Getting-and-cleaning-Data-Course-Project/run_analysis.R at master · niisaban/Getting-and-cleaning-Data-Course-Project · GitHub

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## Download the file and put the file in the data folder
dataset_url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(dataset_url, "Dataset.zip")

## Unzip the file
unzip("Dataset.zip", exdir = "Dataset")

## set working directory
setwd("C:/Users/Abdulrahman/Documents/Coursera/Getting and Cleaning Data/Dataset")

## Unzipped files are in the folder UCI HAR Dataset. Get the list of files
files <- list.files("UCI HAR Dataset", recursive = TRUE)
files

[1] "activity_labels.txt"
[2] "features.txt"
[3] "features_info.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"

## The README.txt file has detailed information on the dataset. The Inertial  Signals files will not be used.
##Files that will be used are:
## 'test/X_test.txt', 'test/y_test.txt',
## 'train/subject_train.txt', 'train/X_train.txt', 'train/y_train.txt'

##Read data from targeted files into the variables
## Read Activity files
dataActivityTest <- read.table(file.path("UCI HAR Dataset", "test", "Y_test.txt"), header = FALSE)
dataActivityTrain <- read.table(file.path("UCI HAR Dataset", "train", "Y_train.txt"), header = FALSE)
## Read Subject files
dataSubjectTrain <- read.table(file.path("UCI HAR Dataset", "train", "subject_train.txt"), header = FALSE)
dataSubjectTest <- read.table(file.path("UCI HAR Dataset", "test", "subject_test.txt"), header = FALSE)
## Read Features files
dataFeaturesTest <- read.table(file.path("UCI HAR Dataset", "test", "X_test.txt"), header = FALSE)
dataFeaturesTrain <- read.table(file.path("UCI HAR Dataset", "train", "X_train.txt"), header = FALSE)

## Look at the properties of the above variables
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:
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  [list output truncated]

str(dataFeaturesTrain)
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 $ 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]

## Merge 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("UCI HAR Dataset", "features.txt"), header = FALSE)
names(dataFeatures) <- dataFeaturesNames$V2
## 3. Merge coloumns to obtain the data frame Data for all data
dataAll <- cbind(dataSubject, dataActivity)
Data <- cbind(dataFeatures, dataAll)

## Extract only the measurements on the mean and standard deviation for each measurement
## 1. Subset Name of the Features by measurements on the mean and standard deviation
## The above can be achieved by using Names of Features with "mean()" or "std()"
subdataFeaturesNames <- dataFeaturesNames$V2[grep("mean\\(\\) | std\\(\\)", dataFeaturesNames$V2)]
## 2. Subset the data frame (Data) by selected names of Features
selectedNames <- c(as.character(subdataFeaturesNames), "subject", "activity")
Data <- subset(Data, "select" == selectedNames)
str(Data)
$ 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 ...
 $ tBodyAcc-mad()-X                    : num  -0.995 -0.999 -0.997 -0.997 -0.998 ...
 $ tBodyAcc-mad()-Y                    : num  -0.983 -0.975 -0.964 -0.983 -0.98 ...
 $ tBodyAcc-mad()-Z                    : num  -0.924 -0.958 -0.977 -0.989 -0.99 ...
 $ tBodyAcc-max()-X                    : num  -0.935 -0.943 -0.939 -0.939 -0.942 ...
 $ tBodyAcc-max()-Y                    : num  -0.567 -0.558 -0.558 -0.576 -0.569 ...
 $ tBodyAcc-max()-Z                    : num  -0.744 -0.818 -0.818 -0.83 -0.825 ...
 $ tBodyAcc-min()-X                    : num  0.853 0.849 0.844 0.844 0.849 ...
 $ tBodyAcc-min()-Y                    : num  0.686 0.686 0.682 0.682 0.683 ...
 $ tBodyAcc-min()-Z                    : num  0.814 0.823 0.839 0.838 0.838 ...
 $ tBodyAcc-sma()                      : num  -0.966 -0.982 -0.983 -0.986 -0.993 ...
 $ tBodyAcc-energy()-X                 : num  -1 -1 -1 -1 -1 ...
 $ tBodyAcc-energy()-Y                 : num  -1 -1 -1 -1 -1 ...
 $ tBodyAcc-energy()-Z                 : num  -0.995 -0.998 -0.999 -1 -1 ...
 $ tBodyAcc-iqr()-X                    : num  -0.994 -0.999 -0.997 -0.997 -0.998 ...
 $ tBodyAcc-iqr()-Y                    : num  -0.988 -0.978 -0.965 -0.984 -0.981 ...
 $ tBodyAcc-iqr()-Z                    : num  -0.943 -0.948 -0.975 -0.986 -0.991 ...
 $ tBodyAcc-entropy()-X                : num  -0.408 -0.715 -0.592 -0.627 -0.787 ...
 $ tBodyAcc-entropy()-Y                : num  -0.679 -0.501 -0.486 -0.851 -0.559 ...
 $ tBodyAcc-entropy()-Z                : num  -0.602 -0.571 -0.571 -0.912 -0.761 ...
 $ tBodyAcc-arCoeff()-X,1              : num  0.9293 0.6116 0.273 0.0614 0.3133 ...
 $ tBodyAcc-arCoeff()-X,2              : num  -0.853 -0.3295 -0.0863 0.0748 -0.1312 ...
 $ tBodyAcc-arCoeff()-X,3              : num  0.36 0.284 0.337 0.198 0.191 ...
 $ tBodyAcc-arCoeff()-X,4              : num  -0.0585 0.2846 -0.1647 -0.2643 0.0869 ...
 $ tBodyAcc-arCoeff()-Y,1              : num  0.2569 0.1157 0.0172 0.0725 0.2576 ...
 $ tBodyAcc-arCoeff()-Y,2              : num  -0.2248 -0.091 -0.0745 -0.1553 -0.2725 ...
 $ tBodyAcc-arCoeff()-Y,3              : num  0.264 0.294 0.342 0.323 0.435 ...
 $ tBodyAcc-arCoeff()-Y,4              : num  -0.0952 -0.2812 -0.3326 -0.1708 -0.3154 ...
 $ tBodyAcc-arCoeff()-Z,1              : num  0.279 0.086 0.239 0.295 0.44 ...
 $ tBodyAcc-arCoeff()-Z,2              : num  -0.4651 -0.0222 -0.1362 -0.3061 -0.2691 ...
 $ tBodyAcc-arCoeff()-Z,3              : num  0.4919 -0.0167 0.1739 0.4821 0.1794 ...
 $ tBodyAcc-arCoeff()-Z,4              : num  -0.191 -0.221 -0.299 -0.47 -0.089 ...
 $ tBodyAcc-correlation()-X,Y          : num  0.3763 -0.0134 -0.1247 -0.3057 -0.1558 ...
 $ tBodyAcc-correlation()-X,Z          : num  0.4351 -0.0727 -0.1811 -0.3627 -0.1898 ...
 $ tBodyAcc-correlation()-Y,Z          : num  0.661 0.579 0.609 0.507 0.599 ...
 $ 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 ...
 $ tGravityAcc-mad()-X                 : num  -0.985 -0.998 -1 -0.996 -0.998 ...
 $ tGravityAcc-mad()-Y                 : num  -0.984 -0.99 -0.993 -0.981 -0.989 ...
 $ tGravityAcc-mad()-Z                 : num  -0.895 -0.933 -0.993 -0.978 -0.979 ...
 $ tGravityAcc-max()-X                 : num  0.892 0.892 0.892 0.894 0.894 ...
 $ tGravityAcc-max()-Y                 : num  -0.161 -0.161 -0.164 -0.164 -0.167 ...
 $ tGravityAcc-max()-Z                 : num  0.1247 0.1226 0.0946 0.0934 0.0917 ...
 $ tGravityAcc-min()-X                 : num  0.977 0.985 0.987 0.987 0.987 ...
 $ tGravityAcc-min()-Y                 : num  -0.123 -0.115 -0.115 -0.121 -0.122 ...
 $ tGravityAcc-min()-Z                 : num  0.0565 0.1028 0.1028 0.0958 0.0941 ...
 $ tGravityAcc-sma()                   : num  -0.375 -0.383 -0.402 -0.4 -0.4 ...
 $ tGravityAcc-energy()-X              : num  0.899 0.908 0.909 0.911 0.912 ...
 $ tGravityAcc-energy()-Y              : num  -0.971 -0.971 -0.97 -0.969 -0.967 ...
 $ tGravityAcc-energy()-Z              : num  -0.976 -0.979 -0.982 -0.982 -0.984 ...
 $ tGravityAcc-iqr()-X                 : num  -0.984 -0.999 -1 -0.996 -0.998 ...
 $ tGravityAcc-iqr()-Y                 : num  -0.989 -0.99 -0.992 -0.981 -0.991 ...
 $ tGravityAcc-iqr()-Z                 : num  -0.918 -0.942 -0.993 -0.98 -0.98 ...
 $ tGravityAcc-entropy()-X             : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
 $ tGravityAcc-entropy()-Y             : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
 $ tGravityAcc-entropy()-Z             : num  0.114 -0.21 -0.927 -0.596 -0.617 ...
 $ tGravityAcc-arCoeff()-X,1           : num  -0.59042 -0.41006 0.00223 -0.06493 -0.25727 ...
 $ tGravityAcc-arCoeff()-X,2           : num  0.5911 0.4139 0.0275 0.0754 0.2689 ...
 $ tGravityAcc-arCoeff()-X,3           : num  -0.5918 -0.4176 -0.0567 -0.0858 -0.2807 ...
 $ tGravityAcc-arCoeff()-X,4           : num  0.5925 0.4213 0.0855 0.0962 0.2926 ...
 $ tGravityAcc-arCoeff()-Y,1           : num  -0.745 -0.196 -0.329 -0.295 -0.167 ...
 $ tGravityAcc-arCoeff()-Y,2           : num  0.7209 0.1253 0.2705 0.2283 0.0899 ...
 $ tGravityAcc-arCoeff()-Y,3           : num  -0.7124 -0.1056 -0.2545 -0.2063 -0.0663 ...
 $ tGravityAcc-arCoeff()-Y,4           : num  0.7113 0.1091 0.2576 0.2048 0.0671 ...
 $ tGravityAcc-arCoeff()-Z,1           : num  -0.995 -0.834 -0.705 -0.385 -0.237 ...
 $ tGravityAcc-arCoeff()-Z,2           : num  0.996 0.834 0.714 0.386 0.239 ...
 $ tGravityAcc-arCoeff()-Z,3           : num  -0.996 -0.834 -0.723 -0.387 -0.241 ...
 $ tGravityAcc-arCoeff()-Z,4           : num  0.992 0.83 0.729 0.385 0.241 ...
 $ tGravityAcc-correlation()-X,Y       : num  0.57 -0.831 -0.181 -0.991 -0.408 ...
 $ tGravityAcc-correlation()-X,Z       : num  0.439 -0.866 0.338 -0.969 -0.185 ...
 $ tGravityAcc-correlation()-Y,Z       : num  0.987 0.974 0.643 0.984 0.965 ...
 $ 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 ...
 $ tBodyAccJerk-mad()-X                : num  -0.994 -0.996 -0.991 -0.994 -0.997 ...
 $ tBodyAccJerk-mad()-Y                : num  -0.986 -0.979 -0.979 -0.986 -0.987 ...
 $ tBodyAccJerk-mad()-Z                : num  -0.993 -0.991 -0.987 -0.991 -0.991 ...
 $ tBodyAccJerk-max()-X                : num  -0.985 -0.995 -0.987 -0.987 -0.997 ...
 $ tBodyAccJerk-max()-Y                : num  -0.992 -0.979 -0.979 -0.992 -0.992 ...
 $ tBodyAccJerk-max()-Z                : num  -0.993 -0.992 -0.992 -0.99 -0.99 ...
 $ tBodyAccJerk-min()-X                : num  0.99 0.993 0.988 0.988 0.994 ...
 $ tBodyAccJerk-min()-Y                : num  0.992 0.992 0.992 0.993 0.993 ...
 $ tBodyAccJerk-min()-Z                : num  0.991 0.989 0.989 0.993 0.986 ...
 $ tBodyAccJerk-sma()                  : num  -0.994 -0.991 -0.988 -0.993 -0.994 ...
 $ tBodyAccJerk-energy()-X             : num  -1 -1 -1 -1 -1 ...
 $ tBodyAccJerk-energy()-Y             : num  -1 -1 -1 -1 -1 ...
 $ tBodyAccJerk-energy()-Z             : num  -1 -1 -1 -1 -1 ...
  [list output truncated]


## Name the activities in the data set with descriptive activity names()
## Read descriptive activity names from "activity_labels.txt"
activityLabels <- read.table(file.path("UCI HAR Dataset", "activity_labels.txt"), header = FALSE)
## Use descriptive activity names to factorize variables (activity) in the data frame (Data)
## Check
head(Data$activity, 30)
## Label the data set (names of features) with descriptive variable names
## replace prefix t by time
## replace Acc by Accelerometer
## replace Gyro by Gyroscope
## replace prefix f by frequency
## replace Mag by Magnitude
## replace BodyBody 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))
## Print the result to the console to check
names(Data)

## Create a second, independent tidy data set with the average of each activity and each subject
library(dplyr)
Data2 <- aggregate(Data, list(subject, activity), fun = mean)
Data2 <- Data2[order(Data2$subject, Data2$activity),]
write.table(Data2, file = "tidydata.txt", row.name = FALSE)