Getting-and-Cleaning-Data-Course-Project.R

## run_analysis.R
## 
## 
## The purpose of this project is to demonstrate course students' ability to collect,
## work with, and clean a data set. The goal is to prepare tidy data that can be used
## for later analysis.
## This script is written for creating a tidy data file that can be used for later analysis.
## There are five main steps to fulfill the above goal.
##
## input files: x_train.txt,x_test.txt,y_train.txt,y_test.txt,subject_train.txt,subject_test.txt
##              features.txt, activity_labels.txt
## output files: tidy_average_data.txt, combinedcleaningdata.txt
##
## required special package: reshape2
##
## usage in R:
## Requirement: the data files and run_analysis.R should be in the same working directory in Rstudio
##
## > source("run_analysis.R")
## > run_analysis()
##  OR
## > ra <- run_analysis()
## > ra
## [1] "TRUE" ## re-confirm the cleaning process has been performed and a tidy data file
##               has been created.
##
#########################################################################################
#########################################################################################
##
run_analysis <- function() {
#
#########################################################################################
## Step 0: load the required special package: reshape2 (to use melt fuction and dcast function)
##         melt function and dcast function will be used in Step 5
##
     if (!("reshape2" %in% rownames(installed.packages())) ) {
        stop ("Please install required package: reshape2!\n")
     } 

## Load the required package: reshape2
     library(reshape2)

#########################################################################################
## Step 1: Merges the training and the test sets to create one data set
##
## Three pairs of data (training and test data sets) need to be merged together
## They are: X_train.txt, X_test.txt; (data set)
##           y_train.txt, y_test.txt; (label-data set)
##           subject_train.txt, subject_test.txt (subject-data set)
##   

## load/read the data sets and then merge the data sets
     cat("\n")
     cat("Step1: Merges the training and the test set to create on data set.\n")
####### read and merge data set pair##################################
     traindata <- read.table("./train/X_train.txt")
     testdata  <- read.table("./test/X_test.txt")
     joindata  <- rbind(traindata, testdata) 

## cross-check dimensions: (observations/rows, variables/columns)
     dim(traindata) ##  (7352, 561)
     dim(testdata)  ##  (2947, 561)
     dim(joindata)  ## (10299, 561)

####### read and merge label-data set pair ##############################
     trainlabel <- read.table("./train/y_train.txt")
     testlabel  <- read.table("./test/y_test.txt")
     joinlabel  <- rbind(trainlabel, testlabel)

## cross-check dimensions: (observations/rows, variables/columns)
     dim(trainlabel) ## (7352, 1)
     dim(testlabel)  ## (2947, 1)
     dim(joinlabel)  ##(10299, 1)

### read and merge subject-data set pair #################################
     trainsubject <- read.table("./train/subject_train.txt")
     testsubject  <- read.table("./test/subject_test.txt")
     joinsubject  <- rbind(trainsubject, testsubject)

##  cross-check dimensions: meanStdIndex <- grep("-mean\\(\\) | -std\\(\\)", features[, 2])
     dim(trainsubject) ##  (7352, 1)
     dim(testsubject)  ##  (2947, 1)
     dim(joinsubject)  ## (10299, 1)

#########################################################################################
## Step 2: Extracts only the measurements on the mean and standard deviation for 
##      each measurement.
##

     cat("\n")
     cat("Step2: Extracts only the measurements on the mean and standard deviation for each measurement.\n")
##
### In order to extract the measurements on the mean and standard deviation,
##  I need locate variable names (column names) related to "mean" and
##  "standard deviation" stored in the file "features.txt" via the command grep

     features <- read.table("features.txt") ##read/load the file
## cross-check dimensions: (rows, columns)
     dim(features) ## (561, 2)

## locate column names with "-mean()" or "-std()" in any rows in column 2
     meanstdindex <- grep("-mean\\(\\)|-std\\(\\)", features[, 2])

##cross-check length(meanstdindex)
     length(meanstdindex) ## 66 variables

## only select those columns/measurements on the mean and standard deviation
## make a new data frame called joindatanew
     joindatanew <- joindata[, meanstdindex] 

## cross-check dimensions: (rows, columns)
     dim(joindatanew)  ##(10299,66)

## put the column names into the new data frame joindatanew
     colnames(joindatanew) <- features[meanstdindex, 2] 

## remove the bad characters such as "()", "-" in colnames, also lower the case if possible
##   in order to avoid any unnecessary errors in later analysis
##
     colnames(joindatanew) <- gsub("\\(|\\)", "", colnames(joindatanew)) 
     colnames(joindatanew) <- gsub("-", ".", colnames(joindatanew))
     colnames(joindatanew) <- tolower(colnames(joindatanew))

#########################################################################################
## Step 3: Uses descriptive activity names to name the activities in the data set.

     cat("\n")
     cat("Step3: Uses descriptive activity names to name the activities in the data set.\n")

## Firstly, it is necessary to load/read the file containing full activity names
     activity <- read.table("activity_labels.txt")

## Remove bad characters such as "_" and also lower case in the activity names/row names
     activity[, 2] <- tolower(gsub("_", "", activity[, 2]))

## Create a new activitylabel vector containing descriptive activity names with
## the length of rows of joinlabel[, 1] 
     activitylabel <- activity[joinlabel[, 1], 2]

## Replace the 'activity numbers' to descriptive activity names in joinlabel data frame
     joinlabel[, 1] <- activitylabel 

## Give a column name to the column in the joinlabel data frame (one column data frame)
    colnames(joinlabel) <- "activity"

#########################################################################################
## Step 4: Appropriately labels the data set with descriptive activity names.
##      (i.e.: create a "clean" data set with labels (colnames and rownames))

     cat("\n")
     cat("Step4: Appropriately labels the data set with descriptiv activity names.\n")

##  Firstly, give a column name to the column of the joinsubject data frame (one column data frame)
     colnames(joinsubject) <- "subject"

##  Combine three working dataframes (joinsubject, joinlabel and joindatanew) into one 
##  single data frame via command cbind

     cleandata <- cbind(joinsubject, joinlabel, joindatanew)

## Cross-check dimensions: (nrows, ncolumns)
     dim(cleandata) ## (10299    68)


##  Optional: produce an output file that is stored the the above 'merged data set with labels'
##            in case it will be useful.

     write.table(cleandata, "combinedcleandata.txt")

#########################################################################################
## Step 5: Creates a second, independent tidy data set with the average of each variable
##      for each activity and each subject. 
##
     cat("\n")
     cat("Step5: Creates a independent tidy data set with the average of each variable for each activity and each subject.\n")

## Reshape the data: generate skinny data via melt function
## Melt the cleadata data set before decasting
     meltdfrm <- melt(cleandata, id=c("activity", "subject"))

## Cast the melt dataset based on the average of each variable for each activity and each subject
     tidydfrm <- dcast(meltdfrm, activity + subject ~ variable, mean)

## Create a file containing the tidy data set
     write.table(tidydfrm, "tidy_average_data.txt", row.names = F, col.names= T, sep = "\t")

     cat("")
     cat("DONE: a tidy data file has been created in the working directory!\n")
     cat("")
     workdone <- "TRUE"

} ##end of run_analysis
###### End of R script ##################################################################
#########################################################################################