One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, our goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants.
To do this, we are going to predict the manner in which they did the
exercise, this is the classe variable in the training
set.
The training and testing data for this project are provided here and here, respectively.
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, our goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here.
Set up directory for downloading data. The trainingURL
and testingURL variables contain the links to the training
and test data, respectively.
dir.create("./data", showWarnings = FALSE)
# data url
trainingURL <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
testingURL <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
# data file destinations
trainingFile <- "./data/pml-training.csv"
testingFile <- "./data/pml-testing.csv"Download the data:
#library(R.utils)
if(!file.exists(trainingFile)) {
download.file(trainingURL, trainingFile, method = "curl")
}
if(!file.exists(testingFile)) {
download.file(testingURL, testingFile, method = "curl")
}trainingData <- read.csv(trainingFile, na.strings=c("NA", "#DIV/0!", ""))
testingData <- read.csv(testingFile, na.strings=c("NA", "#DIV/0!", ""))Let’s take a quick look at our data:
head(trainingData)## X user_name raw_timestamp_part_1 raw_timestamp_part_2 cvtd_timestamp
## 1 1 carlitos 1323084231 788290 05/12/2011 11:23
## 2 2 carlitos 1323084231 808298 05/12/2011 11:23
## 3 3 carlitos 1323084231 820366 05/12/2011 11:23
## 4 4 carlitos 1323084232 120339 05/12/2011 11:23
## 5 5 carlitos 1323084232 196328 05/12/2011 11:23
## 6 6 carlitos 1323084232 304277 05/12/2011 11:23
## new_window num_window roll_belt pitch_belt yaw_belt total_accel_belt
## 1 no 11 1.41 8.07 -94.4 3
## 2 no 11 1.41 8.07 -94.4 3
## 3 no 11 1.42 8.07 -94.4 3
## 4 no 12 1.48 8.05 -94.4 3
## 5 no 12 1.48 8.07 -94.4 3
## 6 no 12 1.45 8.06 -94.4 3
## kurtosis_roll_belt kurtosis_picth_belt kurtosis_yaw_belt skewness_roll_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## skewness_roll_belt.1 skewness_yaw_belt max_roll_belt max_picth_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## max_yaw_belt min_roll_belt min_pitch_belt min_yaw_belt amplitude_roll_belt
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## amplitude_pitch_belt amplitude_yaw_belt var_total_accel_belt avg_roll_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_roll_belt var_roll_belt avg_pitch_belt stddev_pitch_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## var_pitch_belt avg_yaw_belt stddev_yaw_belt var_yaw_belt gyros_belt_x
## 1 NA NA NA NA 0.00
## 2 NA NA NA NA 0.02
## 3 NA NA NA NA 0.00
## 4 NA NA NA NA 0.02
## 5 NA NA NA NA 0.02
## 6 NA NA NA NA 0.02
## gyros_belt_y gyros_belt_z accel_belt_x accel_belt_y accel_belt_z
## 1 0.00 -0.02 -21 4 22
## 2 0.00 -0.02 -22 4 22
## 3 0.00 -0.02 -20 5 23
## 4 0.00 -0.03 -22 3 21
## 5 0.02 -0.02 -21 2 24
## 6 0.00 -0.02 -21 4 21
## magnet_belt_x magnet_belt_y magnet_belt_z roll_arm pitch_arm yaw_arm
## 1 -3 599 -313 -128 22.5 -161
## 2 -7 608 -311 -128 22.5 -161
## 3 -2 600 -305 -128 22.5 -161
## 4 -6 604 -310 -128 22.1 -161
## 5 -6 600 -302 -128 22.1 -161
## 6 0 603 -312 -128 22.0 -161
## total_accel_arm var_accel_arm avg_roll_arm stddev_roll_arm var_roll_arm
## 1 34 NA NA NA NA
## 2 34 NA NA NA NA
## 3 34 NA NA NA NA
## 4 34 NA NA NA NA
## 5 34 NA NA NA NA
## 6 34 NA NA NA NA
## avg_pitch_arm stddev_pitch_arm var_pitch_arm avg_yaw_arm stddev_yaw_arm
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## var_yaw_arm gyros_arm_x gyros_arm_y gyros_arm_z accel_arm_x accel_arm_y
## 1 NA 0.00 0.00 -0.02 -288 109
## 2 NA 0.02 -0.02 -0.02 -290 110
## 3 NA 0.02 -0.02 -0.02 -289 110
## 4 NA 0.02 -0.03 0.02 -289 111
## 5 NA 0.00 -0.03 0.00 -289 111
## 6 NA 0.02 -0.03 0.00 -289 111
## accel_arm_z magnet_arm_x magnet_arm_y magnet_arm_z kurtosis_roll_arm
## 1 -123 -368 337 516 NA
## 2 -125 -369 337 513 NA
## 3 -126 -368 344 513 NA
## 4 -123 -372 344 512 NA
## 5 -123 -374 337 506 NA
## 6 -122 -369 342 513 NA
## kurtosis_picth_arm kurtosis_yaw_arm skewness_roll_arm skewness_pitch_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## skewness_yaw_arm max_roll_arm max_picth_arm max_yaw_arm min_roll_arm
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## min_pitch_arm min_yaw_arm amplitude_roll_arm amplitude_pitch_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## amplitude_yaw_arm roll_dumbbell pitch_dumbbell yaw_dumbbell
## 1 NA 13.05217 -70.49400 -84.87394
## 2 NA 13.13074 -70.63751 -84.71065
## 3 NA 12.85075 -70.27812 -85.14078
## 4 NA 13.43120 -70.39379 -84.87363
## 5 NA 13.37872 -70.42856 -84.85306
## 6 NA 13.38246 -70.81759 -84.46500
## kurtosis_roll_dumbbell kurtosis_picth_dumbbell kurtosis_yaw_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## skewness_roll_dumbbell skewness_pitch_dumbbell skewness_yaw_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## max_roll_dumbbell max_picth_dumbbell max_yaw_dumbbell min_roll_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## min_pitch_dumbbell min_yaw_dumbbell amplitude_roll_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## amplitude_pitch_dumbbell amplitude_yaw_dumbbell total_accel_dumbbell
## 1 NA NA 37
## 2 NA NA 37
## 3 NA NA 37
## 4 NA NA 37
## 5 NA NA 37
## 6 NA NA 37
## var_accel_dumbbell avg_roll_dumbbell stddev_roll_dumbbell var_roll_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_dumbbell stddev_pitch_dumbbell var_pitch_dumbbell avg_yaw_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_dumbbell var_yaw_dumbbell gyros_dumbbell_x gyros_dumbbell_y
## 1 NA NA 0 -0.02
## 2 NA NA 0 -0.02
## 3 NA NA 0 -0.02
## 4 NA NA 0 -0.02
## 5 NA NA 0 -0.02
## 6 NA NA 0 -0.02
## gyros_dumbbell_z accel_dumbbell_x accel_dumbbell_y accel_dumbbell_z
## 1 0.00 -234 47 -271
## 2 0.00 -233 47 -269
## 3 0.00 -232 46 -270
## 4 -0.02 -232 48 -269
## 5 0.00 -233 48 -270
## 6 0.00 -234 48 -269
## magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z roll_forearm
## 1 -559 293 -65 28.4
## 2 -555 296 -64 28.3
## 3 -561 298 -63 28.3
## 4 -552 303 -60 28.1
## 5 -554 292 -68 28.0
## 6 -558 294 -66 27.9
## pitch_forearm yaw_forearm kurtosis_roll_forearm kurtosis_picth_forearm
## 1 -63.9 -153 NA NA
## 2 -63.9 -153 NA NA
## 3 -63.9 -152 NA NA
## 4 -63.9 -152 NA NA
## 5 -63.9 -152 NA NA
## 6 -63.9 -152 NA NA
## kurtosis_yaw_forearm skewness_roll_forearm skewness_pitch_forearm
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## skewness_yaw_forearm max_roll_forearm max_picth_forearm max_yaw_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## min_roll_forearm min_pitch_forearm min_yaw_forearm amplitude_roll_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## amplitude_pitch_forearm amplitude_yaw_forearm total_accel_forearm
## 1 NA NA 36
## 2 NA NA 36
## 3 NA NA 36
## 4 NA NA 36
## 5 NA NA 36
## 6 NA NA 36
## var_accel_forearm avg_roll_forearm stddev_roll_forearm var_roll_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_forearm stddev_pitch_forearm var_pitch_forearm avg_yaw_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_forearm var_yaw_forearm gyros_forearm_x gyros_forearm_y
## 1 NA NA 0.03 0.00
## 2 NA NA 0.02 0.00
## 3 NA NA 0.03 -0.02
## 4 NA NA 0.02 -0.02
## 5 NA NA 0.02 0.00
## 6 NA NA 0.02 -0.02
## gyros_forearm_z accel_forearm_x accel_forearm_y accel_forearm_z
## 1 -0.02 192 203 -215
## 2 -0.02 192 203 -216
## 3 0.00 196 204 -213
## 4 0.00 189 206 -214
## 5 -0.02 189 206 -214
## 6 -0.03 193 203 -215
## magnet_forearm_x magnet_forearm_y magnet_forearm_z classe
## 1 -17 654 476 A
## 2 -18 661 473 A
## 3 -18 658 469 A
## 4 -16 658 469 A
## 5 -17 655 473 A
## 6 -9 660 478 Ahead(testingData)## X user_name raw_timestamp_part_1 raw_timestamp_part_2 cvtd_timestamp
## 1 1 pedro 1323095002 868349 05/12/2011 14:23
## 2 2 jeremy 1322673067 778725 30/11/2011 17:11
## 3 3 jeremy 1322673075 342967 30/11/2011 17:11
## 4 4 adelmo 1322832789 560311 02/12/2011 13:33
## 5 5 eurico 1322489635 814776 28/11/2011 14:13
## 6 6 jeremy 1322673149 510661 30/11/2011 17:12
## new_window num_window roll_belt pitch_belt yaw_belt total_accel_belt
## 1 no 74 123.00 27.00 -4.75 20
## 2 no 431 1.02 4.87 -88.90 4
## 3 no 439 0.87 1.82 -88.50 5
## 4 no 194 125.00 -41.60 162.00 17
## 5 no 235 1.35 3.33 -88.60 3
## 6 no 504 -5.92 1.59 -87.70 4
## kurtosis_roll_belt kurtosis_picth_belt kurtosis_yaw_belt skewness_roll_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## skewness_roll_belt.1 skewness_yaw_belt max_roll_belt max_picth_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## max_yaw_belt min_roll_belt min_pitch_belt min_yaw_belt amplitude_roll_belt
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## amplitude_pitch_belt amplitude_yaw_belt var_total_accel_belt avg_roll_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_roll_belt var_roll_belt avg_pitch_belt stddev_pitch_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## var_pitch_belt avg_yaw_belt stddev_yaw_belt var_yaw_belt gyros_belt_x
## 1 NA NA NA NA -0.50
## 2 NA NA NA NA -0.06
## 3 NA NA NA NA 0.05
## 4 NA NA NA NA 0.11
## 5 NA NA NA NA 0.03
## 6 NA NA NA NA 0.10
## gyros_belt_y gyros_belt_z accel_belt_x accel_belt_y accel_belt_z
## 1 -0.02 -0.46 -38 69 -179
## 2 -0.02 -0.07 -13 11 39
## 3 0.02 0.03 1 -1 49
## 4 0.11 -0.16 46 45 -156
## 5 0.02 0.00 -8 4 27
## 6 0.05 -0.13 -11 -16 38
## magnet_belt_x magnet_belt_y magnet_belt_z roll_arm pitch_arm yaw_arm
## 1 -13 581 -382 40.7 -27.80 178
## 2 43 636 -309 0.0 0.00 0
## 3 29 631 -312 0.0 0.00 0
## 4 169 608 -304 -109.0 55.00 -142
## 5 33 566 -418 76.1 2.76 102
## 6 31 638 -291 0.0 0.00 0
## total_accel_arm var_accel_arm avg_roll_arm stddev_roll_arm var_roll_arm
## 1 10 NA NA NA NA
## 2 38 NA NA NA NA
## 3 44 NA NA NA NA
## 4 25 NA NA NA NA
## 5 29 NA NA NA NA
## 6 14 NA NA NA NA
## avg_pitch_arm stddev_pitch_arm var_pitch_arm avg_yaw_arm stddev_yaw_arm
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## var_yaw_arm gyros_arm_x gyros_arm_y gyros_arm_z accel_arm_x accel_arm_y
## 1 NA -1.65 0.48 -0.18 16 38
## 2 NA -1.17 0.85 -0.43 -290 215
## 3 NA 2.10 -1.36 1.13 -341 245
## 4 NA 0.22 -0.51 0.92 -238 -57
## 5 NA -1.96 0.79 -0.54 -197 200
## 6 NA 0.02 0.05 -0.07 -26 130
## accel_arm_z magnet_arm_x magnet_arm_y magnet_arm_z kurtosis_roll_arm
## 1 93 -326 385 481 NA
## 2 -90 -325 447 434 NA
## 3 -87 -264 474 413 NA
## 4 6 -173 257 633 NA
## 5 -30 -170 275 617 NA
## 6 -19 396 176 516 NA
## kurtosis_picth_arm kurtosis_yaw_arm skewness_roll_arm skewness_pitch_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## skewness_yaw_arm max_roll_arm max_picth_arm max_yaw_arm min_roll_arm
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## min_pitch_arm min_yaw_arm amplitude_roll_arm amplitude_pitch_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## amplitude_yaw_arm roll_dumbbell pitch_dumbbell yaw_dumbbell
## 1 NA -17.73748 24.96085 126.23596
## 2 NA 54.47761 -53.69758 -75.51480
## 3 NA 57.07031 -51.37303 -75.20287
## 4 NA 43.10927 -30.04885 -103.32003
## 5 NA -101.38396 -53.43952 -14.19542
## 6 NA 62.18750 -50.55595 -71.12063
## kurtosis_roll_dumbbell kurtosis_picth_dumbbell kurtosis_yaw_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## skewness_roll_dumbbell skewness_pitch_dumbbell skewness_yaw_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## max_roll_dumbbell max_picth_dumbbell max_yaw_dumbbell min_roll_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## min_pitch_dumbbell min_yaw_dumbbell amplitude_roll_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## amplitude_pitch_dumbbell amplitude_yaw_dumbbell total_accel_dumbbell
## 1 NA NA 9
## 2 NA NA 31
## 3 NA NA 29
## 4 NA NA 18
## 5 NA NA 4
## 6 NA NA 29
## var_accel_dumbbell avg_roll_dumbbell stddev_roll_dumbbell var_roll_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_dumbbell stddev_pitch_dumbbell var_pitch_dumbbell avg_yaw_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_dumbbell var_yaw_dumbbell gyros_dumbbell_x gyros_dumbbell_y
## 1 NA NA 0.64 0.06
## 2 NA NA 0.34 0.05
## 3 NA NA 0.39 0.14
## 4 NA NA 0.10 -0.02
## 5 NA NA 0.29 -0.47
## 6 NA NA -0.59 0.80
## gyros_dumbbell_z accel_dumbbell_x accel_dumbbell_y accel_dumbbell_z
## 1 -0.61 21 -15 81
## 2 -0.71 -153 155 -205
## 3 -0.34 -141 155 -196
## 4 0.05 -51 72 -148
## 5 -0.46 -18 -30 -5
## 6 1.10 -138 166 -186
## magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z roll_forearm
## 1 523 -528 -56 141
## 2 -502 388 -36 109
## 3 -506 349 41 131
## 4 -576 238 53 0
## 5 -424 252 312 -176
## 6 -543 262 96 150
## pitch_forearm yaw_forearm kurtosis_roll_forearm kurtosis_picth_forearm
## 1 49.30 156.0 NA NA
## 2 -17.60 106.0 NA NA
## 3 -32.60 93.0 NA NA
## 4 0.00 0.0 NA NA
## 5 -2.16 -47.9 NA NA
## 6 1.46 89.7 NA NA
## kurtosis_yaw_forearm skewness_roll_forearm skewness_pitch_forearm
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## skewness_yaw_forearm max_roll_forearm max_picth_forearm max_yaw_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## min_roll_forearm min_pitch_forearm min_yaw_forearm amplitude_roll_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## amplitude_pitch_forearm amplitude_yaw_forearm total_accel_forearm
## 1 NA NA 33
## 2 NA NA 39
## 3 NA NA 34
## 4 NA NA 43
## 5 NA NA 24
## 6 NA NA 43
## var_accel_forearm avg_roll_forearm stddev_roll_forearm var_roll_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_forearm stddev_pitch_forearm var_pitch_forearm avg_yaw_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_forearm var_yaw_forearm gyros_forearm_x gyros_forearm_y
## 1 NA NA 0.74 -3.34
## 2 NA NA 1.12 -2.78
## 3 NA NA 0.18 -0.79
## 4 NA NA 1.38 0.69
## 5 NA NA -0.75 3.10
## 6 NA NA -0.88 4.26
## gyros_forearm_z accel_forearm_x accel_forearm_y accel_forearm_z
## 1 -0.59 -110 267 -149
## 2 -0.18 212 297 -118
## 3 0.28 154 271 -129
## 4 1.80 -92 406 -39
## 5 0.80 131 -93 172
## 6 1.35 230 322 -144
## magnet_forearm_x magnet_forearm_y magnet_forearm_z problem_id
## 1 -714 419 617 1
## 2 -237 791 873 2
## 3 -51 698 783 3
## 4 -233 783 521 4
## 5 375 -787 91 5
## 6 -300 800 884 6Number of rows:
nRows <- nrow(trainingData)
nRows## [1] 19622The training data set is very sparsely populated. We should find the percentage of NAs in each column and the delete all the column in which the NA percentages greater than 50%.
# find the NA percentage in each column
naPer <- colSums(is.na(trainingData)) / nRows
# get the columns with the NA percentages less than 50%
lessNACols <- naPer < 0.5
# filter the data and reject all the columns having the large NA proportion
trainingData <- trainingData[,lessNACols]
testingData <- testingData[,lessNACols]Re-count the number of NA values within each column in the training data and find the columns that have NAs:
# check if a column has NAs
haveNA <- colSums(is.na(trainingData)) > 0
# get the columns with NAs
names(trainingData)[haveNA]## character(0)Fortunately, there is no column that has NAs.
head(trainingData)## X user_name raw_timestamp_part_1 raw_timestamp_part_2 cvtd_timestamp
## 1 1 carlitos 1323084231 788290 05/12/2011 11:23
## 2 2 carlitos 1323084231 808298 05/12/2011 11:23
## 3 3 carlitos 1323084231 820366 05/12/2011 11:23
## 4 4 carlitos 1323084232 120339 05/12/2011 11:23
## 5 5 carlitos 1323084232 196328 05/12/2011 11:23
## 6 6 carlitos 1323084232 304277 05/12/2011 11:23
## new_window num_window roll_belt pitch_belt yaw_belt total_accel_belt
## 1 no 11 1.41 8.07 -94.4 3
## 2 no 11 1.41 8.07 -94.4 3
## 3 no 11 1.42 8.07 -94.4 3
## 4 no 12 1.48 8.05 -94.4 3
## 5 no 12 1.48 8.07 -94.4 3
## 6 no 12 1.45 8.06 -94.4 3
## gyros_belt_x gyros_belt_y gyros_belt_z accel_belt_x accel_belt_y accel_belt_z
## 1 0.00 0.00 -0.02 -21 4 22
## 2 0.02 0.00 -0.02 -22 4 22
## 3 0.00 0.00 -0.02 -20 5 23
## 4 0.02 0.00 -0.03 -22 3 21
## 5 0.02 0.02 -0.02 -21 2 24
## 6 0.02 0.00 -0.02 -21 4 21
## magnet_belt_x magnet_belt_y magnet_belt_z roll_arm pitch_arm yaw_arm
## 1 -3 599 -313 -128 22.5 -161
## 2 -7 608 -311 -128 22.5 -161
## 3 -2 600 -305 -128 22.5 -161
## 4 -6 604 -310 -128 22.1 -161
## 5 -6 600 -302 -128 22.1 -161
## 6 0 603 -312 -128 22.0 -161
## total_accel_arm gyros_arm_x gyros_arm_y gyros_arm_z accel_arm_x accel_arm_y
## 1 34 0.00 0.00 -0.02 -288 109
## 2 34 0.02 -0.02 -0.02 -290 110
## 3 34 0.02 -0.02 -0.02 -289 110
## 4 34 0.02 -0.03 0.02 -289 111
## 5 34 0.00 -0.03 0.00 -289 111
## 6 34 0.02 -0.03 0.00 -289 111
## accel_arm_z magnet_arm_x magnet_arm_y magnet_arm_z roll_dumbbell
## 1 -123 -368 337 516 13.05217
## 2 -125 -369 337 513 13.13074
## 3 -126 -368 344 513 12.85075
## 4 -123 -372 344 512 13.43120
## 5 -123 -374 337 506 13.37872
## 6 -122 -369 342 513 13.38246
## pitch_dumbbell yaw_dumbbell total_accel_dumbbell gyros_dumbbell_x
## 1 -70.49400 -84.87394 37 0
## 2 -70.63751 -84.71065 37 0
## 3 -70.27812 -85.14078 37 0
## 4 -70.39379 -84.87363 37 0
## 5 -70.42856 -84.85306 37 0
## 6 -70.81759 -84.46500 37 0
## gyros_dumbbell_y gyros_dumbbell_z accel_dumbbell_x accel_dumbbell_y
## 1 -0.02 0.00 -234 47
## 2 -0.02 0.00 -233 47
## 3 -0.02 0.00 -232 46
## 4 -0.02 -0.02 -232 48
## 5 -0.02 0.00 -233 48
## 6 -0.02 0.00 -234 48
## accel_dumbbell_z magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z
## 1 -271 -559 293 -65
## 2 -269 -555 296 -64
## 3 -270 -561 298 -63
## 4 -269 -552 303 -60
## 5 -270 -554 292 -68
## 6 -269 -558 294 -66
## roll_forearm pitch_forearm yaw_forearm total_accel_forearm gyros_forearm_x
## 1 28.4 -63.9 -153 36 0.03
## 2 28.3 -63.9 -153 36 0.02
## 3 28.3 -63.9 -152 36 0.03
## 4 28.1 -63.9 -152 36 0.02
## 5 28.0 -63.9 -152 36 0.02
## 6 27.9 -63.9 -152 36 0.02
## gyros_forearm_y gyros_forearm_z accel_forearm_x accel_forearm_y
## 1 0.00 -0.02 192 203
## 2 0.00 -0.02 192 203
## 3 -0.02 0.00 196 204
## 4 -0.02 0.00 189 206
## 5 0.00 -0.02 189 206
## 6 -0.02 -0.03 193 203
## accel_forearm_z magnet_forearm_x magnet_forearm_y magnet_forearm_z classe
## 1 -215 -17 654 476 A
## 2 -216 -18 661 473 A
## 3 -213 -18 658 469 A
## 4 -214 -16 658 469 A
## 5 -214 -17 655 473 A
## 6 -215 -9 660 478 AFinally, we will delete some more useless (or irrelevant) columns. These are the first 7 columns which are the user names or time stamps when the user took the exercises.
trainingData <- trainingData[,-c(1:7)]
testingData <- testingData[,-c(1:7)]trainingData$classe = factor(trainingData$classe)We will split the training with the ratio of 4:1 which is 4 for the actual training data and 1 for the validation set.
library("caret")
inTrain <- createDataPartition(y=trainingData$classe, p=0.8, list=FALSE)
train <- trainingData[inTrain, ]
val <- trainingData[-inTrain, ]
dim(train)## [1] 15699 53dim(val) ## [1] 3923 53In this part, I will use 2 types of classifier:
Decision tree
and Random Forest
Fit the model:
library("rpart")
fitDT <- rpart(classe ~ ., data=train, method="class")Predict and evaluate on the val set:
predsDT <- predict(fitDT, val, type = "class")
mean(predsDT == val$classe)## [1] 0.7410145Fit the model:
library("randomForest")
set.seed(42)
fitRF <- randomForest(classe ~ ., data=train, ntree=500)Predict and evaluate on the val set:
predsRF <- predict(fitRF, val)
mean(predsRF == val$classe)## [1] 0.9964313Since the Random Forest model did very well on the
val set. We will use it as the final model to predict 20
test cases in the testingData set.
submission <- predict(fitRF, testingData)
submission## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## B A B A A E D B A A B C B A E E A B B B
## Levels: A B C D EHere are the libraries and their versions that I am using in this project:
data.frame(Library = c("caret", "rpart", "randomForest"),
Version = c(packageVersion("caret"), packageVersion("rpart"), packageVersion("randomForest")))## Library Version
## 1 caret 6.0.91
## 2 rpart 4.1.15
## 3 randomForest 4.7.1