Google Data Analytics Capstone Project
Peniel
2024-06-03
Introduction to Bellabeat Tech Company
Bellabeat is a tech company which wants to build marketing strategy in a way that will facilitate growth. I will identify the trends in non- Bellabeat smart device usage using external datasets and find out how these trends affect or apply to Bellabeat customers. I will also find out how those trends can help influence Bellabeat marketing strategy to enhance productivity and growth.
Data Sources used for Analysis
FitBit Fitness Tracker Data, a public dataset on Kaggle made available through Mobius. This Kaggle data set contains personal fitness tracker from thirty Fitbit users who consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.
Summary, Key Findings and Recommendations
From the dataset, I found that the average person walks 6,546 steps, 4.7km distance and burns 2189 calories daily.
I also discovered that the average hours for sleep was 3 although the data provided for this makes the results seem a bit inconclusive because as a result of insufficient data.
The average weight by individuals was 73kg and the average heartrate, 80pm.
I recommend that clients should be encouraged more to sleep and make sure the records are entered on the devices to enable data accuracy. Bellabeat can promote ads which inform clients to make proper use of their devices.
Although the average heartrate was healthy, the maximum showed that some users have unhealthy heartrates which should be checked to prevent health complications.
These are the factors that Bellabeat should consider while creating or modifying their fitness tracker device as it gives them an edge over competitors and an opprotunity to do better.
Step 1: Install tidyverse packages
The first thing I did before I began my analysis was to install the tidyverse packages which enabled me run my analysis in R seamlessly.
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(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(skimr)Step 2: Import the dataset.
Next, I imported the dataset which I needed for the analysis to begin the process of cleaning the data.
bellabeat_activity <- read.csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/dailyActivity_merged.csv")
bellabeat_heartrate <- read_csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/heartrate_seconds_merged.csv")## Rows: 1154681 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Time
## dbl (2): Id, Value
##
## 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.bellabeat_calories <- read.csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/hourlyCalories_merged.csv")
bellabeat_steps <- read.csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/hourlySteps_merged.csv")
bellabeat_sleep <- read.csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/minuteSleep_merged.csv")
bellabeat_weight <- read.csv("/cloud/project/mturkfitbit_export_3.12.16-4.11.16/Fitabase Data 3.12.16-4.11.16/weightLogInfo_merged.csv")Step 3: View the data to understand it.
This was done using the head function
head(bellabeat_activity)head(bellabeat_calories)head(bellabeat_heartrate)head(bellabeat_sleep)head(bellabeat_steps)head(bellabeat_weight)Step 3: Clean the data
I carried this out by using the skimr and lubridate(to align the date formats in each table) packages.
bellabeat_sleep$date=as.POSIXct(bellabeat_sleep$date, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
bellabeat_sleep$date <- format(bellabeat_sleep$date, format = "%m/%d/%y")
bellabeat_calories$ActivityHour=as.POSIXct(bellabeat_calories$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
bellabeat_calories$ActivityHour <- format(bellabeat_calories$ActivityHour, format = "%m/%d/%y")
bellabeat_heartrate$Time=as.POSIXct(bellabeat_heartrate$Time, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
bellabeat_heartrate$Time <- format(bellabeat_heartrate$Time, format = "%m/%d/%y")
bellabeat_steps$ActivityHour=as.POSIXct(bellabeat_steps$ActivityHour, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
bellabeat_steps$ActivityHour <- format(bellabeat_steps$ActivityHour, format = "%m/%d/%y")
bellabeat_weight$Date=as.POSIXct(bellabeat_weight$Date, format="%m/%d/%Y %I:%M:%S %p", tz=Sys.timezone())
bellabeat_weight$Date <- format(bellabeat_weight$Date, format = "%m/%d/%y")
skim(bellabeat_activity)| Name | bellabeat_activity |
| Number of rows | 457 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityDate | 0 | 1 | 8 | 9 | 0 | 32 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.628595e+09 | 2.293781e+09 | 1503960366 | 2.347168e+09 | 4.057193e+09 | 6.391747e+09 | 8.877689e+09 | ▇▇▃▅▃ |
| TotalSteps | 0 | 1 | 6.546560e+03 | 5.398490e+03 | 0 | 1.988000e+03 | 5.986000e+03 | 1.019800e+04 | 2.849700e+04 | ▇▆▂▁▁ |
| TotalDistance | 0 | 1 | 4.660000e+00 | 4.080000e+00 | 0 | 1.410000e+00 | 4.090000e+00 | 7.160000e+00 | 2.753000e+01 | ▇▅▁▁▁ |
| TrackerDistance | 0 | 1 | 4.610000e+00 | 4.070000e+00 | 0 | 1.280000e+00 | 4.090000e+00 | 7.110000e+00 | 2.753000e+01 | ▇▅▁▁▁ |
| LoggedActivitiesDistance | 0 | 1 | 1.800000e-01 | 8.500000e-01 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 6.730000e+00 | ▇▁▁▁▁ |
| VeryActiveDistance | 0 | 1 | 1.180000e+00 | 2.490000e+00 | 0 | 0.000000e+00 | 0.000000e+00 | 1.310000e+00 | 2.192000e+01 | ▇▁▁▁▁ |
| ModeratelyActiveDistance | 0 | 1 | 4.800000e-01 | 8.300000e-01 | 0 | 0.000000e+00 | 2.000000e-02 | 6.700000e-01 | 6.400000e+00 | ▇▁▁▁▁ |
| LightActiveDistance | 0 | 1 | 2.890000e+00 | 2.240000e+00 | 0 | 8.700000e-01 | 2.930000e+00 | 4.460000e+00 | 1.251000e+01 | ▇▆▂▁▁ |
| SedentaryActiveDistance | 0 | 1 | 0.000000e+00 | 1.000000e-02 | 0 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.000000e-01 | ▇▁▁▁▁ |
| VeryActiveMinutes | 0 | 1 | 1.662000e+01 | 2.892000e+01 | 0 | 0.000000e+00 | 0.000000e+00 | 2.500000e+01 | 2.020000e+02 | ▇▁▁▁▁ |
| FairlyActiveMinutes | 0 | 1 | 1.307000e+01 | 3.621000e+01 | 0 | 0.000000e+00 | 1.000000e+00 | 1.600000e+01 | 6.600000e+02 | ▇▁▁▁▁ |
| LightlyActiveMinutes | 0 | 1 | 1.700700e+02 | 1.222100e+02 | 0 | 6.400000e+01 | 1.810000e+02 | 2.570000e+02 | 7.200000e+02 | ▇▇▂▁▁ |
| SedentaryMinutes | 0 | 1 | 9.952800e+02 | 3.370200e+02 | 32 | 7.280000e+02 | 1.057000e+03 | 1.285000e+03 | 1.440000e+03 | ▁▂▆▅▇ |
| Calories | 0 | 1 | 2.189450e+03 | 8.154800e+02 | 0 | 1.776000e+03 | 2.062000e+03 | 2.667000e+03 | 4.562000e+03 | ▁▅▇▃▁ |
skim(bellabeat_calories)| Name | bellabeat_calories |
| Number of rows | 24084 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityHour | 0 | 1 | 8 | 8 | 0 | 32 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.889424e+09 | 2421565819.2 | 1503960366 | 2347167796 | 4558609924 | 6962181067 | 8877689391 | ▇▅▃▅▅ |
| Calories | 0 | 1 | 9.427000e+01 | 59.4 | 42 | 61 | 77 | 104 | 933 | ▇▁▁▁▁ |
skim(bellabeat_heartrate)| Name | bellabeat_heartrate |
| Number of rows | 1154681 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Time | 0 | 1 | 8 | 8 | 0 | 15 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 5.352122e+09 | 2.033584e+09 | 2022484408 | 4020332650 | 5553957443 | 6962181067 | 8877689391 | ▅▇▆▅▃ |
| Value | 0 | 1 | 7.976000e+01 | 1.873000e+01 | 36 | 66 | 77 | 90 | 185 | ▃▇▂▁▁ |
skim(bellabeat_sleep)| Name | bellabeat_sleep |
| Number of rows | 198559 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| date | 0 | 1 | 8 | 8 | 0 | 33 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.824304e+09 | 2.173935e+09 | 1503960366 | 2347167796 | 4702921684 | 6775888955 | 8792009665 | ▇▃▇▆▃ |
| value | 0 | 1 | 1.090000e+00 | 3.100000e-01 | 1 | 1 | 1 | 1 | 3 | ▇▁▁▁▁ |
| logId | 0 | 1 | 1.124161e+10 | 7.969858e+07 | 11103653021 | 11165512026 | 11243951252 | 11310735495 | 11374876178 | ▇▆▇▇▇ |
skim(bellabeat_steps)| Name | bellabeat_steps |
| Number of rows | 24084 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ActivityHour | 0 | 1 | 8 | 8 | 0 | 32 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1 | 4.889424e+09 | 2.421566e+09 | 1503960366 | 2347167796 | 4558609924 | 6962181067 | 8877689391 | ▇▅▃▅▅ |
| StepTotal | 0 | 1 | 2.862200e+02 | 6.649200e+02 | 0 | 0 | 10 | 289 | 10565 | ▇▁▁▁▁ |
skim(bellabeat_weight)| Name | bellabeat_weight |
| Number of rows | 33 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Date | 0 | 1 | 8 | 8 | 0 | 14 | 0 |
| IsManualReport | 0 | 1 | 4 | 5 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Id | 0 | 1.00 | 6.477156e+09 | 2.308888e+09 | 1.503960e+09 | 4.702922e+09 | 6.962181e+09 | 8.877689e+09 | 8.877689e+09 | ▃▁▁▇▆ |
| WeightKg | 0 | 1.00 | 7.344000e+01 | 1.653000e+01 | 5.330000e+01 | 6.170000e+01 | 6.250000e+01 | 8.580000e+01 | 1.296000e+02 | ▇▁▅▁▁ |
| WeightPounds | 0 | 1.00 | 1.619100e+02 | 3.644000e+01 | 1.175100e+02 | 1.360300e+02 | 1.377900e+02 | 1.891600e+02 | 2.857200e+02 | ▇▁▅▁▁ |
| Fat | 31 | 0.06 | 1.600000e+01 | 8.490000e+00 | 1.000000e+01 | 1.300000e+01 | 1.600000e+01 | 1.900000e+01 | 2.200000e+01 | ▇▁▁▁▇ |
| BMI | 0 | 1.00 | 2.573000e+01 | 4.330000e+00 | 2.145000e+01 | 2.410000e+01 | 2.439000e+01 | 2.576000e+01 | 4.617000e+01 | ▇▁▁▁▁ |
| LogId | 0 | 1.00 | 1.459959e+12 | 3.088072e+08 | 1.459382e+12 | 1.459753e+12 | 1.459987e+12 | 1.460160e+12 | 1.460506e+12 | ▅▆▇▇▆ |
Step 4 :Summarize the data
The data was summarized to gain insights by using some aggregate functions in the tidyverse package.
activity_summary <-
bellabeat_activity %>%
summarise(average_TotalSteps=mean(TotalSteps),
min_TotalSteps=min(TotalSteps),
max_TotalSteps=max(TotalSteps),
average_TotalDistance=mean(TotalDistance),
min_TotalDistance=min(TotalDistance),
max_TotalDistance=max(TotalDistance),
average_Calories=mean(Calories),
min_Calories=min(Calories),
max_Calories=max(Calories))
head(activity_summary)calories_summary <-
bellabeat_calories %>%
summarise(average_Calories=mean(Calories),
min_Calories=min(Calories),
max_Calories=max(Calories))
head(calories_summary)heartrate_summary <-
bellabeat_heartrate %>%
summarise(average_heartrate=mean(Value),
min_heartrate=min(Value),
max_heartrate=max(Value))
head(heartrate_summary)weight_summary <-
bellabeat_weight %>%
summarise(average_weight=mean(WeightKg),
min_weight=min(WeightKg),
max_weight=max(WeightKg),
average_BMI=mean(BMI),
min_BMI=min(BMI),
max_BMI=max(BMI))
head(weight_summary)sleep_summary <-
bellabeat_sleep %>%
summarise(average_sleep=mean(value),
min_sleep=min(value),
max_sleep=max(value))
head(sleep_summary)Step 5: Understanding trends with visualization
The data had been summarized and some insights gotten from it. The next thing I did was visualize the trends using the gglpot package. I merged some tables before I began the visualization.
merged_activity_weight <-merge(bellabeat_activity, bellabeat_weight, by=c('Id'))
head(merged_activity_weight)Using the ggplot function, I created Visualizations to better understand trends in the dataset.
ggplot(data = bellabeat_activity, aes(x = TotalSteps, y = Calories))+
geom_point(color="blue")+
labs(title = "TotalSteps vs. Calories", x = "TotalSteps", y = "Calories")+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data= merged_activity_weight, aes(x= WeightKg, y = Calories))+
geom_point(color="green")+
labs(title = "WeightKg vs. Calories", x = "WeightKg", y = "Calories")+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'