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-run_analysis.r

## Create one R script called run_analysis.R that does the following: ## 1. Merges the training and the test sets to create one data set. ## 2. Extracts only the measurements on the mean and standard deviation for each measurement. ## 3. Uses descriptive activity names to name the activities in the data set ## 4. Appropriately labels the data set with descriptive activity names. ## 5. Creates a second, independent tidy data set with the average of each variable for each activity and each subject. if (!require("data.table")) { install.packages("data.table") } if (!require("reshape2")) { install.packages("reshape2") } require("data.table") require("reshape2") # Load: activity labels activity_labels <- read.table("./UCI HAR Dataset/activity_labels.txt")[,2] # Load: data column names features <- read.table("./UCI HAR Dataset/features.txt")[,2] # Extract only the measurements on the mean and standard deviation for each measurement. extract_features <- grepl("mean|std", features) # Load and process X_test & y_test data. X_test <- read.table("./UCI HAR Dataset/test/X_test.txt") y_test <- read.table("./UCI HAR Dataset/test/y_test.txt") subject_test <- read.table("./UCI HAR Dataset/test/subject_test.txt") names(X_test) = features # Extract only the measurements on the mean and standard deviation for each measurement. X_test = X_test[,extract_features] # Load activity labels y_test[,2] = activity_labels[y_test[,1]] names(y_test) = c("Activity_ID", "Activity_Label") names(subject_test) = "subject" # Bind data test_data <- cbind(as.data.table(subject_test), y_test, X_test) # Load and process X_train & y_train data. X_train <- read.table("./UCI HAR Dataset/train/X_train.txt") y_train <- read.table("./UCI HAR Dataset/train/y_train.txt") subject_train <- read.table("./UCI HAR Dataset/train/subject_train.txt") names(X_train) = features # Extract only the measurements on the mean and standard deviation for each measurement. X_train = X_train[,extract_features] # Load activity data y_train[,2] = activity_labels[y_train[,1]] names(y_train) = c("Activity_ID", "Activity_Label") names(subject_train) = "subject" # Bind data train_data <- cbind(as.data.table(subject_train), y_train, X_train) # Merge test and train data data = rbind(test_data, train_data) id_labels = c("subject", "Activity_ID", "Activity_Label") data_labels = setdiff(colnames(data), id_labels) melt_data = melt(data, id = id_labels, measure.vars = data_labels) # Apply mean function to dataset using dcast function tidy_data = dcast(melt_data, subject + Activity_Label ~ variable, mean) write.table(tidy_data, file = "./tidy_data.txt")

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