Bellabeat Case Study
Ashley Higgins
2024-05-20
Hi, I’m Ashley Higgins and this is my first R Markdown project
1. Introduction
This is the capstone project for the Google Data Analytics Course. I
will follow the six steps of Data Analysis for the Bellabeat Case Study:
Ask, Prepare, Process, Analyze, Share, and Act.
I am a junior data analyst working on the marketing analysis team at Bellabeat, a high-tech manufacturer of health-focused products for women. I have been asked to focus on one of Bellabeat’s products and analyze the user data of a competing product, FitBit, to gain insight on how consumers are using their smart devices. My discoveries and recommendations for marketing strategy will be presented to the Bellabeat executive team.
About Bellabeat
- Bellabeat team members:
- Urška Sršen: Bellabeat’s cofounder and Chief Creative Officer
- Sando Mur: Mathematician and Bellabeat’s cofounder; key member of the Bellabeat executive team
- Bellabeat marketing analytics team: A team of data
analysts responsible for collecting, analyzing, and reporting data that
helps guide Bellabeat’s marketing strategy. I joined this team six
months ago and have been busy learning about Bellabeat’s mission and
business goals to help Bellabeat achieve them.
- Bellabeat offers the following products:
- Bellabeat App: provides users with health data related to their activity, sleep, stress, menstrual cycle, and mindfullness habits. The app connects to the line of smart wellness products, Leaf, Time, and Spring. The data gathered by the app helps users better understand their current habits to make healthy decisions.
- Leaf: Bellabeat’s classic wellness tracker that can be worn as a bracelet, necklace, or clip. Connects to the app to track activity, sleep, and stress.
- Time: This wellness watch combines the timeless look of a classic timepiece with smart technology to track user activity, sleep, and stress. Connects with the app to provide insights into daily wellness.
- Spring: A water bottle that tracks daily water intake using smart technology to ensure that you are appropriately hydrated throughout the day. Connects to the app to track hydration levels.
- Bellabeat Membership: Subscription-based membership
program for users. Gives 24/7 access to fully personalized guidance on
nutrition, activity, sleep, health and beauty, and mindfullness based on
their lifestyle and goals.
2. Ask
Sršen asked me to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants me to select one Bellabeat product to apply these insights to in my presentation.
- Some questions to guide my 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?
- I will 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 my analysis
- Supporting visualizations and key findings
- My top high-level content recommendations based on my analysis
3. Prepare
The data used for my analysis is public data that explores 33 users of the FitBit tracker (CC0, public domain made available through Mobius)
Set up my environment
install.packages("tidyverse")
library(tidyverse)
library(here)
library(skimr)
library(janitor)
library(lubridate)
library(scales)
library(ggplot2)Review the Data
We’ll be focusing on 30 days of data from 7 files during the dates April 12, 2016 to May 12, 2016.
Import the data
daily_activity <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv", header = TRUE)
daily_steps <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv", header = TRUE)
daily_sleep <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv", header = TRUE)
hourly_steps <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv", header = TRUE)
hourly_calories <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/hourlyCalories_merged.csv", header = TRUE)
hourly_intensities <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv", header = TRUE)
weight <- read.csv("mturkfitbit_export_4.12.16-5.12.16/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv", header = TRUE)Preview the data
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 1728head(daily_sleep)## Id SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM 1 327
## 2 1503960366 4/13/2016 12:00:00 AM 2 384
## 3 1503960366 4/15/2016 12:00:00 AM 1 412
## 4 1503960366 4/16/2016 12:00:00 AM 2 340
## 5 1503960366 4/17/2016 12:00:00 AM 1 700
## 6 1503960366 4/19/2016 12:00:00 AM 1 304
## TotalTimeInBed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320head(daily_steps)## Id ActivityDay StepTotal
## 1 1503960366 4/12/2016 13162
## 2 1503960366 4/13/2016 10735
## 3 1503960366 4/14/2016 10460
## 4 1503960366 4/15/2016 9762
## 5 1503960366 4/16/2016 12669
## 6 1503960366 4/17/2016 9705head(hourly_calories)## Id ActivityHour Calories
## 1 1503960366 4/12/2016 12:00:00 AM 81
## 2 1503960366 4/12/2016 1:00:00 AM 61
## 3 1503960366 4/12/2016 2:00:00 AM 59
## 4 1503960366 4/12/2016 3:00:00 AM 47
## 5 1503960366 4/12/2016 4:00:00 AM 48
## 6 1503960366 4/12/2016 5:00:00 AM 48head(hourly_intensities)## Id ActivityHour TotalIntensity AverageIntensity
## 1 1503960366 4/12/2016 12:00:00 AM 20 0.333333
## 2 1503960366 4/12/2016 1:00:00 AM 8 0.133333
## 3 1503960366 4/12/2016 2:00:00 AM 7 0.116667
## 4 1503960366 4/12/2016 3:00:00 AM 0 0.000000
## 5 1503960366 4/12/2016 4:00:00 AM 0 0.000000
## 6 1503960366 4/12/2016 5:00:00 AM 0 0.000000head(hourly_steps)## Id ActivityHour StepTotal
## 1 1503960366 4/12/2016 12:00:00 AM 373
## 2 1503960366 4/12/2016 1:00:00 AM 160
## 3 1503960366 4/12/2016 2:00:00 AM 151
## 4 1503960366 4/12/2016 3:00:00 AM 0
## 5 1503960366 4/12/2016 4:00:00 AM 0
## 6 1503960366 4/12/2016 5:00:00 AM 0head(weight)## Id Date WeightKg WeightPounds Fat BMI
## 1 1503960366 5/2/2016 11:59:59 PM 52.6 115.9631 22 22.65
## 2 1503960366 5/3/2016 11:59:59 PM 52.6 115.9631 NA 22.65
## 3 1927972279 4/13/2016 1:08:52 AM 133.5 294.3171 NA 47.54
## 4 2873212765 4/21/2016 11:59:59 PM 56.7 125.0021 NA 21.45
## 5 2873212765 5/12/2016 11:59:59 PM 57.3 126.3249 NA 21.69
## 6 4319703577 4/17/2016 11:59:59 PM 72.4 159.6147 25 27.45
## IsManualReport LogId
## 1 True 1.462234e+12
## 2 True 1.462320e+12
## 3 False 1.460510e+12
## 4 True 1.461283e+12
## 5 True 1.463098e+12
## 6 True 1.460938e+12str(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 ...str(daily_sleep)## '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 ...str(daily_steps)## '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" ...
## $ StepTotal : int 13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...str(hourly_calories)## 'data.frame': 22099 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour: chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ Calories : int 81 61 59 47 48 48 48 47 68 141 ...str(hourly_intensities)## 'data.frame': 22099 obs. of 4 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour : chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ TotalIntensity : int 20 8 7 0 0 0 0 0 13 30 ...
## $ AverageIntensity: num 0.333 0.133 0.117 0 0 ...str(hourly_steps)## 'data.frame': 22099 obs. of 3 variables:
## $ Id : num 1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
## $ ActivityHour: chr "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
## $ StepTotal : int 373 160 151 0 0 0 0 0 250 1864 ...str(weight)## '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 ...Check the unique number of users in each dataframe
n_unique(daily_activity$Id)## [1] 33n_unique(daily_sleep$Id)## [1] 24n_unique(daily_steps$Id)## [1] 33n_unique(hourly_calories$Id)## [1] 33n_unique(hourly_intensities$Id)## [1] 33n_unique(hourly_steps$Id)## [1] 33n_unique(weight$Id)## [1] 8All data sets have 33 participants each except the daily_sleep and weight data set which has 24 and 8 participants respectively.
I will drop the weight data set because 8 participants is too small a sample to draw conclusions and make recommendations.
Check for duplicates in data
sum(duplicated(daily_activity))## [1] 0sum(duplicated(daily_sleep))## [1] 3sum(duplicated(daily_steps))## [1] 0sum(duplicated(hourly_calories))## [1] 0sum(duplicated(hourly_intensities))## [1] 0sum(duplicated(hourly_steps))## [1] 04. Clean the data
Starting with daily_activity dataframe
Remove all duplicates and missing data from all dataframes
daily_activity <- daily_activity %>%
distinct() %>%
drop_na()
daily_sleep <- daily_sleep %>%
distinct() %>%
drop_na()
daily_steps <- daily_steps %>%
distinct() %>%
drop_na()
hourly_calories <- hourly_calories %>%
distinct() %>%
drop_na()
hourly_intensities <- hourly_intensities %>%
distinct() %>%
drop_na()
hourly_steps <- hourly_steps %>%
distinct() %>%
drop_na()Clean and rename columns of all dataframes
I will clean date-time format for daily_activity and daily_sleep since they are the wrong data type and both dataframes will be merged. I will not be using the time from daily_sleep.
For our hourly_calories, hourly_intensities and hourly_steps dataset, I will convert date string to date-time.
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()))
hourly_calories <- hourly_calories %>%
rename(date_time = activityhour) %>%
mutate(date_time = as.POSIXct(date_time, format = "%m/%d/%Y %I:%M:%S %p" , tz = Sys.timezone()))
hourly_intensities <- hourly_intensities %>%
rename(date_time = activityhour) %>%
mutate(date_time = as.POSIXct(date_time, format = "%m/%d/%Y %I:%M:%S %p" , tz = Sys.timezone()))
hourly_steps<- hourly_steps %>%
rename(date_time = activityhour) %>%
mutate(date_time = as.POSIXct(date_time, format = "%m/%d/%Y %I:%M:%S %p" , tz = Sys.timezone()))Merge the daily_activity and daily_sleep data sets together.
daily_activity_sleep <- merge(daily_activity, daily_sleep, by=c ("id", "date"))
view(daily_activity_sleep)Summarize and Explore daily activity
daily_activity %>%
select(totalsteps,
totaldistance,
sedentaryminutes, calories) %>%
summary()## totalsteps totaldistance sedentaryminutes calories
## Min. : 0 Min. : 0.000 Min. : 0.0 Min. : 0
## 1st Qu.: 3790 1st Qu.: 2.620 1st Qu.: 729.8 1st Qu.:1828
## Median : 7406 Median : 5.245 Median :1057.5 Median :2134
## Mean : 7638 Mean : 5.490 Mean : 991.2 Mean :2304
## 3rd Qu.:10727 3rd Qu.: 7.713 3rd Qu.:1229.5 3rd Qu.:2793
## Max. :36019 Max. :28.030 Max. :1440.0 Max. :49004. Analyze
Create chart for number of activities per day
daily_activity %>% count(date, sort = TRUE) %>%
ggplot() + geom_col(aes(x = date, y = n), color = "black", fill = "white") +
scale_y_continuous() +
scale_x_date(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "Count of Activities Per Day By All Users", x = "Activity Date", y = "Number of Activities")
Steps Per day plot
total_steps <- daily_activity %>% group_by(date) %>%
summarize(Steps = sum(totalsteps), Mean = mean(totalsteps, na.rm = TRUE))
ggplot(total_steps) + geom_step(aes(x = date, y = Steps), color = "darkgreen") +
scale_x_date(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "Total Steps Per Day By All Users", x = "Activity Date", y = "Number of Steps") +
scale_y_continuous(labels = comma)
Avg Steps Per Day
ggplot(total_steps) + geom_step(aes(x = date, y = Mean), color = "green") +
scale_x_date(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "Average Steps Per Day", x = "Activity Date", y = "Number of Steps") +
scale_y_continuous(labels = comma)
Total Distance Plot
total_distance <- daily_activity %>% group_by(date) %>%
summarise(Total = sum(totaldistance, na.rm = TRUE), Mean = mean(totaldistance, na.rm = TRUE))ggplot(total_distance) +
geom_step(aes(x = date, y = Total), color = "red") +
scale_x_date(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "Average Distance Per Day", x = "Activity Date", y = "Distance")
Average Distance Plot
ggplot(total_distance) +
geom_step(aes(x = date, y = Mean), color = "hotpink") +
scale_x_date(date_breaks = "1 day") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "Average Distance Per Day", x = "Activity Date", y = "Distance")
Hourly Steps through the day Separate the datetime column into date and
time column
hourly_steps <- hourly_steps %>%
separate(date_time, into = c("date", "time"), sep= " ") %>%
mutate(date = ymd(date)) ## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 934 rows [1, 25, 49, 73,
## 97, 121, 145, 169, 193, 217, 241, 265, 289, 313, 337, 361, 385, 409, 433, 457,
## ...].head(hourly_steps)## id date time steptotal
## 1 1503960366 2016-04-12 <NA> 373
## 2 1503960366 2016-04-12 01:00:00 160
## 3 1503960366 2016-04-12 02:00:00 151
## 4 1503960366 2016-04-12 03:00:00 0
## 5 1503960366 2016-04-12 04:00:00 0
## 6 1503960366 2016-04-12 05:00:00 0hourly_steps %>%
group_by(time) %>%
summarize(average_steps = mean(steptotal)) %>%
ggplot() +
geom_col(mapping = aes(x=time, y = average_steps, fill = average_steps)) +
labs(title = "Hourly steps throughout the day", x="Time of Day", y="Number of Steps") +
scale_fill_gradient(low = "orange", high = "purple")+
theme(axis.text.x = element_text(angle = 90))
Calories vs Steps
ggplot(data = daily_activity, aes(x = totalsteps, y = calories)) +
geom_point() +
geom_smooth() +
labs(title ="Total Steps vs. Calories By All Users")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
To know what day of the week users are more active.
weekday_steps <- daily_activity %>%
mutate(weekday = weekdays(date))
weekday_steps$weekday <- ordered(weekday_steps$weekday, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
weekday_steps <-weekday_steps %>%
group_by(weekday) %>%
summarize (daily_steps = mean(totalsteps))
head(weekday_steps)## # A tibble: 6 × 2
## weekday daily_steps
## <ord> <dbl>
## 1 Monday 7781.
## 2 Tuesday 8125.
## 3 Wednesday 7559.
## 4 Thursday 7406.
## 5 Friday 7448.
## 6 Saturday 8153.ggplot(weekday_steps, aes(weekday, daily_steps)) +
geom_col(fill = "purple") +
geom_hline(yintercept = 7500, color ="red") +
labs(title = "Average Daily Steps By Weekday", x= "", y = "") +
theme(axis.text.x = element_text(angle = 45,vjust = 0.5, hjust = 1)) +
annotate("text", x = 4, y = 8500, label = "Recommended Amount of Steps Daily is 7500", color = "red")
Summarize and Explore sleep
daily_activity_sleep %>%
select(totalminutesasleep,
totaltimeinbed) %>%
summary()## totalminutesasleep totaltimeinbed
## Min. : 58.0 Min. : 61.0
## 1st Qu.:361.0 1st Qu.:403.8
## Median :432.5 Median :463.0
## Mean :419.2 Mean :458.5
## 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :796.0 Max. :961.0To know what day of the week users sleep the most
weekday_sleep <- daily_sleep %>%
mutate(weekday = weekdays(date))
weekday_sleep$weekday <- ordered(weekday_sleep$weekday, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
weekday_sleep <-weekday_sleep %>%
group_by(weekday) %>%
summarize(daily_sleep = mean(totalminutesasleep/60))
head(weekday_sleep)## # A tibble: 6 × 2
## weekday daily_sleep
## <ord> <dbl>
## 1 Monday 6.99
## 2 Tuesday 6.74
## 3 Wednesday 7.24
## 4 Thursday 6.69
## 5 Friday 6.76
## 6 Saturday 6.98ggplot(weekday_sleep, aes(weekday, daily_sleep)) +
geom_col(fill = "skyblue") +
geom_hline(yintercept = 8, color = "darkblue") +
labs(title = "Average Hours Asleep By Day", x= "", y = "Hours") +
theme(axis.text.x = element_text(angle = 45,vjust = 0.5, hjust = 1)) +
annotate("text", x = 2, y = 7.5, label = "Recommended Amount of Sleep is 8 Hours", color = "darkblue")
Summarize and Explore hourly intensities throughout the day
First, split the datetime column into date and time columns
hourly_intensities <- hourly_intensities %>%
separate(date_time, into = c("date", "time"), sep= " ") ## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 934 rows [1, 25, 49, 73,
## 97, 121, 145, 169, 193, 217, 241, 265, 289, 313, 337, 361, 385, 409, 433, 457,
## ...].head(hourly_intensities)## id date time totalintensity averageintensity
## 1 1503960366 2016-04-12 <NA> 20 0.333333
## 2 1503960366 2016-04-12 01:00:00 8 0.133333
## 3 1503960366 2016-04-12 02:00:00 7 0.116667
## 4 1503960366 2016-04-12 03:00:00 0 0.000000
## 5 1503960366 2016-04-12 04:00:00 0 0.000000
## 6 1503960366 2016-04-12 05:00:00 0 0.000000hourly_intensities <- hourly_intensities %>%
group_by(time) %>%
drop_na() %>%
summarize(mean_total_int = mean(totalintensity))ggplot(data = hourly_intensities, aes(x = time, y = mean_total_int)) +
geom_histogram(stat = "identity", fill = "orange") +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Average Total Intensity vs. Time", x = "Time of Day", y = "Average Intensity")## Warning in geom_histogram(stat = "identity", fill = "orange"): Ignoring unknown
## parameters: `binwidth`, `bins`, and `pad`
### User Information
daily_use <- daily_activity_sleep %>%
group_by(id) %>%
summarize(days_used=sum(n())) %>%
mutate(user_type= case_when(
days_used >= 1 & days_used <= 10 ~ "low user",
days_used >= 11 & days_used <= 20 ~ "moderate user",
days_used >= 21 & days_used <= 31 ~ "high user",
))
head(daily_use)## # A tibble: 6 × 3
## id days_used user_type
## <dbl> <int> <chr>
## 1 1503960366 25 high user
## 2 1644430081 4 low user
## 3 1844505072 3 low user
## 4 1927972279 5 low user
## 5 2026352035 28 high user
## 6 2320127002 1 low userdaily_use_percent <- daily_use %>%
group_by(user_type) %>%
summarize(total = n()) %>%
mutate(totals = sum(total)) %>%
group_by(user_type) %>%
summarise(total_percent = total / totals) %>%
mutate(labels = scales::percent(total_percent))
daily_use_percent$user_type <- factor(daily_use_percent$user_type, levels = c("high user", "moderate user", "low user"))
head(daily_use_percent)## # A tibble: 3 × 3
## user_type total_percent labels
## <fct> <dbl> <chr>
## 1 high user 0.5 50%
## 2 low user 0.375 38%
## 3 moderate user 0.125 12%daily_use_percent %>%
ggplot(aes(x = "",y = total_percent, fill = user_type)) +
geom_bar(stat = "identity", width = 1)+
coord_polar("y", start=0)+
theme_minimal()+
theme(axis.title.x= element_blank(),
axis.title.y = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
plot.title = element_text(hjust = 0.5, size=14, face = "bold")) +
geom_text(aes(label = labels),
position = position_stack(vjust = 0.5))+
scale_fill_manual(values = c( "lightgreen","yellow","red"),
labels = c("High user - 21 to 31 days",
"Moderate user - 11 to 20 days",
"Low user - 1 to 10 days"))+
labs(title="Daily use of smart device")