## ----global_options, warning = FALSE, eval = FALSE, echo = FALSE-------------- # knitr::opts_chunk$get("root.dir") ## ----installing the dependencies, eval = FALSE-------------------------------- # # install.packages("GENEAread", repos = "http://cran.us.r-project.org") # install.packages("changepoint", repos = "http://cran.us.r-project.org") # install.packages("signal", repos = "http://cran.us.r-project.org") # install.packages("mmap", repos = "http://cran.us.r-project.org") # # # Load in the libraries # library(GENEAread) # library(changepoint) # library(signal) # library(mmap) ## ----Installing from Source, eval = FALSE------------------------------------- # # You will need to change the folder location inside setwd("") to the directory where you saved the tar.gz file # # Note that R only uses / not \ when refering to a file/directory location # setwd("/Users/owner/Documents/GENEActiv") # install.packages("GENEAclassify_1.5.1.tar.gz", repos=NULL, type="source") ## ----loading in the GENEAclassify library, eval = FALSE----------------------- # library(GENEAclassify) ## ----installing from GitHub, eval = FALSE------------------------------------- # install.packages("devtools",repos = "http://cran.us.r-project.org") # library(devtools) # # install_github("https://github.com/Langford/GENEAclassify_1.41.git", # auth_token = "7f0051aaca453eaabf0e60d49bcf752c0fea0668") # ## ----Run library function again GENEAclassify library,eval=FALSE-------------- # # library(GENEAclassify) # ## ----run the vignette,eval = FALSE-------------------------------------------- # # vignette("GENEAclassifyDemo", package = NULL, lib.loc = NULL, all = TRUE) # ## ----Loading Data then Segmenting, eval = FALSE------------------------------- # # Name of the file to analyse # DataFile = "DataDirectory/jl_left wrist_010094_2012-01-30 20-39-54.bin" # ImportedData = dataImport(DataFile, downsample = 100, start = 0, end = 0.1) # head(ImportData) ## ----eval = FALSE------------------------------------------------------------- # # These are some of the output variables from segmentation and getGENEAsegments # dataCols <- c("UpDown.mean", # "UpDown.var", # "UpDown.sd", # "Degrees.mean", # "Degrees.var", # "Degrees.sd", # "Magnitude.mean", # # Frequency Variables # "Principal.Frequency.median", # "Principal.Frequency.mad", # "Principal.Frequency.GENEAratio", # "Principal.Frequency.sumdiff", # "Principal.Frequency.meandiff", # "Principal.Frequency.abssumdiff", # "Principal.Frequency.sddiff", # # Light Variables # "Light.mean", # "Light.max", # # Temperature Variables # "Temp.mean", # "Temp.sumdiff", # "Temp.meandiff", # "Temp.abssumdiff", # "Temp.sddiff", # # Step Variables # "Step.GENEAcount", # "Step.sd", # "Step.mean") # # # Performing the segmentation now given the dataCols we want to find. # # SegDataFile = segmentation(ImportedData, dataCols) # # View the data from the segmentation # head(SegDataFile) ## ----segment a datafile, eval = FALSE----------------------------------------- # # Name of the file to analyse # DataFile = "DataDirectory/jl_left wrist_010094_2012-01-30 20-39-54.bin" # SegDataFile = getGENEAsegments(DataFile, dataCols, start = 0, end = 0.1) ## ----Displaying varying step counting alogrithms, eval = FALSE---------------- # # WalkingData = "TrainingData/Walking/walking_jl_right wrist_024603_2015-12-12 15-36-47.bin" # # # Starting with default filter # W1 = getGENEAsegments(WalkingData, plot.it = TRUE) # # # plot.it Shows the crossing points. Turn this on for all plots to see how each filter works # # List the step outputs here. # W1$Step.GENEAcount; W1$Step.sd; W1$Step.mean # # W2 = getGENEAsegments(WalkingData, filteroder = 4) # # Changing the filterorder changes the order of the chebyshev filter applied. # W2$Step.GENEAcount; W2$Step.sd; W2$Step.mean # # W3 = getGENEAsegments(WalkingData, boundaries = c(0.15, 1)) # # List the step outputs here. # W3$Step.GENEAcount; W3$Step.sd; W3$Step.mean # # # Changing the deicbel paramter # W4 = getGENEAsegments(WalkingData, Rp = 3) # W4$Step.GENEAcount; W4$Step.sd; W4$Step.mean # # # Increasing the hystersis # W5 = getGENEAsegments(WalkingData, hysteresis = 0.1) # W5$Step.GENEAcount; W5$Step.sd; W5$Step.mean ## ----loading TrainingData.csv, eval = FALSE----------------------------------- # # Change the file path to the location of GENEAclassify. # setwd("/Users/owner/Documents/GENEActiv/GENEAclassify_1.41/Data") # TrainingData = read.table("TrainingData.csv", sep = ",") # # # The data can also be called through from the package. # data(TrainingData) # TrainingData ## ----eval = FALSE------------------------------------------------------------- # ClassificationModel = createGENEAmodel(TrainingData, # features = c("Segment.Duration", # "UpDown.mean", "UpDown.sd", # "Degrees.mean", "Degrees.sd", # "Magnitude.mean", # "Light.mean", # "Temp.mean", # "Step.sd", "Step.count", "Step.mean", # "Principal.Frequency.median", "Principal.Frequency.mad") # ) ## ----eval = FALSE------------------------------------------------------------- # ClassificationModel = createGENEAmodel(TrainingData, # features = c("UpDown.mean", "UpDown.sd", # "Degrees.mean", "Degrees.sd", # "Magnitude.mean", # "Step.sd", "Step.mean", # "Principal.Frequency.median", # "Principal.Frequency.mad")) ## ----classifying a File, eval = FALSE----------------------------------------- # DataFile = "jl_left wrist_010094_2012-01-30 20-39-54.bin" # Change to the file to classify # ClassifiedFile = classifyGENEA(DataFile, # trainingfit = ClassificationModel, # start = "3:00", # end = "1 3:00") ## ----classifying a Directory, eval = FALSE------------------------------------ # ClassifiedDirectory = classifyGENEA(DataDirectory, # trainingfit = ClassificationModel, # start = "3:00", # end = "1 3:00") ## ----Segmentation RunWalk file, echo = FALSE, eval = FALSE-------------------- # SegData = getGENEAsegments("RunWalk.bin", end = "9:23") # head(SegData) ## ----List creation,eval = FALSE----------------------------------------------- # Activity = c("Running", # "Running", # "Walking") ## ----Attaching Activities, eval = FALSE--------------------------------------- # SegData = cbind(SegData, ActivitiesListed) ## ----eval = FALSE------------------------------------------------------------- # SegData$Activity[1:2] = "Running" # SegData$Activity[3] = "Walking" ## ----eval = FALSE------------------------------------------------------------- # Cycling = getGENEAsegments("TrainingData/Cycling") # Cycling$Activity = "Cycling" # # NonWear = getGENEAsegments("TrainingData/NonWear") # NonWear$Activity = "NonWear" # # onthego = getGENEAsegments("TrainingData/onthego") # onthego$Activity = "onthego" # # Running = getGENEAsegments("TrainingData/Running") # Running$Activity = "Running" # # Sitting = getGENEAsegments("TrainingData/Sitting") # Sitting$Activity = "Sitting" # # Sleep = getGENEAsegments("TrainingData/Sleep") # Sleep$Activity = "Sleep" # # Standing = getGENEAsegments("TrainingData/Standing") # Standing$Activity = "Standing" # # Swimming = getGENEAsegments("TrainingData/Swimming") # Swimming$Activity = "Swimming" # # Transport = getGENEAsegments("TrainingData/Transport") # Transport$Activity = "Transport" # # Walking = getGENEAsegments("TrainingData/Walking") # Walking$Activity = "Walking" # # Workingout = getGENEAsegments("TrainingData/Workingout") # Workingout$Activity = "Workingout" ## ----Combining Segments, eval = FALSE----------------------------------------- # TrainingData = rbind(Cycling, # NonWear, # onthego, # Running, # Sitting, # Sleep, # Standing, # Swimming, # Transport, # Walking, # Workingout) ## ----eval = FALSE------------------------------------------------------------- # ClassificationModel = createGENEAmodel(TrainingData, # features = c("UpDown.mean", # "UpDown.sd","Degrees.mean", # "Degrees.sd","Magnitude.mean", # "Step.sd","Step.mean", # "Principal.Frequency.median", # "Principal.Frequency.mad"))