Per Rynning
19 mai 2016
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, your 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: http://groupware.les.inf.puc-rio.br/har
The required R packages to reproduce this analysis are:
library(knitr)
library(caret)
library(doParallel)
registerDoParallel()The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
Unless the data is available in the current working directory, the files will be automaticallt downloaded.
## Downloads training data if not available in the current folder
if(!file.exists("pml-training.csv")){
download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv",
destfile = "pml-training.csv")
}
## Downloads testing data if not available in the current folder
if(!file.exists("pml-testing.csv")){
download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv",
destfile = "pml-testing.csv")
}The data was downloaded 19.05.2016 - 09:50. The data may change in the future.
We'll read the training and testing datasets. We will however refer to to test data as validation data from this point on, as we will pick out some additional testing data from the training data.
training <- read.csv(file = "pml-training.csv", header = TRUE)
validation <- read.csv(file = "pml-testing.csv", header = TRUE)##Exploratory Data Analysis
str(training)## 'data.frame': 19622 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : Factor w/ 6 levels "adelmo","carlitos",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ raw_timestamp_part_1 : int 1323084231 1323084231 1323084231 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 ...
## $ raw_timestamp_part_2 : int 788290 808298 820366 120339 196328 304277 368296 440390 484323 484434 ...
## $ cvtd_timestamp : Factor w/ 20 levels "02/12/2011 13:32",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ new_window : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ num_window : int 11 11 11 12 12 12 12 12 12 12 ...
## $ roll_belt : num 1.41 1.41 1.42 1.48 1.48 1.45 1.42 1.42 1.43 1.45 ...
## $ pitch_belt : num 8.07 8.07 8.07 8.05 8.07 8.06 8.09 8.13 8.16 8.17 ...
## $ yaw_belt : num -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 ...
## $ total_accel_belt : int 3 3 3 3 3 3 3 3 3 3 ...
## $ kurtosis_roll_belt : Factor w/ 397 levels "","-0.016850",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_belt : Factor w/ 317 levels "","-0.021887",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt : Factor w/ 395 levels "","-0.003095",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt.1 : Factor w/ 338 levels "","-0.005928",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_belt : Factor w/ 4 levels "","#DIV/0!","0.00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ var_total_accel_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_belt_x : num 0 0.02 0 0.02 0.02 0.02 0.02 0.02 0.02 0.03 ...
## $ gyros_belt_y : num 0 0 0 0 0.02 0 0 0 0 0 ...
## $ gyros_belt_z : num -0.02 -0.02 -0.02 -0.03 -0.02 -0.02 -0.02 -0.02 -0.02 0 ...
## $ accel_belt_x : int -21 -22 -20 -22 -21 -21 -22 -22 -20 -21 ...
## $ accel_belt_y : int 4 4 5 3 2 4 3 4 2 4 ...
## $ accel_belt_z : int 22 22 23 21 24 21 21 21 24 22 ...
## $ magnet_belt_x : int -3 -7 -2 -6 -6 0 -4 -2 1 -3 ...
## $ magnet_belt_y : int 599 608 600 604 600 603 599 603 602 609 ...
## $ magnet_belt_z : int -313 -311 -305 -310 -302 -312 -311 -313 -312 -308 ...
## $ roll_arm : num -128 -128 -128 -128 -128 -128 -128 -128 -128 -128 ...
## $ pitch_arm : num 22.5 22.5 22.5 22.1 22.1 22 21.9 21.8 21.7 21.6 ...
## $ yaw_arm : num -161 -161 -161 -161 -161 -161 -161 -161 -161 -161 ...
## $ total_accel_arm : int 34 34 34 34 34 34 34 34 34 34 ...
## $ var_accel_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_arm_x : num 0 0.02 0.02 0.02 0 0.02 0 0.02 0.02 0.02 ...
## $ gyros_arm_y : num 0 -0.02 -0.02 -0.03 -0.03 -0.03 -0.03 -0.02 -0.03 -0.03 ...
## $ gyros_arm_z : num -0.02 -0.02 -0.02 0.02 0 0 0 0 -0.02 -0.02 ...
## $ accel_arm_x : int -288 -290 -289 -289 -289 -289 -289 -289 -288 -288 ...
## $ accel_arm_y : int 109 110 110 111 111 111 111 111 109 110 ...
## $ accel_arm_z : int -123 -125 -126 -123 -123 -122 -125 -124 -122 -124 ...
## $ magnet_arm_x : int -368 -369 -368 -372 -374 -369 -373 -372 -369 -376 ...
## $ magnet_arm_y : int 337 337 344 344 337 342 336 338 341 334 ...
## $ magnet_arm_z : int 516 513 513 512 506 513 509 510 518 516 ...
## $ kurtosis_roll_arm : Factor w/ 330 levels "","-0.02438",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_arm : Factor w/ 328 levels "","-0.00484",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_arm : Factor w/ 395 levels "","-0.01548",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_arm : Factor w/ 331 levels "","-0.00051",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_arm : Factor w/ 328 levels "","-0.00184",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_arm : Factor w/ 395 levels "","-0.00311",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ roll_dumbbell : num 13.1 13.1 12.9 13.4 13.4 ...
## $ pitch_dumbbell : num -70.5 -70.6 -70.3 -70.4 -70.4 ...
## $ yaw_dumbbell : num -84.9 -84.7 -85.1 -84.9 -84.9 ...
## $ kurtosis_roll_dumbbell : Factor w/ 398 levels "","-0.0035","-0.0073",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_dumbbell : Factor w/ 401 levels "","-0.0163","-0.0233",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_dumbbell : Factor w/ 401 levels "","-0.0082","-0.0096",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_dumbbell : Factor w/ 402 levels "","-0.0053","-0.0084",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]
str(validation)## 'data.frame': 20 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : Factor w/ 6 levels "adelmo","carlitos",..: 6 5 5 1 4 5 5 5 2 3 ...
## $ raw_timestamp_part_1 : int 1323095002 1322673067 1322673075 1322832789 1322489635 1322673149 1322673128 1322673076 1323084240 1322837822 ...
## $ raw_timestamp_part_2 : int 868349 778725 342967 560311 814776 510661 766645 54671 916313 384285 ...
## $ cvtd_timestamp : Factor w/ 11 levels "02/12/2011 13:33",..: 5 10 10 1 6 11 11 10 3 2 ...
## $ new_window : Factor w/ 1 level "no": 1 1 1 1 1 1 1 1 1 1 ...
## $ num_window : int 74 431 439 194 235 504 485 440 323 664 ...
## $ roll_belt : num 123 1.02 0.87 125 1.35 -5.92 1.2 0.43 0.93 114 ...
## $ pitch_belt : num 27 4.87 1.82 -41.6 3.33 1.59 4.44 4.15 6.72 22.4 ...
## $ yaw_belt : num -4.75 -88.9 -88.5 162 -88.6 -87.7 -87.3 -88.5 -93.7 -13.1 ...
## $ total_accel_belt : int 20 4 5 17 3 4 4 4 4 18 ...
## $ kurtosis_roll_belt : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_belt : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_belt : logi NA NA NA NA NA NA ...
## $ skewness_roll_belt : logi NA NA NA NA NA NA ...
## $ skewness_roll_belt.1 : logi NA NA NA NA NA NA ...
## $ skewness_yaw_belt : logi NA NA NA NA NA NA ...
## $ max_roll_belt : logi NA NA NA NA NA NA ...
## $ max_picth_belt : logi NA NA NA NA NA NA ...
## $ max_yaw_belt : logi NA NA NA NA NA NA ...
## $ min_roll_belt : logi NA NA NA NA NA NA ...
## $ min_pitch_belt : logi NA NA NA NA NA NA ...
## $ min_yaw_belt : logi NA NA NA NA NA NA ...
## $ amplitude_roll_belt : logi NA NA NA NA NA NA ...
## $ amplitude_pitch_belt : logi NA NA NA NA NA NA ...
## $ amplitude_yaw_belt : logi NA NA NA NA NA NA ...
## $ var_total_accel_belt : logi NA NA NA NA NA NA ...
## $ avg_roll_belt : logi NA NA NA NA NA NA ...
## $ stddev_roll_belt : logi NA NA NA NA NA NA ...
## $ var_roll_belt : logi NA NA NA NA NA NA ...
## $ avg_pitch_belt : logi NA NA NA NA NA NA ...
## $ stddev_pitch_belt : logi NA NA NA NA NA NA ...
## $ var_pitch_belt : logi NA NA NA NA NA NA ...
## $ avg_yaw_belt : logi NA NA NA NA NA NA ...
## $ stddev_yaw_belt : logi NA NA NA NA NA NA ...
## $ var_yaw_belt : logi NA NA NA NA NA NA ...
## $ gyros_belt_x : num -0.5 -0.06 0.05 0.11 0.03 0.1 -0.06 -0.18 0.1 0.14 ...
## $ gyros_belt_y : num -0.02 -0.02 0.02 0.11 0.02 0.05 0 -0.02 0 0.11 ...
## $ gyros_belt_z : num -0.46 -0.07 0.03 -0.16 0 -0.13 0 -0.03 -0.02 -0.16 ...
## $ accel_belt_x : int -38 -13 1 46 -8 -11 -14 -10 -15 -25 ...
## $ accel_belt_y : int 69 11 -1 45 4 -16 2 -2 1 63 ...
## $ accel_belt_z : int -179 39 49 -156 27 38 35 42 32 -158 ...
## $ magnet_belt_x : int -13 43 29 169 33 31 50 39 -6 10 ...
## $ magnet_belt_y : int 581 636 631 608 566 638 622 635 600 601 ...
## $ magnet_belt_z : int -382 -309 -312 -304 -418 -291 -315 -305 -302 -330 ...
## $ roll_arm : num 40.7 0 0 -109 76.1 0 0 0 -137 -82.4 ...
## $ pitch_arm : num -27.8 0 0 55 2.76 0 0 0 11.2 -63.8 ...
## $ yaw_arm : num 178 0 0 -142 102 0 0 0 -167 -75.3 ...
## $ total_accel_arm : int 10 38 44 25 29 14 15 22 34 32 ...
## $ var_accel_arm : logi NA NA NA NA NA NA ...
## $ avg_roll_arm : logi NA NA NA NA NA NA ...
## $ stddev_roll_arm : logi NA NA NA NA NA NA ...
## $ var_roll_arm : logi NA NA NA NA NA NA ...
## $ avg_pitch_arm : logi NA NA NA NA NA NA ...
## $ stddev_pitch_arm : logi NA NA NA NA NA NA ...
## $ var_pitch_arm : logi NA NA NA NA NA NA ...
## $ avg_yaw_arm : logi NA NA NA NA NA NA ...
## $ stddev_yaw_arm : logi NA NA NA NA NA NA ...
## $ var_yaw_arm : logi NA NA NA NA NA NA ...
## $ gyros_arm_x : num -1.65 -1.17 2.1 0.22 -1.96 0.02 2.36 -3.71 0.03 0.26 ...
## $ gyros_arm_y : num 0.48 0.85 -1.36 -0.51 0.79 0.05 -1.01 1.85 -0.02 -0.5 ...
## $ gyros_arm_z : num -0.18 -0.43 1.13 0.92 -0.54 -0.07 0.89 -0.69 -0.02 0.79 ...
## $ accel_arm_x : int 16 -290 -341 -238 -197 -26 99 -98 -287 -301 ...
## $ accel_arm_y : int 38 215 245 -57 200 130 79 175 111 -42 ...
## $ accel_arm_z : int 93 -90 -87 6 -30 -19 -67 -78 -122 -80 ...
## $ magnet_arm_x : int -326 -325 -264 -173 -170 396 702 535 -367 -420 ...
## $ magnet_arm_y : int 385 447 474 257 275 176 15 215 335 294 ...
## $ magnet_arm_z : int 481 434 413 633 617 516 217 385 520 493 ...
## $ kurtosis_roll_arm : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_arm : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_arm : logi NA NA NA NA NA NA ...
## $ skewness_roll_arm : logi NA NA NA NA NA NA ...
## $ skewness_pitch_arm : logi NA NA NA NA NA NA ...
## $ skewness_yaw_arm : logi NA NA NA NA NA NA ...
## $ max_roll_arm : logi NA NA NA NA NA NA ...
## $ max_picth_arm : logi NA NA NA NA NA NA ...
## $ max_yaw_arm : logi NA NA NA NA NA NA ...
## $ min_roll_arm : logi NA NA NA NA NA NA ...
## $ min_pitch_arm : logi NA NA NA NA NA NA ...
## $ min_yaw_arm : logi NA NA NA NA NA NA ...
## $ amplitude_roll_arm : logi NA NA NA NA NA NA ...
## $ amplitude_pitch_arm : logi NA NA NA NA NA NA ...
## $ amplitude_yaw_arm : logi NA NA NA NA NA NA ...
## $ roll_dumbbell : num -17.7 54.5 57.1 43.1 -101.4 ...
## $ pitch_dumbbell : num 25 -53.7 -51.4 -30 -53.4 ...
## $ yaw_dumbbell : num 126.2 -75.5 -75.2 -103.3 -14.2 ...
## $ kurtosis_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ kurtosis_picth_dumbbell : logi NA NA NA NA NA NA ...
## $ kurtosis_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_pitch_dumbbell : logi NA NA NA NA NA NA ...
## $ skewness_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ max_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ max_picth_dumbbell : logi NA NA NA NA NA NA ...
## $ max_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ min_roll_dumbbell : logi NA NA NA NA NA NA ...
## $ min_pitch_dumbbell : logi NA NA NA NA NA NA ...
## $ min_yaw_dumbbell : logi NA NA NA NA NA NA ...
## $ amplitude_roll_dumbbell : logi NA NA NA NA NA NA ...
## [list output truncated]
Let's have a look at the training data and see if there are any Zero, or Near-zero covariates:
nzv <- nearZeroVar(training,
saveMetrics=TRUE)
nzv## freqRatio percentUnique zeroVar nzv
## X 1.000000 100.00000000 FALSE FALSE
## user_name 1.100679 0.03057792 FALSE FALSE
## raw_timestamp_part_1 1.000000 4.26562022 FALSE FALSE
## raw_timestamp_part_2 1.000000 85.53154622 FALSE FALSE
## cvtd_timestamp 1.000668 0.10192641 FALSE FALSE
## new_window 47.330049 0.01019264 FALSE TRUE
## num_window 1.000000 4.37264295 FALSE FALSE
## roll_belt 1.101904 6.77810621 FALSE FALSE
## pitch_belt 1.036082 9.37722964 FALSE FALSE
## yaw_belt 1.058480 9.97349913 FALSE FALSE
## total_accel_belt 1.063160 0.14779329 FALSE FALSE
## kurtosis_roll_belt 1921.600000 2.02323922 FALSE TRUE
## kurtosis_picth_belt 600.500000 1.61553358 FALSE TRUE
## kurtosis_yaw_belt 47.330049 0.01019264 FALSE TRUE
## skewness_roll_belt 2135.111111 2.01304658 FALSE TRUE
## skewness_roll_belt.1 600.500000 1.72255631 FALSE TRUE
## skewness_yaw_belt 47.330049 0.01019264 FALSE TRUE
## max_roll_belt 1.000000 0.99378249 FALSE FALSE
## max_picth_belt 1.538462 0.11211905 FALSE FALSE
## max_yaw_belt 640.533333 0.34654979 FALSE TRUE
## min_roll_belt 1.000000 0.93772296 FALSE FALSE
## min_pitch_belt 2.192308 0.08154113 FALSE FALSE
## min_yaw_belt 640.533333 0.34654979 FALSE TRUE
## amplitude_roll_belt 1.290323 0.75425543 FALSE FALSE
## amplitude_pitch_belt 3.042254 0.06625217 FALSE FALSE
## amplitude_yaw_belt 50.041667 0.02038528 FALSE TRUE
## var_total_accel_belt 1.426829 0.33126083 FALSE FALSE
## avg_roll_belt 1.066667 0.97339721 FALSE FALSE
## stddev_roll_belt 1.039216 0.35164611 FALSE FALSE
## var_roll_belt 1.615385 0.48924676 FALSE FALSE
## avg_pitch_belt 1.375000 1.09061258 FALSE FALSE
## stddev_pitch_belt 1.161290 0.21914178 FALSE FALSE
## var_pitch_belt 1.307692 0.32106819 FALSE FALSE
## avg_yaw_belt 1.200000 1.22311691 FALSE FALSE
## stddev_yaw_belt 1.693878 0.29558659 FALSE FALSE
## var_yaw_belt 1.500000 0.73896647 FALSE FALSE
## gyros_belt_x 1.058651 0.71348486 FALSE FALSE
## gyros_belt_y 1.144000 0.35164611 FALSE FALSE
## gyros_belt_z 1.066214 0.86127816 FALSE FALSE
## accel_belt_x 1.055412 0.83579655 FALSE FALSE
## accel_belt_y 1.113725 0.72877383 FALSE FALSE
## accel_belt_z 1.078767 1.52379982 FALSE FALSE
## magnet_belt_x 1.090141 1.66649679 FALSE FALSE
## magnet_belt_y 1.099688 1.51870350 FALSE FALSE
## magnet_belt_z 1.006369 2.32901845 FALSE FALSE
## roll_arm 52.338462 13.52563449 FALSE FALSE
## pitch_arm 87.256410 15.73234125 FALSE FALSE
## yaw_arm 33.029126 14.65701763 FALSE FALSE
## total_accel_arm 1.024526 0.33635715 FALSE FALSE
## var_accel_arm 5.500000 2.01304658 FALSE FALSE
## avg_roll_arm 77.000000 1.68178575 FALSE TRUE
## stddev_roll_arm 77.000000 1.68178575 FALSE TRUE
## var_roll_arm 77.000000 1.68178575 FALSE TRUE
## avg_pitch_arm 77.000000 1.68178575 FALSE TRUE
## stddev_pitch_arm 77.000000 1.68178575 FALSE TRUE
## var_pitch_arm 77.000000 1.68178575 FALSE TRUE
## avg_yaw_arm 77.000000 1.68178575 FALSE TRUE
## stddev_yaw_arm 80.000000 1.66649679 FALSE TRUE
## var_yaw_arm 80.000000 1.66649679 FALSE TRUE
## gyros_arm_x 1.015504 3.27693405 FALSE FALSE
## gyros_arm_y 1.454369 1.91621649 FALSE FALSE
## gyros_arm_z 1.110687 1.26388747 FALSE FALSE
## accel_arm_x 1.017341 3.95984099 FALSE FALSE
## accel_arm_y 1.140187 2.73672409 FALSE FALSE
## accel_arm_z 1.128000 4.03628580 FALSE FALSE
## magnet_arm_x 1.000000 6.82397309 FALSE FALSE
## magnet_arm_y 1.056818 4.44399144 FALSE FALSE
## magnet_arm_z 1.036364 6.44684538 FALSE FALSE
## kurtosis_roll_arm 246.358974 1.68178575 FALSE TRUE
## kurtosis_picth_arm 240.200000 1.67159311 FALSE TRUE
## kurtosis_yaw_arm 1746.909091 2.01304658 FALSE TRUE
## skewness_roll_arm 249.558442 1.68688207 FALSE TRUE
## skewness_pitch_arm 240.200000 1.67159311 FALSE TRUE
## skewness_yaw_arm 1746.909091 2.01304658 FALSE TRUE
## max_roll_arm 25.666667 1.47793293 FALSE TRUE
## max_picth_arm 12.833333 1.34033228 FALSE FALSE
## max_yaw_arm 1.227273 0.25991234 FALSE FALSE
## min_roll_arm 19.250000 1.41677709 FALSE TRUE
## min_pitch_arm 19.250000 1.47793293 FALSE TRUE
## min_yaw_arm 1.000000 0.19366018 FALSE FALSE
## amplitude_roll_arm 25.666667 1.55947406 FALSE TRUE
## amplitude_pitch_arm 20.000000 1.49831821 FALSE TRUE
## amplitude_yaw_arm 1.037037 0.25991234 FALSE FALSE
## roll_dumbbell 1.022388 84.20650290 FALSE FALSE
## pitch_dumbbell 2.277372 81.74498012 FALSE FALSE
## yaw_dumbbell 1.132231 83.48282540 FALSE FALSE
## kurtosis_roll_dumbbell 3843.200000 2.02833554 FALSE TRUE
## kurtosis_picth_dumbbell 9608.000000 2.04362450 FALSE TRUE
## kurtosis_yaw_dumbbell 47.330049 0.01019264 FALSE TRUE
## skewness_roll_dumbbell 4804.000000 2.04362450 FALSE TRUE
## skewness_pitch_dumbbell 9608.000000 2.04872082 FALSE TRUE
## skewness_yaw_dumbbell 47.330049 0.01019264 FALSE TRUE
## max_roll_dumbbell 1.000000 1.72255631 FALSE FALSE
## max_picth_dumbbell 1.333333 1.72765263 FALSE FALSE
## max_yaw_dumbbell 960.800000 0.37203139 FALSE TRUE
## min_roll_dumbbell 1.000000 1.69197839 FALSE FALSE
## min_pitch_dumbbell 1.666667 1.81429008 FALSE FALSE
## min_yaw_dumbbell 960.800000 0.37203139 FALSE TRUE
## amplitude_roll_dumbbell 8.000000 1.97227602 FALSE FALSE
## amplitude_pitch_dumbbell 8.000000 1.95189073 FALSE FALSE
## amplitude_yaw_dumbbell 47.920200 0.01528896 FALSE TRUE
## total_accel_dumbbell 1.072634 0.21914178 FALSE FALSE
## var_accel_dumbbell 6.000000 1.95698706 FALSE FALSE
## avg_roll_dumbbell 1.000000 2.02323922 FALSE FALSE
## stddev_roll_dumbbell 16.000000 1.99266130 FALSE FALSE
## var_roll_dumbbell 16.000000 1.99266130 FALSE FALSE
## avg_pitch_dumbbell 1.000000 2.02323922 FALSE FALSE
## stddev_pitch_dumbbell 16.000000 1.99266130 FALSE FALSE
## var_pitch_dumbbell 16.000000 1.99266130 FALSE FALSE
## avg_yaw_dumbbell 1.000000 2.02323922 FALSE FALSE
## stddev_yaw_dumbbell 16.000000 1.99266130 FALSE FALSE
## var_yaw_dumbbell 16.000000 1.99266130 FALSE FALSE
## gyros_dumbbell_x 1.003268 1.22821323 FALSE FALSE
## gyros_dumbbell_y 1.264957 1.41677709 FALSE FALSE
## gyros_dumbbell_z 1.060100 1.04984201 FALSE FALSE
## accel_dumbbell_x 1.018018 2.16593619 FALSE FALSE
## accel_dumbbell_y 1.053061 2.37488533 FALSE FALSE
## accel_dumbbell_z 1.133333 2.08949139 FALSE FALSE
## magnet_dumbbell_x 1.098266 5.74864948 FALSE FALSE
## magnet_dumbbell_y 1.197740 4.30129447 FALSE FALSE
## magnet_dumbbell_z 1.020833 3.44511263 FALSE FALSE
## roll_forearm 11.589286 11.08959331 FALSE FALSE
## pitch_forearm 65.983051 14.85577413 FALSE FALSE
## yaw_forearm 15.322835 10.14677403 FALSE FALSE
## kurtosis_roll_forearm 228.761905 1.64101519 FALSE TRUE
## kurtosis_picth_forearm 226.070588 1.64611151 FALSE TRUE
## kurtosis_yaw_forearm 47.330049 0.01019264 FALSE TRUE
## skewness_roll_forearm 231.518072 1.64611151 FALSE TRUE
## skewness_pitch_forearm 226.070588 1.62572623 FALSE TRUE
## skewness_yaw_forearm 47.330049 0.01019264 FALSE TRUE
## max_roll_forearm 27.666667 1.38110284 FALSE TRUE
## max_picth_forearm 2.964286 0.78992967 FALSE FALSE
## max_yaw_forearm 228.761905 0.22933442 FALSE TRUE
## min_roll_forearm 27.666667 1.37091020 FALSE TRUE
## min_pitch_forearm 2.862069 0.87147080 FALSE FALSE
## min_yaw_forearm 228.761905 0.22933442 FALSE TRUE
## amplitude_roll_forearm 20.750000 1.49322189 FALSE TRUE
## amplitude_pitch_forearm 3.269231 0.93262664 FALSE FALSE
## amplitude_yaw_forearm 59.677019 0.01528896 FALSE TRUE
## total_accel_forearm 1.128928 0.35674243 FALSE FALSE
## var_accel_forearm 3.500000 2.03343186 FALSE FALSE
## avg_roll_forearm 27.666667 1.64101519 FALSE TRUE
## stddev_roll_forearm 87.000000 1.63082255 FALSE TRUE
## var_roll_forearm 87.000000 1.63082255 FALSE TRUE
## avg_pitch_forearm 83.000000 1.65120783 FALSE TRUE
## stddev_pitch_forearm 41.500000 1.64611151 FALSE TRUE
## var_pitch_forearm 83.000000 1.65120783 FALSE TRUE
## avg_yaw_forearm 83.000000 1.65120783 FALSE TRUE
## stddev_yaw_forearm 85.000000 1.64101519 FALSE TRUE
## var_yaw_forearm 85.000000 1.64101519 FALSE TRUE
## gyros_forearm_x 1.059273 1.51870350 FALSE FALSE
## gyros_forearm_y 1.036554 3.77637346 FALSE FALSE
## gyros_forearm_z 1.122917 1.56457038 FALSE FALSE
## accel_forearm_x 1.126437 4.04647844 FALSE FALSE
## accel_forearm_y 1.059406 5.11160942 FALSE FALSE
## accel_forearm_z 1.006250 2.95586586 FALSE FALSE
## magnet_forearm_x 1.012346 7.76679238 FALSE FALSE
## magnet_forearm_y 1.246914 9.54031189 FALSE FALSE
## magnet_forearm_z 1.000000 8.57710733 FALSE FALSE
## classe 1.469581 0.02548160 FALSE FALSE
##Data Processing
###Near Zero Values From the nzv column above we can see that there are several variables that we want to throw out, as these are not useful when we want to construct our prediction model.
We'll also remove the same variables from the test data.
training.noNZV <- training[,!nzv$nzv]
validation.noNZV <- validation[,!nzv$nzv]###Unnecessary columns From looking at the 6 first column names of the training set, we can see that there is data that we'd like to exclude.
names(training.noNZV[,1:6])## [1] "X" "user_name" "raw_timestamp_part_1"
## [4] "raw_timestamp_part_2" "cvtd_timestamp" "num_window"
names(validation.noNZV[,1:6])## [1] "X" "user_name" "raw_timestamp_part_1"
## [4] "raw_timestamp_part_2" "cvtd_timestamp" "num_window"
This data is probably important for analysing other things, but may adversely affect our prediction model. Away they go!
training.noNZV <- training.noNZV[,-c(1:6)]
validation.noNZV <- validation.noNZV[,-c(1:6)]###High NA columns From briefly looking at the data earlier, we could see that there may be a lot of NAs in the data. We will exclude any column that has >70% NAs from both the training and validation sets.
# Finds columns where NAs account for more than 70%
manyNAs <- colMeans(is.na(training.noNZV))>.70
training.noNZV.NAred <- training.noNZV[,!manyNAs]
validation.noNZV.NAred <- validation.noNZV[,!manyNAs]###Splitting the data into training- and testing datasets As mentioned earlier, we will separate out a portion of the training data as a test-set. This is because we want to be able to measure how accurate our model is. This will be hard to do with a test dataset with only 20 entries. We will use 70% of the data for training, and the remaining 30% for testing.
inTrain <- createDataPartition(y=training.noNZV.NAred$classe,
p=0.7,
list=FALSE)
# We'll call the datasets .ready, as they are ready to be used in the training process
training.ready <- training.noNZV.NAred[inTrain,]
testing.ready <- training.noNZV.NAred[-inTrain,]#Prediction models
##Prediction with trees The first model we'll create is a general tree.
rpart.model <- train(classe ~.,
data=training.ready,
method="rpart")## Loading required package: rpart
Let's see how accurate this model is by testing it versus the testing dataset we separated out:
confusionMatrix(testing.ready$classe,
predict(rpart.model,
testing.ready))## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1495 24 146 0 9
## B 494 365 280 0 0
## C 472 37 517 0 0
## D 435 160 369 0 0
## E 140 146 296 0 500
##
## Overall Statistics
##
## Accuracy : 0.4889
## 95% CI : (0.476, 0.5017)
## No Information Rate : 0.5159
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.3324
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.4924 0.49863 0.32152 NA 0.98232
## Specificity 0.9372 0.84980 0.88099 0.8362 0.89174
## Pos Pred Value 0.8931 0.32046 0.50390 NA 0.46211
## Neg Pred Value 0.6341 0.92267 0.77547 NA 0.99813
## Prevalence 0.5159 0.12438 0.27324 0.0000 0.08649
## Detection Rate 0.2540 0.06202 0.08785 0.0000 0.08496
## Detection Prevalence 0.2845 0.19354 0.17434 0.1638 0.18386
## Balanced Accuracy 0.7148 0.67422 0.60125 NA 0.93703
49.2% accuracy... Worse than a coin toss. We'll have to do better than that!
##Random forest The second model we'll create is a random forest model.
rf.model <- train(classe ~.,
data=training.ready,
method="rf")## Loading required package: randomForest
## Warning: package 'randomForest' was built under R version 3.2.5
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
##
## margin
Let's have a look at how accurate this model is:
confusionMatrix(testing.ready$classe,
predict(rf.model,
testing.ready))## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1672 1 0 0 1
## B 5 1133 1 0 0
## C 0 6 1020 0 0
## D 0 0 7 957 0
## E 0 0 0 6 1076
##
## Overall Statistics
##
## Accuracy : 0.9954
## 95% CI : (0.9933, 0.997)
## No Information Rate : 0.285
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9942
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9970 0.9939 0.9922 0.9938 0.9991
## Specificity 0.9995 0.9987 0.9988 0.9986 0.9988
## Pos Pred Value 0.9988 0.9947 0.9942 0.9927 0.9945
## Neg Pred Value 0.9988 0.9985 0.9984 0.9988 0.9998
## Prevalence 0.2850 0.1937 0.1747 0.1636 0.1830
## Detection Rate 0.2841 0.1925 0.1733 0.1626 0.1828
## Detection Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Balanced Accuracy 0.9983 0.9963 0.9955 0.9962 0.9989
99.2% accuracy seems acceptable. Let's use this model to find the actions from the validation dataset:
predict(rf.model,
validation.noNZV.NAred)## [1] 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 E