run_analysis.R

library(dplyr)

# This script,run_analysis.R, 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 variable names.
# 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.

# This script depends on utility script which has all the helper functions
source("./run_analysis_util.R")

data_path = "./data/UCIDataset/"
train_data_file = "/train/X_train.txt"
test_data_file = "/test/X_test.txt"
train_label_file = "/train/y_train.txt"
test_label_file = "/test/y_test.txt"
features_file = "/features.txt"
activilty_label_file = "/activity_labels.txt"
train_subject_file = "/train/subject_train.txt"
test_subject_file = "/test/subject_test.txt"

# Read data from the files
#observations
total_data = join_data(data_path, train_data_file,test_data_file)
#labels
total_labels = join_data(data_path, train_label_file,test_label_file)
#subjects 
total_subjects = join_data(data_path,train_subject_file,test_subject_file)
#activity text labels
activity_labels = get_activity_names(data_path,activilty_label_file)


# Get the columns to be kept i.e. only with means and standard deviation
cols = get_feature_cols_means_std(data_path,features_file)
cols_to_keep= cols[[1]]
# select the given columns as needed
total_data = total_data %>% select(cols_to_keep)

# change the integer labels with text labels
total_labels = activity_labels[match(total_labels[,1],activity_labels[,1]),2]
#total_labels = as.factor(total_labels)

# Combine observations and labels
total_set = cbind(total_data,total_labels,total_subjects)

# Give descriptive names to variables
cols_names = cols[[2]]
names(total_set) =c(cols_names,"activitylabel","subject")

averages = total_set %>% group_by(activitylabel,subject) %>% summarize_all(funs(mean))