Getting and Cleaning Data Course Project

Last updated 2014-10-26 15:02:47 using R version 3.1.1 (2014-07-10).

Instructions for project

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected.

One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Here are the data for the project:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

You should create one R script called run_analysis.R that does the following.

Merges the training and the test sets to create one data set.
Extracts only the measurements on the mean and standard deviation for each measurement.
Uses descriptive activity names to name the activities in the data set
Appropriately labels the data set with descriptive variable names.
From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Get the data

1.Download the file and put the file in the data folder

if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl,destfile="./data/Dataset.zip",method="curl")

2.Unzip the file

unzip(zipfile="./data/Dataset.zip",exdir="./data")

3.unzipped files are in the folderUCI HAR Dataset. Get the list of the files

path_rf <- file.path("./data" , "UCI HAR Dataset")
files<-list.files(path_rf, recursive=TRUE)
files

##  [1] "activity_labels.txt"                         
##  [2] "features_info.txt"                           
##  [3] "features.txt"                                
##  [4] "README.txt"                                  
##  [5] "test/Inertial Signals/body_acc_x_test.txt"   
##  [6] "test/Inertial Signals/body_acc_y_test.txt"   
##  [7] "test/Inertial Signals/body_acc_z_test.txt"   
##  [8] "test/Inertial Signals/body_gyro_x_test.txt"  
##  [9] "test/Inertial Signals/body_gyro_y_test.txt"  
## [10] "test/Inertial Signals/body_gyro_z_test.txt"  
## [11] "test/Inertial Signals/total_acc_x_test.txt"  
## [12] "test/Inertial Signals/total_acc_y_test.txt"  
## [13] "test/Inertial Signals/total_acc_z_test.txt"  
## [14] "test/subject_test.txt"                       
## [15] "test/X_test.txt"                             
## [16] "test/y_test.txt"                             
## [17] "train/Inertial Signals/body_acc_x_train.txt" 
## [18] "train/Inertial Signals/body_acc_y_train.txt" 
## [19] "train/Inertial Signals/body_acc_z_train.txt" 
## [20] "train/Inertial Signals/body_gyro_x_train.txt"
## [21] "train/Inertial Signals/body_gyro_y_train.txt"
## [22] "train/Inertial Signals/body_gyro_z_train.txt"
## [23] "train/Inertial Signals/total_acc_x_train.txt"
## [24] "train/Inertial Signals/total_acc_y_train.txt"
## [25] "train/Inertial Signals/total_acc_z_train.txt"
## [26] "train/subject_train.txt"                     
## [27] "train/X_train.txt"                           
## [28] "train/y_train.txt"

See the README.txt file for the detailed information on the dataset. For the purposes of this project, the files in the Inertial Signals folders are not used. The files that will be used to load data are listed as follows:

test/subject_test.txt
test/X_test.txt
test/y_test.txt
train/subject_train.txt
train/X_train.txt
train/y_train.txt

Read data from the targeted files

Get the big picture of the structure of the data frame that will be used in this project.

the picture below comes from the picture post on forum by Community TA David Hood.

Reference link: https://class.coursera.org/getdata-008/forum/thread?thread_id=24

alternate text

From the picture and the related files, we can see:

Values of Varible Activity consist of data from “Y_train.txt” and “Y_test.txt”
values of Varible Subject consist of data from “subject_train.txt” and subject_test.txt"
Values of Varibles Features consist of data from “X_train.txt” and “X_test.txt”
Names of Varibles Features come from “features.txt”
levels of Varible Activity come from “activity_labels.txt”

So we will use Activity, Subject and Features as part of descriptive variable names for data in data frame.

2.Read data from the files into the variables

Read the Activity files

dataActivityTest  <- read.table(file.path(path_rf, "test" , "Y_test.txt" ),header = FALSE)
dataActivityTrain <- read.table(file.path(path_rf, "train", "Y_train.txt"),header = FALSE)

Read the Subject files

dataSubjectTrain <- read.table(file.path(path_rf, "train", "subject_train.txt"),header = FALSE)
dataSubjectTest  <- read.table(file.path(path_rf, "test" , "subject_test.txt"),header = FALSE)

Read Fearures files

dataFeaturesTest  <- read.table(file.path(path_rf, "test" , "X_test.txt" ),header = FALSE)
dataFeaturesTrain <- read.table(file.path(path_rf, "train", "X_train.txt"),header = FALSE)

Look at the properties of the above varibles

str(dataActivityTest)

## 'data.frame':    2947 obs. of  1 variable:
##  $ V1: int  5 5 5 5 5 5 5 5 5 5 ...

str(dataActivityTrain)

## 'data.frame':    7352 obs. of  1 variable:
##  $ V1: int  5 5 5 5 5 5 5 5 5 5 ...

str(dataSubjectTrain)

## 'data.frame':    7352 obs. of  1 variable:
##  $ V1: int  1 1 1 1 1 1 1 1 1 1 ...

str(dataSubjectTest)

## 'data.frame':    2947 obs. of  1 variable:
##  $ V1: int  2 2 2 2 2 2 2 2 2 2 ...

str(dataFeaturesTest)

## 'data.frame':    2947 obs. of  561 variables:
##  $ V1  : num  0.257 0.286 0.275 0.27 0.275 ...
##  $ V2  : num  -0.0233 -0.0132 -0.0261 -0.0326 -0.0278 ...
##  $ V3  : num  -0.0147 -0.1191 -0.1182 -0.1175 -0.1295 ...
##  $ V4  : num  -0.938 -0.975 -0.994 -0.995 -0.994 ...
##  $ V5  : num  -0.92 -0.967 -0.97 -0.973 -0.967 ...
##  $ V6  : num  -0.668 -0.945 -0.963 -0.967 -0.978 ...
##  $ V7  : num  -0.953 -0.987 -0.994 -0.995 -0.994 ...
##  $ V8  : num  -0.925 -0.968 -0.971 -0.974 -0.966 ...
##  $ V9  : num  -0.674 -0.946 -0.963 -0.969 -0.977 ...
##  $ V10 : num  -0.894 -0.894 -0.939 -0.939 -0.939 ...
##  $ V11 : num  -0.555 -0.555 -0.569 -0.569 -0.561 ...
##  $ V12 : num  -0.466 -0.806 -0.799 -0.799 -0.826 ...
##  $ V13 : num  0.717 0.768 0.848 0.848 0.849 ...
##  $ V14 : num  0.636 0.684 0.668 0.668 0.671 ...
##  $ V15 : num  0.789 0.797 0.822 0.822 0.83 ...
##  $ V16 : num  -0.878 -0.969 -0.977 -0.974 -0.975 ...
##  $ V17 : num  -0.998 -1 -1 -1 -1 ...
##  $ V18 : num  -0.998 -1 -1 -0.999 -0.999 ...
##  $ V19 : num  -0.934 -0.998 -0.999 -0.999 -0.999 ...
##  $ V20 : num  -0.976 -0.994 -0.993 -0.995 -0.993 ...
##  $ V21 : num  -0.95 -0.974 -0.974 -0.979 -0.967 ...
##  $ V22 : num  -0.83 -0.951 -0.965 -0.97 -0.976 ...
##  $ V23 : num  -0.168 -0.302 -0.618 -0.75 -0.591 ...
##  $ V24 : num  -0.379 -0.348 -0.695 -0.899 -0.74 ...
##  $ V25 : num  0.246 -0.405 -0.537 -0.554 -0.799 ...
##  $ V26 : num  0.521 0.507 0.242 0.175 0.116 ...
##  $ V27 : num  -0.4878 -0.1565 -0.115 -0.0513 -0.0289 ...
##  $ V28 : num  0.4823 0.0407 0.0327 0.0342 -0.0328 ...
##  $ V29 : num  -0.0455 0.273 0.1924 0.1536 0.2943 ...
##  $ V30 : num  0.21196 0.19757 -0.01194 0.03077 0.00063 ...
##  $ V31 : num  -0.1349 -0.1946 -0.0634 -0.1293 -0.0453 ...
##  $ V32 : num  0.131 0.411 0.471 0.446 0.168 ...
##  $ V33 : num  -0.0142 -0.3405 -0.5074 -0.4195 -0.0682 ...
##  $ V34 : num  -0.106 0.0776 0.1885 0.2715 0.0744 ...
##  $ V35 : num  0.0735 -0.084 -0.2316 -0.2258 0.0271 ...
##  $ V36 : num  -0.1715 0.0353 0.6321 0.4164 -0.1459 ...
##  $ V37 : num  0.0401 -0.0101 -0.5507 -0.2864 -0.0502 ...
##  $ V38 : num  0.077 -0.105 0.3057 -0.0638 0.2352 ...
##  $ V39 : num  -0.491 -0.429 -0.324 -0.167 0.29 ...
##  $ V40 : num  -0.709 0.399 0.28 0.545 0.458 ...
##  $ V41 : num  0.936 0.927 0.93 0.929 0.927 ...
##  $ V42 : num  -0.283 -0.289 -0.288 -0.293 -0.303 ...
##  $ V43 : num  0.115 0.153 0.146 0.143 0.138 ...
##  $ V44 : num  -0.925 -0.989 -0.996 -0.993 -0.996 ...
##  $ V45 : num  -0.937 -0.984 -0.988 -0.97 -0.971 ...
##  $ V46 : num  -0.564 -0.965 -0.982 -0.992 -0.968 ...
##  $ V47 : num  -0.93 -0.989 -0.996 -0.993 -0.996 ...
##  $ V48 : num  -0.938 -0.983 -0.989 -0.971 -0.971 ...
##  $ V49 : num  -0.606 -0.965 -0.98 -0.993 -0.969 ...
##  $ V50 : num  0.906 0.856 0.856 0.856 0.854 ...
##  $ V51 : num  -0.279 -0.305 -0.305 -0.305 -0.313 ...
##  $ V52 : num  0.153 0.153 0.139 0.136 0.134 ...
##  $ V53 : num  0.944 0.944 0.949 0.947 0.946 ...
##  $ V54 : num  -0.262 -0.262 -0.262 -0.273 -0.279 ...
##  $ V55 : num  -0.0762 0.149 0.145 0.1421 0.1309 ...
##  $ V56 : num  -0.0178 0.0577 0.0406 0.0461 0.0554 ...
##  $ V57 : num  0.829 0.806 0.812 0.809 0.804 ...
##  $ V58 : num  -0.865 -0.858 -0.86 -0.854 -0.843 ...
##  $ V59 : num  -0.968 -0.957 -0.961 -0.963 -0.965 ...
##  $ V60 : num  -0.95 -0.988 -0.996 -0.992 -0.996 ...
##  $ V61 : num  -0.946 -0.982 -0.99 -0.973 -0.972 ...
##  $ V62 : num  -0.76 -0.971 -0.979 -0.996 -0.969 ...
##  $ V63 : num  -0.425 -0.729 -0.823 -0.823 -0.83 ...
##  $ V64 : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V65 : num  0.219 -0.465 -0.53 -0.7 -0.302 ...
##  $ V66 : num  -0.43 -0.51 -0.295 -0.343 -0.482 ...
##  $ V67 : num  0.431 0.525 0.305 0.359 0.539 ...
##  $ V68 : num  -0.432 -0.54 -0.315 -0.375 -0.596 ...
##  $ V69 : num  0.433 0.554 0.326 0.392 0.655 ...
##  $ V70 : num  -0.795 -0.746 -0.232 -0.233 -0.493 ...
##  $ V71 : num  0.781 0.733 0.169 0.176 0.463 ...
##  $ V72 : num  -0.78 -0.737 -0.155 -0.169 -0.465 ...
##  $ V73 : num  0.785 0.749 0.164 0.185 0.483 ...
##  $ V74 : num  -0.984 -0.845 -0.429 -0.297 -0.536 ...
##  $ V75 : num  0.987 0.869 0.44 0.304 0.544 ...
##  $ V76 : num  -0.989 -0.893 -0.451 -0.311 -0.553 ...
##  $ V77 : num  0.988 0.913 0.458 0.315 0.559 ...
##  $ V78 : num  0.981 0.945 0.548 0.986 0.998 ...
##  $ V79 : num  -0.996 -0.911 -0.335 0.653 0.916 ...
##  $ V80 : num  -0.96 -0.739 0.59 0.747 0.929 ...
##  $ V81 : num  0.072 0.0702 0.0694 0.0749 0.0784 ...
##  $ V82 : num  0.04575 -0.01788 -0.00491 0.03227 0.02228 ...
##  $ V83 : num  -0.10604 -0.00172 -0.01367 0.01214 0.00275 ...
##  $ V84 : num  -0.907 -0.949 -0.991 -0.991 -0.992 ...
##  $ V85 : num  -0.938 -0.973 -0.971 -0.973 -0.979 ...
##  $ V86 : num  -0.936 -0.978 -0.973 -0.976 -0.987 ...
##  $ V87 : num  -0.916 -0.969 -0.991 -0.99 -0.991 ...
##  $ V88 : num  -0.937 -0.974 -0.973 -0.973 -0.977 ...
##  $ V89 : num  -0.949 -0.979 -0.975 -0.978 -0.985 ...
##  $ V90 : num  -0.903 -0.915 -0.992 -0.992 -0.994 ...
##  $ V91 : num  -0.95 -0.981 -0.975 -0.975 -0.986 ...
##  $ V92 : num  -0.891 -0.978 -0.962 -0.962 -0.986 ...
##  $ V93 : num  0.898 0.898 0.994 0.994 0.994 ...
##  $ V94 : num  0.95 0.968 0.976 0.976 0.98 ...
##  $ V95 : num  0.946 0.966 0.966 0.97 0.985 ...
##  $ V96 : num  -0.931 -0.974 -0.982 -0.983 -0.987 ...
##  $ V97 : num  -0.995 -0.998 -1 -1 -1 ...
##  $ V98 : num  -0.997 -0.999 -0.999 -0.999 -1 ...
##  $ V99 : num  -0.997 -0.999 -0.999 -0.999 -1 ...
##   [list output truncated]

str(dataFeaturesTrain)

## 'data.frame':    7352 obs. of  561 variables:
##  $ V1  : num  0.289 0.278 0.28 0.279 0.277 ...
##  $ V2  : num  -0.0203 -0.0164 -0.0195 -0.0262 -0.0166 ...
##  $ V3  : num  -0.133 -0.124 -0.113 -0.123 -0.115 ...
##  $ V4  : num  -0.995 -0.998 -0.995 -0.996 -0.998 ...
##  $ V5  : num  -0.983 -0.975 -0.967 -0.983 -0.981 ...
##  $ V6  : num  -0.914 -0.96 -0.979 -0.991 -0.99 ...
##  $ V7  : num  -0.995 -0.999 -0.997 -0.997 -0.998 ...
##  $ V8  : num  -0.983 -0.975 -0.964 -0.983 -0.98 ...
##  $ V9  : num  -0.924 -0.958 -0.977 -0.989 -0.99 ...
##  $ V10 : num  -0.935 -0.943 -0.939 -0.939 -0.942 ...
##  $ V11 : num  -0.567 -0.558 -0.558 -0.576 -0.569 ...
##  $ V12 : num  -0.744 -0.818 -0.818 -0.83 -0.825 ...
##  $ V13 : num  0.853 0.849 0.844 0.844 0.849 ...
##  $ V14 : num  0.686 0.686 0.682 0.682 0.683 ...
##  $ V15 : num  0.814 0.823 0.839 0.838 0.838 ...
##  $ V16 : num  -0.966 -0.982 -0.983 -0.986 -0.993 ...
##  $ V17 : num  -1 -1 -1 -1 -1 ...
##  $ V18 : num  -1 -1 -1 -1 -1 ...
##  $ V19 : num  -0.995 -0.998 -0.999 -1 -1 ...
##  $ V20 : num  -0.994 -0.999 -0.997 -0.997 -0.998 ...
##  $ V21 : num  -0.988 -0.978 -0.965 -0.984 -0.981 ...
##  $ V22 : num  -0.943 -0.948 -0.975 -0.986 -0.991 ...
##  $ V23 : num  -0.408 -0.715 -0.592 -0.627 -0.787 ...
##  $ V24 : num  -0.679 -0.501 -0.486 -0.851 -0.559 ...
##  $ V25 : num  -0.602 -0.571 -0.571 -0.912 -0.761 ...
##  $ V26 : num  0.9293 0.6116 0.273 0.0614 0.3133 ...
##  $ V27 : num  -0.853 -0.3295 -0.0863 0.0748 -0.1312 ...
##  $ V28 : num  0.36 0.284 0.337 0.198 0.191 ...
##  $ V29 : num  -0.0585 0.2846 -0.1647 -0.2643 0.0869 ...
##  $ V30 : num  0.2569 0.1157 0.0172 0.0725 0.2576 ...
##  $ V31 : num  -0.2248 -0.091 -0.0745 -0.1553 -0.2725 ...
##  $ V32 : num  0.264 0.294 0.342 0.323 0.435 ...
##  $ V33 : num  -0.0952 -0.2812 -0.3326 -0.1708 -0.3154 ...
##  $ V34 : num  0.279 0.086 0.239 0.295 0.44 ...
##  $ V35 : num  -0.4651 -0.0222 -0.1362 -0.3061 -0.2691 ...
##  $ V36 : num  0.4919 -0.0167 0.1739 0.4821 0.1794 ...
##  $ V37 : num  -0.191 -0.221 -0.299 -0.47 -0.089 ...
##  $ V38 : num  0.3763 -0.0134 -0.1247 -0.3057 -0.1558 ...
##  $ V39 : num  0.4351 -0.0727 -0.1811 -0.3627 -0.1898 ...
##  $ V40 : num  0.661 0.579 0.609 0.507 0.599 ...
##  $ V41 : num  0.963 0.967 0.967 0.968 0.968 ...
##  $ V42 : num  -0.141 -0.142 -0.142 -0.144 -0.149 ...
##  $ V43 : num  0.1154 0.1094 0.1019 0.0999 0.0945 ...
##  $ V44 : num  -0.985 -0.997 -1 -0.997 -0.998 ...
##  $ V45 : num  -0.982 -0.989 -0.993 -0.981 -0.988 ...
##  $ V46 : num  -0.878 -0.932 -0.993 -0.978 -0.979 ...
##  $ V47 : num  -0.985 -0.998 -1 -0.996 -0.998 ...
##  $ V48 : num  -0.984 -0.99 -0.993 -0.981 -0.989 ...
##  $ V49 : num  -0.895 -0.933 -0.993 -0.978 -0.979 ...
##  $ V50 : num  0.892 0.892 0.892 0.894 0.894 ...
##  $ V51 : num  -0.161 -0.161 -0.164 -0.164 -0.167 ...
##  $ V52 : num  0.1247 0.1226 0.0946 0.0934 0.0917 ...
##  $ V53 : num  0.977 0.985 0.987 0.987 0.987 ...
##  $ V54 : num  -0.123 -0.115 -0.115 -0.121 -0.122 ...
##  $ V55 : num  0.0565 0.1028 0.1028 0.0958 0.0941 ...
##  $ V56 : num  -0.375 -0.383 -0.402 -0.4 -0.4 ...
##  $ V57 : num  0.899 0.908 0.909 0.911 0.912 ...
##  $ V58 : num  -0.971 -0.971 -0.97 -0.969 -0.967 ...
##  $ V59 : num  -0.976 -0.979 -0.982 -0.982 -0.984 ...
##  $ V60 : num  -0.984 -0.999 -1 -0.996 -0.998 ...
##  $ V61 : num  -0.989 -0.99 -0.992 -0.981 -0.991 ...
##  $ V62 : num  -0.918 -0.942 -0.993 -0.98 -0.98 ...
##  $ V63 : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V64 : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V65 : num  0.114 -0.21 -0.927 -0.596 -0.617 ...
##  $ V66 : num  -0.59042 -0.41006 0.00223 -0.06493 -0.25727 ...
##  $ V67 : num  0.5911 0.4139 0.0275 0.0754 0.2689 ...
##  $ V68 : num  -0.5918 -0.4176 -0.0567 -0.0858 -0.2807 ...
##  $ V69 : num  0.5925 0.4213 0.0855 0.0962 0.2926 ...
##  $ V70 : num  -0.745 -0.196 -0.329 -0.295 -0.167 ...
##  $ V71 : num  0.7209 0.1253 0.2705 0.2283 0.0899 ...
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##  $ V73 : num  0.7113 0.1091 0.2576 0.2048 0.0671 ...
##  $ V74 : num  -0.995 -0.834 -0.705 -0.385 -0.237 ...
##  $ V75 : num  0.996 0.834 0.714 0.386 0.239 ...
##  $ V76 : num  -0.996 -0.834 -0.723 -0.387 -0.241 ...
##  $ V77 : num  0.992 0.83 0.729 0.385 0.241 ...
##  $ V78 : num  0.57 -0.831 -0.181 -0.991 -0.408 ...
##  $ V79 : num  0.439 -0.866 0.338 -0.969 -0.185 ...
##  $ V80 : num  0.987 0.974 0.643 0.984 0.965 ...
##  $ V81 : num  0.078 0.074 0.0736 0.0773 0.0734 ...
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##  $ V84 : num  -0.994 -0.996 -0.991 -0.993 -0.996 ...
##  $ V85 : num  -0.988 -0.981 -0.981 -0.988 -0.988 ...
##  $ V86 : num  -0.994 -0.992 -0.99 -0.993 -0.992 ...
##  $ V87 : num  -0.994 -0.996 -0.991 -0.994 -0.997 ...
##  $ V88 : num  -0.986 -0.979 -0.979 -0.986 -0.987 ...
##  $ V89 : num  -0.993 -0.991 -0.987 -0.991 -0.991 ...
##  $ V90 : num  -0.985 -0.995 -0.987 -0.987 -0.997 ...
##  $ V91 : num  -0.992 -0.979 -0.979 -0.992 -0.992 ...
##  $ V92 : num  -0.993 -0.992 -0.992 -0.99 -0.99 ...
##  $ V93 : num  0.99 0.993 0.988 0.988 0.994 ...
##  $ V94 : num  0.992 0.992 0.992 0.993 0.993 ...
##  $ V95 : num  0.991 0.989 0.989 0.993 0.986 ...
##  $ V96 : num  -0.994 -0.991 -0.988 -0.993 -0.994 ...
##  $ V97 : num  -1 -1 -1 -1 -1 ...
##  $ V98 : num  -1 -1 -1 -1 -1 ...
##  $ V99 : num  -1 -1 -1 -1 -1 ...
##   [list output truncated]

Merges the training and the test sets to create one data set

1.Concatenate the data tables by rows

dataSubject <- rbind(dataSubjectTrain, dataSubjectTest)
dataActivity<- rbind(dataActivityTrain, dataActivityTest)
dataFeatures<- rbind(dataFeaturesTrain, dataFeaturesTest)

2.set names to variables

names(dataSubject)<-c("subject")
names(dataActivity)<- c("activity")
dataFeaturesNames <- read.table(file.path(path_rf, "features.txt"),head=FALSE)
names(dataFeatures)<- dataFeaturesNames$V2

3.Merge columns to get the data frame Data for all data

dataCombine <- cbind(dataSubject, dataActivity)
Data <- cbind(dataFeatures, dataCombine)

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

Subset Name of Features by measurements on the mean and standard deviation

i.e taken Names of Features with “mean()” or “std()”

subdataFeaturesNames<-dataFeaturesNames$V2[grep("mean\\(\\)|std\\(\\)", dataFeaturesNames$V2)]

Subset the data frame Data by seleted names of Features

selectedNames<-c(as.character(subdataFeaturesNames), "subject", "activity" )
Data<-subset(Data,select=selectedNames)

Check the structures of the data frame Data

str(Data)

## 'data.frame':    10299 obs. of  68 variables:
##  $ tBodyAcc-mean()-X          : num  0.289 0.278 0.28 0.279 0.277 ...
##  $ tBodyAcc-mean()-Y          : num  -0.0203 -0.0164 -0.0195 -0.0262 -0.0166 ...
##  $ tBodyAcc-mean()-Z          : num  -0.133 -0.124 -0.113 -0.123 -0.115 ...
##  $ tBodyAcc-std()-X           : num  -0.995 -0.998 -0.995 -0.996 -0.998 ...
##  $ tBodyAcc-std()-Y           : num  -0.983 -0.975 -0.967 -0.983 -0.981 ...
##  $ tBodyAcc-std()-Z           : num  -0.914 -0.96 -0.979 -0.991 -0.99 ...
##  $ tGravityAcc-mean()-X       : num  0.963 0.967 0.967 0.968 0.968 ...
##  $ tGravityAcc-mean()-Y       : num  -0.141 -0.142 -0.142 -0.144 -0.149 ...
##  $ tGravityAcc-mean()-Z       : num  0.1154 0.1094 0.1019 0.0999 0.0945 ...
##  $ tGravityAcc-std()-X        : num  -0.985 -0.997 -1 -0.997 -0.998 ...
##  $ tGravityAcc-std()-Y        : num  -0.982 -0.989 -0.993 -0.981 -0.988 ...
##  $ tGravityAcc-std()-Z        : num  -0.878 -0.932 -0.993 -0.978 -0.979 ...
##  $ tBodyAccJerk-mean()-X      : num  0.078 0.074 0.0736 0.0773 0.0734 ...
##  $ tBodyAccJerk-mean()-Y      : num  0.005 0.00577 0.0031 0.02006 0.01912 ...
##  $ tBodyAccJerk-mean()-Z      : num  -0.06783 0.02938 -0.00905 -0.00986 0.01678 ...
##  $ tBodyAccJerk-std()-X       : num  -0.994 -0.996 -0.991 -0.993 -0.996 ...
##  $ tBodyAccJerk-std()-Y       : num  -0.988 -0.981 -0.981 -0.988 -0.988 ...
##  $ tBodyAccJerk-std()-Z       : num  -0.994 -0.992 -0.99 -0.993 -0.992 ...
##  $ tBodyGyro-mean()-X         : num  -0.0061 -0.0161 -0.0317 -0.0434 -0.034 ...
##  $ tBodyGyro-mean()-Y         : num  -0.0314 -0.0839 -0.1023 -0.0914 -0.0747 ...
##  $ tBodyGyro-mean()-Z         : num  0.1077 0.1006 0.0961 0.0855 0.0774 ...
##  $ tBodyGyro-std()-X          : num  -0.985 -0.983 -0.976 -0.991 -0.985 ...
##  $ tBodyGyro-std()-Y          : num  -0.977 -0.989 -0.994 -0.992 -0.992 ...
##  $ tBodyGyro-std()-Z          : num  -0.992 -0.989 -0.986 -0.988 -0.987 ...
##  $ tBodyGyroJerk-mean()-X     : num  -0.0992 -0.1105 -0.1085 -0.0912 -0.0908 ...
##  $ tBodyGyroJerk-mean()-Y     : num  -0.0555 -0.0448 -0.0424 -0.0363 -0.0376 ...
##  $ tBodyGyroJerk-mean()-Z     : num  -0.062 -0.0592 -0.0558 -0.0605 -0.0583 ...
##  $ tBodyGyroJerk-std()-X      : num  -0.992 -0.99 -0.988 -0.991 -0.991 ...
##  $ tBodyGyroJerk-std()-Y      : num  -0.993 -0.997 -0.996 -0.997 -0.996 ...
##  $ tBodyGyroJerk-std()-Z      : num  -0.992 -0.994 -0.992 -0.993 -0.995 ...
##  $ tBodyAccMag-mean()         : num  -0.959 -0.979 -0.984 -0.987 -0.993 ...
##  $ tBodyAccMag-std()          : num  -0.951 -0.976 -0.988 -0.986 -0.991 ...
##  $ tGravityAccMag-mean()      : num  -0.959 -0.979 -0.984 -0.987 -0.993 ...
##  $ tGravityAccMag-std()       : num  -0.951 -0.976 -0.988 -0.986 -0.991 ...
##  $ tBodyAccJerkMag-mean()     : num  -0.993 -0.991 -0.989 -0.993 -0.993 ...
##  $ tBodyAccJerkMag-std()      : num  -0.994 -0.992 -0.99 -0.993 -0.996 ...
##  $ tBodyGyroMag-mean()        : num  -0.969 -0.981 -0.976 -0.982 -0.985 ...
##  $ tBodyGyroMag-std()         : num  -0.964 -0.984 -0.986 -0.987 -0.989 ...
##  $ tBodyGyroJerkMag-mean()    : num  -0.994 -0.995 -0.993 -0.996 -0.996 ...
##  $ tBodyGyroJerkMag-std()     : num  -0.991 -0.996 -0.995 -0.995 -0.995 ...
##  $ fBodyAcc-mean()-X          : num  -0.995 -0.997 -0.994 -0.995 -0.997 ...
##  $ fBodyAcc-mean()-Y          : num  -0.983 -0.977 -0.973 -0.984 -0.982 ...
##  $ fBodyAcc-mean()-Z          : num  -0.939 -0.974 -0.983 -0.991 -0.988 ...
##  $ fBodyAcc-std()-X           : num  -0.995 -0.999 -0.996 -0.996 -0.999 ...
##  $ fBodyAcc-std()-Y           : num  -0.983 -0.975 -0.966 -0.983 -0.98 ...
##  $ fBodyAcc-std()-Z           : num  -0.906 -0.955 -0.977 -0.99 -0.992 ...
##  $ fBodyAccJerk-mean()-X      : num  -0.992 -0.995 -0.991 -0.994 -0.996 ...
##  $ fBodyAccJerk-mean()-Y      : num  -0.987 -0.981 -0.982 -0.989 -0.989 ...
##  $ fBodyAccJerk-mean()-Z      : num  -0.99 -0.99 -0.988 -0.991 -0.991 ...
##  $ fBodyAccJerk-std()-X       : num  -0.996 -0.997 -0.991 -0.991 -0.997 ...
##  $ fBodyAccJerk-std()-Y       : num  -0.991 -0.982 -0.981 -0.987 -0.989 ...
##  $ fBodyAccJerk-std()-Z       : num  -0.997 -0.993 -0.99 -0.994 -0.993 ...
##  $ fBodyGyro-mean()-X         : num  -0.987 -0.977 -0.975 -0.987 -0.982 ...
##  $ fBodyGyro-mean()-Y         : num  -0.982 -0.993 -0.994 -0.994 -0.993 ...
##  $ fBodyGyro-mean()-Z         : num  -0.99 -0.99 -0.987 -0.987 -0.989 ...
##  $ fBodyGyro-std()-X          : num  -0.985 -0.985 -0.977 -0.993 -0.986 ...
##  $ fBodyGyro-std()-Y          : num  -0.974 -0.987 -0.993 -0.992 -0.992 ...
##  $ fBodyGyro-std()-Z          : num  -0.994 -0.99 -0.987 -0.989 -0.988 ...
##  $ fBodyAccMag-mean()         : num  -0.952 -0.981 -0.988 -0.988 -0.994 ...
##  $ fBodyAccMag-std()          : num  -0.956 -0.976 -0.989 -0.987 -0.99 ...
##  $ fBodyBodyAccJerkMag-mean() : num  -0.994 -0.99 -0.989 -0.993 -0.996 ...
##  $ fBodyBodyAccJerkMag-std()  : num  -0.994 -0.992 -0.991 -0.992 -0.994 ...
##  $ fBodyBodyGyroMag-mean()    : num  -0.98 -0.988 -0.989 -0.989 -0.991 ...
##  $ fBodyBodyGyroMag-std()     : num  -0.961 -0.983 -0.986 -0.988 -0.989 ...
##  $ fBodyBodyGyroJerkMag-mean(): num  -0.992 -0.996 -0.995 -0.995 -0.995 ...
##  $ fBodyBodyGyroJerkMag-std() : num  -0.991 -0.996 -0.995 -0.995 -0.995 ...
##  $ subject                    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ activity                   : int  5 5 5 5 5 5 5 5 5 5 ...

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

1.Read descriptive activity names from “activity_labels.txt”

activityLabels <- read.table(file.path(path_rf, "activity_labels.txt"),header = FALSE)

facorize Variale activity in the data frame Data using descriptive activity names
check

head(Data$activity,30)

##  [1] STANDING STANDING STANDING STANDING STANDING STANDING STANDING
##  [8] STANDING STANDING STANDING STANDING STANDING STANDING STANDING
## [15] STANDING STANDING STANDING STANDING STANDING STANDING STANDING
## [22] STANDING STANDING STANDING STANDING STANDING STANDING SITTING 
## [29] SITTING  SITTING 
## 6 Levels: WALKING WALKING_UPSTAIRS WALKING_DOWNSTAIRS ... LAYING

Appropriately labels the data set with descriptive variable names

In the former part, variables activity and subject and names of the activities have been labelled using descriptive names.In this part, Names of Feteatures will labelled using descriptive variable names.

prefix t is replaced by time
Acc is replaced by Accelerometer
Gyro is replaced by Gyroscope
prefix f is replaced by frequency
Mag is replaced by Magnitude
BodyBody is replaced by Body

names(Data)<-gsub("^t", "time", names(Data))
names(Data)<-gsub("^f", "frequency", names(Data))
names(Data)<-gsub("Acc", "Accelerometer", names(Data))
names(Data)<-gsub("Gyro", "Gyroscope", names(Data))
names(Data)<-gsub("Mag", "Magnitude", names(Data))
names(Data)<-gsub("BodyBody", "Body", names(Data))

check

names(Data)

##  [1] "timeBodyAccelerometer-mean()-X"                
##  [2] "timeBodyAccelerometer-mean()-Y"                
##  [3] "timeBodyAccelerometer-mean()-Z"                
##  [4] "timeBodyAccelerometer-std()-X"                 
##  [5] "timeBodyAccelerometer-std()-Y"                 
##  [6] "timeBodyAccelerometer-std()-Z"                 
##  [7] "timeGravityAccelerometer-mean()-X"             
##  [8] "timeGravityAccelerometer-mean()-Y"             
##  [9] "timeGravityAccelerometer-mean()-Z"             
## [10] "timeGravityAccelerometer-std()-X"              
## [11] "timeGravityAccelerometer-std()-Y"              
## [12] "timeGravityAccelerometer-std()-Z"              
## [13] "timeBodyAccelerometerJerk-mean()-X"            
## [14] "timeBodyAccelerometerJerk-mean()-Y"            
## [15] "timeBodyAccelerometerJerk-mean()-Z"            
## [16] "timeBodyAccelerometerJerk-std()-X"             
## [17] "timeBodyAccelerometerJerk-std()-Y"             
## [18] "timeBodyAccelerometerJerk-std()-Z"             
## [19] "timeBodyGyroscope-mean()-X"                    
## [20] "timeBodyGyroscope-mean()-Y"                    
## [21] "timeBodyGyroscope-mean()-Z"                    
## [22] "timeBodyGyroscope-std()-X"                     
## [23] "timeBodyGyroscope-std()-Y"                     
## [24] "timeBodyGyroscope-std()-Z"                     
## [25] "timeBodyGyroscopeJerk-mean()-X"                
## [26] "timeBodyGyroscopeJerk-mean()-Y"                
## [27] "timeBodyGyroscopeJerk-mean()-Z"                
## [28] "timeBodyGyroscopeJerk-std()-X"                 
## [29] "timeBodyGyroscopeJerk-std()-Y"                 
## [30] "timeBodyGyroscopeJerk-std()-Z"                 
## [31] "timeBodyAccelerometerMagnitude-mean()"         
## [32] "timeBodyAccelerometerMagnitude-std()"          
## [33] "timeGravityAccelerometerMagnitude-mean()"      
## [34] "timeGravityAccelerometerMagnitude-std()"       
## [35] "timeBodyAccelerometerJerkMagnitude-mean()"     
## [36] "timeBodyAccelerometerJerkMagnitude-std()"      
## [37] "timeBodyGyroscopeMagnitude-mean()"             
## [38] "timeBodyGyroscopeMagnitude-std()"              
## [39] "timeBodyGyroscopeJerkMagnitude-mean()"         
## [40] "timeBodyGyroscopeJerkMagnitude-std()"          
## [41] "frequencyBodyAccelerometer-mean()-X"           
## [42] "frequencyBodyAccelerometer-mean()-Y"           
## [43] "frequencyBodyAccelerometer-mean()-Z"           
## [44] "frequencyBodyAccelerometer-std()-X"            
## [45] "frequencyBodyAccelerometer-std()-Y"            
## [46] "frequencyBodyAccelerometer-std()-Z"            
## [47] "frequencyBodyAccelerometerJerk-mean()-X"       
## [48] "frequencyBodyAccelerometerJerk-mean()-Y"       
## [49] "frequencyBodyAccelerometerJerk-mean()-Z"       
## [50] "frequencyBodyAccelerometerJerk-std()-X"        
## [51] "frequencyBodyAccelerometerJerk-std()-Y"        
## [52] "frequencyBodyAccelerometerJerk-std()-Z"        
## [53] "frequencyBodyGyroscope-mean()-X"               
## [54] "frequencyBodyGyroscope-mean()-Y"               
## [55] "frequencyBodyGyroscope-mean()-Z"               
## [56] "frequencyBodyGyroscope-std()-X"                
## [57] "frequencyBodyGyroscope-std()-Y"                
## [58] "frequencyBodyGyroscope-std()-Z"                
## [59] "frequencyBodyAccelerometerMagnitude-mean()"    
## [60] "frequencyBodyAccelerometerMagnitude-std()"     
## [61] "frequencyBodyAccelerometerJerkMagnitude-mean()"
## [62] "frequencyBodyAccelerometerJerkMagnitude-std()" 
## [63] "frequencyBodyGyroscopeMagnitude-mean()"        
## [64] "frequencyBodyGyroscopeMagnitude-std()"         
## [65] "frequencyBodyGyroscopeJerkMagnitude-mean()"    
## [66] "frequencyBodyGyroscopeJerkMagnitude-std()"     
## [67] "subject"                                       
## [68] "activity"

Creates a second,independent tidy data set and ouput it

In this part,a second, independent tidy data set will be created with the average of each variable for each activity and each subject based on the data set in step 4.

library(plyr);
Data2<-aggregate(. ~subject + activity, Data, mean)
Data2<-Data2[order(Data2$subject,Data2$activity),]
write.table(Data2, file = "tidydata.txt",row.name=FALSE)

Prouduce Codebook

library(knitr)
knit2html("codebook.Rmd");

## 
## 
## processing file: codebook.Rmd

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##    inline R code fragments
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## label: unnamed-chunk-21
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##   ordinary text without R code

## output file: codebook.md