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<!DOCTYPE html>
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<title>run_analysis</title>
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<h1>run_analysis</h1>
<h2>Preliminaries</h2>
<p>Load packages.</p>
<pre><code class="r">packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
</code></pre>
<pre><code>## Warning: package 'data.table' was built under R version 3.2.3
</code></pre>
<pre><code>## data.table 1.9.6 For help type ?data.table or https://github.com/Rdatatable/data.table/wiki
</code></pre>
<pre><code>## The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way
</code></pre>
<pre><code>##
## Attaching package: 'reshape2'
</code></pre>
<pre><code>## The following objects are masked from 'package:data.table':
##
## dcast, melt
</code></pre>
<pre><code>## data.table reshape2
## TRUE TRUE
</code></pre>
<p>Set path.</p>
<pre><code class="r">path <- getwd()
path
</code></pre>
<pre><code>## [1] "/Users/ricadomanhaes/Documents/Coursera/Data_Science/Getting_and_Cleaning_Data/Project"
</code></pre>
<h2>Get the data</h2>
<p>Download the file. Put it in the <code>Data</code> folder. </p>
<pre><code class="r">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))
</code></pre>
<p>Unzip the file.</p>
<pre><code class="r">unzip(f)
</code></pre>
<p>The archive put the files in a folder named <code>Dataset</code>. Set this folder as the input path.</p>
<pre><code class="r">pathIn <- file.path(path, "UCI HAR Dataset")
list.files(pathIn, recursive=TRUE)
</code></pre>
<pre><code>## [1] "activity_labels.txt"
## [2] "features_info.txt"
## [3] "features.txt"
## [4] "README.txt"
## [5] "test/Inertial Signals/body_acc_x_test.txt"
## [6] "test/Inertial Signals/body_acc_y_test.txt"
## [7] "test/Inertial Signals/body_acc_z_test.txt"
## [8] "test/Inertial Signals/body_gyro_x_test.txt"
## [9] "test/Inertial Signals/body_gyro_y_test.txt"
## [10] "test/Inertial Signals/body_gyro_z_test.txt"
## [11] "test/Inertial Signals/total_acc_x_test.txt"
## [12] "test/Inertial Signals/total_acc_y_test.txt"
## [13] "test/Inertial Signals/total_acc_z_test.txt"
## [14] "test/subject_test.txt"
## [15] "test/X_test.txt"
## [16] "test/y_test.txt"
## [17] "train/Inertial Signals/body_acc_x_train.txt"
## [18] "train/Inertial Signals/body_acc_y_train.txt"
## [19] "train/Inertial Signals/body_acc_z_train.txt"
## [20] "train/Inertial Signals/body_gyro_x_train.txt"
## [21] "train/Inertial Signals/body_gyro_y_train.txt"
## [22] "train/Inertial Signals/body_gyro_z_train.txt"
## [23] "train/Inertial Signals/total_acc_x_train.txt"
## [24] "train/Inertial Signals/total_acc_y_train.txt"
## [25] "train/Inertial Signals/total_acc_z_train.txt"
## [26] "train/subject_train.txt"
## [27] "train/X_train.txt"
## [28] "train/y_train.txt"
</code></pre>
<h2>Read the files</h2>
<p>Read the subject files.</p>
<pre><code class="r">fileToDataTable <- function (f) {
df <- read.table(f)
dt <- data.table(df)
}
dtSubjectTrain <- fileToDataTable(file.path(pathIn, "train", "subject_train.txt"))
dtSubjectTest <- fileToDataTable(file.path(pathIn, "test" , "subject_test.txt" ))
</code></pre>
<p>Read the label files.</p>
<pre><code class="r">dtActivityTrain <- fileToDataTable(file.path(pathIn, "train", "y_train.txt"))
dtActivityTest <- fileToDataTable(file.path(pathIn, "test" , "y_test.txt" ))
</code></pre>
<p>Read the data files.</p>
<pre><code class="r">dtTrain <- fileToDataTable(file.path(pathIn, "train", "X_train.txt"))
dtTest <- fileToDataTable(file.path(pathIn, "test" , "X_test.txt" ))
</code></pre>
<p>Read the key files.</p>
<pre><code class="r">dtFeatures <- fileToDataTable(file.path(pathIn, "features.txt"))
dtActivityNames <- fileToDataTable(file.path(pathIn, "activity_labels.txt"))
</code></pre>
<h2>1. Merge the training and the test sets</h2>
<p>Concatenate the data tables.</p>
<pre><code class="r">dtSubject <- rbind(dtSubjectTrain, dtSubjectTest)
setnames(dtSubject, "V1", "subject")
dtActivity <- rbind(dtActivityTrain, dtActivityTest)
setnames(dtActivity, "V1", "activityNum")
dt <- rbind(dtTrain, dtTest)
</code></pre>
<p>Merge columns.</p>
<pre><code class="r">dtSubject <- cbind(dtSubject, dtActivity)
dt <- cbind(dtSubject, dt)
</code></pre>
<p>Set key.</p>
<pre><code class="r">setkey(dt, subject, activityNum)
</code></pre>
<h2>2. Extract only the mean and standard deviation</h2>
<p>Read the <code>features.txt</code> file. This tells which variables in <code>dt</code> are measurements for the mean and standard deviation.</p>
<pre><code class="r">setnames(dtFeatures, names(dtFeatures), c("featureNum", "featureName"))
</code></pre>
<p>Subset only measurements for the mean and standard deviation.</p>
<pre><code class="r">dtFeatures <- dtFeatures[grepl("mean\\(\\)|std\\(\\)", featureName)]
</code></pre>
<p>Convert the column numbers to a vector of variable names matching columns in <code>dt</code>.</p>
<pre><code class="r">dtFeatures$featureCode <- dtFeatures[, paste0("V", featureNum)]
head(dtFeatures)
</code></pre>
<pre><code>## featureNum featureName featureCode
## 1: 1 tBodyAcc-mean()-X V1
## 2: 2 tBodyAcc-mean()-Y V2
## 3: 3 tBodyAcc-mean()-Z V3
## 4: 4 tBodyAcc-std()-X V4
## 5: 5 tBodyAcc-std()-Y V5
## 6: 6 tBodyAcc-std()-Z V6
</code></pre>
<pre><code class="r">dtFeatures$featureCode
</code></pre>
<pre><code>## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V41" "V42" "V43" "V44"
## [11] "V45" "V46" "V81" "V82" "V83" "V84" "V85" "V86" "V121" "V122"
## [21] "V123" "V124" "V125" "V126" "V161" "V162" "V163" "V164" "V165" "V166"
## [31] "V201" "V202" "V214" "V215" "V227" "V228" "V240" "V241" "V253" "V254"
## [41] "V266" "V267" "V268" "V269" "V270" "V271" "V345" "V346" "V347" "V348"
## [51] "V349" "V350" "V424" "V425" "V426" "V427" "V428" "V429" "V503" "V504"
## [61] "V516" "V517" "V529" "V530" "V542" "V543"
</code></pre>
<p>Subset these variables using variable names.</p>
<pre><code class="r">select <- c(key(dt), dtFeatures$featureCode)
dt <- dt[, select, with=FALSE]
</code></pre>
<h2>3. Use descriptive activity names</h2>
<p>Use the <code>activity_labels.txt</code> file. This will be used to add descriptive names to the activities.</p>
<pre><code class="r">setnames(dtActivityNames, names(dtActivityNames), c("activityNum", "activityName"))
</code></pre>
<h2>4. Appropriately labels the data set with descriptive variable names.</h2>
<p>Merge activity labels.</p>
<pre><code class="r">dt <- merge(dt, dtActivityNames, by="activityNum", all.x=TRUE)
</code></pre>
<p>Add <code>activityName</code> as a key.</p>
<pre><code class="r">setkey(dt, subject, activityNum, activityName)
</code></pre>
<p>Melt the data table to reshape it from a short and wide format to a tall and narrow format.</p>
<pre><code class="r">dt <- data.table(melt(dt, key(dt), variable.name="featureCode"))
</code></pre>
<p>Merge activity name.</p>
<pre><code class="r">dt <- merge(dt, dtFeatures[, list(featureNum, featureCode, featureName)], by="featureCode", all.x=TRUE)
</code></pre>
<p>Create a new variable, <code>activity</code> that is equivalent to <code>activityName</code> as a factor class.
Create a new variable, <code>feature</code> that is equivalent to <code>featureName</code> as a factor class.</p>
<pre><code class="r">dt$activity <- factor(dt$activityName)
dt$feature <- factor(dt$featureName)
</code></pre>
<p>Separate features from <code>featureName</code> using the helper function <code>grepthis</code>.</p>
<pre><code class="r">grepthis <- function (regex) {
grepl(regex, dt$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"))
</code></pre>
<p>Check to make sure all possible combinations of <code>feature</code> are accounted for by all possible combinations of the factor class variables.</p>
<pre><code class="r">r1 <- nrow(dt[, .N, by=c("feature")])
r2 <- nrow(dt[, .N, by=c("featDomain", "featAcceleration", "featInstrument", "featJerk", "featMagnitude", "featVariable", "featAxis")])
r1 == r2
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<h2>5. Create a new independent tidy data set</h2>
<p>Create a data set with the average of each variable for each activity and each subject.</p>
<pre><code class="r">setkey(dt, subject, activity, featDomain, featAcceleration, featInstrument, featJerk, featMagnitude, featVariable, featAxis)
dtTidy <- dt[, list(count = .N, average = mean(value)), by=key(dt)]
</code></pre>
<p>Make codebook.</p>
<pre><code class="r">knit("createCodebook.Rmd", output="codebook.md", encoding="ISO8859-1", quiet = TRUE)
</code></pre>
<pre><code>## [1] "codebook.md"
</code></pre>
<pre><code class="r">markdownToHTML("codebook.md", "codebook.html")
</code></pre>
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