getdata-015-courseproject

Course Project submission repository for Getting and Cleaning Data courese.

run_analysis.R

R script for the Course Project of "Getting and Cleaning data" by Sungwook Moon

Task 1.

Merges the training and the test sets to create one data set.

1. Download project data

    temp <- tempfile()
    fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
    download.file(fileUrl, temp, method="curl")
   

2. Read train data files which are under the folder train and bind subject id, activity level and train data

    subject_train <- read.table(unz(temp, "UCI HAR Dataset/train/subject_train.txt"), stringsAsFactors=FALSE)
    ylabel_train <- read.table(unz(temp, "UCI HAR Dataset/train/y_train.txt")) 
    ds_train <- read.table(unz(temp, "UCI HAR Dataset/train/X_train.txt"))  # read train dataset 7,352 obs.
    ds_train <- cbind(subject_train, ylabel_train, ds_train)
   

3. Read test data files which are under the folder test and bind subject id, activity level and test data

    subject_test <- read.table("./data/test/subject_test.txt", stringsAsFactors=FALSE)
    ylabel_test <- read.table("./data/test/y_test.txt") 
    ds_test <- read.table("./data/test/X_test.txt")
    ds_test <- cbind(subject_test, ylabel_test, ds_test)
   

4. Merge both datasets and set column names

    ds_merged <- rbind(ds_train, ds_test)
    hdr <- read.table(unz(temp, "UCI HAR Dataset/features.txt"), stringsAsFactors=FALSE)  # read column header
    names(ds_merged) <- c("subjectID", "activity", hdr$V2)
   

Task 2.

Extracts only the measurements on the mean and standard deviation for each measurement.

1. Make column names unique before select

    names(ds_merged) <- make.names(names=names(ds_merged), unique=TRUE)
   

2. Select columns which contains "mean" and "std"

    ds_extracted <- select(ds_merged, subjectID, activity, 
                   contains(".mean"), contains(".std"), -contains(".meanFreq"))
   

Task 3.

Uses descriptive activity names to name the activities in the data set

1. Read activity labels from file

    activity_labels <- read.table(unz(temp, "UCI HAR Dataset/activity_labels.txt"), stringsAsFactors=FALSE)
   

2. Change activity levels to factor type with descriptive labels

    ds_extracted$activity <- as.factor(ds_extracted$activity)
    levels(ds_extracted$activity) <- activity_labels$V2
   

Task 4.

Appropriately labels the data set with descriptive variable names. Using gsub function, change the column names of dataset

names(ds_extracted) <- gsub(".mean","Mean", names(ds_extracted))
names(ds_extracted) <- gsub(".std","Std", names(ds_extracted))
names(ds_extracted) <- gsub("\\.","", names(ds_extracted))
names(ds_extracted) <- gsub("BodyBody","Body", names(ds_extracted))

Task 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.

1. Create tidy dataset

        ds_tidy <- ddply(ds_extracted, .(subjectID, activity), colwise(mean))
   

2. Write dataset to a txt file

        write.table(ds_tidy, file="./tidy_dataset.txt", row.names=FALSE, col.names=TRUE, sep="\t", quote=FALSE)
   

3. Disconnect and remove tempfile

        unlink(temp)
        rm(temp,fileUrl)
   

That is it.