Portfolio_Smartwatch

About the Project and Dataset

This is a detailed analysis of spreadsheet data for a fitness smartwatch that tracks data including: calories, hours slept, intensity levels, and overall data activity. The original dataset can be found online on Kaggle, which is modified and cleaned to my profile at Fitbit Project Portfolio.

In this analysis, I proceed to clean the data and aggregate datetime columns from the various spreadsheets to ensure each of the csv files (dailyActivity, dailySleep, dailyCalories, dailyIntensities, dailySteps) have uniform units of per day. Additionally, I create a column that combines the activity day and Id to create a unique identifier (there are multiple observations per Id - see hourlyIntensities. After cleaning the data, I use R code to merge and join the CSV files to begin looking for trends and patterns (joining could have been done in SQL, but the number of observations is relatively sparse).

library(ggplot2)
library(ggridges)
library(tidyr)
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
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ 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 errors

library(skimr)
library(dplyr)
library(viridis)

## Loading required package: viridisLite

library(SmartEDA)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(dlookr)

## Registered S3 methods overwritten by 'dlookr':
##   method          from  
##   plot.transform  scales
##   print.transform scales
## 
## Attaching package: 'dlookr'
## 
## The following object is masked from 'package:tidyr':
## 
##     extract
## 
## The following object is masked from 'package:base':
## 
##     transform

library(DataExplorer)
library(moments)

## 
## Attaching package: 'moments'
## 
## The following objects are masked from 'package:dlookr':
## 
##     kurtosis, skewness

library(flextable)

## 
## Attaching package: 'flextable'
## 
## The following object is masked from 'package:purrr':
## 
##     compose

library(forcats)
library(corrplot)

## corrplot 0.92 loaded

options(warn = -1)

# -- Load and Clean Data -- #
f1 <- "C:/Users/Anthony Morciglio/OneDrive/Coursera Google Data Analytics/Fitabase Data 3.12.16-4.11.16/dailyActivity_merged.csv"
f2 <- "C:/Users/Anthony Morciglio/OneDrive/Coursera Google Data Analytics/Fitabase Data 3.12.16-4.11.16/dailySleep_merged.csv"
f3 <- "C:/Users/Anthony Morciglio/OneDrive/Coursera Google Data Analytics/Fitabase Data 3.12.16-4.11.16/dailyCalories_merged.csv"
f4 <- "C:/Users/Anthony Morciglio/OneDrive/Coursera Google Data Analytics/Fitabase Data 3.12.16-4.11.16/dailyIntensities_merged.csv"
f5 <- "C:/Users/Anthony Morciglio/OneDrive/Coursera Google Data Analytics/Fitabase Data 3.12.16-4.11.16/dailySteps_merged.csv"


df_daily_act <- read_csv(f1)

## Rows: 457 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): ActivityDate
## dbl (15): ID-AD, Id, TotalSteps, TotalDistance, TrackerDistance, LoggedActiv...
## 
## 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.

df_daily_sleep <- read_csv(f2)

## Rows: 198559 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (4): ID-AD, Id, logId, hours
## 
## 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.

df_daily_cal <- read_csv(f3)

## Rows: 1021 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): ID-AD, date
## dbl (2): Id, Calories
## 
## 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.

df_daily_int <- read_csv(f4)

## Rows: 1021 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (4): ID-AD, 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.

df_daily_steps <- read_csv(f5)

## Rows: 1021 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (3): ID-AD, Id, StepTotal
## 
## 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.

# -- Combine data using join or merge functions -- #
# Remark: joining on the unique identifier, ID-AD, obtained by combining Id with date

df_tot <- df_daily_act %>% 
   dplyr::inner_join(df_daily_sleep, by = "ID-AD") %>% select(-Id.x, -Id.y) %>% 
     dplyr::inner_join(df_daily_int, by = "ID-AD") %>% select(-date.x, -date.y, -TrackerDistance) 

colnames(df_tot)[16] <- "HoursSlept"
colnames(df_tot)[11] <- "ModeratelyActiveMinutes"
colnames(df_tot)[8] <- "LightlyActiveDistance"

Observations after Modification

After joining the data using dplyr::inner_join(), we change a few of the column names and observe most of the columns are numeric types. Before creating visualizations, we will generate a few preliminary reports using functions from libraries such as DataExplorer, SmartEDA, and dlookr.

It would be beneficial to create categorical variables based on the activity levels of the people using the fitbits. Since we are interested in the number of Calories burned (to market the product in the wellness and fitness industry), we can categorize activity levels hierarchically such as Sedentary, Lightly Active, Moderately Active, and Very Active using binning.

## Rows: 228
## Columns: 16
## $ ActivityDate             <chr> "3/25/2016", "3/26/2016", "3/26/2016", "3/27/…
## $ TotalSteps               <dbl> 11004, 17609, 17609, 12736, 12736, 13231, 120…
## $ TotalDistance            <dbl> 7.11, 11.55, 11.55, 8.53, 8.53, 8.93, 7.85, 7…
## $ LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveDistance       <dbl> 2.57, 6.92, 6.92, 4.66, 4.66, 3.19, 2.16, 2.2…
## $ ModeratelyActiveDistance <dbl> 0.46, 0.73, 0.73, 0.16, 0.16, 0.79, 1.09, 0.4…
## $ LightlyActiveDistance    <dbl> 4.07, 3.91, 3.91, 3.71, 3.71, 4.95, 4.61, 5.0…
## $ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveMinutes        <dbl> 33, 89, 89, 56, 56, 39, 28, 33, 47, 40, 15, 4…
## $ ModeratelyActiveMinutes  <dbl> 12, 17, 17, 5, 5, 20, 28, 12, 21, 11, 30, 18,…
## $ LightlyActiveMinutes     <dbl> 205, 274, 274, 268, 268, 224, 243, 239, 200, …
## $ SedentaryMinutes         <dbl> 804, 588, 588, 605, 605, 1080, 763, 820, 866,…
## $ Calories                 <dbl> 1819, 2154, 2154, 1944, 1944, 1932, 1886, 188…
## $ HoursSlept               <dbl> 6.42, 7.60, 0.23, 2.13, 5.48, 1.27, 6.28, 5.5…
## $ TotalIntensity           <dbl> 139, 130, 130, 97, 97, 76, 56, 61, 52, 129, 9…
## $ AverageIntensity         <dbl> 0.070, 0.066, 0.066, 0.049, 0.049, 0.038, 0.0…

ActivityDate	TotalSteps	TotalDistance	LoggedActivitiesDistance	VeryActiveDistance	ModeratelyActiveDistance	LightlyActiveDistance	SedentaryActiveDistance	VeryActiveMinutes	ModeratelyActiveMinutes	LightlyActiveMinutes	SedentaryMinutes	Calories	HoursSlept	TotalIntensity	AverageIntensity
3/25/2016	11,004	7.11	0.000000	2.57	0.46	4.07	0.00	33	12	205	804	1,819	6.42	139	0.070
3/26/2016	17,609	11.55	0.000000	6.92	0.73	3.91	0.00	89	17	274	588	2,154	7.60	130	0.066
3/26/2016	17,609	11.55	0.000000	6.92	0.73	3.91	0.00	89	17	274	588	2,154	0.23	130	0.066
3/27/2016	12,736	8.53	0.000000	4.66	0.16	3.71	0.00	56	5	268	605	1,944	2.13	97	0.049
3/27/2016	12,736	8.53	0.000000	4.66	0.16	3.71	0.00	56	5	268	605	1,944	5.48	97	0.049
3/28/2016	13,231	8.93	0.000000	3.19	0.79	4.95	0.00	39	20	224	1,080	1,932	1.27	76	0.038
3/29/2016	12,041	7.85	0.000000	2.16	1.09	4.61	0.00	28	28	243	763	1,886	6.28	56	0.028
3/31/2016	12,256	7.86	0.000000	2.29	0.49	5.04	0.00	33	12	239	820	1,889	5.58	61	0.031
4/1/2016	12,262	7.87	0.000000	3.32	0.83	3.64	0.00	47	21	200	866	1,868	5.08	52	0.026
4/2/2016	11,248	7.25	0.000000	3.00	0.45	3.74	0.00	40	11	244	636	1,843	8.47	129	0.065
4/3/2016	10,016	6.37	0.000000	0.91	1.28	4.18	0.00	15	30	314	655	1,850	7.08	96	0.050
4/4/2016	14,557	9.80	0.000000	3.39	0.70	5.69	0.00	43	18	285	757	2,030	5.60	69	0.036
4/5/2016	14,844	9.73	0.000000	2.94	0.76	6.04	0.00	36	18	341	736	2,083	5.13	46	0.023
4/9/2016	12,432	8.10	0.000000	2.59	0.59	4.92	0.00	32	15	248	738	1,883	6.77	45	0.025
4/10/2016	10,057	6.98	0.000000	4.00	0.49	2.48	0.00	44	13	168	737	1,755	7.95	120	0.074
4/11/2016	10,990	7.26	0.000000	2.04	0.57	4.65	0.00	26	14	216	855	1,811	5.47	67	0.045
4/3/2016	2,841	1.88	0.000000	0.00	0.00	1.88	0.00	0	0	136	1,114	1,636	3.15	96	0.050
4/9/2016	4,979	3.29	0.000000	0.00	0.00	3.29	0.00	0	0	184	620	1,807	10.58	45	0.025
4/2/2016	5,662	3.92	0.000000	0.00	0.00	3.92	0.00	0	0	267	858	2,783	5.23	129	0.065
4/3/2016	3,198	2.21	0.000000	0.00	0.00	2.21	0.00	0	0	146	1,183	2,449	1.83	96	0.050
4/4/2016	2,352	1.63	0.000000	0.00	0.00	1.63	0.00	0	0	128	829	2,380	3.13	69	0.036
4/5/2016	2,234	1.55	0.000000	0.00	0.00	1.55	0.00	0	0	108	912	2,344	6.98	46	0.023
4/6/2016	1,259	0.87	0.000000	0.00	0.00	0.87	0.00	0	0	73	938	2,202	7.13	122	0.064
4/9/2016	2,523	1.75	0.000000	0.00	0.37	1.38	0.00	0	9	134	1,250	2,443	0.77	45	0.025
4/10/2016	2,105	1.46	0.000000	0.00	0.00	1.46	0.00	0	0	139	805	2,442	5.13	120	0.074
4/10/2016	2,105	1.46	0.000000	0.00	0.00	1.46	0.00	0	0	139	805	2,442	1.03	120	0.074
4/11/2016	1,209	0.84	0.000000	0.00	0.00	0.84	0.00	0	0	73	842	2,255	2.05	67	0.045
4/12/2016	24	0.02	0.000000	0.00	0.00	0.02	0.00	0	0	3	161	942	2.13	25	0.021
4/1/2016	2,605	1.62	0.000000	0.00	0.00	1.62	0.00	0	0	166	729	1,407	2.45	52	0.026
4/2/2016	1,229	0.76	0.000000	0.00	0.00	0.76	0.00	0	0	69	795	1,237	2.55	129	0.065
4/3/2016	2,308	1.43	0.000000	0.00	0.00	1.43	0.00	0	0	115	720	1,330	2.97	96	0.050
4/4/2016	6,679	4.14	0.000000	0.00	0.00	4.14	0.00	0	0	263	644	1,583	2.00	69	0.036
4/5/2016	5,645	3.50	0.000000	0.00	0.00	3.50	0.00	0	0	248	621	1,538	2.57	46	0.023
4/6/2016	3,180	1.97	0.000000	0.00	0.00	1.97	0.00	0	0	185	738	1,421	2.17	122	0.064
4/7/2016	2,767	1.72	0.000000	0.00	0.00	1.72	0.00	0	0	170	673	1,392	2.95	80	0.042
4/8/2016	3,762	2.33	0.000000	0.00	0.00	2.33	0.00	0	0	173	719	1,425	2.92	45	0.023
4/9/2016	3,098	1.92	0.000000	0.00	0.00	1.92	0.00	0	0	185	700	1,413	2.82	45	0.025
4/10/2016	5,142	3.19	0.000000	0.00	0.00	3.19	0.00	0	0	230	654	1,515	2.72	120	0.074
4/11/2016	3,279	2.03	0.000000	0.00	0.00	2.03	0.00	0	0	164	694	1,405	3.18	67	0.045
3/29/2016	10,272	6.79	0.000000	0.16	3.12	3.50	0.00	2	58	208	700	2,041	1.82	56	0.028
3/30/2016	10,533	7.10	0.000000	1.77	2.06	3.27	0.00	21	35	255	615	2,187	1.57	99	0.050
3/31/2016	6,760	4.47	0.000000	0.00	0.00	4.47	0.00	0	0	250	613	1,929	2.28	61	0.031
4/2/2016	15,459	10.22	0.000000	3.59	0.81	5.82	0.00	51	16	327	583	2,438	7.63	129	0.065
4/2/2016	15,459	10.22	0.000000	3.59	0.81	5.82	0.00	51	16	327	583	2,438	0.05	129	0.065
4/3/2016	7,485	4.95	0.000000	0.00	0.00	4.95	0.00	0	0	324	491	2,035	2.50	96	0.050
4/4/2016	10,254	6.80	0.000000	1.42	1.23	4.16	0.00	21	25	231	638	2,099	2.08	69	0.036
4/5/2016	10,114	6.82	0.000000	1.64	0.48	4.68	0.00	18	9	269	696	2,096	0.62	46	0.023
4/7/2016	10,320	6.85	0.000000	0.68	1.23	4.94	0.00	15	32	315	1,002	2,338	1.25	80	0.042
4/9/2016	16,081	10.63	0.000000	1.25	1.82	7.56	0.00	16	32	401	970	2,488	0.33	45	0.025
4/10/2016	10,078	6.83	0.000000	1.02	0.12	5.69	0.00	12	3	303	463	2,164	1.93	120	0.074
4/1/2016	4,499	3.01	0.000000	0.00	0.00	3.01	0.00	0	0	168	842	1,288	7.15	52	0.026
4/2/2016	7,618	5.10	0.000000	0.00	0.18	4.91	0.00	0	6	302	842	1,490	4.82	129	0.065
4/3/2016	11,508	7.70	0.000000	2.17	0.84	4.69	0.00	29	22	282	748	1,630	5.97	96	0.050
4/4/2016	11,943	7.99	0.000000	1.03	2.01	4.95	0.00	16	43	297	710	1,648	6.22	69	0.036
4/5/2016	12,303	8.23	0.000000	1.71	1.35	5.17	0.00	26	32	291	605	1,649	8.08	46	0.023
4/6/2016	15,425	10.32	0.000000	1.62	1.82	6.87	0.00	24	45	331	614	1,783	7.08	122	0.064
4/7/2016	8,422	5.63	0.000000	0.00	3.60	2.04	0.00	0	81	139	815	1,431	6.73	80	0.042
4/8/2016	10,226	6.84	0.000000	0.00	4.44	2.40	0.00	0	101	174	685	1,524	7.98	45	0.023
4/9/2016	14,583	9.76	0.000000	2.48	1.74	5.53	0.00	35	36	254	697	1,697	6.95	45	0.025
4/10/2016	3,573	2.39	0.000000	0.94	0.41	0.97	0.00	15	8	99	866	1,232	7.52	120	0.074
4/11/2016	3,108	2.14	0.000000	0.07	0.04	1.96	0.00	1	1	129	894	1,223	6.90	67	0.045
3/12/2016	5,543	3.97	0.000000	0.00	0.00	3.96	0.01	0	0	254	757	2,990	6.00	98	0.049
3/12/2016	5,543	3.97	0.000000	0.00	0.00	3.96	0.01	0	0	254	757	2,990	1.12	98	0.049
3/13/2016	3,226	2.31	0.000000	0.00	0.00	2.28	0.00	0	0	136	771	2,480	5.37	53	0.027
3/14/2016	3,023	2.17	0.000000	0.00	0.00	2.14	0.00	0	0	145	1,005	2,570	4.82	27	0.014
3/15/2016	5,906	4.23	0.000000	0.00	0.16	4.04	0.00	0	10	215	874	3,016	5.67	53	0.027
3/16/2016	12,483	8.99	0.000000	1.45	0.57	6.90	0.00	25	14	309	599	3,830	8.20	73	0.037
3/17/2016	8,940	6.41	0.000000	0.00	0.00	0.61	0.04	0	0	47	986	3,706	6.77	70	0.035
3/30/2016	5,400	3.87	0.000000	0.00	0.00	3.85	0.00	0	0	258	795	3,418	2.43	99	0.050
3/30/2016	5,400	3.87	0.000000	0.00	0.00	3.85	0.00	0	0	258	795	3,418	3.98	99	0.050
3/31/2016	7,428	5.33	0.000000	0.87	0.92	3.51	0.00	18	33	261	860	3,439	4.45	61	0.031
4/1/2016	5,351	3.84	0.000000	0.62	0.18	3.01	0.00	15	17	210	748	3,338	4.08	52	0.026
4/1/2016	5,351	3.84	0.000000	0.62	0.18	3.01	0.00	15	17	210	748	3,338	2.92	52	0.026
4/1/2016	5,351	3.84	0.000000	0.62	0.18	3.01	0.00	15	17	210	748	3,338	0.45	52	0.026
4/2/2016	4,299	3.10	0.000000	0.09	0.21	2.74	0.04	3	15	162	752	2,892	3.12	129	0.065
4/3/2016	6,107	4.38	0.000000	0.00	0.00	4.36	0.00	0	0	265	776	3,313	5.00	96	0.050
4/3/2016	6,107	4.38	0.000000	0.00	0.00	4.36	0.00	0	0	265	776	3,313	1.62	96	0.050
4/4/2016	6,429	4.60	0.000000	0.00	0.95	3.65	0.00	0	28	193	741	3,118	7.95	69	0.036
4/6/2016	7,476	5.36	0.000000	0.00	0.00	5.31	0.00	0	0	263	920	3,253	4.27	122	0.064
4/10/2016	5,129	3.68	0.000000	0.18	0.24	3.21	0.01	5	7	176	1,178	2,817	1.22	120	0.074
4/11/2016	2,993	2.15	0.000000	0.00	0.00	2.09	0.00	0	0	114	888	2,507	3.75	67	0.045
3/29/2016	5,643	3.79	0.000000	0.23	0.33	3.23	0.00	3	8	199	683	1,958	1.60	56	0.028
3/29/2016	5,643	3.79	0.000000	0.23	0.33	3.23	0.00	3	8	199	683	1,958	1.73	56	0.028
3/31/2016	8,144	5.46	0.000000	0.25	0.61	4.61	0.00	4	17	247	1,125	2,129	0.77	61	0.031
4/1/2016	9,343	6.27	0.000000	0.96	0.85	4.46	0.00	14	20	268	654	2,216	1.55	52	0.026
4/1/2016	9,343	6.27	0.000000	0.96	0.85	4.46	0.00	14	20	268	654	2,216	0.72	52	0.026
4/2/2016	8,405	5.66	0.000000	1.13	0.19	4.34	0.00	15	4	257	621	2,154	0.97	129	0.065
4/3/2016	8,223	5.52	0.000000	0.80	1.51	3.21	0.00	12	37	257	551	2,178	1.03	96	0.050
4/4/2016	10,067	6.76	0.000000	0.36	1.42	4.97	0.00	5	39	313	578	2,308	1.52	69	0.036
4/5/2016	8,359	5.63	0.000000	0.23	0.53	4.87	0.00	3	14	311	638	2,201	1.15	46	0.023
4/6/2016	10,946	7.35	0.000000	0.54	2.09	4.72	0.00	8	51	308	569	2,341	4.30	122	0.064
4/6/2016	10,946	7.35	0.000000	0.54	2.09	4.72	0.00	8	51	308	569	2,341	0.80	122	0.064
3/29/2016	2,303	1.55	0.000000	0.00	0.00	1.55	0.00	0	0	155	807	2,010	3.75	56	0.028
3/29/2016	2,303	1.55	0.000000	0.00	0.00	1.55	0.00	0	0	155	807	2,010	4.18	56	0.028
3/31/2016	4,804	3.22	0.000000	0.00	0.00	3.22	0.00	0	0	238	786	2,227	4.90	61	0.031
3/31/2016	4,804	3.22	0.000000	0.00	0.00	3.22	0.00	0	0	238	786	2,227	2.00	61	0.031
4/1/2016	3,271	2.19	0.000000	0.00	0.00	2.19	0.00	0	0	205	898	2,133	5.60	52	0.026
4/2/2016	5,406	3.63	0.000000	0.00	0.00	3.61	0.00	0	0	273	672	2,317	8.23	129	0.065
4/5/2016	4,239	2.84	0.000000	0.06	0.24	2.53	0.00	1	6	235	702	2,217	8.25	46	0.023
4/6/2016	6,911	4.78	0.000000	2.32	0.10	2.36	0.00	28	2	215	745	2,374	7.48	122	0.064
4/7/2016	6,667	4.61	0.000000	2.02	0.05	2.55	0.00	24	1	208	939	2,335	4.45	80	0.042
4/8/2016	5,543	3.72	0.000000	0.00	0.00	3.72	0.00	0	0	266	941	2,303	1.68	45	0.023
4/8/2016	5,543	3.72	0.000000	0.00	0.00	3.72	0.00	0	0	266	941	2,303	2.17	45	0.023
4/9/2016	4,195	2.81	0.000000	0.00	0.00	2.81	0.00	0	0	239	758	2,210	3.38	45	0.025
4/9/2016	4,195	2.81	0.000000	0.00	0.00	2.81	0.00	0	0	239	758	2,210	1.15	45	0.025
4/9/2016	4,195	2.81	0.000000	0.00	0.00	2.81	0.00	0	0	239	758	2,210	2.80	45	0.025
4/10/2016	6,625	4.45	0.000000	0.00	0.00	4.45	0.00	0	0	316	755	2,424	4.87	120	0.074
4/10/2016	6,625	4.45	0.000000	0.00	0.00	4.45	0.00	0	0	316	755	2,424	1.25	120	0.074
4/11/2016	5,986	4.13	0.000000	1.95	0.23	1.95	0.00	23	5	195	789	2,297	4.55	67	0.045
4/11/2016	5,986	4.13	0.000000	1.95	0.23	1.95	0.00	23	5	195	789	2,297	2.55	67	0.045
4/12/2016	278	0.19	0.000000	0.00	0.00	0.19	0.00	0	0	20	253	745	5.52	25	0.021
4/2/2016	3,183	2.10	0.000000	0.00	0.00	2.10	0.00	0	0	173	1,180	1,680	1.43	129	0.065
3/29/2016	6,506	5.28	0.000000	0.33	0.27	4.67	0.00	4	5	241	760	2,886	1.15	56	0.028
3/30/2016	7,583	6.15	0.000000	0.25	0.55	5.35	0.00	3	13	227	742	2,915	1.53	99	0.050
3/31/2016	6,963	5.65	0.000000	0.00	0.00	5.65	0.00	0	0	256	759	2,895	1.00	61	0.031
4/1/2016	7,165	5.81	0.000000	0.00	0.37	5.44	0.00	0	9	248	708	2,923	2.00	52	0.026
4/2/2016	10,328	8.38	0.000000	0.00	1.46	6.92	0.00	0	32	367	475	3,323	2.00	129	0.065
4/3/2016	12,116	9.83	0.000000	0.25	2.85	6.72	0.00	3	57	323	471	3,357	1.95	96	0.050
4/4/2016	7,810	6.36	0.000000	0.48	1.20	4.68	0.00	6	27	216	746	2,931	1.33	69	0.036
4/5/2016	6,670	5.41	0.000000	1.24	0.80	3.37	0.00	15	16	199	770	2,848	1.32	46	0.023
4/6/2016	7,605	6.18	0.000000	0.33	0.21	5.63	0.00	4	4	248	708	2,943	1.75	122	0.064
4/7/2016	6,585	5.34	0.000000	0.00	0.00	5.34	0.00	0	0	210	770	2,822	1.63	80	0.042
4/9/2016	14,002	11.36	0.000000	0.61	2.68	8.06	0.00	8	56	381	407	3,597	7.20	45	0.025
4/9/2016	14,002	11.36	0.000000	0.61	2.68	8.06	0.00	8	56	381	407	3,597	2.57	45	0.025
4/10/2016	11,135	9.03	0.000000	0.08	1.16	7.79	0.00	1	25	331	528	3,224	2.02	120	0.074
4/10/2016	11,135	9.03	0.000000	0.08	1.16	7.79	0.00	1	25	331	528	3,224	0.87	120	0.074
4/11/2016	6,499	5.27	0.000000	0.00	0.00	5.27	0.00	0	0	207	809	2,677	3.80	67	0.045
4/2/2016	2,937	1.92	0.000000	0.00	0.00	1.91	0.00	0	0	181	356	1,615	11.58	129	0.065
4/2/2016	2,937	1.92	0.000000	0.00	0.00	1.91	0.00	0	0	181	356	1,615	3.43	129	0.065
4/3/2016	1,515	0.99	0.000000	0.00	0.00	0.99	0.00	0	0	93	507	1,481	12.50	96	0.050
4/3/2016	1,515	0.99	0.000000	0.00	0.00	0.99	0.00	0	0	93	507	1,481	1.47	96	0.050
4/4/2016	8,921	5.88	0.000000	2.07	0.44	3.36	0.00	27	8	198	754	1,892	1.53	69	0.036
4/5/2016	11,306	7.38	0.000000	2.31	0.98	4.09	0.00	40	26	218	772	2,086	0.38	46	0.023
4/6/2016	12,252	8.00	0.000000	2.41	1.70	3.89	0.00	33	28	229	745	2,044	0.58	122	0.064
4/7/2016	15,444	10.08	0.000000	3.27	1.73	5.08	0.00	51	39	243	731	2,249	0.08	80	0.042
4/9/2016	4,599	3.00	0.000000	0.00	0.00	3.00	0.00	0	0	176	578	1,692	9.12	45	0.025
4/9/2016	4,599	3.00	0.000000	0.00	0.00	3.00	0.00	0	0	176	578	1,692	2.28	45	0.025
4/10/2016	5,594	3.65	0.000000	0.28	1.48	1.89	0.00	4	25	150	552	1,712	10.35	120	0.074
4/10/2016	5,594	3.65	0.000000	0.28	1.48	1.89	0.00	4	25	150	552	1,712	1.43	120	0.074
4/11/2016	11,906	7.77	0.000000	2.26	1.52	3.99	0.00	32	27	234	688	2,065	1.65	67	0.045
4/1/2016	10,461	7.87	0.000000	4.76	0.85	2.25	0.00	99	31	142	721	3,625	2.20	52	0.026
4/2/2016	14,873	11.11	0.000000	8.19	0.60	2.31	0.00	202	36	153	663	4,430	5.17	129	0.065
4/2/2016	14,873	11.11	0.000000	8.19	0.60	2.31	0.00	202	36	153	663	4,430	1.17	129	0.065
4/3/2016	9,917	7.41	0.000000	1.99	0.97	4.44	0.00	58	27	240	700	3,427	2.20	96	0.050
4/4/2016	7,401	5.56	0.000000	2.32	0.36	2.88	0.00	70	19	204	689	3,492	2.27	69	0.036
4/5/2016	8,964	6.70	0.000000	3.97	0.19	2.53	0.00	107	18	145	756	3,597	2.17	46	0.023
4/5/2016	8,964	6.70	0.000000	3.97	0.19	2.53	0.00	107	18	145	756	3,597	0.95	46	0.023
4/6/2016	11,080	8.30	0.000000	3.13	1.37	3.81	0.00	83	52	190	695	3,765	1.60	122	0.064
4/7/2016	4,499	3.36	0.000000	0.89	0.26	2.21	0.00	31	11	146	756	2,775	2.35	80	0.042
4/8/2016	4,363	3.26	0.000000	0.13	0.06	3.08	0.00	8	2	156	813	2,486	2.22	45	0.023
4/9/2016	10,494	7.84	0.000000	4.91	0.78	2.15	0.00	123	62	138	601	3,817	4.43	45	0.025
4/9/2016	10,494	7.84	0.000000	4.91	0.78	2.15	0.00	123	62	138	601	3,817	1.88	45	0.025
4/9/2016	10,494	7.84	0.000000	4.91	0.78	2.15	0.00	123	62	138	601	3,817	0.28	45	0.025
4/10/2016	9,776	7.38	0.000000	2.93	1.43	3.02	0.00	80	35	162	721	3,378	1.53	120	0.074
4/1/2016	7,444	5.62	0.000000	0.00	0.00	5.62	0.00	0	0	286	568	2,210	4.28	52	0.026
4/1/2016	7,444	5.62	0.000000	0.00	0.00	5.62	0.00	0	0	286	568	2,210	3.93	52	0.026
4/1/2016	7,444	5.62	0.000000	0.00	0.00	5.62	0.00	0	0	286	568	2,210	1.50	52	0.026
4/5/2016	9,910	7.48	0.000000	0.00	0.00	7.48	0.00	0	0	384	990	2,445	1.05	46	0.023
4/5/2016	9,910	7.48	0.000000	0.00	0.00	7.48	0.00	0	0	384	990	2,445	0.02	46	0.023
4/6/2016	12,409	9.37	0.000000	0.00	0.00	9.37	0.00	0	0	491	388	2,694	1.72	122	0.064
4/9/2016	10,789	8.15	0.000000	0.00	0.00	8.15	0.00	0	0	506	413	2,617	3.73	45	0.025
4/9/2016	10,789	8.15	0.000000	0.00	0.00	8.15	0.00	0	0	506	413	2,617	2.95	45	0.025
4/9/2016	10,789	8.15	0.000000	0.00	0.00	8.15	0.00	0	0	506	413	2,617	1.05	45	0.025
4/1/2016	7,225	5.18	0.000000	1.73	1.27	2.18	0.00	25	50	163	1,189	3,065	0.20	52	0.026
4/6/2016	11,761	8.43	0.000000	1.31	2.44	4.68	0.00	24	99	300	550	3,920	7.58	122	0.064
4/6/2016	11,761	8.43	0.000000	1.31	2.44	4.68	0.00	24	99	300	550	3,920	0.17	122	0.064
4/7/2016	13,987	10.03	0.000000	2.87	3.34	3.82	0.00	46	114	250	739	3,856	1.82	80	0.042
4/7/2016	13,987	10.03	0.000000	2.87	3.34	3.82	0.00	46	114	250	739	3,856	2.13	80	0.042
3/30/2016	15,491	10.24	0.000000	1.29	4.49	4.46	0.00	18	77	272	641	2,244	6.75	99	0.050
3/30/2016	15,491	10.24	0.000000	1.29	4.49	4.46	0.00	18	77	272	641	2,244	0.42	99	0.050
3/31/2016	14,097	9.32	0.000000	3.50	1.92	3.90	0.00	50	32	234	595	2,188	1.90	61	0.031
3/31/2016	14,097	9.32	0.000000	3.50	1.92	3.90	0.00	50	32	234	595	2,188	0.07	61	0.031
4/2/2016	12,437	8.30	0.000000	3.76	1.29	3.24	0.00	50	24	219	732	2,115	6.90	129	0.065
4/3/2016	12,307	8.14	0.000000	1.01	3.26	3.87	0.00	14	51	218	589	2,055	8.25	96	0.050
4/3/2016	12,307	8.14	0.000000	1.01	3.26	3.87	0.00	14	51	218	589	2,055	1.18	96	0.050
4/5/2016	12,010	7.94	0.000000	2.38	0.95	4.61	0.00	38	17	287	692	2,158	5.90	46	0.023
4/5/2016	12,010	7.94	0.000000	2.38	0.95	4.61	0.00	38	17	287	692	2,158	0.83	46	0.023
4/6/2016	12,480	8.86	3.972795	4.18	0.69	4.00	0.00	55	14	276	593	2,170	0.22	122	0.064
4/7/2016	13,417	9.65	1.926302	3.39	1.25	4.91	0.10	45	16	284	665	2,244	0.33	80	0.042
4/9/2016	12,495	8.26	0.000000	2.21	1.09	4.96	0.00	33	19	324	609	2,231	6.18	45	0.025
4/9/2016	12,495	8.26	0.000000	2.21	1.09	4.96	0.00	33	19	324	609	2,231	1.37	45	0.025
4/10/2016	10,148	6.71	0.000000	1.36	0.22	5.13	0.00	19	7	306	679	2,100	7.02	120	0.074
4/10/2016	10,148	6.71	0.000000	1.36	0.22	5.13	0.00	19	7	306	679	2,100	0.10	120	0.074
4/12/2016	5,893	3.90	0.000000	2.88	0.56	0.46	0.00	43	9	48	146	917	6.43	25	0.021
4/2/2016	10,976	7.34	0.000000	0.55	0.67	6.13	0.00	9	16	271	1,075	2,575	1.13	129	0.065
4/3/2016	16,806	11.24	0.000000	6.41	1.37	3.47	0.00	104	31	230	1,002	3,086	1.20	96	0.050
4/5/2016	12,084	9.14	4.836380	4.39	0.46	4.29	0.00	50	6	284	1,030	2,676	1.15	46	0.023
4/7/2016	14,100	10.58	4.875990	5.10	1.19	4.28	0.00	61	33	265	1,015	2,840	1.08	80	0.042
4/1/2016	11,463	7.67	0.000000	2.83	1.89	2.93	0.00	38	30	154	777	2,496	0.60	52	0.026
4/5/2016	7,478	4.99	0.000000	1.80	0.53	2.61	0.03	25	33	132	1,159	2,439	1.50	46	0.023
4/6/2016	7,352	4.73	0.000000	1.41	0.78	2.53	0.00	19	14	124	814	2,233	1.08	122	0.064
4/7/2016	14,604	10.82	0.000000	7.23	1.12	2.43	0.03	76	30	127	695	2,862	1.75	80	0.042
4/9/2016	7,338	4.67	0.000000	1.08	0.13	3.46	0.00	27	5	204	717	2,519	8.10	45	0.025
4/10/2016	569	0.35	0.000000	0.00	0.00	0.34	0.00	0	0	27	1,368	1,799	0.73	120	0.074
4/11/2016	6,242	3.92	0.000000	0.74	0.23	2.93	0.00	46	22	126	741	2,543	1.80	67	0.045
4/1/2016	14,179	11.24	0.000000	6.23	1.06	3.96	0.00	73	21	195	719	3,669	2.52	52	0.026
4/1/2016	14,179	11.24	0.000000	6.23	1.06	3.96	0.00	73	21	195	719	3,669	0.30	52	0.026
4/2/2016	3,358	2.66	0.000000	0.00	0.00	2.66	0.00	0	0	144	657	2,702	3.23	129	0.065
4/2/2016	3,358	2.66	0.000000	0.00	0.00	2.66	0.00	0	0	144	657	2,702	1.30	129	0.065
4/2/2016	3,358	2.66	0.000000	0.00	0.00	2.66	0.00	0	0	144	657	2,702	2.03	129	0.065
4/3/2016	9,152	7.26	0.000000	1.99	0.82	4.45	0.00	25	13	243	600	3,304	2.05	96	0.050
4/4/2016	13,935	11.05	2.092147	4.09	0.79	6.17	0.00	105	16	220	658	4,234	3.00	69	0.036
4/5/2016	12,846	10.19	2.253081	5.00	0.75	4.44	0.00	113	15	189	686	4,128	2.10	46	0.023
4/5/2016	12,846	10.19	2.253081	5.00	0.75	4.44	0.00	113	15	189	686	4,128	0.90	46	0.023
4/6/2016	9,124	7.24	2.092147	0.00	0.25	6.99	0.00	55	6	230	728	3,798	1.25	122	0.064
4/6/2016	9,124	7.24	2.092147	0.00	0.25	6.99	0.00	55	6	230	728	3,798	1.40	122	0.064
4/7/2016	9,725	7.71	2.253081	2.41	0.53	4.77	0.00	83	11	181	670	3,839	1.52	80	0.042
4/7/2016	9,725	7.71	2.253081	2.41	0.53	4.77	0.00	83	11	181	670	3,839	1.45	80	0.042
4/7/2016	9,725	7.71	2.253081	2.41	0.53	4.77	0.00	83	11	181	670	3,839	0.87	80	0.042
4/8/2016	8,350	6.62	2.092147	1.49	0.53	4.61	0.00	72	10	167	868	3,713	1.10	45	0.023
4/9/2016	2,240	1.78	0.000000	0.00	0.00	1.78	0.00	0	0	110	843	2,606	2.95	45	0.025
4/9/2016	2,240	1.78	0.000000	0.00	0.00	1.78	0.00	0	0	110	843	2,606	1.55	45	0.025
4/10/2016	2,631	2.09	0.000000	0.00	0.00	2.09	0.00	0	0	117	727	2,624	8.17	120	0.074
4/10/2016	2,631	2.09	0.000000	0.00	0.00	2.09	0.00	0	0	117	727	2,624	1.65	120	0.074
4/11/2016	8,837	7.01	2.092147	1.54	0.88	4.59	0.00	74	17	182	811	3,775	1.67	67	0.045
4/1/2016	4,592	2.94	0.000000	0.10	0.14	2.70	0.00	4	8	176	748	2,260	5.63	52	0.026
4/1/2016	4,592	2.94	0.000000	0.10	0.14	2.70	0.00	4	8	176	748	2,260	2.73	52	0.026
4/3/2016	7,238	4.63	0.000000	0.11	0.23	4.29	0.00	2	6	279	636	2,667	8.60	96	0.050
4/4/2016	3,821	2.45	0.000000	0.00	0.22	2.23	0.00	0	9	161	675	2,229	5.58	69	0.036
4/4/2016	3,821	2.45	0.000000	0.00	0.22	2.23	0.00	0	9	161	675	2,229	4.30	69	0.036
4/5/2016	2,332	1.49	0.000000	0.01	0.18	1.30	0.00	1	10	111	904	2,100	6.88	46	0.023
4/6/2016	2,121	1.36	0.000000	0.00	0.00	1.36	0.00	0	0	122	855	2,114	7.70	122	0.064
4/7/2016	1,291	0.83	0.000000	0.00	0.00	0.83	0.00	0	0	77	888	1,961	7.90	80	0.042
4/8/2016	1,467	0.94	0.000000	0.10	0.21	0.63	0.00	2	8	71	912	1,953	2.95	45	0.023
4/8/2016	1,467	0.94	0.000000	0.10	0.21	0.63	0.00	2	8	71	912	1,953	4.47	45	0.023
4/9/2016	1,022	0.65	0.000000	0.00	0.00	0.65	0.00	0	0	63	739	1,890	4.67	45	0.025
4/9/2016	1,022	0.65	0.000000	0.00	0.00	0.65	0.00	0	0	63	739	1,890	4.60	45	0.025
4/9/2016	1,022	0.65	0.000000	0.00	0.00	0.65	0.00	0	0	63	739	1,890	1.32	45	0.025

##      ID-AD           ActivityDate         TotalSteps    TotalDistance   
##  Min.   :1.504e+14   Length:228         Min.   :   24   Min.   : 0.020  
##  1st Qu.:3.977e+14   Class :character   1st Qu.: 4499   1st Qu.: 3.000  
##  Median :4.703e+14   Mode  :character   Median : 7482   Median : 5.620  
##  Mean   :4.985e+14                      Mean   : 7977   Mean   : 5.613  
##  3rd Qu.:6.962e+14                      3rd Qu.:11262   3rd Qu.: 7.888  
##  Max.   :8.792e+14                      Max.   :17609   Max.   :11.550  
##  LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
##  Min.   :0.0000           Min.   :0.000      Min.   :0.0000          
##  1st Qu.:0.0000           1st Qu.:0.000      1st Qu.:0.0000          
##  Median :0.0000           Median :0.265      Median :0.2400          
##  Mean   :0.1638           Mean   :1.275      Mean   :0.6450          
##  3rd Qu.:0.0000           3rd Qu.:2.180      3rd Qu.:0.9275          
##  Max.   :4.8760           Max.   :8.190      Max.   :4.4900          
##  LightlyActiveDistance SedentaryActiveDistance VeryActiveMinutes
##  Min.   :0.020         Min.   :0.000000        Min.   :  0.00   
##  1st Qu.:2.245         1st Qu.:0.000000        1st Qu.:  0.00   
##  Median :3.715         Median :0.000000        Median :  4.50   
##  Mean   :3.662         Mean   :0.001184        Mean   : 22.73   
##  3rd Qu.:4.683         3rd Qu.:0.000000        3rd Qu.: 33.00   
##  Max.   :9.370         Max.   :0.100000        Max.   :202.00   
##  ModeratelyActiveMinutes LightlyActiveMinutes SedentaryMinutes    Calories   
##  Min.   :  0.00          Min.   :  3.0        Min.   : 146.0   Min.   : 745  
##  1st Qu.:  0.00          1st Qu.:155.8        1st Qu.: 632.2   1st Qu.:1958  
##  Median :  9.00          Median :217.0        Median : 727.5   Median :2260  
##  Mean   : 16.18          Mean   :216.9        Mean   : 727.4   Mean   :2462  
##  3rd Qu.: 24.25          3rd Qu.:268.0        3rd Qu.: 804.2   3rd Qu.:2893  
##  Max.   :114.00          Max.   :506.0        Max.   :1368.0   Max.   :4430  
##      logId             HoursSlept           Id            TotalIntensity  
##  Min.   :1.111e+10   Min.   : 0.020   Min.   :1.504e+09   Min.   : 25.00  
##  1st Qu.:1.129e+10   1st Qu.: 1.393   1st Qu.:3.977e+09   1st Qu.: 46.00  
##  Median :1.132e+10   Median : 2.315   Median :4.703e+09   Median : 69.00  
##  Mean   :1.132e+10   Mean   : 3.401   Mean   :4.985e+09   Mean   : 78.88  
##  3rd Qu.:1.135e+10   3rd Qu.: 5.265   3rd Qu.:6.962e+09   3rd Qu.:120.00  
##  Max.   :1.137e+10   Max.   :12.500   Max.   :8.792e+09   Max.   :139.00  
##  AverageIntensity 
##  Min.   :0.01400  
##  1st Qu.:0.02500  
##  Median :0.04200  
##  Mean   :0.04231  
##  3rd Qu.:0.06400  
##  Max.   :0.07400

described_variables	n	na	mean	sd	se_mean	IQR	skewness	kurtosis	p00	p01	p05	p10	p20	p25	p30	p40	p50	p60	p70	p75	p80	p90	p95	p99	p100
TotalIntensity	228	0	78.88	31.99	2.12	74.00	0.36	-1.36	25.00	25.54	45.00	45.00	46.00	46.00	52.00	61.00	69.00	80.00	97.00	120.00	120.00	124.10	129.00	129.73	139.00
AverageIntensity	228	0	0.04	0.02	0.00	0.04	0.45	-1.23	0.01	0.02	0.02	0.02	0.03	0.03	0.03	0.03	0.04	0.04	0.05	0.06	0.06	0.06	0.07	0.07	0.07
TotalDistance	228	0	5.61	3.03	0.20	4.89	0.10	-1.05	0.02	0.43	0.96	1.63	2.45	3.00	3.65	4.44	5.62	6.80	7.70	7.89	8.25	9.89	10.61	11.36	11.55
TotalSteps	228	0	7,976.65	4,247.19	281.28	6,763.50	0.12	-0.99	24.00	691.31	1,483.80	2,324.80	3,444.00	4,499.00	5,351.00	6,485.00	7,481.50	9,725.00	10,763.40	11,262.50	12,103.20	13,991.50	14,862.85	16,610.25	17,609.00
Calories	228	0	2,461.91	745.15	49.35	934.75	0.54	-0.24	745.00	1,017.87	1,415.80	1,615.00	1,889.40	1,958.00	2,083.30	2,201.80	2,260.00	2,445.00	2,701.20	2,892.75	3,181.60	3,669.00	3,835.85	4,205.38	4,430.00
HoursSlept	228	0	3.40	2.63	0.17	3.87	0.91	0.05	0.02	0.07	0.33	0.79	1.19	1.39	1.53	1.95	2.32	3.13	4.59	5.27	5.94	7.49	8.15	10.52	12.50
LightlyActiveDistance	228	0	3.66	1.79	0.12	2.44	0.46	0.17	0.02	0.37	0.83	1.52	2.09	2.25	2.53	3.07	3.72	4.05	4.58	4.68	4.95	5.69	6.99	8.15	9.37
LightlyActiveMinutes	228	0	216.94	85.22	5.64	112.25	0.42	1.09	3.00	32.40	73.00	116.40	145.00	155.75	170.30	195.00	217.00	239.20	258.00	268.00	280.80	314.30	331.00	501.95	506.00
SedentaryMinutes	228	0	727.36	173.73	11.51	172.00	0.31	1.97	146.00	280.81	465.80	552.00	602.60	632.25	657.00	691.40	727.50	748.00	771.90	804.25	836.80	938.30	1,024.75	1,187.38	1,368.00
VeryActiveDistance	228	0	1.27	1.78	0.12	2.18	1.65	2.48	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.27	0.97	1.73	2.18	2.41	3.82	4.91	7.15	8.19
ModeratelyActiveDistance	228	0	0.65	0.89	0.06	0.93	2.07	4.75	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.16	0.24	0.53	0.79	0.93	1.14	1.76	2.60	4.21	4.49
ModeratelyActiveMinutes	228	0	16.18	21.74	1.44	24.25	2.14	5.40	0.00	0.00	0.00	0.00	0.00	0.00	0.00	5.00	9.00	15.00	18.90	24.25	28.00	40.20	57.65	100.46	114.00
VeryActiveMinutes	228	0	22.73	34.23	2.27	33.00	2.26	6.55	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	4.50	15.20	26.00	33.00	43.00	72.30	95.50	123.00	202.00
LoggedActivitiesDistance	228	0	0.16	0.69	0.05	0.00	4.72	24.12	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	2.09	3.51	4.88
SedentaryActiveDistance	228	0	0.00	0.01	0.00	0.00	9.38	102.16	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.04	0.10

variables	min	Q1	mean	median	Q3	max	zero	minus	outlier
TotalSteps	24.00	4,499.00	7,976.65	7,481.50	11,262.50	17,609.00	0	0	0
TotalDistance	0.02	3.00	5.61	5.62	7.89	11.55	0	0	0
LoggedActivitiesDistance	0.00	0.00	0.16	0.00	0.00	4.88	214	0	14
VeryActiveDistance	0.00	0.00	1.27	0.27	2.18	8.19	94	0	8
ModeratelyActiveDistance	0.00	0.00	0.65	0.24	0.93	4.49	82	0	14
LightlyActiveDistance	0.02	2.25	3.66	3.72	4.68	9.37	0	0	1
SedentaryActiveDistance	0.00	0.00	0.00	0.00	0.00	0.10	220	0	8
VeryActiveMinutes	0.00	0.00	22.73	4.50	33.00	202.00	92	0	18
ModeratelyActiveMinutes	0.00	0.00	16.18	9.00	24.25	114.00	82	0	11
LightlyActiveMinutes	3.00	155.75	216.94	217.00	268.00	506.00	0	0	4
SedentaryMinutes	146.00	632.25	727.36	727.50	804.25	1,368.00	0	0	16
Calories	745.00	1,958.00	2,461.91	2,260.00	2,892.75	4,430.00	0	0	2
HoursSlept	0.02	1.39	3.40	2.32	5.27	12.50	0	0	2
TotalIntensity	25.00	46.00	78.88	69.00	120.00	139.00	0	0	0
AverageIntensity	0.01	0.03	0.04	0.04	0.06	0.07	0	0	0

## 
##  Anscombe-Glynn kurtosis test
## 
## data:  df_tot$TotalSteps
## kurt = 2.0065, z = -6.2249, p-value = 4.82e-10
## alternative hypothesis: kurtosis is not equal to 3

## 
##  Anscombe-Glynn kurtosis test
## 
## data:  df_tot$Calories
## kurt = 2.73955, z = -0.72288, p-value = 0.4698
## alternative hypothesis: kurtosis is not equal to 3

## 
##  Anscombe-Glynn kurtosis test
## 
## data:  df_tot$VeryActiveDistance
## kurt = 5.3966, z = 4.1207, p-value = 3.777e-05
## alternative hypothesis: kurtosis is not equal to 3

## 
##  Anscombe-Glynn kurtosis test
## 
## data:  df_tot$LightlyActiveMinutes
## kurt = 4.0398, z = 2.5349, p-value = 0.01125
## alternative hypothesis: kurtosis is not equal to 3

Our reports suggest we do not have any negative data and the variables with greatest change after removing outliers include: VeryActiveMinutes, SedentaryMinutes, LoggedActiviesDistance, ModeratelyActiveDistance, and ModeratelyActiveMinutes (see: FitnessSmartEDA from SmartEDA library) with VeryActiveMinutes having the greatest change as the Kurtosis has a value of around 9.38 (using kurtosis()). Some of the data is somewhat normally distributed after making observations when testing for Kurtosis close to 3 using anscombe.test() for Calories, TotalSteps, VeryActiveDistance, and LightlyActiveDistance. Most of the data appears to be skewed right with VeryActiveMiinutes having skewness of around 2.25 (using skewness()).

We can now explore the patterns within the dataset such as how much distance should a person travel in order to burn a certain amount of calories and how is sleep related to the activity levels of a person, but first we will need to explore the heterogeneity of the distributions of the columns of data.

# -- Generate density functions 
# We are interested in the properties of Calories burned.
# The distribution appears to be bimodally distributed: 

ggplot(data = df_tot, aes(x = Calories)) + 
  geom_density(data = df_tot, aes(x = Calories)) + 
  labs(x = "Calories", title = "PDF: Calories", y = "Density") + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$SedentaryMinutes
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Sedentary Minutes", y = "Calories", title = "Box Plot: Sedentary Minutes vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$LightlyActiveMinutes
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Light Activity Minutes", y = "Calories", title = "Box Plot: Light Activity Minutes vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$ModeratelyActiveMinutes
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Moderate Activity Minutes", y = "Calories", title = "Box Plot: Moderate Activity Minutes vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$VeryActiveMinutes
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Very Activity Minutes", y = "Calories", title = "Box Plot: Very Activity Minutes vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

# Remark: SedentaryActiveDistance does not work due to the distribution properties.

cvar <- df_tot$LightlyActiveDistance
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Light Activity Distance", y = "Calories", title = "Box Plot: Light Activity Distance vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$ModeratelyActiveDistance
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Moderate Activity Distance", y = "Calories", title = "Box Plot: Moderate Activity Distance vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

cvar <- df_tot$VeryActiveDistance
quartiles <- quantile(cvar, c(0.25, 0.5, 0.75))
df_tot$qrts <- cut(cvar, breaks = c(-Inf, quartiles, Inf), labels = c("Q1", "Q2", "Q3", "Q4"))
ggplot(data = df_tot, aes(x = df_tot$qrts, y = Calories, fill = df_tot$qrts)) + 
  geom_violin(data = df_tot, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df_tot, aes(x = df_tot$qrts), width = 0.10) + 
  labs(x = "Very Activity Distance", y = "Calories", title = "Box Plot: Very Activity Distance vs Calories", fill = "Quantiles") +
  scale_fill_manual(values = viridis(n = length(unique(df_tot$qrts)))) + 
    theme_linedraw(base_size = 18)

We observe the LightActivityMinutes plot to be bimodally distributed at the first and third quartile with a significant proportion of the highest forth quartile to have a skinny tail (violin plot in yellow) suggesting a uniform spread. In the FairlyActivityMinutes plot, we observe similar distributions for the third and forth quartiles and similar results are obtained in the VeryActiveMinutes plot for the first and second quartiles. This suggests the upper and lower tails of the variables are not similarly distributed and we can categorize the activity levels based on multiple columns. Similar results are obtained when substituting minutes for distance, but the qualitative shape of the distribution is less heterogeneous.

After exploring with various methods, I started by taking the greatest of the measures of central tendency (average and median) for each variable (VeryActiveMinutes, ModeratelyActiveMinutes, and LightlyActiveMinutes). The Very Active population will be greater than or equal to the max of average and median of VeryActiveMinutes, otherwise it would fall into the Moderately Active or Lightly Active population. To classify if it should fall into the Moderately Active population, the population should have greater ModeratelyActiveMinutes than the max of median and average of its respective column. A similar approach is applied for the Lightly Active categorization. If it does not fall into the three classifications, it is Sedentary.
A similar method could be applied when substituting Minutes for Distance, but the results do not change too much (just a slight difference in where each member is categorized - can be seen in the bar chart).

# -- Group by using the binning method -- #
# In the absence of categorical data, we will create factor variables based on the numeric variables.

df_tot_user_mins <- df_tot %>% 
  summarise(
    user_type = factor(case_when(
      VeryActiveMinutes >= max(median(VeryActiveMinutes), mean(VeryActiveMinutes)) ~ "Very Active",
      VeryActiveMinutes < max(median(VeryActiveMinutes), mean(VeryActiveMinutes)) & 
        ModeratelyActiveMinutes >= max(median(ModeratelyActiveMinutes), mean(ModeratelyActiveMinutes))  ~ "Moderately Active",
      VeryActiveMinutes < max(median(VeryActiveMinutes), mean(VeryActiveMinutes)) & 
        ModeratelyActiveMinutes < max(median(ModeratelyActiveMinutes), mean(ModeratelyActiveMinutes)) & 
          LightlyActiveMinutes >= max(median(LightlyActiveMinutes), mean(LightlyActiveMinutes))   ~ "Lightly Active", 
      VeryActiveMinutes < max(median(VeryActiveMinutes), mean(VeryActiveMinutes)) & 
        ModeratelyActiveMinutes < max(median(ModeratelyActiveMinutes), mean(ModeratelyActiveMinutes)) & 
          LightlyActiveMinutes < max(median(LightlyActiveMinutes), mean(LightlyActiveMinutes)) ~ "Sedentary",
        ),
      levels = c("Very Active", "Moderately Active", "Lightly Active", "Sedentary")), 
    Calories, 
    HoursSlept, 
    TotalDistance, 
    TotalIntensity
  )

# Observe the aggregated data categorized by minutes

glimpse(df_tot_user_mins) %>% flextable()

## Rows: 228
## Columns: 5
## $ user_type      <fct> Very Active, Very Active, Very Active, Very Active, Ver…
## $ Calories       <dbl> 1819, 2154, 2154, 1944, 1944, 1932, 1886, 1889, 1868, 1…
## $ HoursSlept     <dbl> 6.42, 7.60, 0.23, 2.13, 5.48, 1.27, 6.28, 5.58, 5.08, 8…
## $ TotalDistance  <dbl> 7.11, 11.55, 11.55, 8.53, 8.53, 8.93, 7.85, 7.86, 7.87,…
## $ TotalIntensity <dbl> 139, 130, 130, 97, 97, 76, 56, 61, 52, 129, 96, 69, 46,…

user_type	Calories	HoursSlept	TotalDistance	TotalIntensity
Very Active	1,819	6.42	7.11	139
Very Active	2,154	7.60	11.55	130
Very Active	2,154	0.23	11.55	130
Very Active	1,944	2.13	8.53	97
Very Active	1,944	5.48	8.53	97
Very Active	1,932	1.27	8.93	76
Very Active	1,886	6.28	7.85	56
Very Active	1,889	5.58	7.86	61
Very Active	1,868	5.08	7.87	52
Very Active	1,843	8.47	7.25	129
Moderately Active	1,850	7.08	6.37	96
Very Active	2,030	5.60	9.80	69
Very Active	2,083	5.13	9.73	46
Very Active	1,883	6.77	8.10	45
Very Active	1,755	7.95	6.98	120
Very Active	1,811	5.47	7.26	67
Sedentary	1,636	3.15	1.88	96
Sedentary	1,807	10.58	3.29	45
Lightly Active	2,783	5.23	3.92	129
Sedentary	2,449	1.83	2.21	96
Sedentary	2,380	3.13	1.63	69
Sedentary	2,344	6.98	1.55	46
Sedentary	2,202	7.13	0.87	122
Sedentary	2,443	0.77	1.75	45
Sedentary	2,442	5.13	1.46	120
Sedentary	2,442	1.03	1.46	120
Sedentary	2,255	2.05	0.84	67
Sedentary	942	2.13	0.02	25
Sedentary	1,407	2.45	1.62	52
Sedentary	1,237	2.55	0.76	129
Sedentary	1,330	2.97	1.43	96
Lightly Active	1,583	2.00	4.14	69
Lightly Active	1,538	2.57	3.50	46
Sedentary	1,421	2.17	1.97	122
Sedentary	1,392	2.95	1.72	80
Sedentary	1,425	2.92	2.33	45
Sedentary	1,413	2.82	1.92	45
Lightly Active	1,515	2.72	3.19	120
Sedentary	1,405	3.18	2.03	67
Moderately Active	2,041	1.82	6.79	56
Moderately Active	2,187	1.57	7.10	99
Lightly Active	1,929	2.28	4.47	61
Very Active	2,438	7.63	10.22	129
Very Active	2,438	0.05	10.22	129
Lightly Active	2,035	2.50	4.95	96
Moderately Active	2,099	2.08	6.80	69
Lightly Active	2,096	0.62	6.82	46
Moderately Active	2,338	1.25	6.85	80
Moderately Active	2,488	0.33	10.63	45
Lightly Active	2,164	1.93	6.83	120
Sedentary	1,288	7.15	3.01	52
Lightly Active	1,490	4.82	5.10	129
Very Active	1,630	5.97	7.70	96
Moderately Active	1,648	6.22	7.99	69
Very Active	1,649	8.08	8.23	46
Very Active	1,783	7.08	10.32	122
Moderately Active	1,431	6.73	5.63	80
Moderately Active	1,524	7.98	6.84	45
Very Active	1,697	6.95	9.76	45
Sedentary	1,232	7.52	2.39	120
Sedentary	1,223	6.90	2.14	67
Lightly Active	2,990	6.00	3.97	98
Lightly Active	2,990	1.12	3.97	98
Sedentary	2,480	5.37	2.31	53
Sedentary	2,570	4.82	2.17	27
Sedentary	3,016	5.67	4.23	53
Very Active	3,830	8.20	8.99	73
Sedentary	3,706	6.77	6.41	70
Lightly Active	3,418	2.43	3.87	99
Lightly Active	3,418	3.98	3.87	99
Moderately Active	3,439	4.45	5.33	61
Moderately Active	3,338	4.08	3.84	52
Moderately Active	3,338	2.92	3.84	52
Moderately Active	3,338	0.45	3.84	52
Sedentary	2,892	3.12	3.10	129
Lightly Active	3,313	5.00	4.38	96
Lightly Active	3,313	1.62	4.38	96
Moderately Active	3,118	7.95	4.60	69
Lightly Active	3,253	4.27	5.36	122
Sedentary	2,817	1.22	3.68	120
Sedentary	2,507	3.75	2.15	67
Sedentary	1,958	1.60	3.79	56
Sedentary	1,958	1.73	3.79	56
Moderately Active	2,129	0.77	5.46	61
Moderately Active	2,216	1.55	6.27	52
Moderately Active	2,216	0.72	6.27	52
Lightly Active	2,154	0.97	5.66	129
Moderately Active	2,178	1.03	5.52	96
Moderately Active	2,308	1.52	6.76	69
Lightly Active	2,201	1.15	5.63	46
Moderately Active	2,341	4.30	7.35	122
Moderately Active	2,341	0.80	7.35	122
Sedentary	2,010	3.75	1.55	56
Sedentary	2,010	4.18	1.55	56
Lightly Active	2,227	4.90	3.22	61
Lightly Active	2,227	2.00	3.22	61
Sedentary	2,133	5.60	2.19	52
Lightly Active	2,317	8.23	3.63	129
Lightly Active	2,217	8.25	2.84	46
Very Active	2,374	7.48	4.78	122
Very Active	2,335	4.45	4.61	80
Lightly Active	2,303	1.68	3.72	45
Lightly Active	2,303	2.17	3.72	45
Lightly Active	2,210	3.38	2.81	45
Lightly Active	2,210	1.15	2.81	45
Lightly Active	2,210	2.80	2.81	45
Lightly Active	2,424	4.87	4.45	120
Lightly Active	2,424	1.25	4.45	120
Very Active	2,297	4.55	4.13	67
Very Active	2,297	2.55	4.13	67
Sedentary	745	5.52	0.19	25
Sedentary	1,680	1.43	2.10	129
Lightly Active	2,886	1.15	5.28	56
Lightly Active	2,915	1.53	6.15	99
Lightly Active	2,895	1.00	5.65	61
Lightly Active	2,923	2.00	5.81	52
Moderately Active	3,323	2.00	8.38	129
Moderately Active	3,357	1.95	9.83	96
Moderately Active	2,931	1.33	6.36	69
Sedentary	2,848	1.32	5.41	46
Lightly Active	2,943	1.75	6.18	122
Sedentary	2,822	1.63	5.34	80
Moderately Active	3,597	7.20	11.36	45
Moderately Active	3,597	2.57	11.36	45
Moderately Active	3,224	2.02	9.03	120
Moderately Active	3,224	0.87	9.03	120
Sedentary	2,677	3.80	5.27	67
Sedentary	1,615	11.58	1.92	129
Sedentary	1,615	3.43	1.92	129
Sedentary	1,481	12.50	0.99	96
Sedentary	1,481	1.47	0.99	96
Very Active	1,892	1.53	5.88	69
Very Active	2,086	0.38	7.38	46
Very Active	2,044	0.58	8.00	122
Very Active	2,249	0.08	10.08	80
Sedentary	1,692	9.12	3.00	45
Sedentary	1,692	2.28	3.00	45
Moderately Active	1,712	10.35	3.65	120
Moderately Active	1,712	1.43	3.65	120
Very Active	2,065	1.65	7.77	67
Very Active	3,625	2.20	7.87	52
Very Active	4,430	5.17	11.11	129
Very Active	4,430	1.17	11.11	129
Very Active	3,427	2.20	7.41	96
Very Active	3,492	2.27	5.56	69
Very Active	3,597	2.17	6.70	46
Very Active	3,597	0.95	6.70	46
Very Active	3,765	1.60	8.30	122
Very Active	2,775	2.35	3.36	80
Sedentary	2,486	2.22	3.26	45
Very Active	3,817	4.43	7.84	45
Very Active	3,817	1.88	7.84	45
Very Active	3,817	0.28	7.84	45
Very Active	3,378	1.53	7.38	120
Lightly Active	2,210	4.28	5.62	52
Lightly Active	2,210	3.93	5.62	52
Lightly Active	2,210	1.50	5.62	52
Lightly Active	2,445	1.05	7.48	46
Lightly Active	2,445	0.02	7.48	46
Lightly Active	2,694	1.72	9.37	122
Lightly Active	2,617	3.73	8.15	45
Lightly Active	2,617	2.95	8.15	45
Lightly Active	2,617	1.05	8.15	45
Very Active	3,065	0.20	5.18	52
Very Active	3,920	7.58	8.43	122
Very Active	3,920	0.17	8.43	122
Very Active	3,856	1.82	10.03	80
Very Active	3,856	2.13	10.03	80
Moderately Active	2,244	6.75	10.24	99
Moderately Active	2,244	0.42	10.24	99
Very Active	2,188	1.90	9.32	61
Very Active	2,188	0.07	9.32	61
Very Active	2,115	6.90	8.30	129
Moderately Active	2,055	8.25	8.14	96
Moderately Active	2,055	1.18	8.14	96
Very Active	2,158	5.90	7.94	46
Very Active	2,158	0.83	7.94	46
Very Active	2,170	0.22	8.86	122
Very Active	2,244	0.33	9.65	80
Very Active	2,231	6.18	8.26	45
Very Active	2,231	1.37	8.26	45
Lightly Active	2,100	7.02	6.71	120
Lightly Active	2,100	0.10	6.71	120
Very Active	917	6.43	3.90	25
Lightly Active	2,575	1.13	7.34	129
Very Active	3,086	1.20	11.24	96
Very Active	2,676	1.15	9.14	46
Very Active	2,840	1.08	10.58	80
Very Active	2,496	0.60	7.67	52
Very Active	2,439	1.50	4.99	46
Sedentary	2,233	1.08	4.73	122
Very Active	2,862	1.75	10.82	80
Very Active	2,519	8.10	4.67	45
Sedentary	1,799	0.73	0.35	120
Very Active	2,543	1.80	3.92	67
Very Active	3,669	2.52	11.24	52
Very Active	3,669	0.30	11.24	52
Sedentary	2,702	3.23	2.66	129
Sedentary	2,702	1.30	2.66	129
Sedentary	2,702	2.03	2.66	129
Very Active	3,304	2.05	7.26	96
Very Active	4,234	3.00	11.05	69
Very Active	4,128	2.10	10.19	46
Very Active	4,128	0.90	10.19	46
Very Active	3,798	1.25	7.24	122
Very Active	3,798	1.40	7.24	122
Very Active	3,839	1.52	7.71	80
Very Active	3,839	1.45	7.71	80
Very Active	3,839	0.87	7.71	80
Very Active	3,713	1.10	6.62	45
Sedentary	2,606	2.95	1.78	45
Sedentary	2,606	1.55	1.78	45
Sedentary	2,624	8.17	2.09	120
Sedentary	2,624	1.65	2.09	120
Very Active	3,775	1.67	7.01	67
Sedentary	2,260	5.63	2.94	52
Sedentary	2,260	2.73	2.94	52
Lightly Active	2,667	8.60	4.63	96
Sedentary	2,229	5.58	2.45	69
Sedentary	2,229	4.30	2.45	69
Sedentary	2,100	6.88	1.49	46
Sedentary	2,114	7.70	1.36	122
Sedentary	1,961	7.90	0.83	80
Sedentary	1,953	2.95	0.94	45
Sedentary	1,953	4.47	0.94	45
Sedentary	1,890	4.67	0.65	45
Sedentary	1,890	4.60	0.65	45
Sedentary	1,890	1.32	0.65	45

summary(df_tot_user_mins) 

##              user_type     Calories      HoursSlept     TotalDistance   
##  Very Active      :80   Min.   : 745   Min.   : 0.020   Min.   : 0.020  
##  Moderately Active:34   1st Qu.:1958   1st Qu.: 1.393   1st Qu.: 3.000  
##  Lightly Active   :47   Median :2260   Median : 2.315   Median : 5.620  
##  Sedentary        :67   Mean   :2462   Mean   : 3.401   Mean   : 5.613  
##                         3rd Qu.:2893   3rd Qu.: 5.265   3rd Qu.: 7.888  
##                         Max.   :4430   Max.   :12.500   Max.   :11.550  
##  TotalIntensity  
##  Min.   : 25.00  
##  1st Qu.: 46.00  
##  Median : 69.00  
##  Mean   : 78.88  
##  3rd Qu.:120.00  
##  Max.   :139.00

df_tot_user_dist <- df_tot %>% 
  summarise(
    user_type = factor(case_when(
      VeryActiveDistance >= max(median(VeryActiveDistance), mean(VeryActiveDistance)) ~ "Very Active",
      VeryActiveDistance < max(median(VeryActiveDistance), mean(VeryActiveDistance)) & 
        ModeratelyActiveDistance >= max(median(ModeratelyActiveDistance), mean(ModeratelyActiveDistance))  ~ "Moderately Active",
      VeryActiveDistance < max(median(VeryActiveDistance), mean(VeryActiveDistance)) & 
        ModeratelyActiveDistance < max(median(ModeratelyActiveDistance), mean(ModeratelyActiveDistance)) & 
        LightlyActiveDistance >= max(median(LightlyActiveDistance), mean(LightlyActiveDistance))   ~ "Lightly Active", 
      VeryActiveDistance < max(median(VeryActiveDistance), mean(VeryActiveDistance)) & 
        ModeratelyActiveDistance < max(median(ModeratelyActiveDistance), mean(ModeratelyActiveDistance)) & 
        LightlyActiveDistance < max(median(LightlyActiveDistance), mean(LightlyActiveDistance)) ~ "Sedentary",
    ),
    levels = c("Very Active", "Moderately Active", "Lightly Active", "Sedentary")), 
    Calories, 
    HoursSlept, 
    TotalDistance, 
    TotalIntensity
  )

# Observe the aggregated data:
glimpse(df_tot_user_dist) %>% flextable()

## Rows: 228
## Columns: 5
## $ user_type      <fct> Very Active, Very Active, Very Active, Very Active, Ver…
## $ Calories       <dbl> 1819, 2154, 2154, 1944, 1944, 1932, 1886, 1889, 1868, 1…
## $ HoursSlept     <dbl> 6.42, 7.60, 0.23, 2.13, 5.48, 1.27, 6.28, 5.58, 5.08, 8…
## $ TotalDistance  <dbl> 7.11, 11.55, 11.55, 8.53, 8.53, 8.93, 7.85, 7.86, 7.87,…
## $ TotalIntensity <dbl> 139, 130, 130, 97, 97, 76, 56, 61, 52, 129, 96, 69, 46,…

user_type	Calories	HoursSlept	TotalDistance	TotalIntensity
Very Active	1,819	6.42	7.11	139
Very Active	2,154	7.60	11.55	130
Very Active	2,154	0.23	11.55	130
Very Active	1,944	2.13	8.53	97
Very Active	1,944	5.48	8.53	97
Very Active	1,932	1.27	8.93	76
Very Active	1,886	6.28	7.85	56
Very Active	1,889	5.58	7.86	61
Very Active	1,868	5.08	7.87	52
Very Active	1,843	8.47	7.25	129
Moderately Active	1,850	7.08	6.37	96
Very Active	2,030	5.60	9.80	69
Very Active	2,083	5.13	9.73	46
Very Active	1,883	6.77	8.10	45
Very Active	1,755	7.95	6.98	120
Very Active	1,811	5.47	7.26	67
Sedentary	1,636	3.15	1.88	96
Sedentary	1,807	10.58	3.29	45
Lightly Active	2,783	5.23	3.92	129
Sedentary	2,449	1.83	2.21	96
Sedentary	2,380	3.13	1.63	69
Sedentary	2,344	6.98	1.55	46
Sedentary	2,202	7.13	0.87	122
Sedentary	2,443	0.77	1.75	45
Sedentary	2,442	5.13	1.46	120
Sedentary	2,442	1.03	1.46	120
Sedentary	2,255	2.05	0.84	67
Sedentary	942	2.13	0.02	25
Sedentary	1,407	2.45	1.62	52
Sedentary	1,237	2.55	0.76	129
Sedentary	1,330	2.97	1.43	96
Lightly Active	1,583	2.00	4.14	69
Sedentary	1,538	2.57	3.50	46
Sedentary	1,421	2.17	1.97	122
Sedentary	1,392	2.95	1.72	80
Sedentary	1,425	2.92	2.33	45
Sedentary	1,413	2.82	1.92	45
Sedentary	1,515	2.72	3.19	120
Sedentary	1,405	3.18	2.03	67
Moderately Active	2,041	1.82	6.79	56
Very Active	2,187	1.57	7.10	99
Lightly Active	1,929	2.28	4.47	61
Very Active	2,438	7.63	10.22	129
Very Active	2,438	0.05	10.22	129
Lightly Active	2,035	2.50	4.95	96
Very Active	2,099	2.08	6.80	69
Very Active	2,096	0.62	6.82	46
Moderately Active	2,338	1.25	6.85	80
Moderately Active	2,488	0.33	10.63	45
Lightly Active	2,164	1.93	6.83	120
Sedentary	1,288	7.15	3.01	52
Lightly Active	1,490	4.82	5.10	129
Very Active	1,630	5.97	7.70	96
Moderately Active	1,648	6.22	7.99	69
Very Active	1,649	8.08	8.23	46
Very Active	1,783	7.08	10.32	122
Moderately Active	1,431	6.73	5.63	80
Moderately Active	1,524	7.98	6.84	45
Very Active	1,697	6.95	9.76	45
Sedentary	1,232	7.52	2.39	120
Sedentary	1,223	6.90	2.14	67
Lightly Active	2,990	6.00	3.97	98
Lightly Active	2,990	1.12	3.97	98
Sedentary	2,480	5.37	2.31	53
Sedentary	2,570	4.82	2.17	27
Lightly Active	3,016	5.67	4.23	53
Very Active	3,830	8.20	8.99	73
Sedentary	3,706	6.77	6.41	70
Lightly Active	3,418	2.43	3.87	99
Lightly Active	3,418	3.98	3.87	99
Moderately Active	3,439	4.45	5.33	61
Sedentary	3,338	4.08	3.84	52
Sedentary	3,338	2.92	3.84	52
Sedentary	3,338	0.45	3.84	52
Sedentary	2,892	3.12	3.10	129
Lightly Active	3,313	5.00	4.38	96
Lightly Active	3,313	1.62	4.38	96
Moderately Active	3,118	7.95	4.60	69
Lightly Active	3,253	4.27	5.36	122
Sedentary	2,817	1.22	3.68	120
Sedentary	2,507	3.75	2.15	67
Sedentary	1,958	1.60	3.79	56
Sedentary	1,958	1.73	3.79	56
Lightly Active	2,129	0.77	5.46	61
Moderately Active	2,216	1.55	6.27	52
Moderately Active	2,216	0.72	6.27	52
Lightly Active	2,154	0.97	5.66	129
Moderately Active	2,178	1.03	5.52	96
Moderately Active	2,308	1.52	6.76	69
Lightly Active	2,201	1.15	5.63	46
Moderately Active	2,341	4.30	7.35	122
Moderately Active	2,341	0.80	7.35	122
Sedentary	2,010	3.75	1.55	56
Sedentary	2,010	4.18	1.55	56
Sedentary	2,227	4.90	3.22	61
Sedentary	2,227	2.00	3.22	61
Sedentary	2,133	5.60	2.19	52
Sedentary	2,317	8.23	3.63	129
Sedentary	2,217	8.25	2.84	46
Very Active	2,374	7.48	4.78	122
Very Active	2,335	4.45	4.61	80
Lightly Active	2,303	1.68	3.72	45
Lightly Active	2,303	2.17	3.72	45
Sedentary	2,210	3.38	2.81	45
Sedentary	2,210	1.15	2.81	45
Sedentary	2,210	2.80	2.81	45
Lightly Active	2,424	4.87	4.45	120
Lightly Active	2,424	1.25	4.45	120
Very Active	2,297	4.55	4.13	67
Very Active	2,297	2.55	4.13	67
Sedentary	745	5.52	0.19	25
Sedentary	1,680	1.43	2.10	129
Lightly Active	2,886	1.15	5.28	56
Lightly Active	2,915	1.53	6.15	99
Lightly Active	2,895	1.00	5.65	61
Lightly Active	2,923	2.00	5.81	52
Moderately Active	3,323	2.00	8.38	129
Moderately Active	3,357	1.95	9.83	96
Moderately Active	2,931	1.33	6.36	69
Moderately Active	2,848	1.32	5.41	46
Lightly Active	2,943	1.75	6.18	122
Lightly Active	2,822	1.63	5.34	80
Moderately Active	3,597	7.20	11.36	45
Moderately Active	3,597	2.57	11.36	45
Moderately Active	3,224	2.02	9.03	120
Moderately Active	3,224	0.87	9.03	120
Lightly Active	2,677	3.80	5.27	67
Sedentary	1,615	11.58	1.92	129
Sedentary	1,615	3.43	1.92	129
Sedentary	1,481	12.50	0.99	96
Sedentary	1,481	1.47	0.99	96
Very Active	1,892	1.53	5.88	69
Very Active	2,086	0.38	7.38	46
Very Active	2,044	0.58	8.00	122
Very Active	2,249	0.08	10.08	80
Sedentary	1,692	9.12	3.00	45
Sedentary	1,692	2.28	3.00	45
Moderately Active	1,712	10.35	3.65	120
Moderately Active	1,712	1.43	3.65	120
Very Active	2,065	1.65	7.77	67
Very Active	3,625	2.20	7.87	52
Very Active	4,430	5.17	11.11	129
Very Active	4,430	1.17	11.11	129
Very Active	3,427	2.20	7.41	96
Very Active	3,492	2.27	5.56	69
Very Active	3,597	2.17	6.70	46
Very Active	3,597	0.95	6.70	46
Very Active	3,765	1.60	8.30	122
Sedentary	2,775	2.35	3.36	80
Sedentary	2,486	2.22	3.26	45
Very Active	3,817	4.43	7.84	45
Very Active	3,817	1.88	7.84	45
Very Active	3,817	0.28	7.84	45
Very Active	3,378	1.53	7.38	120
Lightly Active	2,210	4.28	5.62	52
Lightly Active	2,210	3.93	5.62	52
Lightly Active	2,210	1.50	5.62	52
Lightly Active	2,445	1.05	7.48	46
Lightly Active	2,445	0.02	7.48	46
Lightly Active	2,694	1.72	9.37	122
Lightly Active	2,617	3.73	8.15	45
Lightly Active	2,617	2.95	8.15	45
Lightly Active	2,617	1.05	8.15	45
Very Active	3,065	0.20	5.18	52
Very Active	3,920	7.58	8.43	122
Very Active	3,920	0.17	8.43	122
Very Active	3,856	1.82	10.03	80
Very Active	3,856	2.13	10.03	80
Very Active	2,244	6.75	10.24	99
Very Active	2,244	0.42	10.24	99
Very Active	2,188	1.90	9.32	61
Very Active	2,188	0.07	9.32	61
Very Active	2,115	6.90	8.30	129
Moderately Active	2,055	8.25	8.14	96
Moderately Active	2,055	1.18	8.14	96
Very Active	2,158	5.90	7.94	46
Very Active	2,158	0.83	7.94	46
Very Active	2,170	0.22	8.86	122
Very Active	2,244	0.33	9.65	80
Very Active	2,231	6.18	8.26	45
Very Active	2,231	1.37	8.26	45
Very Active	2,100	7.02	6.71	120
Very Active	2,100	0.10	6.71	120
Very Active	917	6.43	3.90	25
Moderately Active	2,575	1.13	7.34	129
Very Active	3,086	1.20	11.24	96
Very Active	2,676	1.15	9.14	46
Very Active	2,840	1.08	10.58	80
Very Active	2,496	0.60	7.67	52
Very Active	2,439	1.50	4.99	46
Very Active	2,233	1.08	4.73	122
Very Active	2,862	1.75	10.82	80
Sedentary	2,519	8.10	4.67	45
Sedentary	1,799	0.73	0.35	120
Sedentary	2,543	1.80	3.92	67
Very Active	3,669	2.52	11.24	52
Very Active	3,669	0.30	11.24	52
Sedentary	2,702	3.23	2.66	129
Sedentary	2,702	1.30	2.66	129
Sedentary	2,702	2.03	2.66	129
Very Active	3,304	2.05	7.26	96
Very Active	4,234	3.00	11.05	69
Very Active	4,128	2.10	10.19	46
Very Active	4,128	0.90	10.19	46
Lightly Active	3,798	1.25	7.24	122
Lightly Active	3,798	1.40	7.24	122
Very Active	3,839	1.52	7.71	80
Very Active	3,839	1.45	7.71	80
Very Active	3,839	0.87	7.71	80
Very Active	3,713	1.10	6.62	45
Sedentary	2,606	2.95	1.78	45
Sedentary	2,606	1.55	1.78	45
Sedentary	2,624	8.17	2.09	120
Sedentary	2,624	1.65	2.09	120
Very Active	3,775	1.67	7.01	67
Sedentary	2,260	5.63	2.94	52
Sedentary	2,260	2.73	2.94	52
Lightly Active	2,667	8.60	4.63	96
Sedentary	2,229	5.58	2.45	69
Sedentary	2,229	4.30	2.45	69
Sedentary	2,100	6.88	1.49	46
Sedentary	2,114	7.70	1.36	122
Sedentary	1,961	7.90	0.83	80
Sedentary	1,953	2.95	0.94	45
Sedentary	1,953	4.47	0.94	45
Sedentary	1,890	4.67	0.65	45
Sedentary	1,890	4.60	0.65	45
Sedentary	1,890	1.32	0.65	45

summary(df_tot_user_dist)

##              user_type     Calories      HoursSlept     TotalDistance   
##  Very Active      :83   Min.   : 745   Min.   : 0.020   Min.   : 0.020  
##  Moderately Active:28   1st Qu.:1958   1st Qu.: 1.393   1st Qu.: 3.000  
##  Lightly Active   :40   Median :2260   Median : 2.315   Median : 5.620  
##  Sedentary        :77   Mean   :2462   Mean   : 3.401   Mean   : 5.613  
##                         3rd Qu.:2893   3rd Qu.: 5.265   3rd Qu.: 7.888  
##                         Max.   :4430   Max.   :12.500   Max.   :11.550  
##  TotalIntensity  
##  Min.   : 25.00  
##  1st Qu.: 46.00  
##  Median : 69.00  
##  Mean   : 78.88  
##  3rd Qu.:120.00  
##  Max.   :139.00

# -- Correlation tests before grouping

library(flextable) 
df_tot %>% select(-Id, -"ID-AD", -logId) %>% 
dlookr::normality() %>% 
  mutate(across(is.numeric, ~round(., 2))) %>% 
  arrange(statistic) %>% 
  flextable()

vars	statistic	p_value	sample
SedentaryActiveDistance	0.13	0	228
LoggedActivitiesDistance	0.25	0	228
VeryActiveMinutes	0.71	0	228
ModeratelyActiveDistance	0.74	0	228
ModeratelyActiveMinutes	0.74	0	228
VeryActiveDistance	0.75	0	228
TotalIntensity	0.88	0	228
AverageIntensity	0.88	0	228
HoursSlept	0.90	0	228
SedentaryMinutes	0.96	0	228
Calories	0.96	0	228
TotalSteps	0.97	0	228
TotalDistance	0.97	0	228
LightlyActiveDistance	0.98	0	228
LightlyActiveMinutes	0.98	0	228

# -- Apply similar method after grouping -- #
df_tot_user_mins %>% 
  group_by(user_type) %>% 
  dlookr::normality() %>% 
  mutate(across(is.numeric, ~round(., 2))) %>% 
  arrange(statistic) %>% 
  flextable()

variable	user_type	statistic	p_value	sample
TotalIntensity	Lightly Active	0.82	0.00	47
HoursSlept	Moderately Active	0.84	0.00	34
TotalIntensity	Sedentary	0.86	0.00	67
HoursSlept	Very Active	0.87	0.00	80
HoursSlept	Lightly Active	0.88	0.00	47
TotalIntensity	Very Active	0.88	0.00	80
Calories	Very Active	0.90	0.00	80
Calories	Moderately Active	0.90	0.01	34
HoursSlept	Sedentary	0.90	0.00	67
TotalIntensity	Moderately Active	0.90	0.00	34
TotalDistance	Sedentary	0.93	0.00	67
TotalDistance	Lightly Active	0.95	0.04	47
Calories	Lightly Active	0.96	0.07	47
TotalDistance	Very Active	0.96	0.01	80
TotalDistance	Moderately Active	0.96	0.19	34
Calories	Sedentary	0.98	0.50	67

df_tot_user_dist %>% 
  group_by(user_type) %>% 
  dlookr::normality() %>% 
  mutate(across(is.numeric, ~round(., 2))) %>% 
  arrange(statistic) %>% 
  flextable()

variable	user_type	statistic	p_value	sample
HoursSlept	Moderately Active	0.81	0.00	28
TotalIntensity	Sedentary	0.84	0.00	77
HoursSlept	Very Active	0.86	0.00	83
TotalIntensity	Lightly Active	0.86	0.00	40
Calories	Very Active	0.88	0.00	83
HoursSlept	Lightly Active	0.88	0.00	40
TotalIntensity	Very Active	0.89	0.00	83
TotalIntensity	Moderately Active	0.89	0.01	28
HoursSlept	Sedentary	0.90	0.00	77
TotalDistance	Lightly Active	0.91	0.01	40
Calories	Moderately Active	0.94	0.10	28
TotalDistance	Very Active	0.97	0.03	83
TotalDistance	Moderately Active	0.97	0.46	28
TotalDistance	Sedentary	0.97	0.08	77
Calories	Lightly Active	0.98	0.74	40
Calories	Sedentary	0.98	0.39	77

We apply the Shaprio-Wilk test for Normality before and after categorizing based on minutes and notice that TotalDistance for Moderately Active and Calories for Sedentary population are normally distributed (pvalue of 0.19 and 0.50). As a reminder, the null hypothesis is that the distribution is Gaussian (normal) and if the pvalue is less than or equal to 0.05, then the distribution is not Gaussian.

We will now visualize the plots after binning by minutes, which lead to interesting findings:

# Categorize by Minutes
# C:Q - Calories
df <- df_tot_user_mins

# Bar plots to visualize the samples in the dataset.
ggplot(data = df_tot_user_mins, aes(x = user_type, fill = user_type)) + 
  geom_bar(data = df_tot_user_mins, aes(x = fct_infreq(user_type), fill = user_type)) + 
  labs(x = "UserType", y = "Frequency", title = "Bar Chart: Usertype Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df_tot_user_dist, aes(x = user_type, fill = user_type)) + 
  geom_bar(data = df_tot_user_dist, aes(x = fct_infreq(user_type), fill = user_type)) + 
  labs(x = "UserType", y = "Frequency", title = "Bar Chart: Usertype Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

# Q:Q - Correlogram to visualize the correlation between all numeric columns.
df_num <- df %>% select(-user_type) %>% drop_na()
colnames(df_num) <- c("Calories", "Hours Slept", "Total Distance", "Total Intensity")
corr_mat <- cor(as.matrix(df_num))
corrplot(corr_mat, method = 'square', tl.col = 'black') +
  theme_linedraw(base_size = 18)

## NULL

# Boxplots to visualize the distribution.
ggplot(data = df, aes(x = user_type, y = Calories, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Calories", title = "Box Plot: Usertype vs Calories Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
    theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = Calories, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Calories", title = "Box Plot: Usertype vs Calories Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
    theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = Calories, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = Calories, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Calories", y = "Density", title = "Density Plot: Calories Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = Calories, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = Calories, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Calories", y = "Density", title = "Density Plot: Calories Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = Calories, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=Calories)) + 
  labs(x = "Calories", y = "Probability", title = "CDF: Calories Categorized by Minutes", color = "UserType") +
  #facet_wrap( ~ user_type) + 
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18) 

# C:Q Intensity
ggplot(data = df, aes(x = user_type, y = TotalDistance, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Total Distance", title = "Box Plot: Usertype vs Total Distance Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = TotalDistance, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Total Distance", title = "Box Plot: Usertype vs Total Distance Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalDistance, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = TotalDistance, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Total Distance", y = "Density", title = "Density Plot: Total Distance Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalDistance, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = TotalDistance, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Total Distance", y = "Density", title = "Density Plot: Total Distance Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = TotalDistance, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=TotalDistance)) + 
  labs(x = "Total Distance", y = "Probability", title = "CDF: Total Distance Categorized by Minutes", color = "UserType") +
  #facet_wrap( ~ user_type) + 
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18) 

# C:Q HoursSlept
ggplot(data = df, aes(x = user_type, y = HoursSlept, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Hours Slept", title = "Box Plot: Usertype vs Hours Slept Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = HoursSlept, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Hours Slept", title = "Box Plot: Usertype vs Hours Slept Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = HoursSlept, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Hours Slept", y = "Density", title = "Density Plot: Hours Slept Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = HoursSlept, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Hours Slept", y = "Density", title = "Density Plot: Hours Slept Categorized by Minutes", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = HoursSlept, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=HoursSlept)) + 
  labs(x = "Hours Slept", y = "Probability", title = "CDF: Hours Slept Categorized by Minutes", color = "UserType") +
  #facet_wrap( ~ user_type) + 
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18) 

# C:Q Intensity
ggplot(data = df, aes(x = user_type, y = TotalIntensity, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  labs(x = "UserType", y = "Intensity", title = "Box Plot: Usertype vs Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = TotalIntensity, fill = user_type)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Intensity", title = "Box Plot: Usertype vs Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalIntensity, group = user_type, fill = user_type)) + 
  geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Intensity", y = "Density", title = "Density Plot: Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18)

ggplot(data = df, aes(x = TotalIntensity, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  labs(x = "Intensity", y = "Density", title = "Density Plot: Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = TotalIntensity, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=HoursSlept)) + 
  labs(x = "Intensity", y = "Probability", title = "CDF: Intensity Categorized by Distance", color = "UserType") +
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18)

We notice that the Calories is least correlated with Hours Slept and most correlated with Total Distance traveled. Both of the Very Active and Moderately Active densities are bimodally distributed suggesting those that burn a large amount of calories are getting enough sleep or are sleep deprived. Interestingly, those that are Sedentary are most likely to oversleep (get more than 8 hours of daily sleep - seen by the CDF plot of Hours Slept and Stacked Density plot).

Additionally, we see a greater proportion of Sedentary population when binning by Distance compared to Sleep. This implies when we categorize by Distance, there is a greater weight at the tails of the activity levels, however there is not too much of a difference between the other plots.

The Stacked Density plots of Total Distance suggest most of the Sedentary population does not travel more than 3 miles (around 82% by the CDF plot) compared to about 6 miles for the Lightly Active population (at 75th percentile). The gap (width between groups) is narrowed for Moderately Active and Very Active populations. We also see the facet wrap of the Total Distance is Gaussian like for the Moderately Active group and highly skewed left. In contrast, the Total Distance for Sedentary population is skewed right. These plots suggest we that our categorization coincides with our intuition.

The violin and boxplots of the Calories coincide with our preliminary visualizations. The Very Active group is bimodal and the Sedentary group has the smallest third quartile. Getting light activity in the day increases your likelihood of burning more calories and it is not necessary to highly active to burn more than 2000 calories.

Supplemental Visualizations (Binning by Distance)

Similar visualizations can be obtained when binning by distance instead of minutes:

# Categorize by Distance
df <- df_tot_user_dist
ggplot(data = df, aes(x = user_type, y = Calories, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Calories", title = "Box Plot: Usertype vs Calories Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = Calories, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Calories", title = "Box Plot: Usertype vs Calories Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = Calories, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = Calories, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Calories", y = "Density", title = "Density Plot: Calories Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = Calories, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = Calories, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Calories", y = "Density", title = "Density Plot: Calories Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = Calories, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=Calories)) + 
  labs(x = "Calories", y = "Probability", title = "CDF: Calories Categorized by Distance", color = "UserType") +
  #facet_wrap( ~ user_type) + 
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18)

# C:Q Intensity
ggplot(data = df, aes(x = user_type, y = TotalDistance, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Total Distance", title = "Box Plot: Usertype vs Total Distance Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = TotalDistance, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Total Distance", title = "Box Plot: Usertype vs Total Distance Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalDistance, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = TotalDistance, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Total Distance", y = "Density", title = "Density Plot: Total Distance Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalDistance, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = TotalDistance, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Total Distance", y = "Density", title = "Density Plot: Total Distance Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = TotalDistance, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=TotalDistance)) + 
  labs(x = "Total Distance", y = "Probability", title = "CDF: Total Distance Categorized by Distance", color = "UserType") +
  #facet_wrap( ~ user_type) + 
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18) 

# C:Q HoursSlept
ggplot(data = df, aes(x = user_type, y = HoursSlept, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  #geom_boxplot(data = df, aes(x = user_type)) + 
  labs(x = "UserType", y = "Hours Slept", title = "Box Plot: Usertype vs Hours Slept Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = HoursSlept, fill = user_type)) + 
  #geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Hours Slept", title = "Box Plot: Usertype vs Hours Slept Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = HoursSlept, group = user_type, fill = user_type)) + 
  #geom_density(position = "fill", adjust = 1.5) +
  geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Hours Slept", y = "Density", title = "Density Plot: Hours Slept Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18)

ggplot(data = df, aes(x = HoursSlept, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  #geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  labs(x = "Hours Slept", y = "Density", title = "Density Plot: Hours Slept Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = HoursSlept, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=HoursSlept)) + 
  labs(x = "Hours Slept", y = "Probability", title = "CDF: Hours Slept Categorized by Distance", color = "UserType") +
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18)

# C:Q Intensity
ggplot(data = df, aes(x = user_type, y = TotalIntensity, fill = user_type)) + 
  geom_violin(data = df, trim = FALSE, draw_quantiles = c(0.25, 0.5, 0.75)) + 
  labs(x = "UserType", y = "Intensity", title = "Box Plot: Usertype vs Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = user_type, y = TotalIntensity, fill = user_type)) + 
  geom_boxplot(data = df, aes(x = df$user_type)) + 
  labs(x = "UserType", y = "Intensity", title = "Box Plot: Usertype vs Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data = df, aes(x = TotalIntensity, group = user_type, fill = user_type)) + 
  geom_density(data = df, aes(x = HoursSlept, fill = df$user_type, alpha = 0.2)) + 
  facet_wrap( ~ user_type) +
  labs(x = "Intensity", y = "Density", title = "Density Plot: Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18)

ggplot(data = df, aes(x = TotalIntensity, group = user_type, fill = user_type)) + 
  geom_density(position = "fill", adjust = 1.5) +
  labs(x = "Intensity", y = "Density", title = "Density Plot: Intensity Categorized by Distance", fill = "UserType") +
  scale_fill_manual(values = viridis(n = length(unique(df$user_type)))) + 
  theme_linedraw(base_size = 18) 

ggplot(data=df, aes(x = TotalIntensity, color = df$user_type)) +
  stat_ecdf(data=df, geom = "step", aes(color=df$user_type, x=HoursSlept)) + 
  labs(x = "Intensity", y = "Probability", title = "CDF: Intensity Categorized by Distance", color = "UserType") +
  scale_color_manual(values = viridis(n = length(unique(df$user_type)))) +
  theme_linedraw(base_size = 18)