run_analysis.R

##Programming assignmnt in getting and cleaning data

# Load information to data frames
activity_labels <- read.table("./UCI HAR Dataset/activity_labels.txt")
features <- read.table("./UCI HAR Dataset/features.txt")
subject_train <- read.table("./UCI HAR Dataset/subject_train.txt")
subject_test <- read.table("./UCI HAR Dataset/subject_test.txt")
X_train <- read.table("./UCI HAR Dataset/X_train.txt")
X_test <- read.table("./UCI HAR Dataset/X_test.txt")
y_train <- read.table("./UCI HAR Dataset/y_train.txt")
y_test <- read.table("./UCI HAR Dataset/y_test.txt")

#Add appropriate names
names(subject_train) <- "Subject_ID"
names(subject_test) <- "Subject_ID"
names(X_train) <- features$V2
names(X_test) <- features$V2
names(y_train) <- "Activity"
names(y_test) <- "Activity"

# combine files into one dataset
train <- cbind(subject_train, y_train, X_train)
test <- cbind(subject_test, y_test, X_test)
combined <- rbind(train, test)

# Extract only mean and standard deviation for each measurement.
ex_features <- grepl("mean\\(\\)", names(combined)) | grepl("std\\(\\)", names(combined))
ex_features[1:2] <- TRUE
combined = combined[ ,ex_features]

#Convert valus to factor
act_group <- factor(combined$Activity)
levels(act_group) <- activity_labels[,2]
combined$Activity <- act_group

# Apply mean function to tidy dataset using dcast
library(reshape2)
melt_combined <- melt(combined, (id.vars=c("Subject_ID","Activity")))
tidy_data   = dcast(melt_combined, Subject_ID + Activity ~ variable, mean)
write.table(tidy_data, file = "./tidy_data.txt", row.name=FALSE)