run_analysis.R

library(dplyr)

#if working directory *contains* the data set, go inside
base_dir="UCI HAR Dataset"
if(file.exists(base_dir))
    setwd(base_dir)
#else we assume the working directory is the content of the data folder

#Merge the training and the test sets to create one data set.
    #merge subjects
    train_subjects <- read.table("train/subject_train.txt",col.names="subject_id")
    test_subjects <- read.table("test/subject_test.txt",col.names="subject_id")
    subjects <- bind_rows(train_subjects,test_subjects)

    #merge training activity labels
    train_activities <- read.table("train/y_train.txt",col.names="act_id")
    test_activities  <- read.table("test/y_test.txt",col.names="act_id")
    activities <- bind_rows(train_activities,test_activities)

    # Load measurements set with descriptive features labels
    # Data is appropriately labelled by removing special chars (parenthesis, -)
    features <- read.table("features.txt",col.names=c("id","label"))
    clean_label <- gsub("\\(|\\)","",features$label)
    clean_label <- gsub("-","_",clean_label)
    train_set <- read.table("train/X_train.txt",col.names=clean_label)
    test_set <- read.table("test/X_test.txt",col.names=clean_label)
    measurements <- bind_rows(train_set,test_set)

#create data frame with all attributes and observations
    alltogether <- data.frame(subjects,activities,measurements)

#Extract only the measurements on the mean and standard deviation for each measurement.
    cleanset <- select(alltogether,subject_id,act_id,matches("*(std|mean)($|_)",ignore.case = FALSE))

#Use descriptive activity names to name the activities in the data set
    features <- read.table("activity_labels.txt",col.names=c("act_id","act_name"))
    cleanset<-cleanset %>%
        inner_join(features) %>%
        select(subject_id,act_name,everything(),-act_id)

#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. We prefix each calculated average with the "mbgv_" string for "mean of grouped variable" using the setNames function
avg_va <-cleanset %>%
    group_by(subject_id,act_name) %>%
    summarise_each(funs(mean)) %>%
    setNames(c(names(.)[1:2], paste0("mogo_",names(.)[0:-2])))

#export the clean data set from step 5 as a csv file
#file is exported in parent folder
#since we made sure we went inside the UCI HAR Dataset folder
write.table(avg_va,file="../tidy.csv",row.names=FALSE)