Bellabeat Case Study
Mohamed Shaeldin
31/01/2022
INTRODUCTION
This case study was made as a final project for the Google Data Analytics Certificate. by analyzing FitBit Fitness Tracker Data to make useful insights for Bellabeat.
following the data life cycle steps in this case study :
- ASK
- Prepare
- Process
- Analyze
- Share
- Act
1) ASK
Background
Bellabeat is a high-tech manufacturer of beautifully-designed health-focused smart products for women since 2013.Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for females.
The co-founder and Chief Creative Officer, Urška Sršen is confident that an analysis of non-Bellebeat consumer data (ie. FitBit fitness tracker usage data) would reveal more opportunities for growth.
Business Task:
- Analyze FitBit fitness tracker data to gain insights into how consumers are using the FitBit app.
- discover trends for Bellabeat marketing strategy.
Objectives:
- 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?
key stakeholders:
Urška Sršen (Bellabeat’s cofounder and Chief Creative Officer).
Sando Mur (Mathematician and Bellabeat’s cofounder).
Bellabeat marketing analytics team.
2) Prepare
Data Source:
- Data is publicly available on Kaggle: FitBit Fitness Tracker Data.
- stored in 18 csv files.
- Generated by respondents from a survey via Amazon Mechanical Turk.
- The data is close to 5 years old (12 March 2016 to 12 May 2016).
- 30 FitBit users consented to the submission of personal tracker data.
Checking the data credibility with ROCCC :
(Reliable, Original, Comprehensive, Current, and Cited)
Classification by : (Low -> Medium -> High)
| ROCCC Characteristic | Classification | Comment |
|---|---|---|
| Reliable | LOW | Not reliable as it only has 30 respondents. |
| Original | LOW | Third party provider. |
| Comprehensive | MED | Parameters match most of Bellabeat products’ parameters. |
| Current | LOW | Data is 5 years old and may not be relevant. |
| Cited | LOW | Data collected from third party, hence unknown. |
Final assessment of the data source:
the dataset is considered bad quality data and not recommended for analysis and to produce business recommendations based on this data.
Tool:
We are using Rstudio for data cleaning, transformation and visualization.
3) Process
The Data Must be cleaned for analysis.
Cleaning Steps:
- Install R packages and Observe the data.
- Remove duplicate values
- Remove empty rows
Installing cleaning packages, Loading libraries for processing and analyzing data
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readxl)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionImporting Data
dailyActivity_merged <- read_csv("Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv")## Rows: 940 Columns: 15## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): ActivityDate
## dbl (14): Id, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDi...##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.dailyCalories_merged <- read_csv("Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv")## Rows: 940 Columns: 3## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): ActivityDay
## dbl (2): Id, Calories##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.dailyIntensities_merged <- read_csv("Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv")## Rows: 940 Columns: 10## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): ActivityDay
## dbl (9): Id, SedentaryMinutes, LightlyActiveMinutes, FairlyActiveMinutes, Ve...##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.sleepDay_merged <- read_csv("Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv")## Rows: 413 Columns: 5## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): SleepDay
## dbl (4): Id, TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.weightLogInfo_merged <- read_csv("Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv")## Rows: 67 Columns: 8## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): Date
## dbl (6): Id, WeightKg, WeightPounds, Fat, BMI, LogId
## lgl (1): IsManualReport##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.Over viewing and Getting Familiar with datasets:
daily_activity <- dailyActivity_merged
daily_calories <- dailyCalories_merged
daily_intensities <- dailyIntensities_merged
sleep_day <- sleepDay_merged
weight_log <- weightLogInfo_merged
head(daily_activity)## # A tibble: 6 x 15
## Id ActivityDate TotalSteps TotalDistance TrackerDistance LoggedActivitie~
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1.50e9 4/12/2016 13162 8.5 8.5 0
## 2 1.50e9 4/13/2016 10735 6.97 6.97 0
## 3 1.50e9 4/14/2016 10460 6.74 6.74 0
## 4 1.50e9 4/15/2016 9762 6.28 6.28 0
## 5 1.50e9 4/16/2016 12669 8.16 8.16 0
## 6 1.50e9 4/17/2016 9705 6.48 6.48 0
## # ... with 9 more variables: VeryActiveDistance <dbl>,
## # ModeratelyActiveDistance <dbl>, LightActiveDistance <dbl>,
## # SedentaryActiveDistance <dbl>, VeryActiveMinutes <dbl>,
## # FairlyActiveMinutes <dbl>, LightlyActiveMinutes <dbl>,
## # SedentaryMinutes <dbl>, Calories <dbl>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"glimpse(daily_activity)## Rows: 940
## Columns: 15
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 150396036~
## $ ActivityDate <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/~
## $ 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~head(daily_calories)## # A tibble: 6 x 3
## Id ActivityDay Calories
## <dbl> <chr> <dbl>
## 1 1503960366 4/12/2016 1985
## 2 1503960366 4/13/2016 1797
## 3 1503960366 4/14/2016 1776
## 4 1503960366 4/15/2016 1745
## 5 1503960366 4/16/2016 1863
## 6 1503960366 4/17/2016 1728colnames(daily_calories)## [1] "Id" "ActivityDay" "Calories"glimpse(daily_calories)## Rows: 940
## Columns: 3
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 1503960366~
## $ ActivityDay <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/2016", "4/16/~
## $ Calories <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 1775~head(sleep_day)## # A tibble: 6 x 5
## Id SleepDay TotalSleepRecor~ TotalMinutesAsle~ TotalTimeInBed
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 12:00:~ 1 327 346
## 2 1503960366 4/13/2016 12:00:~ 2 384 407
## 3 1503960366 4/15/2016 12:00:~ 1 412 442
## 4 1503960366 4/16/2016 12:00:~ 2 340 367
## 5 1503960366 4/17/2016 12:00:~ 1 700 712
## 6 1503960366 4/19/2016 12:00:~ 1 304 320colnames(sleep_day)## [1] "Id" "SleepDay" "TotalSleepRecords"
## [4] "TotalMinutesAsleep" "TotalTimeInBed"glimpse(sleep_day)## Rows: 413
## Columns: 5
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150~
## $ SleepDay <chr> "4/12/2016 12:00:00 AM", "4/13/2016 12:00:00 AM", "~
## $ 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~head(weight_log)## # A tibble: 6 x 8
## Id Date WeightKg WeightPounds Fat BMI IsManualReport LogId
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
## 1 1503960366 5/2/2016~ 52.6 116. 22 22.6 TRUE 1.46e12
## 2 1503960366 5/3/2016~ 52.6 116. NA 22.6 TRUE 1.46e12
## 3 1927972279 4/13/201~ 134. 294. NA 47.5 FALSE 1.46e12
## 4 2873212765 4/21/201~ 56.7 125. NA 21.5 TRUE 1.46e12
## 5 2873212765 5/12/201~ 57.3 126. NA 21.7 TRUE 1.46e12
## 6 4319703577 4/17/201~ 72.4 160. 25 27.5 TRUE 1.46e12colnames(weight_log)## [1] "Id" "Date" "WeightKg" "WeightPounds"
## [5] "Fat" "BMI" "IsManualReport" "LogId"glimpse(weight_log)## Rows: 67
## Columns: 8
## $ Id <dbl> 1503960366, 1503960366, 1927972279, 2873212765, 2873212~
## $ Date <chr> "5/2/2016 11:59:59 PM", "5/3/2016 11:59:59 PM", "4/13/2~
## $ 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,~head(daily_intensities)## # A tibble: 6 x 10
## Id ActivityDay SedentaryMinutes LightlyActiveMinutes FairlyActiveMinu~
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 728 328 13
## 2 1503960366 4/13/2016 776 217 19
## 3 1503960366 4/14/2016 1218 181 11
## 4 1503960366 4/15/2016 726 209 34
## 5 1503960366 4/16/2016 773 221 10
## 6 1503960366 4/17/2016 539 164 20
## # ... with 5 more variables: VeryActiveMinutes <dbl>,
## # SedentaryActiveDistance <dbl>, LightActiveDistance <dbl>,
## # ModeratelyActiveDistance <dbl>, VeryActiveDistance <dbl>colnames(daily_intensities)## [1] "Id" "ActivityDay"
## [3] "SedentaryMinutes" "LightlyActiveMinutes"
## [5] "FairlyActiveMinutes" "VeryActiveMinutes"
## [7] "SedentaryActiveDistance" "LightActiveDistance"
## [9] "ModeratelyActiveDistance" "VeryActiveDistance"glimpse(daily_intensities)## Rows: 940
## Columns: 10
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 150396036~
## $ ActivityDay <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/~
## $ SedentaryMinutes <dbl> 728, 776, 1218, 726, 773, 539, 1149, 775, 818~
## $ LightlyActiveMinutes <dbl> 328, 217, 181, 209, 221, 164, 233, 264, 205, ~
## $ FairlyActiveMinutes <dbl> 13, 19, 11, 34, 10, 20, 16, 31, 12, 8, 27, 21~
## $ VeryActiveMinutes <dbl> 25, 21, 30, 29, 36, 38, 42, 50, 28, 19, 66, 4~
## $ SedentaryActiveDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ LightActiveDistance <dbl> 6.06, 4.71, 3.91, 2.83, 5.04, 2.51, 4.71, 5.0~
## $ ModeratelyActiveDistance <dbl> 0.55, 0.69, 0.40, 1.26, 0.41, 0.78, 0.64, 1.3~
## $ VeryActiveDistance <dbl> 1.88, 1.57, 2.44, 2.14, 2.71, 3.19, 3.25, 3.5~Data cleaning
A)Removing duplicates
Id and activity date should be unique (0 observations removed)
daily_activity <- daily_activity %>%
distinct(Id, ActivityDate, .keep_all = TRUE)Id and activity date should be unique (3 observations removed)
sleep_day <- sleep_day %>%
distinct(Id, SleepDay, .keep_all = TRUE)Id and Date should be unique (0 observations removed)
weight_log <- weight_log %>%
distinct(Id, Date, .keep_all = TRUE)B) Checking for NA values and removing if any exist
No NA value was found
which(!complete.cases(daily_activity))## integer(0)which(!complete.cases(sleep_day))## integer(0)which(!complete.cases(daily_intensities))## integer(0)in wight_Log there was (60) NA on FAT column
which(!complete.cases(weight_log))## [1] 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## [26] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
## [51] 53 54 55 56 57 58 59 60 61 62 63 64 65 66 674) Analyze
For this case study I decided to use this merged 5 files:
daily_activity.
weight_log.
daily_intensities.
sleep_day.
Initial observations:
They all contain ID column.
daily_activity, daily_intensities and daily_calories has same Rows Number(940).
daily_activity contain all daily_calories attribute and daily_ intensities.
A) daily activity Trends:
Checking User Activity.
By grouping User into “Active” and “Non-Active” users.
Active users are those with average sum of high and medium daily activity over 50 minutes.
Will assume that user with total steps less than 700 the device was not used).
daily_activity_on <- daily_activity[daily_activity$TotalSteps > 700, ]- Then we group by Id, and finding the mean for each activity
daily_activity_users <- daily_activity_on %>%
group_by(Id) %>%
summarise(VeryActiveMinutes_av = mean(VeryActiveMinutes),
FairlyActiveMinutes_av = mean(FairlyActiveMinutes),
LightlyActiveMinutes_av = mean(LightlyActiveMinutes),
SedentaryMinutes_av = mean(SedentaryMinutes),
Calories_av = mean(Calories))
daily_activity_users <- daily_activity_users %>%
mutate(very_fairly_active_avg = VeryActiveMinutes_av + FairlyActiveMinutes_av)- Creating a variable for active and non-active users
daily_activity_users <- daily_activity_users %>%
mutate(active = ifelse(very_fairly_active_avg > 60, "active", "non-active"))Create a bar chart to visualize the Result :
ggplot(data = daily_activity_users)+
geom_bar(mapping = aes(x = active, fill = active))+
labs(title = "Active VS Non-active users", x = "Activity level", y = "Number of users")+
theme(title = element_text(size=19, face="bold"),
axis.title=element_text(size=17,face="bold"),
legend.text=element_text(size=15))+
guides(fill=guide_legend(title="Activity level"))
Less than 25% of respondents are users, who exercise at least 50 minutes a day.
Non-active group has a majority people in it.
We can assume that either an average user does not have an active lifestyle. Or does not wear a device during training.
B) daily_activity VS daily_calories & daily_intensities
- By creating temp data frame named it daily_activity_Temp
daily_activity_Temp1 <- daily_activity %>%
select(Id, ActivityDate, Calories)
head(daily_activity_Temp1)## # A tibble: 6 x 3
## Id ActivityDate Calories
## <dbl> <chr> <dbl>
## 1 1503960366 4/12/2016 1985
## 2 1503960366 4/13/2016 1797
## 3 1503960366 4/14/2016 1776
## 4 1503960366 4/15/2016 1745
## 5 1503960366 4/16/2016 1863
## 6 1503960366 4/17/2016 1728glimpse(daily_activity_Temp1)## Rows: 940
## Columns: 3
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396036~
## $ ActivityDate <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/2016", "4/16~
## $ Calories <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 177~we check if the two data frames of daily_activity_Temp1 and daily_calories are same.
first we:
install the (sqldf) package for ruining SQL statements on R data frames, optimized for convenience.
library(sqldf)## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLitesql_check1 <- sqldf('SELECT * FROM daily_activity_Temp1 INTERSECT SELECT * FROM daily_calories')
head(sql_check1)## Id ActivityDate Calories
## 1 1503960366 4/12/2016 1985
## 2 1503960366 4/13/2016 1797
## 3 1503960366 4/14/2016 1776
## 4 1503960366 4/15/2016 1745
## 5 1503960366 4/16/2016 1863
## 6 1503960366 4/17/2016 1728glimpse(sql_check1)## Rows: 940
## Columns: 3
## $ Id <dbl> 1503960366, 1503960366, 1503960366, 1503960366, 150396036~
## $ ActivityDate <chr> "4/12/2016", "4/13/2016", "4/14/2016", "4/15/2016", "4/16~
## $ Calories <dbl> 1985, 1797, 1776, 1745, 1863, 1728, 1921, 2035, 1786, 177~it’s safe to assume that the values are the same between the data frames.
the same is true for daily intensities, so we can drop those two data frames from analysis.
That leaves us with (3) data frames:
daily_activity.
sleep_day.
weight_log.
let check the Number of Id’s in daily_activity and sleep_day
n_distinct(daily_activity$Id)## [1] 33n_distinct(sleep_day$Id)## [1] 24n_distinct(weight_log$Id)## [1] 8let check the number of observation in each
nrow(daily_activity)## [1] 940nrow(sleep_day)## [1] 410nrow(weight_log)## [1] 67Statistical Calculation summery
A) daily_activity
daily_activity %>%
select(TotalSteps, TotalDistance, VeryActiveMinutes, SedentaryMinutes, Calories) %>%
summary()## TotalSteps TotalDistance VeryActiveMinutes SedentaryMinutes
## Min. : 0 Min. : 0.000 Min. : 0.00 Min. : 0.0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 0.00 1st Qu.: 729.8
## Median : 7406 Median : 5.245 Median : 4.00 Median :1057.5
## Mean : 7638 Mean : 5.490 Mean : 21.16 Mean : 991.2
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.: 32.00 3rd Qu.:1229.5
## Max. :36019 Max. :28.030 Max. :210.00 Max. :1440.0
## Calories
## Min. : 0
## 1st Qu.:1828
## Median :2134
## Mean :2304
## 3rd Qu.:2793
## Max. :4900B) sleep_day
sleep_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. :961.0C) weight_log
weight_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. :47.54few explorations
Total steps VS sedentary minutes
ggplot(data = daily_activity, aes(x= TotalSteps, y= SedentaryMinutes, color= Calories)) +
geom_point(color="#ca6708") +
labs(title="Total steps VS Sedentary minutes", x="Total Steps", y="Sendentary Minutes")
- Its look like negative relationship between total steps taken and sedentary Minutes.
Total steps VS Calories
ggplot(data=daily_activity, aes(x=TotalSteps, y = Calories))+
geom_point(color="#ca6708") +
labs(title="Total steps VS Calories", x="Total Steps", y="Calories") + stat_smooth(method=lm)## `geom_smooth()` using formula 'y ~ x'
Calories generally trend positively with total steps taking.
we can assume This shows that the data seem fairly accurate when it comes to recording steps and sedentary minutes.
we could also market the devices as a way to let people know how sedentary they actually are.
We can also note that sedentary time is not necessarily related to calories burned.
in general the people who took the most total steps tended to burn the most calories however, there’s a large spread there clustered towards the lower amounts.
calories.lm <- lm(Calories ~ TotalSteps, data = daily_activity)
calories.res <- resid(calories.lm)
plot(daily_activity$TotalSteps, calories.res, ylab="Residuals",xlab = "Total Steps", main = "Calories Burned", )
Now lets plot the density of the residuals
plot(density(calories.res))
Checking for normality
qqnorm(calories.res)
qqline(calories.res)
- looks like the spread isn’t as far statistically as we though
A potential strategy
- In order to burn calories, you don’t have to do high-intensity work outs, you can just get out there and start walking,
Sleep VS Time in Bed
1:1 trend from the amount of time slept and the total time someone spends in bed
ggplot(data=sleep_day, aes(x=TotalMinutesAsleep, y=TotalTimeInBed)) +
geom_point(color="#ca6708") +
labs(title="Total Minutes Sleep VS Total Time In Bed", x="Total Minutes Sleep", y="Total Time In Bed")
there are some outliers.some data points that spent a lot of time in bed, but didn’t actually sleep, and then a small batch that slept a whole bunch and spent time in bed
We could definitely market to consumers to monitor their time in bed with the watch against their sleep time.and could add some apps like night meditation or any sleeping aids voice books.
sedentary minutes VS Sleep
Merging these two datasets together
combined_sleep_day_data <- merge(sleep_day, daily_activity, by="Id")
head(combined_sleep_day_data)## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM 1 327
## 2 1503960366 4/12/2016 12:00:00 AM 1 327
## 3 1503960366 4/12/2016 12:00:00 AM 1 327
## 4 1503960366 4/12/2016 12:00:00 AM 1 327
## 5 1503960366 4/12/2016 12:00:00 AM 1 327
## 6 1503960366 4/12/2016 12:00:00 AM 1 327
## TotalTimeInBed ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 346 5/7/2016 11992 7.71 7.71
## 2 346 5/6/2016 12159 8.03 8.03
## 3 346 5/1/2016 10602 6.81 6.81
## 4 346 4/30/2016 14673 9.25 9.25
## 5 346 4/12/2016 13162 8.50 8.50
## 6 346 4/13/2016 10735 6.97 6.97
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 2.46 2.12
## 2 0 1.97 0.25
## 3 0 2.29 1.60
## 4 0 3.56 1.42
## 5 0 1.88 0.55
## 6 0 1.57 0.69
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 3.13 0 37
## 2 5.81 0 24
## 3 2.92 0 33
## 4 4.27 0 52
## 5 6.06 0 25
## 6 4.71 0 21
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 46 175 833 1821
## 2 6 289 754 1896
## 3 35 246 730 1820
## 4 34 217 712 1947
## 5 13 328 728 1985
## 6 19 217 776 1797n_distinct(combined_sleep_day_data$Id)## [1] 24there are only 24 unique id’s in the combined dataset.
since only 24 users actively used the sleep data.
Sedentary Time vs Time In Bed
sedentary.lm <- lm(SedentaryMinutes ~ TotalTimeInBed, data = combined_sleep_day_data)
sedentary.lm##
## Call:
## lm(formula = SedentaryMinutes ~ TotalTimeInBed, data = combined_sleep_day_data)
##
## Coefficients:
## (Intercept) TotalTimeInBed
## 922.5632 -0.2688now a pearson correlation coefficient
cor(combined_sleep_day_data$TotalTimeInBed,combined_sleep_day_data$SedentaryMinutes, method = "pearson")## [1] -0.1286386- As time in bed goes up, sedentary minutes actually go down, but not to a statistically significant degree.
Very active minutes vs calories burned
ggplot(data = combined_sleep_day_data, aes(x=VeryActiveMinutes, y=Calories)) +
geom_point(color="#ca6708") + geom_smooth(method = 'loess', formula = y ~ x) +
labs(title="Very Active Minutes vs. Calories Burned", x="VeryActiveMinutes", y="Calories Burned")## Warning: Computation failed in `stat_smooth()`:
## 'Calloc' could not allocate memory (228842447 of 8 bytes)
- A Moderate relationship for fairly active minutes.
ggplot(data = combined_sleep_day_data, aes(x=FairlyActiveMinutes, y=Calories)) +
geom_point(color="#ca6708") +
labs(title="Fairly Active Minutes VS Calories", x="Fairly Active Minutes", y=" Calories")+
stat_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
lm(Calories ~ FairlyActiveMinutes, data = combined_sleep_day_data)##
## Call:
## lm(formula = Calories ~ FairlyActiveMinutes, data = combined_sleep_day_data)
##
## Coefficients:
## (Intercept) FairlyActiveMinutes
## 2205.987 6.7396) Act
we will be delivering our insights and providing recommendations based on our analysis.
some trends has been identified and can be applied to Beallabeat.
Trends
Only around 25% of the user can be called active.
Less people track sleep as they become more experienced users.
more people log their calories, steps taken, etc, and fewer users log their sleep data, and only a select few are logging their weight
The proportion of night active users is relatively large.
Takeaway And Recommendations :
Adding some additional data capture techniques like hydration data, that puts Bellabeat way above the competition.
We should focus on marketing the fact that collecting data will help you (customer) set goals.
Marketing team can encourage users by educating and equipping them with knowledge about fitness benefits, suggest different types of exercise and calories intake and burnt rate information on the Bellabeat app. this provides a unique opportunity for individuals to change their behavior, become more physically active and increase their life expectancy.
adding features like notifications,competitions into the wearable’s ( eg. on weekends, Bellabeat app can rompt notification to encourage users to exercise)
Add some sleep aids tools like meditation or audio book and its can be monetized.
Make the device more comfortable on sleep and adding sleep tracking features.
We could market to consumers by telling them smart-devices could help them start their journey by measuring how much they’re already moving.