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##CodeBook

This is a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data.

Describing the data and variables:

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. See 'features_info.txt' for more details.

For each record it is provided:

  • Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
  • Triaxial Angular velocity from the gyroscope.
  • A 561-feature vector with time and frequency domain variables.
  • Its activity label.
  • An identifier of the subject who carried out the experiment.

The dataset includes the following files:

  • 'README.txt'

  • 'features_info.txt': Shows information about the variables used on the feature vector.

  • 'features.txt': List of all features.

  • 'activity_labels.txt': Links the class labels with their activity name.

  • 'train/X_train.txt': Training set.

  • 'train/y_train.txt': Training labels.

  • 'test/X_test.txt': Test set.

  • 'test/y_test.txt': Test labels.

Data detail Information:

activity_labels.txt

  • nrow(activityNames) = 6
  • ncol(activityNames) = 2
  • No column names on the file.
  • Size = 4 KB

features.txt

  • nrow(featureData) = 561
  • ncol(featureData) = 2
  • No column names on the file.
  • Size = 16 KB

subject_train.txt

  • nrow(subTrain) = 7352;
  • ncol(subTrain) = 1;
  • No column names on the file.
  • Size = 20 KB

subject_test.txt

  • nrow(subTest) = 2947;
  • ncol(subTest) = 1;
  • No column names on the file.
  • Size = 8k

Y_train.txt

  • nrow(yTrain) = 7352
  • ncol(yTrain) = 1
  • No column names on the file.
  • Size = 16 KB

Y_test.txt

  • nrow(yTest) = 2947
  • ncol(yTest) = 1
  • No column names on the file.
  • Size = 8 k

X_train.txt

  • nrow(xTrain) = 7352
  • ncol(xTrain) = 561
  • No column names on the file.
  • Size = 66 MB

X_test.txt

  • nrow(xTest) = 2947
  • col(mergeX) = 561
  • No column names on the file.
  • Size = 26.5 MB

Transformations

  • Download data from the https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip;

  • Loading subject_train.txt and subject_test.txt with data.table(..) function with the name "subTrain" and "subTest", respectively:

    • nrow(subTrain) = 7352;
    • ncol(subTrain) = 1;
    • ColName = V1
    • nrow(subTest) = 2947;
    • ncol(subTest) = 1;
    • ColName = V1

  • Mergin subTrain and subTest into mergeSub:

    • nrow(mergeSub) = 10299;
    • ncol(mergeSub) = 1;
      • ColName = V1

  • Changing Column name of table mergeSub from "V1" to "mergeSub".

  • Loading Y_train.txt and Y_test.txt with data.table(..) function with the name "yTrain" and "yTest", respectively:

    • nrow(yTrain) = 7352
    • ncol(yTrain) = 1
    • ColName = V1
    • nrow(yTest) = 2947
    • ncol(yTest) = 1
      • ColName = V1

  • Mergin yTrain and yTest into mergeY:

    • nrow(mergeY) = 10229
    • ncol(mergeY) = 1
    • ColName = V1

  • Changing Column name of table mergeY from "V1" to "mergeY"

  • Loading X_train.txt and X_test.txt with data.table(..) function with the name "xTrain" and "xTest", respectively:

    • nrow(xTrain) = 7352
    • ncol(xTrain) = 561
    • ColName = From "V1" to "V561"
    • nrow(xTest) = 2947
    • ncol(xTest) = 561
    • ColName = From "V1" to "V561"

  • Mergin xTrain and xTest into mergeX:

    • nrow(mergeX) = 10229
    • ncol(mergeX) = 561
      • ColName = From "V1" to "V561"

  • Changing Column name of table mergeX from "V1" to "mergeX"

  • Merging all 3 tables together mergeSub + mergeY + mergeX with the name allData:

    • nrow(allMerge) = 10299
    • ncol(allMerge) = 563
    • ColName = "mergeSub" + "mergeY" + "mergeX" + From "V2" to "V561"

  • Extracts only the measurements on the mean and standard deviation for each measurement:

  • First loading the colnames from features.txt with the name featureData;

  • Selecting all colnames from featureData with the string "mean" or "std" on it. Constructing a vector "posMeanStd" with the position of the colnames selected.

  • Subsetting "allData" using the vector "posMeanStd", including also the first two column of "allData". Naming this subset as "meanAndStd"

    • nrow(meanAndStd) = 10299
    • ncol(meanAndStd) = 81
    • names(meanAndStd) = [1] "mergeSub" "mergeY" "mergeX" "V2" "V3" "V4" "V5" "V6"
      [9] "V41" "V42" "V43" "V44" "V45" "V46" "V81" "V82"
      [17] "V83" "V84" "V85" "V86" "V121" "V122" "V123" "V124"
      [25] "V125" "V126" "V161" "V162" "V163" "V164" "V165" "V166"
      [33] "V201" "V202" "V214" "V215" "V227" "V228" "V240" "V241"
      [41] "V253" "V254" "V266" "V267" "V268" "V269" "V270" "V271"
      [49] "V294" "V295" "V296" "V345" "V346" "V347" "V348" "V349"
      [57] "V350" "V373" "V374" "V375" "V424" "V425" "V426" "V427"
      [65] "V428" "V429" "V452" "V453" "V454" "V503" "V504" "V513"
      [73] "V516" "V517" "V526" "V529" "V530" "V539" "V542" "V543"
      [81] "V552"

  • Uses descriptive activity names to name the activities in the data set:

  • Loading labels from activity_labels.txt with the name activityNames.

    • nrow(activityNames) = 6
    • ncol(activityNames) = 2
    • names(activityNames) = "V1" "V2"

  • Changing activityNames col names from "V1" "V2" to "mergeY" "Activities";

  • Setting "mergeY" as key of the table "meanAndStd";

  • Setting "mergeY" as key of the table "activityNames";

  • Merging "meanAndStd" and "activityNames" by the key "mergeY". Adding a new col with the name "Activities" into "meanAndStd".

    • nrow(meanAndStd) = 10229
    • ncol(meanAndStd) = 82

  • Appropriately labels the data set with descriptive variable names:

  • Changing "meanAndStd" col names to: [1] "Activity_ID" "Subject"
    [3] "tBody_Acc_mean_X" "tBody_Acc_mean_Y"
    [5] "tBody_Acc_mean_Z" "tBody_Acc_std_X"
    [7] "tBody_Acc_std_Y" "tBody_Acc_std_Z"
    [9] "tGravity_Acc_mean_X" "tGravity_Acc_mean_Y"
    [11] "tGravity_Acc_mean_Z" "tGravity_Acc_std_X"
    [13] "tGravity_Acc_std_Y" "tGravity_Acc_std_Z"
    [15] "tBody_Acc_Jerk_mean_X" "tBody_Acc_Jerk_mean_Y"
    [17] "tBody_Acc_Jerk_mean_Z" "tBody_Acc_Jerk_std_X"
    [19] "tBody_Acc_Jerk_std_Y" "tBody_Acc_Jerk_std_Z"
    [21] "tBody_Gyro_mean_X" "tBody_Gyro_mean_Y"
    [23] "tBody_Gyro_mean_Z" "tBody_Gyro_std_X"
    [25] "tBody_Gyro_std_Y" "tBody_Gyro_std_Z"
    [27] "tBody_Gyro_Jerk_mean_X" "tBody_Gyro_Jerk_mean_Y"
    [29] "tBody_Gyro_Jerk_mean_Z" "tBody_Gyro_Jerk_std_X"
    [31] "tBody_Gyro_Jerk_std_Y" "tBody_Gyro_Jerk_std_Z"
    [33] "tBody_Acc_Mag_mean" "tBody_Acc_Mag_std"
    [35] "tGravity_Acc_Mag_mean" "tGravity_Acc_Mag_std"
    [37] "tBody_Acc_Jerk_Mag_mean" "tBody_Acc_Jerk_Mag_std"
    [39] "tBody_Gyro_Mag_mean" "tBody_Gyro_Mag_std"
    [41] "tBody_Gyro_Jerk_Mag_mean" "tBody_Gyro_Jerk_Mag_std"
    [43] "fBody_Acc_mean_X" "fBody_Acc_mean_Y"
    [45] "fBody_Acc_mean_Z" "fBody_Acc_std_X"
    [47] "fBody_Acc_std_Y" "fBody_Acc_std_Z"
    [49] "fBody_Acc_mean_Freq_X" "fBody_Acc_mean_Freq_Y"
    [51] "fBody_Acc_mean_Freq_Z" "fBody_Acc_Jerk_mean_X"
    [53] "fBody_Acc_Jerk_mean_Y" "fBody_Acc_Jerk_mean_Z"
    [55] "fBody_Acc_Jerk_std_X" "fBody_Acc_Jerk_std_Y"
    [57] "fBody_Acc_Jerk_std_Z" "fBody_Acc_Jerk_mean_Freq_X"
    [59] "fBody_Acc_Jerk_mean_Freq_Y" "fBody_Acc_Jerk_mean_Freq_Z"
    [61] "fBody_Gyro_mean_X" "fBody_Gyro_mean_Y"
    [63] "fBody_Gyro_mean_Z" "fBody_Gyro_std_X"
    [65] "fBody_Gyro_std_Y" "fBody_Gyro_std_Z"
    [67] "fBody_Gyro_mean_Freq_X" "fBody_Gyro_mean_Freq_Y"
    [69] "fBody_Gyro_mean_Freq_Z" "fBody_Acc_Mag_mean"
    [71] "fBody_Acc_Mag_std" "fBody_Acc_Mag_mean_Freq"
    [73] "fBody_Acc_Jerk_Mag_mean" "fBody_Acc_Jerk_Mag_std"
    [75] "fBody_Acc_Jerk_Mag_mean_Freq" "fBody_Gyro_Mag_mean"
    [77] "fBody_Gyro_Mag_std" "fBody_Gyro_Mag_mean_Freq"
    [79] "fBody_Gyro_Jerk_Mag_mean" "fBody_Gyro_Jerk_Mag_std"
    [81] "fBody_Gyro_Jerk_Mag_mean_Freq" "Activity_Name"

  • Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

  • Set columns from 3 to penultimate as measure variables;

  • Set columns "Subject","Activity_ID","Activity_Name" as Ids;

  • Melting "meanAndStd" with the ids and measure variables defined. Creating "dataMelt" variable;

  • CASTING "dataMelt" using the formula Subject + Activity_Name ~ variable and the agglomerate function mean.

    • nrow(tidyData) = 180
    • ncol(tidyData) = 81