rprogramming

library(dplyr)

Data Download

filename <- "Coursera_DS3_Final.zip"

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

if (!file.exists("UCI HAR Dataset")) { unzip(filename) }

Data frames

features <- read.table("UCI HAR Dataset/features.txt", col.names = c("n","functions")) activity_labels <- 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")

Creating R script called run_analysis.R:

Step 1: Merging the training and the test data sets.

X <- rbind(x_train, x_test) Y <- rbind(y_train, y_test) Subject <- rbind(subject_train, subject_test) Merged_Data <- cbind(Subject, Y, X)

Step 2: Extracting only measurements on the mean and standard deviation for each measurement.

TidyData <- Merged_Data %>% select(subject, code, contains("mean"), contains("std"))

Step 3: Using descriptive activity names to name the activities in the data set.

TidyData$code <- activity_labels[TidyData$code, 2]

Step 4: Appropriately labeling 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))

Step 5: From the data set in step 4, creating a second, independent tidy data set

with the average of each variable for each activity and each subject.

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

Checking variable names

str(FinalData)

Taking a look at final data

FinalData