Bellabeat Capstone Project
Lakesha Lewis
2024-07-03
Case Study Scenario
As a Junior Analyst working on the marketing analyst team at Bellabeat, I’ve been asked to analyze the smart device fitness data of users to gain insights into how they’re using their devices. The insights discovered will guide in developing a marketing strategy for the company. My task also includes focusing on one of Bellabeat’s products. Urška Sršen, cofounder and Chief Creative Officer of Bellabeat believes that analyzing the data will unlock new growth opportunities for the company.
Step 1: Setting up the Environment
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(dplyr)
library(tidyr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testinstall.packages("readr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)library(readr)Step 2: Upload and Import Data, Create data frames for datasets
In the output pane, click the upload button. Browse and choose file. After selecting the file, click Import. Now run the code chunk below, read the file into a data frame:
Hourly_Intensities<- read_csv("hourlyIntensities_merged.csv")## Rows: 24084 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ActivityHour
## dbl (3): Id, TotalIntensity, AverageIntensity
##
## 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.Sleep_Minutes<- read_csv("minuteSleep_merged[1].csv")## Rows: 198559 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (3): Id, value, logId
##
## 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.Daily_Activity<- read_csv("dailyActivity_Bella.csv")## New names:
## Rows: 457 Columns: 20
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Weight, ActivityDate dbl (16): Id, TotalSteps, TotalDistance,
## TrackerDistance, LoggedActivitiesDi... lgl (2): WeightLogged, ...18
## 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.
## • `` -> `...18`Step 3: Looked at Sample of Data
head(Hourly_Intensities)## # A tibble: 6 × 4
## Id ActivityHour TotalIntensity AverageIntensity
## <dbl> <chr> <dbl> <dbl>
## 1 1503960366 3/12/2016 12:00:00 AM 0 0
## 2 1503960366 3/12/2016 1:00:00 AM 0 0
## 3 1503960366 3/12/2016 2:00:00 AM 0 0
## 4 1503960366 3/12/2016 3:00:00 AM 0 0
## 5 1503960366 3/12/2016 4:00:00 AM 0 0
## 6 1503960366 3/12/2016 5:00:00 AM 0 0str(Hourly_Intensities)## spc_tbl_ [24,084 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:24084] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour : chr [1:24084] "3/12/2016 12:00:00 AM" "3/12/2016 1:00:00 AM" "3/12/2016 2:00:00 AM" "3/12/2016 3:00:00 AM" ...
## $ TotalIntensity : num [1:24084] 0 0 0 0 0 0 0 0 0 1 ...
## $ AverageIntensity: num [1:24084] 0 0 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. ActivityHour = col_character(),
## .. TotalIntensity = col_double(),
## .. AverageIntensity = col_double()
## .. )
## - attr(*, "problems")=<externalptr>colnames(Hourly_Intensities)## [1] "Id" "ActivityHour" "TotalIntensity" "AverageIntensity"head(Sleep_Minutes)## # A tibble: 6 × 4
## Id date value logId
## <dbl> <chr> <dbl> <dbl>
## 1 1503960366 3/13/2016 2:39:30 AM 1 11114919637
## 2 1503960366 3/13/2016 2:40:30 AM 1 11114919637
## 3 1503960366 3/13/2016 2:41:30 AM 1 11114919637
## 4 1503960366 3/13/2016 2:42:30 AM 1 11114919637
## 5 1503960366 3/13/2016 2:43:30 AM 1 11114919637
## 6 1503960366 3/13/2016 2:44:30 AM 1 11114919637str(Sleep_Minutes)## spc_tbl_ [198,559 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:198559] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ date : chr [1:198559] "3/13/2016 2:39:30 AM" "3/13/2016 2:40:30 AM" "3/13/2016 2:41:30 AM" "3/13/2016 2:42:30 AM" ...
## $ value: num [1:198559] 1 1 1 1 1 1 2 2 1 1 ...
## $ logId: num [1:198559] 1.11e+10 1.11e+10 1.11e+10 1.11e+10 1.11e+10 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. date = col_character(),
## .. value = col_double(),
## .. logId = col_double()
## .. )
## - attr(*, "problems")=<externalptr>colnames(Sleep_Minutes)## [1] "Id" "date" "value" "logId"head(Daily_Activity)## # A tibble: 6 × 20
## Id Weight WeightLogged ActivityDate TotalSteps TotalDistance
## <dbl> <chr> <lgl> <chr> <dbl> <dbl>
## 1 1503960366 53.30 TRUE 3/25/2016 11004 7.11
## 2 1503960366 53.30 TRUE 3/26/2016 17609 11.6
## 3 1503960366 53.30 TRUE 3/27/2016 12736 8.53
## 4 1503960366 53.30 TRUE 3/28/2016 13231 8.93
## 5 1503960366 53.30 TRUE 3/29/2016 12041 7.85
## 6 1503960366 53.30 TRUE 3/30/2016 10970 7.16
## # ℹ 14 more variables: TrackerDistance <dbl>, LoggedActivitiesDistance <dbl>,
## # VeryActiveDistance <dbl>, ModeratelyActiveDistance <dbl>,
## # LightActiveDistance <dbl>, SedentaryActiveDistance <dbl>,
## # VeryActiveMinutes <dbl>, FairlyActiveMinutes <dbl>,
## # LightlyActiveMinutes <dbl>, SedentaryMinutes <dbl>, Calories <dbl>,
## # ...18 <lgl>, ID2 <dbl>, WeightKg <dbl>str(Daily_Activity)## spc_tbl_ [457 × 20] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Id : num [1:457] 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ Weight : chr [1:457] "53.30" "53.30" "53.30" "53.30" ...
## $ WeightLogged : logi [1:457] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ ActivityDate : chr [1:457] "3/25/2016" "3/26/2016" "3/27/2016" "3/28/2016" ...
## $ TotalSteps : num [1:457] 11004 17609 12736 13231 12041 ...
## $ TotalDistance : num [1:457] 7.11 11.55 8.53 8.93 7.85 ...
## $ TrackerDistance : num [1:457] 7.11 11.55 8.53 8.93 7.85 ...
## $ LoggedActivitiesDistance: num [1:457] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveDistance : num [1:457] 2.57 6.92 4.66 3.19 2.16 ...
## $ ModeratelyActiveDistance: num [1:457] 0.46 0.73 0.16 0.79 1.09 ...
## $ LightActiveDistance : num [1:457] 4.07 3.91 3.71 4.95 4.61 ...
## $ SedentaryActiveDistance : num [1:457] 0 0 0 0 0 0 0 0 0 0 ...
## $ VeryActiveMinutes : num [1:457] 33 89 56 39 28 30 33 47 40 15 ...
## $ FairlyActiveMinutes : num [1:457] 12 17 5 20 28 13 12 21 11 30 ...
## $ LightlyActiveMinutes : num [1:457] 205 274 268 224 243 223 239 200 244 314 ...
## $ SedentaryMinutes : num [1:457] 804 588 605 1080 763 ...
## $ Calories : num [1:457] 1819 2154 1944 1932 1886 ...
## $ ...18 : logi [1:457] NA NA NA NA NA NA ...
## $ ID2 : num [1:457] 1.50e+09 1.93e+09 2.35e+09 2.87e+09 2.87e+09 ...
## $ WeightKg : num [1:457] 53.3 129.6 63.4 56.7 57.2 ...
## - attr(*, "spec")=
## .. cols(
## .. Id = col_double(),
## .. Weight = col_character(),
## .. WeightLogged = col_logical(),
## .. 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(),
## .. ...18 = col_logical(),
## .. ID2 = col_double(),
## .. WeightKg = col_double()
## .. )
## - attr(*, "problems")=<externalptr>colnames(Daily_Activity)## [1] "Id" "Weight"
## [3] "WeightLogged" "ActivityDate"
## [5] "TotalSteps" "TotalDistance"
## [7] "TrackerDistance" "LoggedActivitiesDistance"
## [9] "VeryActiveDistance" "ModeratelyActiveDistance"
## [11] "LightActiveDistance" "SedentaryActiveDistance"
## [13] "VeryActiveMinutes" "FairlyActiveMinutes"
## [15] "LightlyActiveMinutes" "SedentaryMinutes"
## [17] "Calories" "...18"
## [19] "ID2" "WeightKg"Step 4: I wanted to know how many distinct userids there were in the datasets.
n_distinct(Daily_Activity$Id)## [1] 35n_distinct(Sleep_Minutes$Id)## [1] 23n_distinct(Hourly_Intensities$Id)## [1] 34n_distinct(Daily_Activity$WeightLogged)## [1] 2There were 35 distinct users who submitted information for their daily activities. Only 12 of the users submitted weight tracking information, and 23 users submitted their sleep data. The sleep data for the period 3.12.16-4.11.16 contained limited information regarding the sleep habits of the users, so It was left out of analysis.6
Step 5: I wanted to perform a statisitcal analysis of the datasets
Daily_Activity %>%
select(TotalSteps, TotalDistance, Calories, SedentaryMinutes, VeryActiveMinutes, LightlyActiveMinutes, VeryActiveMinutes)%>%
summary()## TotalSteps TotalDistance Calories SedentaryMinutes
## Min. : 0 Min. : 0.000 Min. : 0 Min. : 32.0
## 1st Qu.: 1988 1st Qu.: 1.410 1st Qu.:1776 1st Qu.: 728.0
## Median : 5986 Median : 4.090 Median :2062 Median :1057.0
## Mean : 6547 Mean : 4.664 Mean :2189 Mean : 995.3
## 3rd Qu.:10198 3rd Qu.: 7.160 3rd Qu.:2667 3rd Qu.:1285.0
## Max. :28497 Max. :27.530 Max. :4562 Max. :1440.0
## VeryActiveMinutes LightlyActiveMinutes
## Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 64.0
## Median : 0.00 Median :181.0
## Mean : 16.62 Mean :170.1
## 3rd Qu.: 25.00 3rd Qu.:257.0
## Max. :202.00 Max. :720.0After reviewing the summary, I also wanted to know which UserId had the Max total steps. I wanted this information to test my hypothesis.
max_Steps_user<-data.frame(which.max(Daily_Activity$TotalSteps))
print(max_Steps_user)## which.max.Daily_Activity.TotalSteps.
## 1 455Hourly_Intensities %>%
select(ActivityHour, TotalIntensity,AverageIntensity)%>%
summary()## ActivityHour TotalIntensity AverageIntensity
## Length:24084 Min. : 0.00 Min. :0.00000
## Class :character 1st Qu.: 0.00 1st Qu.:0.00000
## Mode :character Median : 1.00 Median :0.01667
## Mean : 10.83 Mean :0.18044
## 3rd Qu.: 14.00 3rd Qu.:0.23333
## Max. :180.00 Max. :3.00000Step 6: Create Vectors and Data Frames for Analysis
Distinct_Id2<- c(1503960366,1624580081,1644430081,1844505072,1927972279,2022484408,2026352035,2320127002,2347167796,2873212765,2891001357,3372868164,3977333714,4020332650,
4057192912,4319703577,4388161847,4445114986,4558609924,4702921684,5553957443,5577150313,6117666160,6290855005,6391747486,6775888955,6962181067,7007744171,7086361926,8053475328,
8253242879,8378563200,8583815059,8792009665,8877689391)Total_Calories2<- c(34128,25705,29164,19391,27048,29704,16266,18385,30320,20349,18187,18603,16777,98412,60913,23931,14442,31617,21962,42322,21630,36303,20993,
21656,15868,24523,29249,31525,26124,31825,17556,40274,19130,24884,41414)Total_Steps2<- c(221170,80299,92748,43687,26170,146099,40713,37661,147001,79639,6189,61275,103967,184851,60391,93847,0,64396,69423,119151,100257,94690,82486,16181,12032,
50031,176956,147124,73247,163288,28679,97623,24364,37139,209005)Calories_Burned_From_Activity<-data.frame(Distinct_Id2,Total_Calories2,Total_Steps2)As I began to explore the data, I hypothesized that the users who
burned the most total calories were also one of the 12 users who also
logged their weight using their devices. Right away, I wanted to look
more into what drives customers to log their weight, and how we can
motivate users to track their weights more consistently by enhancing our
current product offerings. In addition, the data shows that most users
recorded the most amount of steps and burned the most calories between
the hours of 12pm-4pm, and I wanted to create a visual to highlight the
positive correlation between calories burned and steps taken.
Furthermore to test my hypothesis I utilized the print function, and it
was determined that User 8877689391 recorded the Max number of steps,
and also tracked their weight.
## Step 7: Data Visualizations
ggplot(data=Calories_Burned_From_Activity)+geom_smooth(mapping=aes(Total_Steps2,y=Total_Calories2))+ labs(title = "Calories Burned vs. Steps" )## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggsave('Calories Burned vs. Steps.pdf')## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'