Case Study 2: Bellabeat
Marlene Seleiro - Google Data Analytics Capstone - Portfolio-Ready Case Study
08/11/2021
Bellabeat - How Can a Wellness Technology Company Play It Smart?
## Introduction and background Urška Sršen and Sando Mur founded Bellabeat, a high-tech company that manufactures health-focused smart products. Sršen used her background as an artist to develop beautifully designed technology that informs and inspires women around the world. Collecting data on activity, sleep, stress, and reproductive health has allowed Bellabeat to empower women with knowledge about their own health and habits. Since it was founded in 2013, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for women. By 2016, Bellabeat had opened offices around the world and launched multiple products. 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.
Key Stakeholders: Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer Sando Mur: Mathematician, Bellabeat’s cofounder and key member of the Bellabeat executive team Bellabeat marketing analytics team: A team of data analysts guiding Bellabeat’s marketing strategy.
Using the Case Study Roadmap as a guide, this analysis will follow the steps of the data analysis process: Ask, Prepare, Process, Analyze, Share, and Act.
Step 1: Ask
Analyze the smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices and select one Bellabeat product to apply these insights to my presentation.
Guiding questions
- What is the problem you are trying to solve?
Sršen knows that an analysis of Bellabeat’s available consumer data would reveal more opportunities for growth. She has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. - How can your insights drive business decisions?
Using this information, would produce high-level recommendations on how these trends can inform Bellabeat marketing strategy. Business Task: Analyze FitBit fitness tracker data to gain insights into how consumers are using the FitBit app and discover trends for Bellabeat marketing strategy.
Business questions:
- 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?
Produce a report with the following deliverables:
- A clear summary of the business task
- A description of all data sources used
- Documentation of any cleaning or manipulation of data
- A summary of your analysis
- Supporting visualizations and key findings
6, Your top high-level content recommendations based on your analysis
Step 2: Prepare
The co-founder and Chief Creative Officer encourages me to use “public data” that explores smart device users’ daily habits and points me to a specific Kaggle data set. Now I prepared the data for analysis using the “Case Study Roadmap” as a guide:
Guiding questions:
- Where is your data stored?
Data is publicly available on Kaggle: FitBit Fitness Tracker Data. - How is the data organized?
Data is stored in 18 csv files. - Are there issues with bias or credibility in this data? Does your data ROCCC?
This Kaggle data set contains personal fitness tracker from 30 fitbit users. They were generated by respondents to a distributed survey via Amazon Mechanical Turk between 12.04.2016-12.05.2016. - How are you addressing licensing, privacy, security, and accessibility?
30 eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. A good data source is ROCCC which stands for Reliable, Original, Comprehensive, Current, and Cited:
Reliable — LOW — Not reliable as it only has 30 respondents Original — LOW — Third party provider (Amazon Mechanical Turk) Comprehensive — MED — Parameters match most of Bellabeat product´s parameters Current — LOW — Data is 5 years old and may not be relevant Cited — LOW — Data collected from third party, hence unknown “Overall, this dataset is considered “bad quality data” and it is not recommended to produce business recommendations based on this data”
- How did you verify the data’s integrity?
As data is collected in a survey, we are unable to ascertain its integrity or accuracy. - How does it help you answer your question?
This data explores smart device users’ daily habits. It includes information about daily activity, steps, sleep habits and heart rate, that can be used to explore users’ habits and find some trends. - Are there any problems with the data?
Data was collected 5 years ago in 2016. Users’ daily activity, fitness and sleeping habits, diet and food consumption may have changed since then. Data may not be timely or relevant. Sample size of 30 FitBit users is not representative of the entire fitness population.
Step 3: Process
Process the data by cleaning and ensuring that it is correct, relevant, complete and free of error and outlier.
Upload CSV files to R
I upload the CSV files to my project from the relevant data source:
https://www.kaggle.com/arashnic/fitbit There are many different CSV files in this dataset, but I decided to concentrate in two CSVs: “dailyActivity_merged.csv” and “sleepDay_merged.csv”
Installing and loading common packages and libraries
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readr)
library(dplyr)
library(ggplot2)
library(knitr)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(rmarkdown)
library(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(skimr)# Import dataset "dailyActivity_merged.csv"
daily_activity <- read_csv("C:\\Users\\MM\\OneDrive\\Documentos\\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.# Import dataset "sleepDay_merged.csv"
daily_sleep <- read_csv("C:\\Users\\MM\\OneDrive\\Documentos\\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.# Explore and preview the first 10 rows of data
head(daily_activity, 10)## # A tibble: 10 x 15
## Id ActivityDate TotalSteps TotalDistance TrackerDistance LoggedActivitie~
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 13162 8.5 8.5 0
## 2 1503960366 4/13/2016 10735 6.97 6.97 0
## 3 1503960366 4/14/2016 10460 6.74 6.74 0
## 4 1503960366 4/15/2016 9762 6.28 6.28 0
## 5 1503960366 4/16/2016 12669 8.16 8.16 0
## 6 1503960366 4/17/2016 9705 6.48 6.48 0
## 7 1503960366 4/18/2016 13019 8.59 8.59 0
## 8 1503960366 4/19/2016 15506 9.88 9.88 0
## 9 1503960366 4/20/2016 10544 6.68 6.68 0
## 10 1503960366 4/21/2016 9819 6.34 6.34 0
## # ... with 9 more variables: VeryActiveDistance <dbl>,
## # ModeratelyActiveDistance <dbl>, LightActiveDistance <dbl>,
## # SedentaryActiveDistance <dbl>, VeryActiveMinutes <dbl>,
## # FairlyActiveMinutes <dbl>, LightlyActiveMinutes <dbl>,
## # SedentaryMinutes <dbl>, Calories <dbl>head(daily_sleep, 10)## # A tibble: 10 x 5
## Id SleepDay TotalSleepRecor~ TotalMinutesAsl~ TotalTimeInBed
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 12:00:00 AM 1 327 346
## 2 1503960366 4/13/2016 12:00:00 AM 2 384 407
## 3 1503960366 4/15/2016 12:00:00 AM 1 412 442
## 4 1503960366 4/16/2016 12:00:00 AM 2 340 367
## 5 1503960366 4/17/2016 12:00:00 AM 1 700 712
## 6 1503960366 4/19/2016 12:00:00 AM 1 304 320
## 7 1503960366 4/20/2016 12:00:00 AM 1 360 377
## 8 1503960366 4/21/2016 12:00:00 AM 1 325 364
## 9 1503960366 4/23/2016 12:00:00 AM 1 361 384
## 10 1503960366 4/24/2016 12:00:00 AM 1 430 449# Familiarize with the data and column datatypes
str(daily_activity)## spec_tbl_df [940 x 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:940] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityDate : chr [1:940] "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
## $ TotalSteps : num [1:940] 13162 10735 10460 9762 12669 ...
## $ TotalDistance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ TrackerDistance : num [1:940] 8.5 6.97 6.74 6.28 8.16 ...
## $ LoggedActivitiesDistance: num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveDistance : num [1:940] 1.88 1.57 2.44 2.14 2.71 ...
## $ ModeratelyActiveDistance: num [1:940] 0.55 0.69 0.4 1.26 0.41 ...
## $ LightActiveDistance : num [1:940] 6.06 4.71 3.91 2.83 5.04 ...
## $ SedentaryActiveDistance : num [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveMinutes : num [1:940] 25 21 30 29 36 38 42 50 28 19 ...
## $ FairlyActiveMinutes : num [1:940] 13 19 11 34 10 20 16 31 12 8 ...
## $ LightlyActiveMinutes : num [1:940] 328 217 181 209 221 164 233 264 205 211 ...
## $ SedentaryMinutes : num [1:940] 728 776 1218 726 773 ...
## $ Calories : num [1:940] 1985 1797 1776 1745 1863 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. ActivityDate = col_character(),
## .. TotalSteps = col_double(),
## .. TotalDistance = col_double(),
## .. TrackerDistance = col_double(),
## .. LoggedActivitiesDistance = col_double(),
## .. VeryActiveDistance = col_double(),
## .. ModeratelyActiveDistance = col_double(),
## .. LightActiveDistance = col_double(),
## .. SedentaryActiveDistance = col_double(),
## .. VeryActiveMinutes = col_double(),
## .. FairlyActiveMinutes = col_double(),
## .. LightlyActiveMinutes = col_double(),
## .. SedentaryMinutes = col_double(),
## .. Calories = col_double()
## .. )
## - attr(*, "problems")=<externalptr>str(daily_sleep)## spec_tbl_df [413 x 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:413] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ SleepDay : chr [1:413] "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 : num [1:413] 1 2 1 2 1 1 1 1 1 1 ...
## $ TotalMinutesAsleep: num [1:413] 327 384 412 340 700 304 360 325 361 430 ...
## $ TotalTimeInBed : num [1:413] 346 407 442 367 712 320 377 364 384 449 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. SleepDay = col_character(),
## .. TotalSleepRecords = col_double(),
## .. TotalMinutesAsleep = col_double(),
## .. TotalTimeInBed = col_double()
## .. )
## - attr(*, "problems")=<externalptr>Understanding some summary statistics
How many unique participants are there in each dataframe? It looks like there may be more participants in the daily activity dataset than the sleep dataset.
n_distinct(daily_activity$Id)## [1] 33n_distinct(daily_sleep$Id)## [1] 24# Check for duplicates
sum(is.na(daily_activity))## [1] 0sum(is.na(daily_sleep))## [1] 0#Check for missing values
sum(duplicated(daily_activity))## [1] 0sum(duplicated(daily_sleep))## [1] 3# Remove duplicates and NA from table 1
daily_activity < daily_activity %>%
distinct() %>%
drop_na()## Id ActivityDate TotalSteps TotalDistance TrackerDistance
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## [820,] FALSE FALSE FALSE FALSE FALSE
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## [824,] FALSE FALSE FALSE FALSE FALSE
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## [827,] FALSE FALSE FALSE FALSE FALSE
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## [829,] FALSE FALSE FALSE FALSE FALSE
## [830,] FALSE FALSE FALSE FALSE FALSE
## [831,] FALSE FALSE FALSE FALSE FALSE
## [832,] FALSE FALSE FALSE FALSE FALSE
## [833,] FALSE FALSE FALSE FALSE FALSE
## [834,] FALSE FALSE FALSE FALSE FALSE
## [835,] FALSE FALSE FALSE FALSE FALSE
## [836,] FALSE FALSE FALSE FALSE FALSE
## [837,] FALSE FALSE FALSE FALSE FALSE
## [838,] FALSE FALSE FALSE FALSE FALSE
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## [840,] FALSE FALSE FALSE FALSE FALSE
## [841,] FALSE FALSE FALSE FALSE FALSE
## [842,] FALSE FALSE FALSE FALSE FALSE
## [843,] FALSE FALSE FALSE FALSE FALSE
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## [846,] FALSE FALSE FALSE FALSE FALSE
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## [861,] FALSE FALSE FALSE FALSE FALSE
## [862,] FALSE FALSE FALSE FALSE FALSE
## [863,] FALSE FALSE FALSE FALSE FALSE
## [864,] FALSE FALSE FALSE FALSE FALSE
## [865,] FALSE FALSE FALSE FALSE FALSE
## [866,] FALSE FALSE FALSE FALSE FALSE
## [867,] FALSE FALSE FALSE FALSE FALSE
## [868,] FALSE FALSE FALSE FALSE FALSE
## [869,] FALSE FALSE FALSE FALSE FALSE
## [870,] FALSE FALSE FALSE FALSE FALSE
## [871,] FALSE FALSE FALSE FALSE FALSE
## [872,] FALSE FALSE FALSE FALSE FALSE
## [873,] FALSE FALSE FALSE FALSE FALSE
## [874,] FALSE FALSE FALSE FALSE FALSE
## [875,] FALSE FALSE FALSE FALSE FALSE
## [876,] FALSE FALSE FALSE FALSE FALSE
## [877,] FALSE FALSE FALSE FALSE FALSE
## [878,] FALSE FALSE FALSE FALSE FALSE
## [879,] FALSE FALSE FALSE FALSE FALSE
## [880,] FALSE FALSE FALSE FALSE FALSE
## [881,] FALSE FALSE FALSE FALSE FALSE
## [882,] FALSE FALSE FALSE FALSE FALSE
## [883,] FALSE FALSE FALSE FALSE FALSE
## [884,] FALSE FALSE FALSE FALSE FALSE
## [885,] FALSE FALSE FALSE FALSE FALSE
## [886,] FALSE FALSE FALSE FALSE FALSE
## [887,] FALSE FALSE FALSE FALSE FALSE
## [888,] FALSE FALSE FALSE FALSE FALSE
## [889,] FALSE FALSE FALSE FALSE FALSE
## [890,] FALSE FALSE FALSE FALSE FALSE
## [891,] FALSE FALSE FALSE FALSE FALSE
## [892,] FALSE FALSE FALSE FALSE FALSE
## [893,] FALSE FALSE FALSE FALSE FALSE
## [894,] FALSE FALSE FALSE FALSE FALSE
## [895,] FALSE FALSE FALSE FALSE FALSE
## [896,] FALSE FALSE FALSE FALSE FALSE
## [897,] FALSE FALSE FALSE FALSE FALSE
## [898,] FALSE FALSE FALSE FALSE FALSE
## [899,] FALSE FALSE FALSE FALSE FALSE
## [900,] FALSE FALSE FALSE FALSE FALSE
## [901,] FALSE FALSE FALSE FALSE FALSE
## [902,] FALSE FALSE FALSE FALSE FALSE
## [903,] FALSE FALSE FALSE FALSE FALSE
## [904,] FALSE FALSE FALSE FALSE FALSE
## [905,] FALSE FALSE FALSE FALSE FALSE
## [906,] FALSE FALSE FALSE FALSE FALSE
## [907,] FALSE FALSE FALSE FALSE FALSE
## [908,] FALSE FALSE FALSE FALSE FALSE
## [909,] FALSE FALSE FALSE FALSE FALSE
## [910,] FALSE FALSE FALSE FALSE FALSE
## [911,] FALSE FALSE FALSE FALSE FALSE
## [912,] FALSE FALSE FALSE FALSE FALSE
## [913,] FALSE FALSE FALSE FALSE FALSE
## [914,] FALSE FALSE FALSE FALSE FALSE
## [915,] FALSE FALSE FALSE FALSE FALSE
## [916,] FALSE FALSE FALSE FALSE FALSE
## [917,] FALSE FALSE FALSE FALSE FALSE
## [918,] FALSE FALSE FALSE FALSE FALSE
## [919,] FALSE FALSE FALSE FALSE FALSE
## [920,] FALSE FALSE FALSE FALSE FALSE
## [921,] FALSE FALSE FALSE FALSE FALSE
## [922,] FALSE FALSE FALSE FALSE FALSE
## [923,] FALSE FALSE FALSE FALSE FALSE
## [924,] FALSE FALSE FALSE FALSE FALSE
## [925,] FALSE FALSE FALSE FALSE FALSE
## [926,] FALSE FALSE FALSE FALSE FALSE
## [927,] FALSE FALSE FALSE FALSE FALSE
## [928,] FALSE FALSE FALSE FALSE FALSE
## [929,] FALSE FALSE FALSE FALSE FALSE
## [930,] FALSE FALSE FALSE FALSE FALSE
## [931,] FALSE FALSE FALSE FALSE FALSE
## [932,] FALSE FALSE FALSE FALSE FALSE
## [933,] FALSE FALSE FALSE FALSE FALSE
## [934,] FALSE FALSE FALSE FALSE FALSE
## [935,] FALSE FALSE FALSE FALSE FALSE
## [936,] FALSE FALSE FALSE FALSE FALSE
## [937,] FALSE FALSE FALSE FALSE FALSE
## [938,] FALSE FALSE FALSE FALSE FALSE
## [939,] FALSE FALSE FALSE FALSE FALSE
## [940,] FALSE FALSE FALSE FALSE FALSE
## LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE
## [8,] FALSE FALSE FALSE
## [9,] FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE
## [20,] FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE
## [22,] FALSE FALSE FALSE
## [23,] FALSE FALSE FALSE
## [24,] FALSE FALSE FALSE
## [25,] FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE
## [36,] FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE
## [38,] FALSE FALSE FALSE
## [39,] FALSE FALSE FALSE
## [40,] FALSE FALSE FALSE
## [41,] FALSE FALSE FALSE
## [42,] FALSE FALSE FALSE
## [43,] FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE
## [47,] FALSE FALSE FALSE
## [48,] FALSE FALSE FALSE
## [49,] FALSE FALSE FALSE
## [50,] FALSE FALSE FALSE
## [51,] FALSE FALSE FALSE
## [52,] FALSE FALSE FALSE
## [53,] FALSE FALSE FALSE
## [54,] FALSE FALSE FALSE
## [55,] FALSE FALSE FALSE
## [56,] FALSE FALSE FALSE
## [57,] FALSE FALSE FALSE
## [58,] FALSE FALSE FALSE
## [59,] FALSE FALSE FALSE
## [60,] FALSE FALSE FALSE
## [61,] FALSE FALSE FALSE
## [62,] FALSE FALSE FALSE
## [63,] FALSE FALSE FALSE
## [64,] FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE
## [67,] FALSE FALSE FALSE
## [68,] FALSE FALSE FALSE
## [69,] FALSE FALSE FALSE
## [70,] FALSE FALSE FALSE
## [71,] FALSE FALSE FALSE
## [72,] FALSE FALSE FALSE
## [73,] FALSE FALSE FALSE
## [74,] FALSE FALSE FALSE
## [75,] FALSE FALSE FALSE
## [76,] FALSE FALSE FALSE
## [77,] FALSE FALSE FALSE
## [78,] FALSE FALSE FALSE
## [79,] FALSE FALSE FALSE
## [80,] FALSE FALSE FALSE
## [81,] FALSE FALSE FALSE
## [82,] FALSE FALSE FALSE
## [83,] FALSE FALSE FALSE
## [84,] FALSE FALSE FALSE
## [85,] FALSE FALSE FALSE
## [86,] FALSE FALSE FALSE
## [87,] FALSE FALSE FALSE
## [88,] FALSE FALSE FALSE
## [89,] FALSE FALSE FALSE
## [90,] FALSE FALSE FALSE
## [91,] FALSE FALSE FALSE
## [92,] FALSE FALSE FALSE
## [93,] FALSE FALSE FALSE
## [94,] FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE
## [97,] FALSE FALSE FALSE
## [98,] FALSE FALSE FALSE
## [99,] FALSE FALSE FALSE
## [100,] FALSE FALSE FALSE
## [101,] FALSE FALSE FALSE
## [102,] FALSE FALSE FALSE
## [103,] FALSE FALSE FALSE
## [104,] FALSE FALSE FALSE
## [105,] FALSE FALSE FALSE
## [106,] FALSE FALSE FALSE
## [107,] FALSE FALSE FALSE
## [108,] FALSE FALSE FALSE
## [109,] FALSE FALSE FALSE
## [110,] FALSE FALSE FALSE
## [111,] FALSE FALSE FALSE
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## [922,] FALSE FALSE FALSE
## [923,] FALSE FALSE FALSE
## [924,] FALSE FALSE FALSE
## [925,] FALSE FALSE FALSE
## [926,] FALSE FALSE FALSE
## [927,] FALSE FALSE FALSE
## [928,] FALSE FALSE FALSE
## [929,] FALSE FALSE FALSE
## [930,] FALSE FALSE FALSE
## [931,] FALSE FALSE FALSE
## [932,] FALSE FALSE FALSE
## [933,] FALSE FALSE FALSE
## [934,] FALSE FALSE FALSE
## [935,] FALSE FALSE FALSE
## [936,] FALSE FALSE FALSE
## [937,] FALSE FALSE FALSE
## [938,] FALSE FALSE FALSE
## [939,] FALSE FALSE FALSE
## [940,] FALSE FALSE FALSE
## LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE
## [8,] FALSE FALSE FALSE
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## [10,] FALSE FALSE FALSE
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## [14,] FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE
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## [18,] FALSE FALSE FALSE
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## [20,] FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE
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## [25,] FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE
## [36,] FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE
## [38,] FALSE FALSE FALSE
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## [40,] FALSE FALSE FALSE
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## [43,] FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE
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## [50,] FALSE FALSE FALSE
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## [64,] FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE
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## [68,] FALSE FALSE FALSE
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## [94,] FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE
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## [116,] FALSE FALSE FALSE
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## [120,] FALSE FALSE FALSE
## [121,] FALSE FALSE FALSE
## [122,] FALSE FALSE FALSE
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## [133,] FALSE FALSE FALSE
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## [135,] FALSE FALSE FALSE
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## [137,] FALSE FALSE FALSE
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## [139,] FALSE FALSE FALSE
## [140,] FALSE FALSE FALSE
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## [144,] FALSE FALSE FALSE
## [145,] FALSE FALSE FALSE
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## [305,] FALSE FALSE FALSE
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## [940,] FALSE FALSE FALSE FALSE# Remove duplicates and NA from table 2
daily_sleep <- daily_sleep %>%
distinct() %>%
drop_na()# Check duplicates were removed from tables
sum(duplicated(daily_activity))## [1] 0sum(duplicated(daily_sleep))## [1] 0# Cleaning and formatting table 1
clean_names(daily_activity)## # A tibble: 940 x 15
## id activity_date total_steps total_distance tracker_distance
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 13162 8.5 8.5
## 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
## 7 1503960366 4/18/2016 13019 8.59 8.59
## 8 1503960366 4/19/2016 15506 9.88 9.88
## 9 1503960366 4/20/2016 10544 6.68 6.68
## 10 1503960366 4/21/2016 9819 6.34 6.34
## # ... 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 <dbl>,
## # fairly_active_minutes <dbl>, lightly_active_minutes <dbl>,
## # sedentary_minutes <dbl>, calories <dbl>daily_activity<- rename_with(daily_activity, tolower)clean_names(daily_sleep)## # A tibble: 410 x 5
## id sleep_day total_sleep_rec~ total_minutes_a~ total_time_in_b~
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1503960366 4/12/2016 12:00:00 AM 1 327 346
## 2 1503960366 4/13/2016 12:00:00 AM 2 384 407
## 3 1503960366 4/15/2016 12:00:00 AM 1 412 442
## 4 1503960366 4/16/2016 12:00:00 AM 2 340 367
## 5 1503960366 4/17/2016 12:00:00 AM 1 700 712
## 6 1503960366 4/19/2016 12:00:00 AM 1 304 320
## 7 1503960366 4/20/2016 12:00:00 AM 1 360 377
## 8 1503960366 4/21/2016 12:00:00 AM 1 325 364
## 9 1503960366 4/23/2016 12:00:00 AM 1 361 384
## 10 1503960366 4/24/2016 12:00:00 AM 1 430 449
## # ... with 400 more rowsdaily_sleep <- rename_with(daily_sleep, tolower)# Change the datatype of the data column, convert format to yyyy-mm-dd and rename it "date"
daily_activity <- daily_activity %>%
rename(date = activitydate) %>%
mutate(date = as_date(date, format = "%m/%d/%Y"))
daily_sleep <- daily_sleep %>%
rename(date = sleepday) %>%
mutate(date = as_date(date,format ="%m/%d/%Y %I:%M:%S %p" , tz=Sys.timezone()))## Warning: `tz` argument is ignored by `as_date()`# Confirm column date is updated correctly
head(daily_activity)## # A tibble: 6 x 15
## id date totalsteps totaldistance trackerdistance loggedactivitie~
## <dbl> <date> <dbl> <dbl> <dbl> <dbl>
## 1 1503960366 2016-04-12 13162 8.5 8.5 0
## 2 1503960366 2016-04-13 10735 6.97 6.97 0
## 3 1503960366 2016-04-14 10460 6.74 6.74 0
## 4 1503960366 2016-04-15 9762 6.28 6.28 0
## 5 1503960366 2016-04-16 12669 8.16 8.16 0
## 6 1503960366 2016-04-17 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>head(daily_sleep)## # A tibble: 6 x 5
## id date totalsleeprecords totalminutesasleep totaltimeinbed
## <dbl> <date> <dbl> <dbl> <dbl>
## 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 320Guiding questions:
- What tools are you choosing and why?
I am using R for data cleaning, transformation and visualization. R provides an accessible language to organize, modify, clean data frames and create insightful data visualizations. - Have you ensured your data’s integrity?
Data was collected in a survey, so I am unable to ascertain its integrity or accuracy. - What steps have you taken to ensure that your data is clean?
I removed duplicates, NA´s, cleaned, formated and converted date formats. - How can you verify that your data is clean and ready to analyze?
I checked all cleaning operations above and assured accuracy. - Have you documented your cleaning process so you can review and share those results?
yes, I used R Markdown and Code Chunks.
Step 4: Analyze
Now that the data is stored appropriately and has been prepared for analysis we can start putting it to work.
How many observations are there in each dataframe?
nrow(daily_activity)## [1] 940nrow(daily_sleep)## [1] 410What are some quick summary statistics we’d want to know about each data frame?
daily_activity %>%
select(totalsteps,
totaldistance,
sedentaryminutes) %>%
summary()## totalsteps totaldistance sedentaryminutes
## Min. : 0 Min. : 0.000 Min. : 0.0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8
## Median : 7406 Median : 5.245 Median :1057.5
## Mean : 7638 Mean : 5.490 Mean : 991.2
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5
## Max. :36019 Max. :28.030 Max. :1440.0daily_sleep %>%
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. :961.0Plotting a few explorations on people’s activities:
What’s the relationship between steps taken in a day and sedentary minutes? How could this help inform the customer segments that we can market to?
- we are observing a negative relationship between total steps taken and sedentary minutes.
- We can also note that sedentary time is not necessarily related to calories burned.
- We also see that calories generally trend positively with total steps taking.
ggplot(data=daily_activity, aes(x=totalsteps, y=sedentaryminutes, color = calories)) + geom_point()
What’s the relationship between minutes asleep and time in bed? You might expect it to be almost completely linear - are there any unexpected trends?
- Observing the next graph We find some outliers. Some of these data points spent a lot of time in bed, but didn’t actually sleep, and then a small batch that slept a whole bunch and spent more time in bed.
ggplot(data=daily_sleep, aes(x=totalminutesasleep, y=totaltimeinbed)) + geom_point(aes(color=date))
What could these trends tell you about how to help market this product?
We could definitely market consumers to use their watch to better monitor their time in bed against their sleep time. ## Or areas where you might want to explore further?
I wonder how this relates to the sedentary minutes data in the last dataset??
Merging the two datasets together, so that data can be more useful and acessible
combined_data <- merge(daily_sleep, daily_activity, by="id").
n_distinct(combined_data$id)## [1] 24We could perform an outer join to include all of the fitbit users in the dataset, but theoretically, their sleep data would be empty (either null or N/A), so this 2nd merge should corrected it to 33 users again.
combined_data2 <- merge(daily_sleep, daily_activity, by="id", all = TRUE)
head(combined_data2)## id date.x totalsleeprecords totalminutesasleep totaltimeinbed
## 1 1503960366 2016-04-12 1 327 346
## 2 1503960366 2016-04-12 1 327 346
## 3 1503960366 2016-04-12 1 327 346
## 4 1503960366 2016-04-12 1 327 346
## 5 1503960366 2016-04-12 1 327 346
## 6 1503960366 2016-04-12 1 327 346
## date.y totalsteps totaldistance trackerdistance loggedactivitiesdistance
## 1 2016-05-07 11992 7.71 7.71 0
## 2 2016-05-06 12159 8.03 8.03 0
## 3 2016-05-01 10602 6.81 6.81 0
## 4 2016-04-30 14673 9.25 9.25 0
## 5 2016-04-12 13162 8.50 8.50 0
## 6 2016-04-13 10735 6.97 6.97 0
## veryactivedistance moderatelyactivedistance lightactivedistance
## 1 2.46 2.12 3.13
## 2 1.97 0.25 5.81
## 3 2.29 1.60 2.92
## 4 3.56 1.42 4.27
## 5 1.88 0.55 6.06
## 6 1.57 0.69 4.71
## sedentaryactivedistance veryactiveminutes fairlyactiveminutes
## 1 0 37 46
## 2 0 24 6
## 3 0 33 35
## 4 0 52 34
## 5 0 25 13
## 6 0 21 19
## lightlyactiveminutes sedentaryminutes calories
## 1 175 833 1821
## 2 289 754 1896
## 3 246 730 1820
## 4 217 712 1947
## 5 328 728 1985
## 6 217 776 1797n_distinct(combined_data2$id)## [1] 33Plotting Total Steps VS Calories:
ggplot(data = combined_data2, aes(x=totalsteps, y=calories)) + geom_point() +stat_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
- From this graph, seems that in general people who took the most total steps tend to burn the most calories; however, there’s a large spread clustered towards the lower amounts.
Plotting Sedentary Time VS Time in Bed:
we’ll use the “combined_data” dataset with the 24 unique IDs, because their the ones that actually logged sleep data.
- I run a correlation to see what the correlation coefficient would be for a linear regression:
sedentary.lm <- lm(sedentaryminutes ~ totaltimeinbed, data = combined_data)
sedentary.lm##
## Call:
## lm(formula = sedentaryminutes ~ totaltimeinbed, data = combined_data)
##
## Coefficients:
## (Intercept) totaltimeinbed
## 922.5632 -0.2688cor(combined_data$totaltimeinbed,combined_data$sedentaryminutes, method = "pearson")## [1] -0.1286386- This correlation coefficient is Negative, meaning Y tends to decrease as X increases, indicating that both variables move in the opposite direction.
So looks like these two variables are not correlated which is an interesting finding, because as time in bed goes up, sedentary minutes actually go down, but not to a statistically significant degree.
Plotting Very Active Minutes VS Calories Burned:
ggplot(data = combined_data, aes(x=veryactiveminutes, 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.
ggplot(data = combined_data, aes(x=totalsteps, y=calories)) + geom_point() +stat_smooth(method = lm)## `geom_smooth()` using formula 'y ~ x'
Guiding questions:
- How should you organize your data to perform analysis on it?
I will merge the two datasets, so data can be useful and accessible. - Has your data been properly formatted? Yes, dates are converted to European format and all names are formatted to lowercase to be more accurate, consistent, and easy to read.
- What surprises did you discover in the data? we are observing a negative relationship between total steps taken and sedentary minutes
- What trends or relationships did you find in the data?
We can also note that sedentary time is not necessarily related to calories burned. We also see that calories generally trend positively with total steps taking. - How will these insights help answer your business questions?
Answering the Business questions:
- What are some trends in smart device usage?
The data tell us that most users log in their calories, steps taken, etc, and fewer log their sleep data. - How could these trends apply to Bellabeat customers?
Data seems fairly accurate when it comes to recording steps and sedentary minutes. We could easily market this to consumers by telling them smart-devices could help them start their journey by measuring how much they’re moving and how these moments of activity would benefit them to live longer! - How could these trends help influence Bellabeat marketing strategy?
It is well documented that moderate-to-vigorous physical activity is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting, so in a need to change both behaviors, self-monitoring of behavior is one of the most robust behavior-change techniques available. Marketing these devices provides a unique opportunity for individuals to change their behavior, become more physically active and increase their life expectancy.