mtroian1 / getting-and-cleaning-data-course-project

Getting-and-Cleaning-Data-Course-Project

run_analysis.R:

This first set of codes creates a temporary file to store the zipped file, downloads and unzips the files, reads then, and then removes the temporary file that is no longer needed.

temp <- tempfile() #Create a temporary file to store the zipped file fileURL <- "http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" download.file(fileURL, destfile = temp, mode = "wb") #downloads it into file "temp" with wb fileUnzipped <- unzip(temp) #unzip testFile <- read.table("./UCI HAR Dataset/test/X_test.txt") trainFile <- read.table("./UCI HAR Dataset/train/X_train.txt") unlink(temp) #remove the temporary file

Now work on the test data. Download the label and subject txt files. The testSubject matches with trainSubject.

testLabel <- read.table("./UCI HAR Dataset/test/y_test.txt") testSubject <- read.table("./UCI HAR Dataset/test/subject_test.txt")

Rename the columns of the label and subject txt files.

colnames(testLabel) <- "labels" colnames(testSubject) <- "subject"

Import variable names for test file.

variableNames <- read.table("./UCI HAR Dataset/features.txt", header = FALSE) newNames <- variableNames[ ,2] colnames(testFile) <- newNames

Bind the test labels and test file together to create new dataframe with correct variable names.

bindedTestData <- cbind(testSubject, testLabel, testFile)

Now work on the train data. Download the label and subject txt files. The trainSubject matches with testSubject

trainLabel <- read.table("./UCI HAR Dataset/train/y_train.txt") trainSubject <- read.table("./UCI HAR Dataset/train/subject_train.txt")

Rename the columns of the label and subject txt files.

colnames(trainLabel) <- "labels" colnames(trainSubject) <- "subject"

Import variable names for test file. Can skip these first two codes and do colnames() since it's the same as test.

#variableNamesTrain <- read.table("./UCI HAR Dataset/features.txt", header = FALSE) #newNames <- variableNames[ ,2]

colnames(trainFile) <- newNames

Bind the train labels and train file together to create a new dataframe with correct variable names.

bindedTrainData <- cbind(trainSubject, trainLabel, trainFile)

Merge the bindedTestData and bindedTrainData together

#Use 1:563 to specify we want to merge them based on their 563 common variables #all=TRUE to merge all data from x and y sets #no.dups prevents duplicates from being added in and doubling the variable amount #sort=FALSE does not sort automatically so we can decide how to order it

mergedData <- merge(bindedTestData, bindedTrainData, by.x = 1:563, by.y = 1:563, all = TRUE, no.dups = TRUE, sort = FALSE)

Order data based on subject

mergedDataSorted <- mergedData[order(mergedData$subject),]

Add descriptive labels to mergedDataSorted$labels.

#$labels is a numeric and needs to be changed to a factor. Assign it the six activity labels. #Assign these updates to mergedDataSorted$labels to update the df

as.factor(mergedDataSorted$labels) mergedDataSorted$labels <- factor(mergedDataSorted$labels, levels = c(1, 2, 3, 4, 5, 6), labels = c("walking", "walking upstairs", "walking downstairs", "sitting", "standing", "laying"))

Extract the mean and standard deviation for each measurement. First 2 variables are for subject/labels. Should be around 68 variables.

#mean(): Mean values and std(): Standard deviation #c(1:8,43:48, 83:88, 123:128, 163:168, 203:205,216:217, 229:230, 242:243, 255:256, 268:273, 347:352, 426:431, 505:506,518:519, 531:532, 544:545)]

library(dplyr) mergedDataExtractedTest <- select(mergedDataSorted, contains(c("subject", "labels", "mean()", "std()")))

Label the data set with descriptive variable names.

#rename(new variable name = existing variable name) #gsub will replace the punctuation marks: dash to period #rename changes labels to activity. Then changes activity and subject to uppercase.

library(stringr) mergedDataExtractedTest <- rename(mergedDataExtractedTest, Subject = subject, Activity = labels) colnames(mergedDataExtractedTest) <- gsub("-", ".", colnames(mergedDataExtractedTest)) colnames(mergedDataExtractedTest) <- gsub("()", "", colnames(mergedDataExtractedTest), fixed = TRUE) colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "tBody", "TimeBody") colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "fBody", "FrequencyBody") colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "tGravity", "TimeGravity") colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "Gyro", "Gyroscope") colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "Acc", "Acceleration") colnames(mergedDataExtractedTest) <- str_replace_all(colnames(mergedDataExtractedTest), "Mag", "Magnitude") colnames(mergedDataExtractedTest)

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

library(data.table) independentData <- copy(mergedDataExtractedTest) independentDataAverages2 <- independentData %>% group_by(Subject, Activity) %>% summarise(across(where(is.numeric), mean)) independentDataAverages2