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Introductions for assignment

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected.

One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Here are the data for the project:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

You should create one R script called run_analysis.R that does the following.

  • Merges the training and the test sets to create one data set
  • Extracts only the measurements on the mean and standard deviation for each measurement
  • Uses descriptive activity names to name the activities in the data set
  • Appropriately labels the data set with descriptive variable names
  • 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

Codebook: the work to clean up the data

The run_analysis.R script contains the code from downloading the data to the clean up steps, the precedure is described as follows:

1. Download and unzip the files

  • Create directory ./data if it doesn't exist.
  • Download the dataset.zip file if it doesn't exist.
  • Unzip the dataset.zip file in the ./data directory.
if (!file.exists("./data")) {
  dir.create("./data")
}

if (!file.exists("./data/dataset.zip")) {
  fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
  download.file(fileUrl, destfile = "./data/dataset.zip", method = "curl")
}

unzip(zipfile = "./data/dataset.zip", exdir = "./data")

2. Read the files into R

The files that will be used in this assignment are listed as follows:

  • activity_labels.txt: names for the six activities
  • features.txt: list of all features (variables)
  • subject_test.txt: the subject number for each row of test group
  • X_test.txt: the data collected of test group
  • y_test.txt: the activity number for each row of test group
  • subject_train.txt: the subject number for each row of train group
  • X_train.txt: the data collected of train group
  • y_train.txt: the activity number for each row of train group

The final data frame will have the following columns and rows:

  • Columns (variables):
    • Subject: number from 1-30 (30 volunteers)
    • Activity: number from 1-6 (6 activities)
    • All the features from features.txt file
  • Rows (observations):
    • Values for each feature: X_test.txt and X_train.txt
    • Numbers of subjects: subject_test.txt and subject_train.txt
    • Numbers of activities: y_test.txt and y_train.txt

The codes in run_analysis.R contain the following steps:

  • Read the data of activities from y_test.txt and y_train.txt
  • Read the data of subjects from subject_test.txt and subject_train.txt
  • Read the data of features from X_test.txt and X_train.txt
  • Read the names of features (variables) from features.txt
datafolder <- "./data/UCI HAR Dataset/"

# Read the data of activities
activity_test <- read.table(file.path(datafolder, "test", "y_test.txt"))
activity_train <- read.table(file.path(datafolder, "train", "y_train.txt"))

# Read the data of subjects
subject_test <- read.table(file.path(datafolder, "test", "subject_test.txt"))
subject_train <- read.table(file.path(datafolder, "train", "subject_train.txt"))

# Read the data of features
feature_test <- read.table(file.path(datafolder, "test", "X_test.txt"))
feature_train <- read.table(file.path(datafolder, "train", "X_train.txt"))

# Read the names of features (variables)
feature_name <- read.table(file.path(datafolder, "features.txt"))

3. Merge all data frames into one data frame

  • Merge rows: merge the train and test data of activity_data, subject_data and feature_data, respectively. The results are three data frames:
    • activity_data: 10299 rows and 1 column
    • subject_data: 10299 rows and 1 column
    • feature_data: 10299 rows and 561 columns
  • Merge columns: merge the three data frames obtained from the last step: the result data frame has 10299 rows and 563 columns
  • Set names to variables: subject as subject, activity as activity, and the feature names from features.txt
# Merge rows
activity_data <- rbind(activity_train, activity_test)
subject_data <- rbind(subject_train, subject_test)
feature_data <- rbind(feature_train, feature_test)

# Merge columns
dataset <- cbind(subject_data, activity_data, feature_data)

# Set names to variables
colnames(dataset) <- c("subject", "activity", feature_name$V2)

4. Extract only the measurements on the mean and standard deviation

Grep columns which have mean(), std(), subject, or activity in their names

subdataset <- dataset[, grep("mean\\(\\)|std\\(\\)|subject|activity", names(dataset))]

5. Use decriptive activity names to name the activities in the data set

Based on the activity names in activity_labels.txt, the values in column activity are replaced as follows:

  • 1 as WALKING
  • 2 as WALKING_UPSTAIRS
  • 3 as WALKING_DOWNSTAIRS
  • 4 as SITTING
  • 5 as STANDING
  • 6 as LAYING
subdataset$activity[subdataset$activity == 1] <- "WALKING"
subdataset$activity[subdataset$activity == 2] <- "WALKING_UPSTAIRS"
subdataset$activity[subdataset$activity == 3] <- "WALKING_DOWNSTAIRS"
subdataset$activity[subdataset$activity == 4] <- "SITTING"
subdataset$activity[subdataset$activity == 5] <- "STANDING"
subdataset$activity[subdataset$activity == 6] <- "LAYING"

6. Appropriately label the data set with dexcriptive variable names

The names of features will be fixed by the following standards:

  • prefix t will be replaced by time
  • Acc will be replaced by Accelerometer
  • Gyro will be replaced by Gyroscope
  • prefix f will be replaced by frequency
  • Mag will be replaced by Magnitude
  • BodyBody will be replaced by Body
  • () will be deleted
  • - will be replaced by _
names(subdataset) <- gsub("^t", "time", names(subdataset))
names(subdataset) <- gsub("Acc", "Accelerometer", names(subdataset))
names(subdataset) <- gsub("Gyro", "Gyroscope", names(subdataset))
names(subdataset) <- gsub("^f", "frequency", names(subdataset))
names(subdataset) <- gsub("Mag", "Magnitude", names(subdataset))
names(subdataset) <- gsub("BodyBody", "Body", names(subdataset))
names(subdataset) <- gsub("\\(\\)", "", names(subdataset))
names(subdataset) <- gsub("-", "_", names(subdataset))

7. Creates a second, independent tidy data set with the average of each variable for each activity and each subject

  • Group the data frame by subject and activity
  • Calculate the average of each variable by summarise_all()
library(dplyr)
group_subject_activity <- group_by(subdataset, subject, activity)
dataset_ave <- summarise_all(group_subject_activity, mean, na.rm = TRUE)

8. Export the result data frame as .txt file

Export using write.table() with row.names = FALSE, the file was named as tidydata_ave.txt.

write.table(dataset_ave, "./tidydata_ave.txt", row.names = FALSE)