run_analysis.R

library(data.table)
library(dplyr)

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

# Checking if folder exists
if (!file.exists("UCI HAR Dataset")) { 
    unzip("./data/UCIHARDataset.zip") 
}

#Reading datasets
features <- read.table("UCI HAR Dataset/features.txt", col.names = c("n","functions"))
activities <- read.table("UCI HAR Dataset/activity_labels.txt", col.names = c("code", "activity"))
subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt", col.names = "subject")
x_test <- read.table("UCI HAR Dataset/test/X_test.txt", col.names = features$functions)
y_test <- read.table("UCI HAR Dataset/test/y_test.txt", col.names = "code")
subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt", col.names = "subject")
x_train <- read.table("UCI HAR Dataset/train/X_train.txt", col.names = features$functions)
y_train <- read.table("UCI HAR Dataset/train/y_train.txt", col.names = "code")

# 1. Merges the training and the test sets to create one data set
X <- rbind(x_train, x_test)
Y <- rbind(y_train, y_test)
Subject <- rbind(subject_train, subject_test)
Merged_Data <- cbind(Subject, Y, X)

# 2. Extracts only the measurements on the mean and standard deviation for each measurement
TidyData <- Merged_Data %>% select(subject, code, contains("mean"), contains("std"))

# 3. Uses descriptive activity names to name the activities in the data set
TidyData$code <- activities[TidyData$code, 2]

# 4. Appropriately labels the data set with descriptive variable names
names(TidyData)[2] = "activity"
names(TidyData)<-gsub("Acc", "Accelerometer", names(TidyData))
names(TidyData)<-gsub("Gyro", "Gyroscope", names(TidyData))
names(TidyData)<-gsub("BodyBody", "Body", names(TidyData))
names(TidyData)<-gsub("Mag", "Magnitude", names(TidyData))
names(TidyData)<-gsub("^t", "Time", names(TidyData))
names(TidyData)<-gsub("^f", "Frequency", names(TidyData))
names(TidyData)<-gsub("tBody", "TimeBody", names(TidyData))
names(TidyData)<-gsub("-mean()", "Mean", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("-std()", "STD", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("-freq()", "Frequency", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("angle", "Angle", names(TidyData))
names(TidyData)<-gsub("gravity", "Gravity", names(TidyData))

# 5. 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
Finaltidydata <- TidyData %>%
  group_by(subject, activity) %>%
  summarise_all(funs(mean))
write.table(Finaltidydata, "Finaltidydata.txt", row.name=FALSE)

# 6. test results
head(Finaltidydata,3)