Instructions
The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. Review criterialess The submitted data set is tidy. The Github repo contains the required scripts. GitHub contains a code book that modifies and updates the available codebooks with the data to indicate all the variables and summaries calculated, along with units, and any other relevant information. The README that explains the analysis files is clear and understandable. The work submitted for this project is the work of the student who submitted it. Getting and Cleaning Data Course Projectless 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.
The codebook is at the end of this document.
Load packages.
packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
Set path.
path <- getwd()
path
Download the file. Put it in the Data folder.
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
f <- "Dataset.zip"
if (!file.exists(path)) {dir.create(path)}
download.file(url, file.path(path, f))
Unzip the file.
The archive put the files in a folder named UCIHARDataset. Set this folder as the input path. List the files here.
pathIn <- file.path(path, "UCI HAR Dataset")
list.files(pathIn, recursive=TRUE)
Read the subject files.
dtSubjectTrain <- fread(file.path(pathIn, "train", "subject_train.txt"))
dtSubjectTest <- fread(file.path(pathIn, "test" , "subject_test.txt" ))
Read the activity files.
dtActivityTrain <- fread(file.path(pathIn, "train", "Y_train.txt"))
dtActivityTest <- fread(file.path(pathIn, "test" , "Y_test.txt" ))
Read the data files. I used a helper function, read the file with read.table instead, then convert the resulting data frame to a data table. Return the data table.
fileToDataTable <- function (f) {
df <- read.table(f)
dt <- data.table(df)
}
dtTrain <- fileToDataTable(file.path(pathIn, "train", "X_train.txt"))
dtTest <- fileToDataTable(file.path(pathIn, "test" , "X_test.txt" ))
Concatenate the data tables.
dtSubject <- rbind(dtSubjectTrain, dtSubjectTest)
setnames(dtSubject, "V1", "subject")
dtActivity <- rbind(dtActivityTrain, dtActivityTest)
setnames(dtActivity, "V1", "activityNum")
dt <- rbind(dtTrain, dtTest)
Merge columns.
dtSubject <- cbind(dtSubject, dtActivity)
dt <- cbind(dtSubject, dt)
Set key.
setkey(dt, subject, activityNum)
Read the features.txt file. This tells which variables in dt are measurements for the mean and standard deviation.
dtFeatures <- fread(file.path(pathIn, "features.txt"))
setnames(dtFeatures, names(dtFeatures), c("featureNum", "featureName"))
Subset only measurements for the mean and standard deviation.
dtFeatures <- dtFeatures[grepl("mean\\(\\)|std\\(\\)", featureName)]
Convert the column numbers to a vector of variable names matching columns in dt.
dtFeatures$featureCode <- dtFeatures[, paste0("V", featureNum)]
head(dtFeatures)
dtFeatures$featureCode
Subset these variables using variable names.
select <- c(key(dt), dtFeatures$featureCode)
dt <- dt[, select, with=FALSE]
Read activity_labels.txt file. This will be used to add descriptive names to the activities.
dtActivityNames <- fread(file.path(pathIn, "activity_labels.txt"))
setnames(dtActivityNames, names(dtActivityNames), c("activityNum", "activityName"))
Merge activity labels.
dt <- merge(dt, dtActivityNames, by="activityNum", all.x=TRUE)
Add activityName as a key.
setkey(dt, subject, activityNum, activityName)
Melt the data table to reshape it from a short and wide format to a tall and narrow format.
dt <- data.table(melt(dt, key(dt), variable.name="featureCode"))
Merge activity name.
dt <- merge(dt, dtFeatures[, list(featureNum, featureCode, featureName)], by="featureCode", all.x=TRUE)
Create a new variable, activity that is equivalent to activityName as a factor class.
Create a new variable, feature that is equivalent to featureName as a factor class.
dt$activity <- factor(dt$activityName)
dt$feature <- factor(dt$featureName)
Seperate features from featureName using the helper function grepthis.
grepthis <- function (regex) {
grepl(regex, dt$feature)
}
## Features with 2 categories
n <- 2
y <- matrix(seq(1, n), nrow=n)
x <- matrix(c(grepthis("^t"), grepthis("^f")), ncol=nrow(y))
dt$featDomain <- factor(x %*% y, labels=c("Time", "Freq"))
x <- matrix(c(grepthis("Acc"), grepthis("Gyro")), ncol=nrow(y))
dt$featInstrument <- factor(x %*% y, labels=c("Accelerometer", "Gyroscope"))
x <- matrix(c(grepthis("BodyAcc"), grepthis("GravityAcc")), ncol=nrow(y))
dt$featAcceleration <- factor(x %*% y, labels=c(NA, "Body", "Gravity"))
x <- matrix(c(grepthis("mean()"), grepthis("std()")), ncol=nrow(y))
dt$featVariable <- factor(x %*% y, labels=c("Mean", "SD"))
## Features with 1 category
dt$featJerk <- factor(grepthis("Jerk"), labels=c(NA, "Jerk"))
dt$featMagnitude <- factor(grepthis("Mag"), labels=c(NA, "Magnitude"))
## Features with 3 categories
n <- 3
y <- matrix(seq(1, n), nrow=n)
x <- matrix(c(grepthis("-X"), grepthis("-Y"), grepthis("-Z")), ncol=nrow(y))
dt$featAxis <- factor(x %*% y, labels=c(NA, "X", "Y", "Z"))
Check to make sure all possible combinations of feature are accounted for by all possible combinations of the factor class variables.
r1 <- nrow(dt[, .N, by=c("feature")])
r2 <- nrow(dt[, .N, by=c("featDomain", "featAcceleration", "featInstrument", "featJerk", "featMagnitude", "featVariable", "featAxis")])
r1 == r2
TRUE, I accounted for all possible combinations. feature is now redundant.
Create a data set with the average of each variable for each activity and each subject.
setkey(dt, subject, activity, featDomain, featAcceleration, featInstrument, featJerk, featMagnitude, featVariable, featAxis)
dtTidy <- dt[, list(count = .N, average = mean(value)), by=key(dt)]
f <- file.path(path, "tidy.txt")
write.table(dtTidy, f, quote=FALSE, sep="\t", row.names=FALSE)
memisc::codebook(dtTidy)
subject
Storage mode: integer
Min.: 1.000
1st Qu.: 8.000
Median: 15.500
Mean: 15.500
3rd Qu.: 23.000
Max.: 30.000
============================================================================================================================
activity
Storage mode: integer Factor with 6 levels
Values and labels N Percent
1 'LAYING' 1980 16.7
2 'SITTING' 1980 16.7
3 'STANDING' 1980 16.7
4 'WALKING' 1980 16.7
5 'WALKING_DOWNSTAIRS' 1980 16.7
6 'WALKING_UPSTAIRS' 1980 16.7
============================================================================================================================
featDomain
Storage mode: integer Factor with 2 levels
Values and labels N Percent
1 'Time' 7200 60.6
2 'Freq' 4680 39.4
============================================================================================================================
featAcceleration
Storage mode: integer Factor with 3 levels
Values and labels N Percent
1 'NA' 4680 39.4
2 'Body' 5760 48.5
3 'Gravity' 1440 12.1
============================================================================================================================
featInstrument
Storage mode: integer Factor with 2 levels
Values and labels N Percent
1 'Accelerometer' 7200 60.6
2 'Gyroscope' 4680 39.4
============================================================================================================================
featJerk
Storage mode: integer Factor with 2 levels
Values and labels N Percent
1 'NA' 7200 60.6
2 'Jerk' 4680 39.4
============================================================================================================================
featMagnitude
Storage mode: integer Factor with 2 levels
Values and labels N Percent
1 'NA' 8640 72.7
2 'Magnitude' 3240 27.3
============================================================================================================================
featVariable
Storage mode: integer Factor with 2 levels
Values and labels N Percent
1 'Mean' 5940 50.0
2 'SD' 5940 50.0
============================================================================================================================
featAxis
Storage mode: integer Factor with 4 levels
Values and labels N Percent
1 'NA' 3240 27.3
2 'X' 2880 24.2
3 'Y' 2880 24.2
4 'Z' 2880 24.2
============================================================================================================================
count
Storage mode: integer
Min.: 36.000
1st Qu.: 49.000
Median: 54.500
Mean: 57.220
3rd Qu.: 63.250
Max.: 95.000
============================================================================================================================
average
Storage mode: double
Min.: -0.998
1st Qu.: -0.962
Median: -0.470
Mean: -0.484
3rd Qu.: -0.078
Max.: 0.975