7.3975, 8.9550, 8.5400, 6.5700, 6.1020, 8.9200, 8.228…- Check the sedentary minutes and most active and less active minutes
bella_data_sed <-final_df%>%
select(Id,Date,Calories,ends_with("Minutes")) %>%
mutate(total_minutes = rowSums(across(ends_with("Minutes"))),
total_hours = total_minutes %/% 60,
total_remaining_minutes = total_minutes %% 60)
glimpse(bella_data_sed)## Rows: 940
## Columns: 10
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-1…
## $ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035…
## $ VeryActiveMinutes <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 41…
## $ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21,…
## $ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, 2…
## $ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818,…
## $ total_minutes <dbl> 1094, 1033, 1440, 998, 1040, 761, 1440, 1120, …
## $ total_hours <dbl> 18, 17, 24, 16, 17, 12, 24, 18, 17, 17, 24, 17…
## $ total_remaining_minutes <dbl> 14, 13, 0, 38, 20, 41, 0, 40, 43, 56, 0, 36, 3…summary(bella_data_sed)## Id Date Calories VeryActiveMinutes
## Min. :1.504e+09 Min. :2016-04-12 Min. : 0 Min. : 0.00
## 1st Qu.:2.320e+09 1st Qu.:2016-04-19 1st Qu.:1828 1st Qu.: 0.00
## Median :4.445e+09 Median :2016-04-26 Median :2134 Median : 4.00
## Mean :4.855e+09 Mean :2016-04-26 Mean :2304 Mean : 21.16
## 3rd Qu.:6.962e+09 3rd Qu.:2016-05-04 3rd Qu.:2793 3rd Qu.: 32.00
## Max. :8.878e+09 Max. :2016-05-12 Max. :4900 Max. :210.00
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes total_minutes
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 2.0
## 1st Qu.: 0.00 1st Qu.:127.0 1st Qu.: 729.8 1st Qu.: 989.8
## Median : 6.00 Median :199.0 Median :1057.5 Median :1440.0
## Mean : 13.56 Mean :192.8 Mean : 991.2 Mean :1218.8
## 3rd Qu.: 19.00 3rd Qu.:264.0 3rd Qu.:1229.5 3rd Qu.:1440.0
## Max. :143.00 Max. :518.0 Max. :1440.0 Max. :1440.0
## total_hours total_remaining_minutes
## Min. : 0.00 Min. : 0.00
## 1st Qu.:16.00 1st Qu.: 0.00
## Median :24.00 Median : 0.00
## Mean :20.07 Mean :14.73
## 3rd Qu.:24.00 3rd Qu.:30.00
## Max. :24.00 Max. :59.00bella_daily_sed <- bella_data_sed %>%
distinct(Id,Date,.keep_all = TRUE) %>%
group_by(Id,Date) %>%
summarize(Max_Active = sum (VeryActiveMinutes),
Median_Active = sum(FairlyActiveMinutes),
min_Active = sum(LightlyActiveMinutes),
Not_ACtive = sum (SedentaryMinutes),
total_active_hr = sum(total_hours)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(bella_daily_sed)## Rows: 940
## Columns: 7
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-15, 2016-…
## $ Max_Active <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 41, 39, 73…
## $ Median_Active <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21, 5, 14, …
## $ min_Active <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, 211, 130,…
## $ Not_ACtive <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818, 838, 12…
## $ total_active_hr <dbl> 18, 17, 24, 16, 17, 12, 24, 18, 17, 17, 24, 17, 16, 18…bella_daily_sed<- bella_data_sed%>%
mutate(Week = weekdays(Date))
glimpse(bella_daily_sed)## Rows: 940
## Columns: 11
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-14, 2016-04-1…
## $ Calories <int> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035…
## $ VeryActiveMinutes <int> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 41…
## $ FairlyActiveMinutes <int> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21,…
## $ LightlyActiveMinutes <int> 328, 217, 181, 209, 221, 164, 233, 264, 205, 2…
## $ SedentaryMinutes <int> 728, 776, 1218, 726, 773, 539, 1149, 775, 818,…
## $ total_minutes <dbl> 1094, 1033, 1440, 998, 1040, 761, 1440, 1120, …
## $ total_hours <dbl> 18, 17, 24, 16, 17, 12, 24, 18, 17, 17, 24, 17…
## $ total_remaining_minutes <dbl> 14, 13, 0, 38, 20, 41, 0, 40, 43, 56, 0, 36, 3…
## $ Week <chr> "Tuesday", "Wednesday", "Thursday", "Friday", …- Identify the sleep Quality
DailySleep_quality <- final_df %>%
select(Id,Date,TotalMinutesAsleep) %>%
drop_na() %>%
mutate(Week = weekdays(Date)) %>%
mutate(sleep_quality = ifelse(TotalMinutesAsleep <= 420, 'Less than 7h',
ifelse(TotalMinutesAsleep <= 540, '7h to 9h',
'More than 9h'))) %>%
mutate(sleep_quality = factor(sleep_quality,
levels = c('Less than 7h','7h to 9h',
'More than 9h'))) %>%
ungroup()
glimpse(DailySleep_quality)## Rows: 410
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-16, 20…
## $ TotalMinutesAsleep <int> 327, 384, 412, 340, 700, 304, 360, 325, 361, 430, 2…
## $ Week <chr> "Tuesday", "Wednesday", "Friday", "Saturday", "Sund…
## $ sleep_quality <fct> Less than 7h, Less than 7h, Less than 7h, Less than…Avrg_sleep <- final_df %>%
select(Id,Date,TotalDistance,TotalMinutesAsleep,TotalTimeInBed) %>%
drop_na() %>%
group_by(Id,Date) %>%
mutate(Week = weekdays(Date)) %>%
summarise(Average_distance = mean(TotalDistance),Average_sleep = mean(TotalMinutesAsleep))%>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(Avrg_sleep)## Rows: 410
## Columns: 4
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 15039…
## $ Date <date> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-16, 2016…
## $ Average_distance <dbl> 8.50, 6.97, 6.28, 8.16, 6.48, 9.88, 6.68, 6.34, 9.04,…
## $ Average_sleep <dbl> 327, 384, 412, 340, 700, 304, 360, 325, 361, 430, 277…- Compare Activity and weight change in every users
weight_data <- final_df %>%
select(Id,Date,WeightKg,TotalSteps) %>%
drop_na() %>%
group_by(Id,Date) %>%
summarise(Average_weight = mean(WeightKg),Average_Steps = mean(TotalSteps)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(weight_data)## Rows: 67
## Columns: 4
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212…
## $ Date <date> 2016-05-02, 2016-05-03, 2016-04-13, 2016-04-21, 2016-0…
## $ Average_weight <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.3, …
## $ Average_Steps <dbl> 14727, 15103, 356, 8859, 7566, 29, 10429, 8940, 8095, 3…Share
5.1Daily Activity of Users | Total Calories Vs Total Steps Relationship
ggplot(data = bella_daily )+geom_smooth(mapping = aes(x = Total_calories, y = Total_Steps))+geom_point(mapping = aes(x = Total_calories, y = Total_Steps,color = Total_calories))+labs(title = 'Calories Burned by Total Steps Taken',
y = 'Total_calories',
x = 'Total Steps',
caption = 'Data Source: FitBit Fitness Tracker Data')
correlation test is used to determine whether and how strongly pairs of variables are related.
Pearson correlation: Measures linear relationship between two continuous variables
correlation_result <- cor( bella_daily$Total_calories, bella_daily$Total_Steps)
print(correlation_result)## [1] 0.5915681This value indicates the strength and direction of the linear relationship between Total Steps and Calories. A value of 0.5929493 suggests a moderate positive correlation, meaning that as the number of steps increases, the number of calories burned tends to increase as well
5.2 Weekly analysis of Daily Activity
##Weekly analysis of daily activity
ggplot(bella_daily_week, aes(x = Week , y = average_distance , fill = as.factor(Id))) +
geom_bar(stat = "summary", fun = "sum") +
labs(title = "Average Distance by Week", x = "Week", y = "Distance") +
theme_minimal()
Users are more active and have move more distance on Saturdays.
5.3 Identifying Daily Active Minutes Distribution
Sed <- final_df %>%
select(VeryActiveMinutes,
FairlyActiveMinutes,
LightlyActiveMinutes,
SedentaryMinutes) %>%
summarise(across(everything(), list(sum))) %>%
gather(active_level, minutes) %>%
mutate(active_level = factor(active_level,labels = c('Moderate Activity','Light Activity', 'Sedentary','Heavy Activity')),
percentage = round(minutes /sum(minutes) * 100, 1)) %>%
ungroup()
glimpse(Sed)## Rows: 4
## Columns: 3
## $ active_level <fct> Heavy Activity, Moderate Activity, Light Activity, Sedent…
## $ minutes <int> 19895, 12751, 181244, 931738
## $ percentage <dbl> 1.7, 1.1, 15.8, 81.3ggplot(Sed,mapping = aes(x = "", y = percentage, fill = active_level)) +
geom_col(color = "black") +
geom_text(aes(label = percentage),
position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y") +
scale_fill_brewer() +
theme_bw()
In the above plot we see the users 81.3% Sedentary and are less active.They are mostly lighty active compared to very active minutes.
5.4 Finding Patterns on their Active Minutes
ggplot(bella_daily_sed, aes(x =VeryActiveMinutes , y = Week ,fill = factor(Id))) +
geom_bar(stat = "summary", fun = "sum") +
labs(title = " Very Active Minutes by Week", x = "Active Minutes", y = "Week") +
theme_minimal()
Most of the users are active on the weekdays.
Time_range <- final_df %>%
select(Id,Time,TotalSteps,Calories) %>%
group_by(Id,Time) %>%
drop_na() %>%
summarise(Avg_step = mean(TotalSteps),Avg_Calories = mean(Calories)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(Time_range)## Rows: 30
## Columns: 4
## $ Id <dbl> 1503960366, 1927972279, 2873212765, 4319703577, 455860992…
## $ Time <chr> "23:59:59", "01:08:52", "23:59:59", "23:59:59", "23:59:59…
## $ Avg_step <dbl> 14915.000, 356.000, 8212.500, 5229.000, 7961.000, 12231.0…
## $ Avg_Calories <dbl> 1997.000, 2151.000, 1700.500, 1873.000, 2064.400, 4552.00…##Comparing Time with daily activity
ggplot(Time_range, aes(x = Time, y = Avg_step, group = 1)) +
geom_line(color = "blue") +
geom_point() +
labs(title = "Total Steps Over the Time",
x = "Time",
y = "Total Steps")+theme(axis.text.x = element_text(angle = 45, hjust = 1))
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## - attr(*, "validate")= logi TRUEggplot(Time_range, aes(x = Time, y = Avg_Calories, group = 1)) +
geom_line(color = "blue") +
geom_point() +
labs(title = "Total Calories Over the Time",
x = "Time",
y = "Total Calories")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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## - attr(*, "validate")= logi TRUEthe above plot gives the relationship of time range with steps and calories. we see the users are more active on first half of the day starting from 6AM to 13 PM and later there is a decrease in the burned calories and steps taken. So ,they are mostly active during first half of a Day.
5.5 Checking Sleep Quality Of Users
## Total minutes of sleep and their quality
ggplot(DailySleep_quality,aes(x = TotalMinutesAsleep, fill = sleep_quality)) +
geom_histogram(position = 'dodge', bins = 50) + labs(title = "Sleep Distribution",
x = "Total Sleep Time",
y = "Count")
## Analysing the sleep quality by comparing their activity
sleep_check <- final_df %>%
select(Id,Date,TotalSteps,TotalMinutesAsleep,TotalTimeInBed) %>%
mutate(Week = weekdays(Date),
No_sleep = TotalTimeInBed-TotalMinutesAsleep)%>%
drop_na() %>%
group_by(Id,Week) %>%
summarise(Avg_Steps = mean(TotalSteps),Avg_Sleep = mean(TotalMinutesAsleep),Avg_Inbed = mean(TotalTimeInBed),No_sleep = mean(No_sleep)) %>%
ungroup()## `summarise()` has grouped output by 'Id'. You can override using the `.groups`
## argument.glimpse(sleep_check)## Rows: 134
## Columns: 6
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, …
## $ Week <chr> "Friday", "Monday", "Saturday", "Sunday", "Thursday", "Tuesd…
## $ Avg_Steps <dbl> 11034.000, 14034.667, 13426.250, 10101.500, 12347.667, 13946…
## $ Avg_Sleep <dbl> 362.3333, 297.3333, 359.0000, 523.2500, 312.6667, 306.4000, …
## $ Avg_Inbed <dbl> 387.6667, 324.6667, 381.2500, 542.0000, 340.3333, 327.8000, …
## $ No_sleep <dbl> 25.33333, 27.33333, 22.25000, 18.75000, 27.66667, 21.40000, …Weekly Sleep Distribution
ggplot(sleep_check, aes(x = Week , y =Avg_Sleep))+
geom_bar(stat = "summary", fun = "sum")+
labs(
title = "Weekly Sleep Distribution",
x = "Week"
)
Weekly analysis of Average Time on bed
Distance Has Impact on Sleep.
## checking whether their distance has impact on sleep.
ggplot(Avrg_sleep) +
geom_bar(aes(x = factor(Id), y =Average_distance), stat = "identity", fill = 'pink', alpha = 0.7) +
geom_point(aes(x = factor(Id), y = Average_sleep/60), color = 'gold4') +
geom_segment(aes(x = factor(Id), xend = factor(Id), y = 0, yend = Average_sleep/60), color = 'gold4' ,group = 1)+ scale_y_continuous(limits=c(0,20), name = "Total Distance",
sec.axis = sec_axis(~.*60, name = "Sleep in minutes"))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
labs(
title = "Average distance vs average sleep",
x = "Users"
)## $x
## [1] "Users"
##
## $title
## [1] "Average distance vs average sleep"
##
## attr(,"class")
## [1] "labels"By the above graph we see very few relation that Average distance covered by user doesn’t make much difference in a better sleep.Users have more time in bed on weekends. Their average sleep is higher on Friday and Sundays.Only few users have a better sleep quality and they spend 7 to 9 hr of sleep. Not everyone has better sleep quality.
5.6 Analyzing Users weight
weight_data$Average_weight <- round(weight_data$Average_weight)
glimpse(weight_data)## Rows: 67
## Columns: 4
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212…
## $ Date <date> 2016-05-02, 2016-05-03, 2016-04-13, 2016-04-21, 2016-0…
## $ Average_weight <dbl> 53, 53, 134, 57, 57, 72, 72, 70, 70, 70, 69, 69, 91, 62…
## $ Average_Steps <dbl> 14727, 15103, 356, 8859, 7566, 29, 10429, 8940, 8095, 3…ggplot(weight_data, aes(x = Date)) +
geom_line(aes(y = Average_weight, color = "Average_weight")) +
geom_line(aes(y = Average_Steps / 200, color = "Average_Steps")) +
# Scale steps for better visualization
scale_y_continuous( limit= c(0,200) ,name = "Average_weight (kg)",
sec.axis = sec_axis(~ . * 200, name = "Average_Steps")
) +
labs(
title = "Average Weight and Average Steps by Date",
x = "Date",
color = "Legend"
) +
scale_color_manual(values = c("Average_weight" = "lightblue", "Average_Steps" = "pink")) +
theme_minimal()
##Distribution of weight on various Date
ggplot(weight_data,aes(x = Date,y= Average_weight ,size =Average_weight, color = factor(Id),alpha(0.07)))+geom_point()+
scale_y_continuous(limits = c(0 ,150), name = " Average_weight")+
labs(
title = "Distribution of weight Over a Month",
x = "Date"
)
user_Weight<-final_df %>%
select(Id,Date,VeryActiveMinutes,WeightKg) %>%
group_by(Id,Date) %>%
drop_na() %>%
ungroup()
glimpse(user_Weight)## Rows: 67
## Columns: 4
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873…
## $ Date <date> 2016-05-02, 2016-05-03, 2016-04-13, 2016-04-21, 201…
## $ VeryActiveMinutes <int> 41, 50, 0, 2, 0, 0, 8, 14, 18, 0, 0, 6, 200, 50, 8, …
## $ WeightKg <dbl> 52.6, 52.6, 133.5, 56.7, 57.3, 72.4, 72.3, 69.7, 70.…ggplot(data = user_Weight,aes(x = VeryActiveMinutes,y= WeightKg , color = factor(Id),size = WeightKg))+geom_point(stat = "summary", fun = "sum") +
scale_y_continuous(limits = c(0,130), name ="WeightKg")+
labs(
title = "Weight Vs VeryActive Minutes of Users",
x = "Very Active Minutes"
)
mean(weight_data$Average_weight)## [1] 72.02985mean(weight_data$Average_Steps)## [1] 12102.15By visualizing the above plots we see that most of the users have average 20 mins of very active minutes and the more obese users maintain more active minutes to reduce their weight.And If we see the data of weight log throughout the month the users maintain constant weight and their isn’t much improvement.
The Average step taken by the users is 12102.The average weight is 72.
Act
Based on the analysis, we have identified several key trends and insights that can be leveraged to enhance marketing strategies:
Key Insights
Calories Burned and Steps Taken:
Insight: There is a statistically significant relationship between the number of steps taken and calories burned.
Application: Highlight this relationship in marketing campaigns to emphasize the importance of physical activity for effective calorie burning.
Weekly Distance Patterns:
Insight: Users tend to cover more distance on Saturdays compared to other days of the week.
Application: Promote weekend challenges or special Saturday fitness events to capitalize on higher activity levels.
Daily Activity Patterns:
Insight: Users are more active in the first half of the day (6 AM to 1 PM) and show a decrease in activity and calories burned in the later part of the day.
Application: Schedule motivational messages and fitness reminders for the morning hours to encourage users to maintain their activity levels throughout the day.
Sleep Patterns:
Insight: Users spend more time in bed on weekends, with average sleep durations higher on Fridays and Sundays. However, not all users have good sleep quality.
Application: Introduce educational content about the benefits of good sleep and how to achieve it, and promote sleep tracking features of the bellabeat device.
Active Minutes and Weight:
Insight: Most users have an average of 20 minutes of very active minutes daily. More obese users tend to maintain more active minutes to manage their weight.
Application: Create targeted fitness programs that encourage short bursts of high-intensity activity, and share success stories of users who have effectively managed their weight through consistent activity.
Weight Management:
Insight: The data shows that users maintain a constant weight with little improvement over time.
Application: Offer personalized weight management plans and tools to help users achieve their weight goals more effectively.
Average Steps and Weight:
Insight: The average steps taken by users are 12,102, and the average weight is 72 kg.
Application: Use these averages as benchmarks in marketing materials, encouraging new users to join the community and reach these fitness milestones.
Marketing Strategy Recommendations
Promote Activity Challenges:
- Introduce weekly or monthly step challenges with rewards for high performers, focusing on increasing user engagement on Saturdays.
Morning Motivation Campaigns:
- Run campaigns that provide motivational content and fitness tips during the morning hours to leverage the users’ higher activity levels in the first half of the day.
Sleep Health Education:
- Launch educational campaigns about the importance of good sleep and how Fitbit’s sleep tracking features can help improve sleep quality.
Personalized Fitness Plans:
- Develop and market personalized fitness and weight management plans based on users’ activity and weight data, emphasizing achievable daily goals like the 20 minutes of active minutes.
Success Stories and Testimonials:
- Share success stories and testimonials from users who have seen significant improvements in their fitness and health through consistent use of bellabeat devices.
Weekend Wellness Programs:
- Create special wellness programs for weekends, encouraging users to stay active and make use of their extra time in bed for better recovery and relaxation.
By aligning marketing strategies with these insights, bellabeat can enhance user engagement, promote healthy habits, and ultimately drive product adoption and satisfaction.