bellabeat_capstoneproject
Ritika Sood
2022-11-30
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.0 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.0
## ✔ readr 2.1.3 ✔ forcats 0.5.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(lubridate)## Loading required package: timechange
##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(dplyr)
library(ggplot2)
library(tidyr)
library(here)## here() starts at /cloud/projectlibrary(skimr)
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testImporting the dataset
Based on certain assumtions,I will VIEW, CLEAN, FORMAT, and ORGANIZE the data.
Activity <- read.csv("dailyActivity_merged.csv")
head(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 1728colnames(Activity)## [1] "Id" "ActivityDate"
## [3] "TotalSteps" "TotalDistance"
## [5] "TrackerDistance" "LoggedActivitiesDistance"
## [7] "VeryActiveDistance" "ModeratelyActiveDistance"
## [9] "LightActiveDistance" "SedentaryActiveDistance"
## [11] "VeryActiveMinutes" "FairlyActiveMinutes"
## [13] "LightlyActiveMinutes" "SedentaryMinutes"
## [15] "Calories"str(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 ...• dailyCalories_merged.csv
Calories <- read.csv("dailyCalories_merged.csv")
head(Calories)## Id ActivityDay Calories
## 1 1503960366 4/12/2016 1985
## 2 1503960366 4/13/2016 1797
## 3 1503960366 4/14/2016 1776
## 4 1503960366 4/15/2016 1745
## 5 1503960366 4/16/2016 1863
## 6 1503960366 4/17/2016 1728colnames(Calories)## [1] "Id" "ActivityDay" "Calories"str(Calories)## '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" ...
## $ Calories : int 1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...• dailyIntensities_merged.csv
Intensities <- read.csv("dailyIntensities_merged.csv")
head(Intensities)## Id ActivityDay SedentaryMinutes LightlyActiveMinutes
## 1 1503960366 4/12/2016 728 328
## 2 1503960366 4/13/2016 776 217
## 3 1503960366 4/14/2016 1218 181
## 4 1503960366 4/15/2016 726 209
## 5 1503960366 4/16/2016 773 221
## 6 1503960366 4/17/2016 539 164
## FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance
## 1 13 25 0
## 2 19 21 0
## 3 11 30 0
## 4 34 29 0
## 5 10 36 0
## 6 20 38 0
## LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
## 1 6.06 0.55 1.88
## 2 4.71 0.69 1.57
## 3 3.91 0.40 2.44
## 4 2.83 1.26 2.14
## 5 5.04 0.41 2.71
## 6 2.51 0.78 3.19colnames(Intensities)## [1] "Id" "ActivityDay"
## [3] "SedentaryMinutes" "LightlyActiveMinutes"
## [5] "FairlyActiveMinutes" "VeryActiveMinutes"
## [7] "SedentaryActiveDistance" "LightActiveDistance"
## [9] "ModeratelyActiveDistance" "VeryActiveDistance"str(Intensities)## 'data.frame': 940 obs. of 10 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" ...
## $ SedentaryMinutes : int 728 776 1218 726 773 539 1149 775 818 838 ...
## $ LightlyActiveMinutes : int 328 217 181 209 221 164 233 264 205 211 ...
## $ FairlyActiveMinutes : int 13 19 11 34 10 20 16 31 12 8 ...
## $ VeryActiveMinutes : int 25 21 30 29 36 38 42 50 28 19 ...
## $ SedentaryActiveDistance : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LightActiveDistance : num 6.06 4.71 3.91 2.83 5.04 ...
## $ ModeratelyActiveDistance: num 0.55 0.69 0.4 1.26 0.41 ...
## $ VeryActiveDistance : num 1.88 1.57 2.44 2.14 2.71 ...• heartrate_seconds_merged.csv
Heartrate <- read.csv("heartrate_seconds_merged.csv")
head(Heartrate)## Id Time Value
## 1 2022484408 4/12/2016 7:21:00 AM 97
## 2 2022484408 4/12/2016 7:21:05 AM 102
## 3 2022484408 4/12/2016 7:21:10 AM 105
## 4 2022484408 4/12/2016 7:21:20 AM 103
## 5 2022484408 4/12/2016 7:21:25 AM 101
## 6 2022484408 4/12/2016 7:22:05 AM 95colnames(Heartrate)## [1] "Id" "Time" "Value"str(Heartrate)## 'data.frame': 2483658 obs. of 3 variables:
## $ Id : num 2.02e+09 2.02e+09 2.02e+09 2.02e+09 2.02e+09 ...
## $ Time : chr "4/12/2016 7:21:00 AM" "4/12/2016 7:21:05 AM" "4/12/2016 7:21:10 AM" "4/12/2016 7:21:20 AM" ...
## $ Value: int 97 102 105 103 101 95 91 93 94 93 ...• sleepDay_merged.csv
Sleep <- read.csv("sleepDay_merged.csv")
head(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 320colnames(Sleep)## [1] "Id" "SleepDay" "TotalSleepRecords"
## [4] "TotalMinutesAsleep" "TotalTimeInBed"str(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 ...# removing the duplictes
Sleep[!duplicated(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
## 7 1503960366 4/20/2016 12:00:00 AM 1 360
## 8 1503960366 4/21/2016 12:00:00 AM 1 325
## 9 1503960366 4/23/2016 12:00:00 AM 1 361
## 10 1503960366 4/24/2016 12:00:00 AM 1 430
## 11 1503960366 4/25/2016 12:00:00 AM 1 277
## 12 1503960366 4/26/2016 12:00:00 AM 1 245
## 13 1503960366 4/28/2016 12:00:00 AM 1 366
## 14 1503960366 4/29/2016 12:00:00 AM 1 341
## 15 1503960366 4/30/2016 12:00:00 AM 1 404
## 16 1503960366 5/1/2016 12:00:00 AM 1 369
## 17 1503960366 5/2/2016 12:00:00 AM 1 277
## 18 1503960366 5/3/2016 12:00:00 AM 1 273
## 19 1503960366 5/5/2016 12:00:00 AM 1 247
## 20 1503960366 5/6/2016 12:00:00 AM 1 334
## 21 1503960366 5/7/2016 12:00:00 AM 1 331
## 22 1503960366 5/8/2016 12:00:00 AM 1 594
## 23 1503960366 5/9/2016 12:00:00 AM 1 338
## 24 1503960366 5/10/2016 12:00:00 AM 1 383
## 25 1503960366 5/11/2016 12:00:00 AM 1 285
## 26 1644430081 4/29/2016 12:00:00 AM 1 119
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## 30 1844505072 4/15/2016 12:00:00 AM 1 644
## 31 1844505072 4/30/2016 12:00:00 AM 1 722
## 32 1844505072 5/1/2016 12:00:00 AM 1 590
## 33 1927972279 4/12/2016 12:00:00 AM 3 750
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## 241 5553957443 4/24/2016 12:00:00 AM 2 553
## 242 5553957443 4/25/2016 12:00:00 AM 1 433
## 243 5553957443 4/26/2016 12:00:00 AM 1 412
## 244 5553957443 4/27/2016 12:00:00 AM 1 347
## 245 5553957443 4/28/2016 12:00:00 AM 1 421
## 246 5553957443 4/29/2016 12:00:00 AM 1 450
## 247 5553957443 4/30/2016 12:00:00 AM 2 775
## 248 5553957443 5/1/2016 12:00:00 AM 2 622
## 249 5553957443 5/2/2016 12:00:00 AM 1 409
## 250 5553957443 5/3/2016 12:00:00 AM 1 380
## 251 5553957443 5/4/2016 12:00:00 AM 1 447
## 252 5553957443 5/5/2016 12:00:00 AM 1 419
## 253 5553957443 5/6/2016 12:00:00 AM 1 400
## 254 5553957443 5/7/2016 12:00:00 AM 1 442
## 255 5553957443 5/8/2016 12:00:00 AM 1 568
## 256 5553957443 5/9/2016 12:00:00 AM 1 453
## 257 5553957443 5/10/2016 12:00:00 AM 1 418
## 258 5553957443 5/11/2016 12:00:00 AM 1 463
## 259 5553957443 5/12/2016 12:00:00 AM 1 438
## 260 5577150313 4/12/2016 12:00:00 AM 1 419
## 261 5577150313 4/13/2016 12:00:00 AM 1 432
## 262 5577150313 4/14/2016 12:00:00 AM 1 477
## 263 5577150313 4/15/2016 12:00:00 AM 1 392
## 264 5577150313 4/16/2016 12:00:00 AM 1 406
## 265 5577150313 4/17/2016 12:00:00 AM 1 549
## 266 5577150313 4/18/2016 12:00:00 AM 1 527
## 267 5577150313 4/19/2016 12:00:00 AM 1 449
## 268 5577150313 4/20/2016 12:00:00 AM 1 447
## 269 5577150313 4/21/2016 12:00:00 AM 1 414
## 270 5577150313 4/22/2016 12:00:00 AM 1 338
## 271 5577150313 4/23/2016 12:00:00 AM 1 384
## 272 5577150313 4/24/2016 12:00:00 AM 1 543
## 273 5577150313 4/25/2016 12:00:00 AM 1 421
## 274 5577150313 4/26/2016 12:00:00 AM 1 354
## 275 5577150313 4/27/2016 12:00:00 AM 1 424
## 276 5577150313 4/28/2016 12:00:00 AM 1 361
## 277 5577150313 4/29/2016 12:00:00 AM 1 459
## 278 5577150313 4/30/2016 12:00:00 AM 1 412
## 279 5577150313 5/1/2016 12:00:00 AM 1 379
## 280 5577150313 5/2/2016 12:00:00 AM 2 525
## 281 5577150313 5/3/2016 12:00:00 AM 1 508
## 282 5577150313 5/4/2016 12:00:00 AM 1 603
## 283 5577150313 5/5/2016 12:00:00 AM 1 74
## 284 5577150313 5/10/2016 12:00:00 AM 1 504
## 285 5577150313 5/11/2016 12:00:00 AM 1 431
## 286 6117666160 4/16/2016 12:00:00 AM 1 380
## 287 6117666160 4/17/2016 12:00:00 AM 2 336
## 288 6117666160 4/18/2016 12:00:00 AM 2 493
## 289 6117666160 4/19/2016 12:00:00 AM 1 465
## 290 6117666160 4/20/2016 12:00:00 AM 1 474
## 291 6117666160 4/21/2016 12:00:00 AM 1 508
## 292 6117666160 4/22/2016 12:00:00 AM 1 480
## 293 6117666160 4/23/2016 12:00:00 AM 1 492
## 294 6117666160 4/24/2016 12:00:00 AM 1 353
## 295 6117666160 4/27/2016 12:00:00 AM 1 542
## 296 6117666160 4/28/2016 12:00:00 AM 1 393
## 297 6117666160 4/29/2016 12:00:00 AM 1 600
## 298 6117666160 5/1/2016 12:00:00 AM 1 507
## 299 6117666160 5/5/2016 12:00:00 AM 1 392
## 300 6117666160 5/6/2016 12:00:00 AM 2 658
## 301 6117666160 5/7/2016 12:00:00 AM 2 498
## 302 6117666160 5/8/2016 12:00:00 AM 1 555
## 303 6117666160 5/9/2016 12:00:00 AM 1 492
## 304 6775888955 4/13/2016 12:00:00 AM 1 235
## 305 6775888955 4/14/2016 12:00:00 AM 1 423
## 306 6775888955 4/15/2016 12:00:00 AM 1 391
## 307 6962181067 4/12/2016 12:00:00 AM 1 366
## 308 6962181067 4/13/2016 12:00:00 AM 3 630
## 309 6962181067 4/14/2016 12:00:00 AM 2 508
## 310 6962181067 4/15/2016 12:00:00 AM 1 370
## 311 6962181067 4/16/2016 12:00:00 AM 1 357
## 312 6962181067 4/17/2016 12:00:00 AM 1 427
## 313 6962181067 4/18/2016 12:00:00 AM 1 442
## 314 6962181067 4/19/2016 12:00:00 AM 1 476
## 315 6962181067 4/20/2016 12:00:00 AM 1 418
## 316 6962181067 4/21/2016 12:00:00 AM 1 451
## 317 6962181067 4/22/2016 12:00:00 AM 1 425
## 318 6962181067 4/23/2016 12:00:00 AM 1 528
## 319 6962181067 4/24/2016 12:00:00 AM 1 511
## 320 6962181067 4/25/2016 12:00:00 AM 1 400
## 321 6962181067 4/26/2016 12:00:00 AM 1 441
## 322 6962181067 4/27/2016 12:00:00 AM 1 455
## 323 6962181067 4/28/2016 12:00:00 AM 1 440
## 324 6962181067 4/29/2016 12:00:00 AM 1 433
## 325 6962181067 4/30/2016 12:00:00 AM 1 422
## 326 6962181067 5/1/2016 12:00:00 AM 1 411
## 327 6962181067 5/2/2016 12:00:00 AM 1 466
## 328 6962181067 5/3/2016 12:00:00 AM 1 394
## 329 6962181067 5/4/2016 12:00:00 AM 1 442
## 330 6962181067 5/5/2016 12:00:00 AM 1 467
## 331 6962181067 5/6/2016 12:00:00 AM 1 443
## 332 6962181067 5/7/2016 12:00:00 AM 1 298
## 333 6962181067 5/8/2016 12:00:00 AM 1 541
## 334 6962181067 5/9/2016 12:00:00 AM 1 489
## 335 6962181067 5/10/2016 12:00:00 AM 1 469
## 336 6962181067 5/11/2016 12:00:00 AM 1 452
## 337 6962181067 5/12/2016 12:00:00 AM 1 516
## 338 7007744171 4/16/2016 12:00:00 AM 1 79
## 339 7007744171 5/1/2016 12:00:00 AM 1 58
## 340 7086361926 4/12/2016 12:00:00 AM 1 514
## 341 7086361926 4/13/2016 12:00:00 AM 1 451
## 342 7086361926 4/14/2016 12:00:00 AM 1 472
## 343 7086361926 4/15/2016 12:00:00 AM 1 377
## 344 7086361926 4/19/2016 12:00:00 AM 1 472
## 345 7086361926 4/20/2016 12:00:00 AM 1 492
## 346 7086361926 4/21/2016 12:00:00 AM 1 390
## 347 7086361926 4/22/2016 12:00:00 AM 1 428
## 348 7086361926 4/24/2016 12:00:00 AM 1 681
## 349 7086361926 4/25/2016 12:00:00 AM 1 446
## 350 7086361926 4/26/2016 12:00:00 AM 1 485
## 351 7086361926 4/27/2016 12:00:00 AM 1 469
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## 353 7086361926 4/30/2016 12:00:00 AM 1 485
## 354 7086361926 5/1/2016 12:00:00 AM 1 388
## 355 7086361926 5/2/2016 12:00:00 AM 1 440
## 356 7086361926 5/3/2016 12:00:00 AM 1 456
## 357 7086361926 5/4/2016 12:00:00 AM 1 420
## 358 7086361926 5/6/2016 12:00:00 AM 1 322
## 359 7086361926 5/7/2016 12:00:00 AM 1 530
## 360 7086361926 5/8/2016 12:00:00 AM 1 481
## 361 7086361926 5/9/2016 12:00:00 AM 1 427
## 362 7086361926 5/11/2016 12:00:00 AM 1 451
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## 364 8053475328 4/20/2016 12:00:00 AM 1 486
## 365 8053475328 4/23/2016 12:00:00 AM 1 331
## 366 8053475328 5/7/2016 12:00:00 AM 1 74
## 367 8378563200 4/12/2016 12:00:00 AM 1 338
## 368 8378563200 4/13/2016 12:00:00 AM 2 447
## 369 8378563200 4/14/2016 12:00:00 AM 1 424
## 370 8378563200 4/15/2016 12:00:00 AM 1 513
## 371 8378563200 4/16/2016 12:00:00 AM 2 611
## 372 8378563200 4/17/2016 12:00:00 AM 2 525
## 373 8378563200 4/18/2016 12:00:00 AM 1 398
## 374 8378563200 4/19/2016 12:00:00 AM 1 387
## 375 8378563200 4/20/2016 12:00:00 AM 1 381
## 376 8378563200 4/21/2016 12:00:00 AM 1 396
## 377 8378563200 4/22/2016 12:00:00 AM 1 441
## 378 8378563200 4/23/2016 12:00:00 AM 1 565
## 379 8378563200 4/24/2016 12:00:00 AM 1 458
## 380 8378563200 4/25/2016 12:00:00 AM 1 388
## 382 8378563200 4/26/2016 12:00:00 AM 1 550
## 383 8378563200 4/27/2016 12:00:00 AM 1 531
## 384 8378563200 4/28/2016 12:00:00 AM 1 506
## 385 8378563200 4/29/2016 12:00:00 AM 1 527
## 386 8378563200 4/30/2016 12:00:00 AM 1 468
## 387 8378563200 5/1/2016 12:00:00 AM 1 475
## 388 8378563200 5/2/2016 12:00:00 AM 1 351
## 389 8378563200 5/3/2016 12:00:00 AM 1 405
## 390 8378563200 5/4/2016 12:00:00 AM 1 441
## 391 8378563200 5/5/2016 12:00:00 AM 1 381
## 392 8378563200 5/6/2016 12:00:00 AM 1 323
## 393 8378563200 5/7/2016 12:00:00 AM 2 459
## 394 8378563200 5/8/2016 12:00:00 AM 1 545
## 395 8378563200 5/9/2016 12:00:00 AM 1 359
## 396 8378563200 5/10/2016 12:00:00 AM 1 342
## 397 8378563200 5/11/2016 12:00:00 AM 1 368
## 398 8378563200 5/12/2016 12:00:00 AM 1 496
## 399 8792009665 4/12/2016 12:00:00 AM 1 458
## 400 8792009665 4/13/2016 12:00:00 AM 1 531
## 401 8792009665 4/14/2016 12:00:00 AM 1 486
## 402 8792009665 4/15/2016 12:00:00 AM 1 363
## 403 8792009665 4/20/2016 12:00:00 AM 1 528
## 404 8792009665 4/22/2016 12:00:00 AM 1 391
## 405 8792009665 4/23/2016 12:00:00 AM 1 339
## 406 8792009665 4/27/2016 12:00:00 AM 1 423
## 407 8792009665 4/28/2016 12:00:00 AM 1 402
## 408 8792009665 4/29/2016 12:00:00 AM 1 398
## 409 8792009665 4/30/2016 12:00:00 AM 1 343
## 410 8792009665 5/1/2016 12:00:00 AM 1 503
## 411 8792009665 5/2/2016 12:00:00 AM 1 415
## 412 8792009665 5/3/2016 12:00:00 AM 1 516
## 413 8792009665 5/4/2016 12:00:00 AM 1 439
## TotalTimeInBed
## 1 346
## 2 407
## 3 442
## 4 367
## 5 712
## 6 320
## 7 377
## 8 364
## 9 384
## 10 449
## 11 323
## 12 274
## 13 393
## 14 354
## 15 425
## 16 396
## 17 309
## 18 296
## 19 264
## 20 367
## 21 349
## 22 611
## 23 342
## 24 403
## 25 306
## 26 127
## 27 142
## 28 961
## 29 154
## 30 961
## 31 961
## 32 961
## 33 775
## 34 422
## 35 499
## 36 315
## 37 178
## 38 546
## 39 565
## 40 568
## 41 573
## 42 567
## 43 498
## 44 540
## 45 510
## 46 514
## 47 545
## 48 554
## 49 591
## 50 531
## 51 545
## 52 545
## 53 510
## 54 607
## 55 546
## 56 543
## 57 560
## 58 485
## 59 548
## 60 521
## 61 568
## 62 556
## 63 380
## 64 553
## 65 485
## 66 69
## 67 531
## 68 489
## 69 504
## 70 602
## 71 557
## 72 514
## 73 484
## 74 461
## 75 386
## 76 459
## 77 471
## 78 490
## 79 499
## 80 450
## 81 473
## 82 469
## 83 456
## 84 397
## 85 556
## 86 510
## 87 566
## 88 522
## 89 395
## 90 305
## 91 512
## 92 476
## 93 372
## 94 526
## 95 467
## 96 371
## 97 540
## 98 423
## 99 478
## 100 382
## 101 626
## 102 384
## 103 500
## 104 336
## 105 480
## 106 512
## 107 443
## 108 456
## 109 452
## 110 541
## 111 77
## 112 332
## 113 536
## 114 248
## 115 408
## 116 402
## 117 494
## 118 557
## 119 491
## 120 522
## 121 551
## 122 498
## 123 543
## 124 65
## 125 550
## 126 722
## 127 501
## 128 506
## 129 516
## 130 307
## 131 522
## 132 546
## 133 516
## 134 500
## 135 506
## 136 512
## 137 491
## 138 530
## 139 638
## 140 565
## 141 517
## 142 558
## 143 321
## 144 526
## 145 448
## 146 641
## 147 104
## 148 338
## 149 451
## 150 458
## 151 85
## 152 501
## 153 595
## 154 346
## 155 500
## 156 458
## 157 430
## 158 597
## 159 376
## 160 414
## 161 495
## 163 496
## 164 541
## 165 65
## 166 375
## 167 494
## 168 457
## 169 406
## 170 492
## 171 379
## 172 499
## 173 107
## 174 424
## 175 462
## 176 469
## 177 417
## 178 345
## 179 391
## 180 374
## 181 442
## 182 108
## 183 353
## 184 459
## 185 542
## 186 450
## 187 363
## 188 513
## 189 402
## 190 436
## 191 391
## 192 533
## 193 426
## 194 530
## 195 501
## 196 137
## 197 121
## 198 179
## 199 129
## 200 134
## 201 439
## 202 430
## 203 415
## 204 257
## 205 406
## 206 612
## 207 312
## 208 487
## 209 468
## 210 434
## 211 475
## 212 506
## 213 380
## 214 429
## 215 449
## 216 461
## 217 447
## 218 501
## 219 373
## 220 434
## 221 428
## 222 449
## 223 543
## 225 458
## 226 431
## 227 366
## 228 442
## 229 464
## 230 488
## 231 418
## 232 409
## 233 686
## 234 402
## 235 541
## 236 410
## 237 678
## 238 431
## 239 353
## 240 725
## 241 640
## 242 468
## 243 453
## 244 391
## 245 457
## 246 495
## 247 843
## 248 686
## 249 471
## 250 429
## 251 470
## 252 464
## 253 434
## 254 470
## 255 608
## 256 494
## 257 443
## 258 486
## 259 475
## 260 438
## 261 458
## 262 497
## 263 413
## 264 445
## 265 583
## 266 553
## 267 465
## 268 480
## 269 437
## 270 366
## 271 402
## 272 615
## 273 461
## 274 377
## 275 452
## 276 372
## 277 485
## 278 433
## 279 398
## 280 553
## 281 543
## 282 634
## 283 78
## 284 562
## 285 476
## 286 398
## 287 350
## 288 510
## 289 492
## 290 502
## 291 550
## 292 546
## 293 539
## 294 367
## 295 557
## 296 416
## 297 636
## 298 575
## 299 415
## 300 698
## 301 507
## 302 603
## 303 522
## 304 260
## 305 441
## 306 406
## 307 387
## 308 679
## 309 535
## 310 386
## 311 366
## 312 446
## 313 458
## 314 535
## 315 424
## 316 457
## 317 435
## 318 546
## 319 514
## 320 415
## 321 446
## 322 467
## 323 453
## 324 447
## 325 424
## 326 426
## 327 482
## 328 418
## 329 455
## 330 491
## 331 462
## 332 334
## 333 569
## 334 497
## 335 481
## 336 480
## 337 535
## 338 82
## 339 61
## 340 525
## 341 465
## 342 476
## 343 386
## 344 483
## 345 502
## 346 411
## 347 448
## 348 704
## 349 447
## 350 500
## 351 479
## 352 367
## 353 489
## 354 407
## 355 459
## 356 461
## 357 436
## 358 333
## 359 548
## 360 510
## 361 438
## 362 463
## 363 457
## 364 493
## 365 337
## 366 75
## 367 356
## 368 487
## 369 455
## 370 533
## 371 689
## 372 591
## 373 451
## 374 421
## 375 409
## 376 417
## 377 469
## 378 591
## 379 492
## 380 402
## 382 584
## 383 600
## 384 556
## 385 562
## 386 555
## 387 539
## 388 385
## 389 429
## 390 477
## 391 417
## 392 355
## 393 513
## 394 606
## 395 399
## 396 391
## 397 387
## 398 546
## 399 493
## 400 552
## 401 503
## 402 377
## 403 547
## 404 407
## 405 360
## 406 428
## 407 416
## 408 406
## 409 360
## 410 527
## 411 423
## 412 545
## 413 463• weightLogInfo_merged.csv
Weight <- read.csv("weightLogInfo_merged.csv")
head(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+12colnames(Weight)## [1] "Id" "Date" "WeightKg" "WeightPounds"
## [5] "Fat" "BMI" "IsManualReport" "LogId"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 ...Cleaning the Dataset (Process phase)
You can also embed plots, for example:
## [1] "Id" "Date" "WeightKg" "WeightPounds"
## [5] "BMI" "IsManualReport" "LogId"## 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
## 7 1503960366 4/20/2016 12:00:00 AM 1 360
## 8 1503960366 4/21/2016 12:00:00 AM 1 325
## 9 1503960366 4/23/2016 12:00:00 AM 1 361
## 10 1503960366 4/24/2016 12:00:00 AM 1 430
## 11 1503960366 4/25/2016 12:00:00 AM 1 277
## 12 1503960366 4/26/2016 12:00:00 AM 1 245
## 13 1503960366 4/28/2016 12:00:00 AM 1 366
## 14 1503960366 4/29/2016 12:00:00 AM 1 341
## 15 1503960366 4/30/2016 12:00:00 AM 1 404
## 16 1503960366 5/1/2016 12:00:00 AM 1 369
## 17 1503960366 5/2/2016 12:00:00 AM 1 277
## 18 1503960366 5/3/2016 12:00:00 AM 1 273
## 19 1503960366 5/5/2016 12:00:00 AM 1 247
## 20 1503960366 5/6/2016 12:00:00 AM 1 334
## 21 1503960366 5/7/2016 12:00:00 AM 1 331
## 22 1503960366 5/8/2016 12:00:00 AM 1 594
## 23 1503960366 5/9/2016 12:00:00 AM 1 338
## 24 1503960366 5/10/2016 12:00:00 AM 1 383
## 25 1503960366 5/11/2016 12:00:00 AM 1 285
## 26 1644430081 4/29/2016 12:00:00 AM 1 119
## 27 1644430081 4/30/2016 12:00:00 AM 1 124
## 28 1644430081 5/2/2016 12:00:00 AM 1 796
## 29 1644430081 5/8/2016 12:00:00 AM 1 137
## 30 1844505072 4/15/2016 12:00:00 AM 1 644
## 31 1844505072 4/30/2016 12:00:00 AM 1 722
## 32 1844505072 5/1/2016 12:00:00 AM 1 590
## 33 1927972279 4/12/2016 12:00:00 AM 3 750
## 34 1927972279 4/13/2016 12:00:00 AM 1 398
## 35 1927972279 4/15/2016 12:00:00 AM 2 475
## 36 1927972279 4/26/2016 12:00:00 AM 1 296
## 37 1927972279 4/28/2016 12:00:00 AM 1 166
## 38 2026352035 4/12/2016 12:00:00 AM 1 503
## 39 2026352035 4/13/2016 12:00:00 AM 1 531
## 40 2026352035 4/14/2016 12:00:00 AM 1 545
## 41 2026352035 4/15/2016 12:00:00 AM 1 523
## 42 2026352035 4/16/2016 12:00:00 AM 1 524
## 43 2026352035 4/17/2016 12:00:00 AM 1 437
## 44 2026352035 4/19/2016 12:00:00 AM 1 498
## 45 2026352035 4/20/2016 12:00:00 AM 1 461
## 46 2026352035 4/21/2016 12:00:00 AM 1 477
## 47 2026352035 4/22/2016 12:00:00 AM 1 520
## 48 2026352035 4/23/2016 12:00:00 AM 1 522
## 49 2026352035 4/24/2016 12:00:00 AM 1 555
## 50 2026352035 4/25/2016 12:00:00 AM 1 506
## 51 2026352035 4/27/2016 12:00:00 AM 1 508
## 52 2026352035 4/28/2016 12:00:00 AM 1 513
## 53 2026352035 4/29/2016 12:00:00 AM 1 490
## 54 2026352035 4/30/2016 12:00:00 AM 1 573
## 55 2026352035 5/1/2016 12:00:00 AM 1 527
## 56 2026352035 5/2/2016 12:00:00 AM 1 511
## 57 2026352035 5/4/2016 12:00:00 AM 1 538
## 58 2026352035 5/5/2016 12:00:00 AM 1 468
## 59 2026352035 5/6/2016 12:00:00 AM 1 524
## 60 2026352035 5/7/2016 12:00:00 AM 1 511
## 61 2026352035 5/8/2016 12:00:00 AM 1 541
## 62 2026352035 5/9/2016 12:00:00 AM 1 531
## 63 2026352035 5/10/2016 12:00:00 AM 1 357
## 64 2026352035 5/11/2016 12:00:00 AM 1 523
## 65 2026352035 5/12/2016 12:00:00 AM 1 456
## 66 2320127002 4/23/2016 12:00:00 AM 1 61
## 67 2347167796 4/13/2016 12:00:00 AM 1 467
## 68 2347167796 4/14/2016 12:00:00 AM 1 445
## 69 2347167796 4/15/2016 12:00:00 AM 1 452
## 70 2347167796 4/17/2016 12:00:00 AM 1 556
## 71 2347167796 4/18/2016 12:00:00 AM 1 500
## 72 2347167796 4/19/2016 12:00:00 AM 1 465
## 73 2347167796 4/21/2016 12:00:00 AM 1 460
## 74 2347167796 4/22/2016 12:00:00 AM 1 405
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## 177 417
## 178 345
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## 413 463Analyse Phase
Summerize the Dataset Let’s look at the total number of participants in each dataset
Activity %>%
summarise(Activity_participants = n_distinct(Activity$Id))## Activity_participants
## 1 33n_distinct(Calories$Id)## [1] 33n_distinct(Intensities$Id)## [1] 33n_distinct(Heartrate$Id)## [1] 14n_distinct(Sleep$Id)## [1] 24n_distinct(Weight$Id)## [1] 8There are 33 participants in the activity, calories and intensities data sets. 24 participants in the Sleep data. 14 participants for Heartrate, and only 8 in the weight data set. 8 and 14 participants are not significant to make any recommendations and conclusions based on these dataset. so we select datasets for my analysis: Activity, Calories, Intensities and Sleep.
# Activity
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. :4900# Explore number of active minutes per category
Intensities %>%
select(VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes, SedentaryMinutes) %>%
summary()## VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:127.0 1st Qu.: 729.8
## Median : 4.00 Median : 6.00 Median :199.0 Median :1057.5
## Mean : 21.16 Mean : 13.56 Mean :192.8 Mean : 991.2
## 3rd Qu.: 32.00 3rd Qu.: 19.00 3rd Qu.:264.0 3rd Qu.:1229.5
## Max. :210.00 Max. :143.00 Max. :518.0 Max. :1440.0# Calories
Calories %>%
select(Calories) %>%
summary()## Calories
## Min. : 0
## 1st Qu.:1828
## Median :2134
## Mean :2304
## 3rd Qu.:2793
## Max. :4900# Sleep
Sleep %>%
select(TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed) %>%
summary()## TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
## Min. :1.000 Min. : 58.0 Min. : 61.0
## 1st Qu.:1.000 1st Qu.:361.0 1st Qu.:403.0
## Median :1.000 Median :433.0 Median :463.0
## Mean :1.119 Mean :419.5 Mean :458.6
## 3rd Qu.:1.000 3rd Qu.:490.0 3rd Qu.:526.0
## Max. :3.000 Max. :796.0 Max. :961.0# Weight
Weight %>%
select(WeightKg) %>%
summary()## WeightKg
## Min. : 52.60
## 1st Qu.: 61.40
## Median : 62.50
## Mean : 72.04
## 3rd Qu.: 85.05
## Max. :133.50From the above analysis, * we can observe average steps are 7638, which are less than recommended 10k steps. * sedentary time is too high, approximately 16hrs.Marketing strategy can focus on sedentary lifestyle of people. Same is the intensity. * Light active time is more than moderate and high intensity. *average sleep time is 7 hrs.
Merging data
This is all the participants as sleep has less participants
Combined_data_outer <- merge(Sleep, Activity, by="Id", all = TRUE)
n_distinct(Combined_data_outer$Id)## [1] 33Share and act (Data visualisation)
Relationship between total steps and sedentary time
ggplot(data=Activity, aes(x=TotalSteps, y=SedentaryMinutes)) + geom_point() + geom_smooth() + labs(title="Total steps VS Sedentary Minutes ")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Here we can see there negative correlation between steps and sedentary
time. As sedentary time increases total steps decreases. So the company
needs to focus on how to decrease sedentary time either through negative
reinforcement.
Relationship between Minutes Asleep and Time in Bed
Relationship between minutes asleep and time in bed?
ggplot(data=Sleep, aes(x=TotalMinutesAsleep, y=TotalTimeInBed)) + geom_point()+ geom_smooth() + labs(title="Minutes Asleep VS Time in bed")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
There is linear relationship. Better time in bed leads to better sleep.
So, setting appropriate bed time can help lead better sleep.
Relationship between Steps and Calories
Relationship between steps taken and Calories
ggplot(data=Activity, aes(x=TotalSteps, y=Calories)) +
geom_point() + geom_smooth() + labs(title="Total Steps vs. Calories")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Here we can see as steps increases more calories are burned. #
Intensities data
Intensities$ActiveIntensity <- (Intensities$VeryActiveMinutes)/60
Combined_data <- merge(Weight, Intensities, by="Id", all=TRUE)
Combined_data$time <- format(Combined_data$Date, format = "%H:%M:%S")
ggplot(data=Combined_data, aes(x=time, y=ActiveIntensity)) + geom_histogram(stat = "identity", fill='darkblue') +
theme(axis.text.x = element_text(angle = 90)) +
labs(title="Total very Active Intensity vs. Time ")## Warning in geom_histogram(stat = "identity", fill = "darkblue"): Ignoring
## unknown parameters: `binwidth`, `bins`, and `pad`
People are mostly active before and after the work.
Conclusion
With time Bellabeat has become leading wearable device. It can significantly help in changing lifestyle of people.
Recommendation
- The data shows high sedantary time. The Bellabeat device can help reduce this time among health conscious people either through nudge or any other negative reinforcement.
*The average sleep time is 7hrs. The quality of sleep can be improved either by devising method to measure deep sleep or notification to go to bed.
*The average steps is less than 8000 whereas 10k steps are recommended in general.
*Customised diet can be recommended as per calorie intake.
*High intensity activity is less than light intensity activity. Target can be set for gradually increasing intensity of activity.