Scenario

Sršen asks you to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants you to select one Bellabeat product to apply these insights to in your presentation. These questions will guide your analysis: 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?

Business task

It’s about analyzing any trend concerning Bellabeat customers, and how it will help for growth.

Data sources used

FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius).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 cleaning

The file used from the data source

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.5     v dplyr   1.0.7
## v tidyr   1.1.4     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)
sleepDay_merged <- read_csv("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.
as_tibble(sleepDay_merged)
## # A tibble: 413 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            320
##  7 1503960366 4/20/2016 12:00~                1               360            377
##  8 1503960366 4/21/2016 12:00~                1               325            364
##  9 1503960366 4/23/2016 12:00~                1               361            384
## 10 1503960366 4/24/2016 12:00~                1               430            449
## # ... with 403 more rows

File cleaning

Excel was used for data cleaning.

library(tidyverse)
library(readr)
Sleep <- read_csv("Sleep.csv")
## Rows: 107 Columns: 3
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): ID
## dbl (2): Date, Sleep
## 
## 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.
as_tibble(Sleep)
## # A tibble: 107 x 3
##    ID     Date Sleep
##    <chr> <dbl> <dbl>
##  1 Jason     1  5.45
##  2 Jason     2  6.4 
##  3 Jason     4  6.87
##  4 Jason     5  5.67
##  5 Jason     6 11.7 
##  6 Jason     8  5.07
##  7 Jason     9  6   
##  8 Jason    10  5.42
##  9 Jason    12  6.02
## 10 Jason    13  7.17
## # ... with 97 more rows
  • Id has been transformed to names
  • Minutes have been transformed to hours
  • Date has been transformed to the number of days

Analysis

R was used for visualizing the data concerning four members sleep hours.

ggplot(data = Sleep)+
  geom_point(mapping = aes(x=Date,y=Sleep,color=ID))+
  facet_wrap(~ID)+
  labs(title = "Hours of sleep",
       subtitle = "A comparison of four persons wearing a Bellabeat product during sleep hours")+
  xlab("Days")+
  ylab("Hours")+
  labs(color="Members")

Findings

The sleeping hours differ over members, there is no specific attention that the sleeping hours should be between 7-8 hours.

Recommendations

Based on the information that everyone should sleep 7-8 hours daily, my recommendations are: