Google Data Analytics Capstone: Case Study 2
Shadi Salek
2022-04-16
How Can a Wellness Company Play it Smart?
The Scenario
Bellabeat is a high-tech manufacturer of health-focused products for women. Urška Sršen and Sando Mur founded Bellabeat in 2013. Urška Sršen believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. I have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights I discover will then help guide marketing strategy for the company. I will present my analysis to the Bellabeat executive team along with my high-level recommendations for Bellabeat’s marketing strategy.
Step 1. Ask
- Define the problem:
- Key stakeholders:
- Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer
- Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
- What are my stakeholders saying their problem are?
- Bellabeat is a small company, but it has the potential to become a larger player in the global smart device market. Urška believes that analyzing smart device fitness data could help unlock new growth opportunities for the company.
- How can I help the stakeholders to resolve their question?
(Business Task)
- Analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information to suggest high-level recommendations for how these trends can inform Bellabeat marketing strategy.
- Key stakeholders:
- Business 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?
Step 2. Prepare
- Data source:
- FitBit Fitness Tracker Data (CC0: Public Domain, dataset meaning the creator has waive his right to the work under the copyright law and made available through Mobius): This dataset 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 format
- Since the data gathered by other people and it lives outside of the company, the data is secondary and external data.
- The dataset has in total 18 files in .csv format organized in long format.
- The data is also quantitative, because it is specific and objective measures of numerical facts.
- Data is organized in a certain format with rows and column, so it is a structured data.
- The data is a long data in which each row is one time point per subject, so each subject will have data in multiple rows.
- ROCCC Data
- Reliable: The data source is not reliable.(Amazon Mechanical Turk)
- Original: The data is not original.(Not Primary data)
- Comprehensive: The data has some critical information.
- Current: The data is collected between 03.12.2016-05.12.2016 and it is not current.
- Cited: The data is not primary so it may not be credible.
- Data Ethics:
- The data has kept subjects’ information and activity private. Thirty three eligible Fitbit users consented to the submission of personal tracker data.
- Data limitations:
- There are million of people who use fitbit to track their health and choosing thirthy three fitbit users is not representative of the population and it leads to sample bias.
- The time frame of 31 days is considered to be short and not enough data has been collected.
- The data is not current and it belongs to 2016.
Step 3. Process
The data I’ll be working with will be as follow:
- dailyActivity_merged.csv
- dailyCalories_merged.csv
- sleepDay_merged.csv
- weightLogInfo_merged.csv
- dailyIntensities_merged.csv
Setting up my R environment:
Setting up my R environment by loading the following files and using naming convention:
daily_activity <- read.csv("image/dailyActivity_merged.csv")
daily_calories <- read.csv("image/dailyCalories_merged.csv")
sleep_day <- read.csv("image/sleepDay_merged.csv")
daily_intensities <- read.csv("image/dailyIntensities_merged.csv")
weight_log <- read.csv("image/weightLogInfo_merged.csv")Installing and loading tidyverse, skimr, janitor, lubridate, dplyr, and sqldf package:
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)install.packages("skimr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)install.packages("janitor")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)install.packages("lubridate")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)install.packages("sqldf")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(skimr)
library(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(sqldf)## Loading required package: gsubfn## Loading required package: proto## Warning in fun(libname, pkgname): couldn't connect to display ":0"## Loading required package: RSQLitelibrary(dplyr)- Data Review:
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 1728colnames(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"str(daily_activity)## '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 ...- Daily_activity file has most of the tracker recorded data, such as calories, intensities, and steps information.
- Above information shows that we have 940 observations and 15 variables and 33 different people logged their daily activities, calories expenditure and steps in 31 days.
daily_calories
str(daily_calories)## 'data.frame': 940 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDay: chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...- daily_calories file has the same information included in daily_activity file with the same observation.
daily_intensities
str(daily_intensities)## 'data.frame': 940 obs. of 10 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDay : chr "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...- daily_intensities file has the same information included in daily_activity file with the same observation.
daily_intensities
str(sleep_day)## '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 ...weight_log
str(weight_log)## '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 ...- weight_log file has less observation (67) and there is a Boolean field (Fat).
Observation
- All the files have Id as a common field.
- The daily_activity table have the same observation and values with calories and intensities, so we should confirm that the values actually match for each ‘Id’ number that can be our primary key. First we make a temporary table from daily_activity with the same columns as daily_calories. Let’s check it out:
daily_activity_df <- daily_activity %>%
select(Id, ActivityDate, Calories)
head(daily_activity_df)## 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 1728check1 <- sqldf('SELECT * FROM daily_activity_df INTERSECT SELECT * FROM daily_calories')
head(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 1728str(check1)## 'data.frame': 940 obs. of 3 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" ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...Now we are sure the daily_calories table is the same as the daily_activity. We do the same with the daily_intensities.
daily_activity_df2 <- daily_activity %>%
select(Id, ActivityDate, SedentaryMinutes, LightlyActiveMinutes, FairlyActiveMinutes, VeryActiveMinutes, SedentaryActiveDistance, LightActiveDistance, ModeratelyActiveDistance, VeryActiveDistance)
head(daily_activity_df2)## Id ActivityDate SedentaryMinutes LightlyActiveMinutes
## 1 1503960366 4/12/2016 728 328
## 2 1503960366 4/13/2016 776 217
## 3 1503960366 4/14/2016 1218 181
## 4 1503960366 4/15/2016 726 209
## 5 1503960366 4/16/2016 773 221
## 6 1503960366 4/17/2016 539 164
## FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance
## 1 13 25 0
## 2 19 21 0
## 3 11 30 0
## 4 34 29 0
## 5 10 36 0
## 6 20 38 0
## LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
## 1 6.06 0.55 1.88
## 2 4.71 0.69 1.57
## 3 3.91 0.40 2.44
## 4 2.83 1.26 2.14
## 5 5.04 0.41 2.71
## 6 2.51 0.78 3.19check_2 <- sqldf('SELECT * FROM daily_activity_df2 INTERSECT SELECT * FROM daily_intensities')
head(check_2)## Id ActivityDate SedentaryMinutes LightlyActiveMinutes
## 1 1503960366 4/12/2016 728 328
## 2 1503960366 4/13/2016 776 217
## 3 1503960366 4/14/2016 1218 181
## 4 1503960366 4/15/2016 726 209
## 5 1503960366 4/16/2016 773 221
## 6 1503960366 4/17/2016 539 164
## FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance
## 1 13 25 0
## 2 19 21 0
## 3 11 30 0
## 4 34 29 0
## 5 10 36 0
## 6 20 38 0
## LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
## 1 6.06 0.55 1.88
## 2 4.71 0.69 1.57
## 3 3.91 0.40 2.44
## 4 2.83 1.26 2.14
## 5 5.04 0.41 2.71
## 6 2.51 0.78 3.19str(check_2)## 'data.frame': 940 obs. of 10 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" ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...Now we are sure the daily_activity table is the same as the daily_intensities. We can now work on daily_activity, sleep_day and weight_log.
- Now we want to check the Id section in all three tables:
n_distinct(daily_activity$Id)## [1] 33n_distinct(sleep_day$Id)## [1] 24n_distinct(weight_log$Id)## [1] 8Then we check the number of observation in each table:
nrow(daily_activity)## [1] 940nrow(sleep_day)## [1] 413nrow(weight_log)## [1] 67Looking at the daily_activity_csv file and using filter, I noticed there are lots of missing data. There are many nulls and missing pieces of information altogether.
After filtering I found out that some days there is no record of steps and activity and even no calories, however the sedentary time has been recorded 1440 minutes. After searching Google, I found fitbit community center and understood that the sedentary time only is calculated on days in which the tracker is worn. Sedentary/Active time is calculated by movement, and you need to be inactive for 10 consecutive minutes before the period is considered stationary. There is a setting on some fitbit trackers that will log a day where the device is not worn as 100% or 1440 minutes of sedentary time. Since this is a case study and I do not have contact with stakeholders, I decided to move on with the data.
The ActivityDate column in daily_activity structure is in Character and we change it to the date format.
daily_activity$ActivityDate <- as.Date(daily_activity$ActivityDate, "%m/%d/%Y")
head(daily_activity)## Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366 2016-04-12 13162 8.50 8.50
## 2 1503960366 2016-04-13 10735 6.97 6.97
## 3 1503960366 2016-04-14 10460 6.74 6.74
## 4 1503960366 2016-04-15 9762 6.28 6.28
## 5 1503960366 2016-04-16 12669 8.16 8.16
## 6 1503960366 2016-04-17 9705 6.48 6.48
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1 0 1.88 0.55
## 2 0 1.57 0.69
## 3 0 2.44 0.40
## 4 0 2.14 1.26
## 5 0 2.71 0.41
## 6 0 3.19 0.78
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1 6.06 0 25
## 2 4.71 0 21
## 3 3.91 0 30
## 4 2.83 0 29
## 5 5.04 0 36
## 6 2.51 0 38
## FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1 13 328 728 1985
## 2 19 217 776 1797
## 3 11 181 1218 1776
## 4 34 209 726 1745
## 5 10 221 773 1863
## 6 20 164 539 1728- We do the same with the SleepDay column in sleep_day and Date column in weight_log:
sleep_day$SleepDay <- as.Date(sleep_day$SleepDay, "%m/%d/%Y")
head(sleep_day)## Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## 1 1503960366 2016-04-12 1 327 346
## 2 1503960366 2016-04-13 2 384 407
## 3 1503960366 2016-04-15 1 412 442
## 4 1503960366 2016-04-16 2 340 367
## 5 1503960366 2016-04-17 1 700 712
## 6 1503960366 2016-04-19 1 304 320weight_log$Date <- as.Date(weight_log$Date, "%m/%d/%Y")
head(weight_log)## Id Date WeightKg WeightPounds Fat BMI IsManualReport
## 1 1503960366 2016-05-02 52.6 115.9631 22 22.65 True
## 2 1503960366 2016-05-03 52.6 115.9631 NA 22.65 True
## 3 1927972279 2016-04-13 133.5 294.3171 NA 47.54 False
## 4 2873212765 2016-04-21 56.7 125.0021 NA 21.45 True
## 5 2873212765 2016-05-12 57.3 126.3249 NA 21.69 True
## 6 4319703577 2016-04-17 72.4 159.6147 25 27.45 True
## LogId
## 1 1.462234e+12
## 2 1.462320e+12
## 3 1.460510e+12
## 4 1.461283e+12
## 5 1.463098e+12
## 6 1.460938e+12- Renaming the data columns:
daily_activity <- daily_activity %>%
rename(activity_date = ActivityDate,
total_steps = TotalSteps,
total_distance = TotalDistance,
tracker_distance = TrackerDistance,
logged_activities_distance = LoggedActivitiesDistance,
very_active_distance = VeryActiveDistance,
moderately_active_distance = ModeratelyActiveDistance,
light_active_distance = LightActiveDistance,
sedentary_active_distance = SedentaryActiveDistance,
very_active_minutes = VeryActiveMinutes,
fairly_active_minutes = FairlyActiveMinutes,
lightly_active_minutes = LightlyActiveMinutes,
sedentary_minutes = SedentaryMinutes
)
head(daily_activity)## Id activity_date total_steps total_distance tracker_distance
## 1 1503960366 2016-04-12 13162 8.50 8.50
## 2 1503960366 2016-04-13 10735 6.97 6.97
## 3 1503960366 2016-04-14 10460 6.74 6.74
## 4 1503960366 2016-04-15 9762 6.28 6.28
## 5 1503960366 2016-04-16 12669 8.16 8.16
## 6 1503960366 2016-04-17 9705 6.48 6.48
## logged_activities_distance very_active_distance moderately_active_distance
## 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
## light_active_distance sedentary_active_distance very_active_minutes
## 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
## fairly_active_minutes lightly_active_minutes sedentary_minutes 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 1728sleep_day <- sleep_day %>%
rename(total_sleep_records = TotalSleepRecords,
total_minutes_asleep = TotalMinutesAsleep,
total_time_in_bed = TotalTimeInBed
)
head(sleep_day)## Id SleepDay total_sleep_records total_minutes_asleep
## 1 1503960366 2016-04-12 1 327
## 2 1503960366 2016-04-13 2 384
## 3 1503960366 2016-04-15 1 412
## 4 1503960366 2016-04-16 2 340
## 5 1503960366 2016-04-17 1 700
## 6 1503960366 2016-04-19 1 304
## total_time_in_bed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320weight_log <- weight_log %>%
rename(weight_kg = WeightKg,
weight_pounds = WeightPounds,
is_manual_report = IsManualReport,
log_id = LogId
)
head(weight_log)## Id Date weight_kg weight_pounds Fat BMI is_manual_report
## 1 1503960366 2016-05-02 52.6 115.9631 22 22.65 True
## 2 1503960366 2016-05-03 52.6 115.9631 NA 22.65 True
## 3 1927972279 2016-04-13 133.5 294.3171 NA 47.54 False
## 4 2873212765 2016-04-21 56.7 125.0021 NA 21.45 True
## 5 2873212765 2016-05-12 57.3 126.3249 NA 21.69 True
## 6 4319703577 2016-04-17 72.4 159.6147 25 27.45 True
## log_id
## 1 1.462234e+12
## 2 1.462320e+12
## 3 1.460510e+12
## 4 1.461283e+12
## 5 1.463098e+12
## 6 1.460938e+12- Organizing and grouping the data:
## Sort by total steps and group by Id
daily_activity <- daily_activity %>%
arrange(-total_steps) %>%
group_by(Id) %>%
drop_na()
head(daily_activity)## # A tibble: 6 × 15
## # Groups: Id [3]
## Id activity_date total_steps total_distance tracker_distance
## <dbl> <date> <int> <dbl> <dbl>
## 1 1624580081 2016-05-01 36019 28.0 28.0
## 2 8877689391 2016-04-16 29326 25.3 25.3
## 3 8877689391 2016-04-30 27745 26.7 26.7
## 4 8877689391 2016-04-27 23629 20.6 20.6
## 5 8877689391 2016-04-12 23186 20.4 20.4
## 6 8053475328 2016-04-24 22988 18.0 18.0
## # … with 10 more variables: logged_activities_distance <dbl>,
## # very_active_distance <dbl>, moderately_active_distance <dbl>,
## # light_active_distance <dbl>, sedentary_active_distance <dbl>,
## # very_active_minutes <int>, fairly_active_minutes <int>,
## # lightly_active_minutes <int>, sedentary_minutes <int>, Calories <int>- Checking for duplicates:
daily_activity## # A tibble: 940 × 15
## # Groups: Id [33]
## Id activity_date total_steps total_distance tracker_distance
## <dbl> <date> <int> <dbl> <dbl>
## 1 1624580081 2016-05-01 36019 28.0 28.0
## 2 8877689391 2016-04-16 29326 25.3 25.3
## 3 8877689391 2016-04-30 27745 26.7 26.7
## 4 8877689391 2016-04-27 23629 20.6 20.6
## 5 8877689391 2016-04-12 23186 20.4 20.4
## 6 8053475328 2016-04-24 22988 18.0 18.0
## 7 4388161847 2016-05-07 22770 17.5 17.5
## 8 8053475328 2016-04-23 22359 17.2 17.2
## 9 2347167796 2016-04-16 22244 15.1 15.1
## 10 8053475328 2016-05-08 22026 17.6 17.6
## # … with 930 more rows, and 10 more variables:
## # logged_activities_distance <dbl>, very_active_distance <dbl>,
## # moderately_active_distance <dbl>, light_active_distance <dbl>,
## # sedentary_active_distance <dbl>, very_active_minutes <int>,
## # fairly_active_minutes <int>, lightly_active_minutes <int>,
## # sedentary_minutes <int>, Calories <int>sum(duplicated(daily_activity))## [1] 0Step 4. Analyze
- Identifying trends and relationships:
## A quick summary statistics
daily_activity %>%
select(total_steps,
total_distance,
sedentary_minutes,
very_active_minutes) %>%
summary()## Adding missing grouping variables: `Id`## Id total_steps total_distance sedentary_minutes
## Min. :1.504e+09 Min. : 0 Min. : 0.000 Min. : 0.0
## 1st Qu.:2.320e+09 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8
## Median :4.445e+09 Median : 7406 Median : 5.245 Median :1057.5
## Mean :4.855e+09 Mean : 7638 Mean : 5.490 Mean : 991.2
## 3rd Qu.:6.962e+09 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5
## Max. :8.878e+09 Max. :36019 Max. :28.030 Max. :1440.0
## very_active_minutes
## Min. : 0.00
## 1st Qu.: 0.00
## Median : 4.00
## Mean : 21.16
## 3rd Qu.: 32.00
## Max. :210.00For daily sleep:
sleep_day %>%
select(total_sleep_records,
total_minutes_asleep,
total_time_in_bed) %>%
summary()## total_sleep_records total_minutes_asleep total_time_in_bed
## Min. :1.000 Min. : 58.0 Min. : 61.0
## 1st Qu.:1.000 1st Qu.:361.0 1st Qu.:403.0
## Median :1.000 Median :433.0 Median :463.0
## Mean :1.119 Mean :419.5 Mean :458.6
## 3rd Qu.:1.000 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :3.000 Max. :796.0 Max. :961.0For weight log:
weight_log %>%
select(weight_kg,
weight_pounds,
BMI) %>%
summary()## weight_kg weight_pounds BMI
## Min. : 52.60 Min. :116.0 Min. :21.45
## 1st Qu.: 61.40 1st Qu.:135.4 1st Qu.:23.96
## Median : 62.50 Median :137.8 Median :24.39
## Mean : 72.04 Mean :158.8 Mean :25.19
## 3rd Qu.: 85.05 3rd Qu.:187.5 3rd Qu.:25.56
## Max. :133.50 Max. :294.3 Max. :47.54Step 5.Data Visualization and Share:
- Negative relationship between total steps and sedentary minutes:
ggplot(data=daily_activity, aes(x=total_steps, y=sedentary_minutes, color = Calories)) + geom_point()
- Calories generally trend positively with total steps:
ggplot(data=daily_activity, aes(x=total_steps, y=Calories)) + geom_point() + geom_smooth() +
labs(title = "Daily Activity: Calories vs. Total Steps")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
+ We can say that physical activity, such as walking, is
important for burning calories.
- We can see a linear trend between the amount of time slept and the total time someone spends in bed:
ggplot(data=sleep_day, aes(x=total_minutes_asleep, y=total_time_in_bed)) + geom_point() + geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
+ We could definitely conclude that by tracking the time we’re
inactive, the devices can record when you fall asleep at night and when
you stir in the morning.
- Now we merge sleep day and daily activity together:
sleep_activity_combined <- merge(sleep_day, daily_activity, by="Id")
head(sleep_activity_combined)## Id SleepDay total_sleep_records total_minutes_asleep
## 1 1503960366 2016-04-12 1 327
## 2 1503960366 2016-04-12 1 327
## 3 1503960366 2016-04-12 1 327
## 4 1503960366 2016-04-12 1 327
## 5 1503960366 2016-04-12 1 327
## 6 1503960366 2016-04-12 1 327
## total_time_in_bed activity_date total_steps total_distance tracker_distance
## 1 346 2016-04-20 10544 6.68 6.68
## 2 346 2016-05-01 10602 6.81 6.81
## 3 346 2016-04-19 15506 9.88 9.88
## 4 346 2016-04-15 9762 6.28 6.28
## 5 346 2016-05-02 14727 9.71 9.71
## 6 346 2016-05-10 12207 7.77 7.77
## logged_activities_distance very_active_distance moderately_active_distance
## 1 0 1.96 0.48
## 2 0 2.29 1.60
## 3 0 3.53 1.32
## 4 0 2.14 1.26
## 5 0 3.21 0.57
## 6 0 3.35 1.16
## light_active_distance sedentary_active_distance very_active_minutes
## 1 4.24 0 28
## 2 2.92 0 33
## 3 5.03 0 50
## 4 2.83 0 29
## 5 5.92 0 41
## 6 3.26 0 46
## fairly_active_minutes lightly_active_minutes sedentary_minutes Calories
## 1 12 205 818 1786
## 2 35 246 730 1820
## 3 31 264 775 2035
## 4 34 209 726 1745
## 5 15 277 798 2004
## 6 31 214 746 1859- Now we plot sedentary time and time in bed together:
sedentary.lm <- lm(sedentary_minutes ~ total_time_in_bed, data = sleep_activity_combined)
sedentary.lm##
## Call:
## lm(formula = sedentary_minutes ~ total_time_in_bed, data = sleep_activity_combined)
##
## Coefficients:
## (Intercept) total_time_in_bed
## 921.9598 -0.2678- It looks like sedentary time and total time in bed are not related much at all.
ggplot(data = sleep_activity_combined, aes(x=very_active_minutes, y=Calories)) + geom_point() + stat_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
+ There’s a strong positive correlation between very active
minutes and calories burned.
Step 6. Act and Recommendation
Final Recommendation to Bellabeat: