Bellabeat Case study
Olaseni Olukogbon
2022-09-26
Summary
Bellabeat, a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, co-founder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. Bellabeat products became available through a growing number of online retailers in addition to their own e-commerce channel on their website. The company has invested in traditional advertising media, such as radio, out-of-home billboards, print, and television, but focuses on digital marketing extensively. Bellabeat invests year-round in Google Search, maintaining active Facebook and Instagram pages, and consistently engages consumers on Twitter. Additionally, Bellabeat runs video ads on YouTube and display ads on the Google Display Network to support campaigns around key marketing dates.
Products
Bellabeat app: The Bellabeat app provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfulness habits. This data can help users better understand their current habits and make healthy decisions. The Bellabeat app connects to their line of smart wellness products.
Leaf: Bellabeat’s classic wellness tracker can be worn as a bracelet, necklace, or clip. The Leaf tracker connects to the Bellabeat app to track activity, sleep, and stress.
Time: This wellness watch combines the timeless look of a classic timepiece with smart technology to track user activity, sleep, and stress. The Time watch connects to the Bellabeat app to provide you with insights into your daily wellness.
Spring: This is a water bottle that tracks daily water intake using smart technology to ensure that you are appropriately hydrated throughout the day. The Spring bottle connects to the Bellabeat app to track your hydration levels.
1. Ask Phase
1.1 Business Task
Analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices in-other to apply insights to Bellabeat a marketing strategy
1.2 Stakeholders
Urška Sršen - Bellabeat’s co-founder and Chief Creative Officer
Sando Mur - Bellabeat’s co-founder and key member of the Bellabeat executive team
Bellabeat marketing analytics team
2. Prepare Phrase
Data source: FitBit Fitness Tracker Data
License: CC0: Public Domain, data-set made available through Mobius
Information about data : This data-set generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016.Thirty eligible FitBit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring.
Data Credibility: We don’t know if the sample is representative of the entire population. Also, due to lack of demographic information and size limitation (30), we believe there could be a sampling bias.
3. Process Phase
Excel was used in formatting dates and removing duplicates from sleep_day data.
3.1 Installing and loading common packages and libraries
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(ggplot2)
install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(dplyr)
install.packages("janitor")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testinstall.packages("ggpubr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(ggpubr)3.2 Import CSV files to R
daily_activity <- read.csv("dailyActivity_merged.csv")
sleep_day <- read.csv("sleepDay_merged.csv")3.2.1 Exploring Dataset table
Take a look at daily_activity
head(daily_activity)## Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366 4/12/2016 13162 8.50 8.50
## 2 1503960366 4/13/2016 10735 6.97 6.97
## 3 1503960366 4/14/2016 10460 6.74 6.74
## 4 1503960366 4/15/2016 9762 6.28 6.28
## 5 1503960366 4/16/2016 12669 8.16 8.16
## 6 1503960366 4/17/2016 9705 6.48 6.48
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 1.88 0.55
## 2 0 1.57 0.69
## 3 0 2.44 0.40
## 4 0 2.14 1.26
## 5 0 2.71 0.41
## 6 0 3.19 0.78
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 6.06 0 25
## 2 4.71 0 21
## 3 3.91 0 30
## 4 2.83 0 29
## 5 5.04 0 36
## 6 2.51 0 38
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 13 328 728 1985
## 2 19 217 776 1797
## 3 11 181 1218 1776
## 4 34 209 726 1745
## 5 10 221 773 1863
## 6 20 164 539 1728identify daily_activity columns
colnames(daily_activity)## [1] "Id" "ActivityDate"
## [3] "TotalSteps" "TotalDistance"
## [5] "TrackerDistance" "LoggedActivitiesDistance"
## [7] "VeryActiveDistance" "ModeratelyActiveDistance"
## [9] "LightActiveDistance" "SedentaryActiveDistance"
## [11] "VeryActiveMinutes" "FairlyActiveMinutes"
## [13] "LightlyActiveMinutes" "SedentaryMinutes"
## [15] "Calories"Take a look at sleep_day
head(sleep_day)## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 0:00:00 1 327
## 2 1503960366 4/13/2016 0:00:00 2 384
## 3 1503960366 4/15/2016 0:00:00 1 412
## 4 1503960366 4/16/2016 0:00:00 2 340
## 5 1503960366 4/17/2016 0:00:00 1 700
## 6 1503960366 4/19/2016 0:00:00 1 304
## TotalTimeInBed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320identify sleep_day columns
colnames(sleep_day)## [1] "Id" "SleepDay" "TotalSleepRecords"
## [4] "TotalMinutesAsleep" "TotalTimeInBed"3.2.2 Understanding some summary statistics
Checking for how many unique participants are in each data-frame?
n_distinct(daily_activity$Id)## [1] 33n_distinct(sleep_day$Id)## [1] 24What is the number of observations in each data-frame?
nrow(daily_activity)## [1] 940nrow(sleep_day)## [1] 410Observation
The daily activity data sets contains 33 participants , while the sleep data set contains 24 participants, according to the report.
There are more participants in the daily activity data (33) than number of participants in the study (30)
3.3 Column Cleaning
For merging reasons,column names will be changed to lower case so they are all unique and consistent.
clean_names(daily_activity)
daily_activity<- rename_with(daily_activity, tolower)
clean_names(sleep_day)
sleep_day <- rename_with(sleep_day, tolower)4. Analyze and Share Phase
Brief summary statistics regarding each data frame that we’d want to know?
daily_activity %>%
select(totalsteps,
totaldistance,
sedentaryminutes,
calories) %>%
summary()## totalsteps totaldistance sedentaryminutes calories
## Min. : 0 Min. : 0.000 Min. : 0.0 Min. : 0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8 1st Qu.:1828
## Median : 7406 Median : 5.245 Median :1057.5 Median :2134
## Mean : 7638 Mean : 5.490 Mean : 991.2 Mean :2304
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5 3rd Qu.:2793
## Max. :36019 Max. :28.030 Max. :1440.0 Max. :4900sleep_day %>%
select(totalsleeprecords,
totalminutesasleep,
totaltimeinbed) %>%
summary()## totalsleeprecords totalminutesasleep totaltimeinbed
## Min. :1.00 Min. : 58.0 Min. : 61.0
## 1st Qu.:1.00 1st Qu.:361.0 1st Qu.:403.8
## Median :1.00 Median :432.5 Median :463.0
## Mean :1.12 Mean :419.2 Mean :458.5
## 3rd Qu.:1.00 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :3.00 Max. :796.0 Max. :961.0daily_activity %>%
select(veryactiveminutes,
fairlyactiveminutes,
lightlyactiveminutes) %>%
summary()## 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. :518.0Key Takeaway
Participants are not adequately active, according to the data shown, also because average sedentary time is 991 minutes, or 16 hours.
According to CDC; Adults should take at least 8000 steps per day, but participants averaged 7638 total steps per day.
Participants sleep 1 time for 7 hours on average.
4.1 Plotting a few explorations
What’s the relationship between steps taken in a day and sedentary minutes?
ggplot(data = daily_activity, mapping = aes(x=totalsteps,y=sedentaryminutes))+geom_point(color = "#0072B2")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
What’s the relationship between minutes asleep and time in bed?
ggplot(data = sleep_day,mapping = aes(x=totalminutesasleep,y=totaltimeinbed))+geom_point(color = "#E69F00")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
What’s the relationship between Total steps and calories?
ggplot(data = daily_activity, mapping = aes(x=totalsteps,y=calories))+geom_point(color= "#CC79A7")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
4.2 Merge both data for further exploration
We want to explore certain areas by merging daily_activity and sleep_day to see any correlation between variables by using id as the primary key
combine_data <- merge(sleep_day,daily_activity, by="id")4.2.1 confirm all data merged
n_distinct(combine_data$id)## [1] 24There were more participant Ids in the daily activity data-set that were filtered out with merging. Outer_join will be used to keep those filtered out back in the data-set.
4.2.2 Use outer join to merge all id
combine_data <- merge(sleep_day, daily_activity, by="id", all = TRUE)
n_distinct(combine_data$id)## [1] 33glimpse(combine_data)## Rows: 12,575
## Columns: 19
## $ id <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ sleepday <chr> "4/12/2016 0:00:00", "4/12/2016 0:00:00", "4/…
## $ totalsleeprecords <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ totalminutesasleep <int> 327, 327, 327, 327, 327, 327, 327, 327, 327, …
## $ totaltimeinbed <int> 346, 346, 346, 346, 346, 346, 346, 346, 346, …
## $ activitydate <chr> "5/7/2016", "5/6/2016", "5/1/2016", "4/30/201…
## $ totalsteps <int> 11992, 12159, 10602, 14673, 13162, 10735, 153…
## $ totaldistance <dbl> 7.71, 8.03, 6.81, 9.25, 8.50, 6.97, 9.80, 8.9…
## $ trackerdistance <dbl> 7.71, 8.03, 6.81, 9.25, 8.50, 6.97, 9.80, 8.9…
## $ loggedactivitiesdistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ veryactivedistance <dbl> 2.46, 1.97, 2.29, 3.56, 1.88, 1.57, 5.29, 2.9…
## $ moderatelyactivedistance <dbl> 2.12, 0.25, 1.60, 1.42, 0.55, 0.69, 0.57, 1.0…
## $ lightactivedistance <dbl> 3.13, 5.81, 2.92, 4.27, 6.06, 4.71, 3.94, 4.8…
## $ sedentaryactivedistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ veryactiveminutes <int> 37, 24, 33, 52, 25, 21, 73, 45, 48, 16, 31, 7…
## $ fairlyactiveminutes <int> 46, 6, 35, 34, 13, 19, 14, 24, 28, 12, 23, 11…
## $ lightlyactiveminutes <int> 175, 289, 246, 217, 328, 217, 216, 250, 189, …
## $ sedentaryminutes <int> 833, 754, 730, 712, 728, 776, 814, 857, 782, …
## $ calories <int> 1821, 1896, 1820, 1947, 1985, 1797, 2013, 195…4.3 Based on the CDC’s guidelines; categorize users by activity level.
- Sedentary - Less than 5000 steps a day.
- Lightly active - Between 5000 and 7499 steps a day.
- Fairly active - Between 7500 and 9999 steps a day.
- Very active - More than 10000 steps a day.
ActivityLevel <- combine_data %>%
group_by(id) %>%
summarise(avg_daily_step= mean(totalsteps)) %>%
mutate (ActivityLevel=case_when (
avg_daily_step <5000 ~ "Sedentary",
avg_daily_step >=5000 & avg_daily_step< 8000 ~"Fairly Active",
avg_daily_step>=8000 & avg_daily_step <12000~"Active",
avg_daily_step >=12000 ~ 'Highly Active'))
head(ActivityLevel)## # A tibble: 6 × 3
## id avg_daily_step ActivityLevel
## <dbl> <dbl> <chr>
## 1 1503960366 12117. Highly Active
## 2 1624580081 5744. Fairly Active
## 3 1644430081 7283. Fairly Active
## 4 1844505072 2580. Sedentary
## 5 1927972279 916. Sedentary
## 6 2022484408 11371. Active4.3.1 Based on the CDC’s guidelines; categorize users by Hours of sleep.
Adults should take 7 or more hours of sleep every night, according to the American Academy of Sleep Medicine and the Sleep Research Society.
- Unhealthy sleepers - Less than 300 minutes(5hrs)
- Average sleepers - Between 300 minutes and 420 minutes(7hrs)
- Healthy Sleepers - More than 420 minutes.
SleepHours <- combine_data %>%
group_by(id) %>%
summarise(avg_time_asleep = mean(totalminutesasleep)) %>%
mutate(type=case_when (
avg_time_asleep < 300 ~ "unhealthy sleep",
avg_time_asleep >=300 & avg_time_asleep <= 420 ~ "average sleep",
avg_time_asleep > 420 ~ "healthy sleep"))
head(SleepHours)## # A tibble: 6 × 3
## id avg_time_asleep type
## <dbl> <dbl> <chr>
## 1 1503960366 360. average sleep
## 2 1624580081 NA <NA>
## 3 1644430081 294 unhealthy sleep
## 4 1844505072 652 healthy sleep
## 5 1927972279 417 average sleep
## 6 2022484408 NA <NA>4.4 Plotting for futher exploration
lets take a look at the average activity Level of each users
ggplot(data = ActivityLevel, mapping = aes(x=ActivityLevel, fill = ActivityLevel))+
geom_bar(width = 0.60)+ scale_fill_manual(values = c("#000000", "#E69F00","#56B4E9","#0072B2"))+labs(title="Activity Level")
Sleeping Hours
ggplot(data = SleepHours, mapping = aes(x= type, fill = type))+
geom_bar()+ scale_fill_manual(values = c("#000000", "#E69F00","#56B4E9"))+labs(title="Sleep Hours")
4.5 Correlations among the data
p1 = ggplot(data = daily_activity, mapping = aes(x=totalsteps,y=sedentaryminutes))+geom_point(color = "#0072B2")+geom_smooth()+labs(title="Total Steps vs. Sedentary Minutes")
p2 = ggplot(data = sleep_day,mapping = aes(x=totalminutesasleep,y=totaltimeinbed))+geom_point(color = "#E69F00")+geom_smooth()+labs(title="Minutes Asleep vs. Time in Bed")
p3 = ggplot(data = daily_activity, mapping = aes(x=totalsteps,y=calories))+geom_point(color= "#CC79A7")+geom_smooth()+labs(title="Total Steps vs. Calories")
ggpubr::ggarrange(p1, p2, p3, ncol = 2, nrow = 2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Summary
The first graph shows there is an inversely associated declining trend, which may indicate that people who took more steps did not spend as many sedentary minutes. The number of sedentary minutes will decrease as the number of steps increases.
The second graph is almost entirely linear; the majority of users spend the same amount of time sleeping as they do in bed. Only a few users spend more time in bed than they do sleeping.
The third graph shows there is a positive correlation between total steps and calories burnt, which suggests the calories burnt will grow as the overall number of steps increases as expected.
5. Act Phase (Conclusion)
Following our analysis, we discovered some trends that may aid marketing strategy and improve the Bellabeat app. Bellabeat can highlight the advantages of a healthy lifestyle by using the correlation between steps taken and calories burnt to urge users to be active and track their daily progress.
I would suggest that Bellabeat upgrade the Time watch and the Bellabeat app to integrate the following features:
- Ability for users to input their desired amount of sleep to create a sleep schedule
- Ability to set a weekly bedtime schedule
- Notification for when to sleep and wake up
- Notification that alerts users when they have been sedentary for an extended period of time and encourages them to take more daily steps
- Reward system; Users can challenge their colleagues to a competition.Participants get points during the competition by completing their Activity.
- Challenge, encourage and share progress with friends
Thank you for taking the time to read about my first R project.