inst/testcode/JUNK.R

In zevross-spatial/rpackage-sensorDataImport: Processes environmental sensor data

get_connection("columbiaBike","spatial",  host="localhost",
               port=5433, user="postgres")


x<-process_microaeth("X:/projects/columbia_bike/notes/issues_w_app/PrePilot_01/MicroAeth Data/Ltest1 - 636/", 
"BIKE0001_MAE36_S01_150616.csv", c("abc", "def"))



process_microaeth<-function(filepath, filename, fileinfo,metainfilename){
  
  #filepath<-"X:/projects/columbia_bike/notes/issues_w_app/PrePilot_01/MicroAeth Data/Ltest1 - 636/BIKE0001_MAE36_S01_150616.csv"
  #filename<-"BIKE0001_MAE36_S01_150616.csv"
  #fileinfo<-c("abc", "def")
  headinfo<-read.csv(filepath,as.is=T, nrow=5, header=FALSE)
  manufacturer<-headinfo[1,1]
  deviceid<-headinfo[2,1]
  softwareV<-headinfo[3,1]
  flowrate<-headinfo[4,1]
  freq<-headinfo[5,1]
  
  colNames<-names(read.csv(filepath, as.is=T, nrow=1, skip=7))
  #BC should be last column
  colNames<-tolower(colNames[1:which(colNames=="BC")])
  data<-read.csv(filepath, as.is=T, skip=9, header=FALSE)
  
  data%<>%dplyr::select(1:length(colNames)) # don't comment
  
  names(data)<-colNames
  
  
  
  
  data%<>%dplyr::mutate(time=addZero(time,8), date.yyyy.mm.dd.=paste(as.Date(date.yyyy.mm.dd.,"%m/%d/%y"), time))%>%
    dplyr::select(-time)%>%
    dplyr::rename(datetime=date.yyyy.mm.dd.)
  
  metadata<-generate_metadata(fileinfo, nrow(data), filename, metainfilename)
  data<-cbind(data, metadata)
  
  return(data)
  
  
}



##############################################

x<-aggregate_data(dat, 
                  aggregation_unit="2 hours",
                  grouping_vars = c("subjectid", "sessionid"),
                  summarize_vars=c("cadence", "breathing_rate"))

clean_first<-TRUE
  get_sensor_data(tablename = "gps", do_aggregate=TRUE, aggregation_unit="complete",
                  xtravars=NULL, summarize_vars=c("latitude", "longitude"))
                            
                            
res<-get_sensor_data("hxi", clean_first=FALSE, xtravars="all") 
res<-get_sensor_data("hxi", xtravars=c("datetime", "subjectid"))  

res<-get_sensor_data("gps")
res<-get_sensor_data(tablename="hxi", 
                   do_aggregate=TRUE, 
                   xtravars= NULL,
                   aggregation_unit="10 min",
                   grouping_vars = c( "subjectid", "sessionid"),
                   summarize_vars=c("cadence", "breathing_rate"))

# this will give you average lat, long (centroid) of each subject/session
res<-get_sensor_data("gps",
                     do_aggregate=TRUE, 
                     xtravars= NULL,
                     aggregation_unit="complete",
                     grouping_vars = c( "subjectid", "sessionid"),
                     summarize_vars=c("latitude", "longitude"))




# gives error, check xtravars
res<-get_sensor_data("hxi", xtravars=c("datetime", "asdfjasf")) #gives error


tablename<-"gps"
do_aggregate <- FALSE
xtravars <- "all"
aggregation_unit="2 hours"
summarize_vars = NULL
grouping_vars = c("datetime", "subjectid", "sessionid")

x<-get_sensor_data("gps")

# gives error check grouping vars
res<-get_sensor_data("hxi", 
                     do_aggregate=TRUE, 
                     xtravars= NULL,
                     aggregation_unit="2 hours",
                     grouping_vars = c("datetime", "subjectid"),
                     summarize_vars=c("cadence", "breathing_rate"))



x<-get_sensor_data("gps")

get_sensor_data <- function(tablename, 
                            do_aggregate = FALSE,
                            clean_first = TRUE,
                            aggregation_unit="15 min",
                            xtravars = "all",
                            summarize_vars = NULL, 
                            grouping_vars = c("datetime", "subjectid", "sessionid")
){
  
  vars_to_get<-NULL
  clean_vars<-NULL #variables needed to do cleaning
  valcon<-valid_connection()
  tableexists<-table_exists(tablename)
  con<-.connection$con
  possiblevars<-get_column_names(tablename)$column_name
  
  if(!valcon || !tableexists) stop(paste("Either you don't have a valid database connection or the table does not exist"), call.=FALSE)
  #if(do_aggregate && !"datetime"%in%grouping_vars) stop("You need the datetime field to aggregate")
  if(!agg_unit_ok(aggregation_unit)) stop("Your aggregation unit is invalid", call.=FALSE)
  if(!is.null(xtravars) & do_aggregate) stop("It looks like you want to aggregate. You should have xtravars=NULL", call.=FALSE)
  if(!is.null(xtravars) && all(tolower(xtravars)!="all") && !all(xtravars%in%possiblevars)){
    stop(paste("One of your extra variables is not in the table. Possible vars are", paste(possiblevars, collapse=",")))
  }
  
  if(do_aggregate && (is.null(grouping_vars) | !all(grouping_vars%in%possiblevars))){
    stop(paste("You have a problem with your grouping variables. Possible vars are", paste(possiblevars, collapse=",")), call.=FALSE)
  }
  
  if(do_aggregate && (is.null(summarize_vars) | !all(summarize_vars%in%possiblevars))){
    stop(paste("You have a problem with your summarize variables. Possible vars are", paste(possiblevars, collapse=",")), call.=FALSE)
  }
  
  
  
  
  thetable<-tbl(.connection, tablename)
  
  # if user has "all" for vars and is not aggregating get all
  # variables
  
  if(!do_aggregate && tolower(xtravars) == "all" ){
    
    
    dat<-collect(thetable)
    vars_to_get <- names(dat)
    
    # otherwise start with the user selected variables
    # if they're aggregating then include the grouping and summarizing vars
    # if they're cleaning then add the vars needed for clearning
  }else{
    vars_to_get <- xtravars #should be NULL if aggregating
    
    # note that I'm manually adding "sessionid" because, even if it's
    # not a grouping variable, we will still need to filter out the
    # non sessions in the cleaning phase
    if(do_aggregate) vars_to_get <- unique(c("datetime", vars_to_get, grouping_vars, summarize_vars))
    
    clean_vars<-NULL
    if(clean_first){
      clean_vars <- cleaning_vars(tablename)
    }
    
    
    dat <- collect(select_(thetable, .dots=unique(c(vars_to_get, clean_vars))))
  }
  
  
  # if we need to clean, we will clean and then drop the variables
  # that are required for cleaning
  
  if(clean_first){
    
    dat <- clean_data(tablename, dat)
    
    # This will get rid of the cleaning variables
    dat <- select_(dat, .dots = vars_to_get)
    
    
  }
  
  
  if(do_aggregate){
    
    dat <- aggregate_data(dat,
                          aggregation_unit = aggregation_unit,
                          summarize_vars = summarize_vars,
                          grouping_vars = grouping_vars)
  }
  
  # if the aggregation unit is complete then we're averaging with
  # no regard to time and then the output should not include time
  # info.
  dat <- ungroup(dat)
  
  if(aggregation_unit == "complete"){
    dat<-dat %>% select(-c(interval, begin, end))
  }
  data.frame(dat)
  
  
}



## filenaming

filenames<-c("BIKE0001_GPS01_S01_150306.gpx", "BIKE0001_ABP01_S01_150306.gpx")
substring(sapply(stringr::str_split(filenames, "_"), "[[",2),1,3)

filenames_split<-strsplit(filenames, "_")

l<-sapply(filenames_split, length)
prefix<-substring(sapply(filenames_split, "[[",2),1,3)


#
curpath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0001_GPS01_S01_150306.gpx"
curfilename<- "BIKE0001_GPS01_S01_150306.gpx"
projectid<-"columbiaBike"
metainfilename<-FALSE


initiate_processing(filepath  = curpath, 
                    filename  = curfilename,
                    projectid = projectid,
                    metainfilename = metainfilename)



# *****************************************************************************
#  process NPM
# *****************************************************************************

filepath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0002_MPM01_S99_BK0001_150306.csv"
filename<-"BIKE0002_MPM01_S99_BK0001_150306.csv"
projectid<-"columbiaBike"

filepath<-"X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/MicroPEM/BIKE0001_MPM01_S01_150626.csv"
filename<-"BIKE0001_MPM01_S01_150626.csv"
fileinfo<-unlist(stringr::str_split(filename, "_"))
fileinfo<-fileinfo[-length(fileinfo)] # we don't need date
fileinfo<-c(fileinfo, projectid)
metainfilename<-TRUE


process_micropem<-function(filepath, filename, fileinfo,metainfilename){
  
  #filepath<-"X:/projects/columbia_bike/data/client_data/20150205_microPem/UGF320417N_KHC0226.csv"
  # Grab the header information -- n=50 to make sure I have all lines
  # but this includes extra lines
  nonParsed<-readLines(file(filepath, "r"), warn=FALSE, n=50)
  
  
  blanks<-!grepl("[a-z0-9A-Z]", nonParsed)# which are blank lines (those starting with two commas)
  numBlanks<-sum(blanks) # number of blank lines
  nonParsed<-nonParsed[!blanks] # remove blanks
  
  
  
  startRowSensor<-grep("Sensor", nonParsed)
  ventilationRow<-grep("Ventilation", nonParsed)
  endHeader<-max(grep("Date", nonParsed))
  
  
  headinfo1<-sapply(splitHeader(nonParsed, 1,startRowSensor-1), collapseHeader)
  
  # this is the table in the header
  headinfo2<-sapply(splitHeader(nonParsed,startRowSensor+1, ventilationRow), collapseHeader, width=6)
  
  headerinfo<-c(headinfo1, headinfo2)
  
  
  headers1<-c("hdr_filename", "hdr_downloaddate", "hdr_downloadtime",  "hdr_deviceserial", "hdr_datetimehard", "hdr_datetimesoft",
              "hdr_participantid", "hdr_filterid", "hdr_participantwt", "hdr_inletsize","hdr_delay_samp_off", "hdr_systimes")
  
  headers2<-c("hdr_nephelometer", "hdr_temperature", "hdr_humidity", 
              "hdr_inlpressure", "hdr_oripressure", "hdr_flow", "hdr_accelerometer", "hdr_battery", "hdr_ventilation")
  
  
  names(headerinfo)<-c(headers1, headers2)
  
  
  data<-read.csv(filepath, as.is=T, skip=endHeader+numBlanks, header=FALSE)
  names(data)<-c("date", "time", "neph_rhcorrect", "temperature", "rh", "battery", "inlpressure", "oripressure", "flow",
                 "xaxis", "yaxis", "zaxis", "vectorsumcomp", "action")
  
  
  #I'm seeing that they might have a line called "Errored Line"
  data %<>% dplyr::filter(!grepl("Errored Line", date))
  
  # from Ashlinn's code -- if the RH is negative we should not have
  # a valid value for nephelometer. File BIKE0002_MPM02_S99_BK0001_150306
  # has examples for testing
  data$neph_rhcorrect[data$rh < 0 & !is.na(data$rh)]<-NA
  data$rh[data$rh<0]<-NA
  
  # need to add the header info, each as it's own record
  # this is bad form making data global
  #     invisible(mapply(function(x,i){
  #       data[[i]]<<-x
  #     }, headerinfo, names(headerinfo)))
  
  h<-data.frame(matrix(headerinfo, nrow=nrow(data), ncol=length(headerinfo), byrow=TRUE))
  names(h)<-names(headerinfo)
  
  data<-cbind(data, h)
  
  # There are two date types possible 2/2/14 or 2-Feb-14. So here I'm testing
  # if the second "piece" of the date is a number and then proceed accordingly
  # Note 5/28/2015 turns out there are sometimes 2/2/14 and sometimes 2/2/2014
  # need to deal with this.
  
  splitdate<-unlist(strsplit(data$date[1], "-|/"))
  isSecondAnumber<-!is.na(suppressWarnings(as.numeric(splitdate[2])))
  isYear2digits<-nchar(splitdate)[3]==2
  
  if(isSecondAnumber){
    
    ifelse(isYear2digits, dateform<-"%m/%d/%y", dateform<-"%m/%d/%Y")
    data%<>%dplyr::mutate(time=addZero(time,8), date=paste(as.Date(date,dateform), time))%>%
      dplyr::select(-time)%>%
      dplyr::rename(datetime=date)
  }else{
    ifelse(isYear2digits, dateform<-"%d-%b-%y", dateform<-"%d-%b-%Y")
    data%<>%dplyr::mutate(time=addZero(time,8), date=paste(as.Date(date,dateform), time))%>%
      dplyr::select(-time)%>%
      dplyr::rename(datetime=date)
  }
  
  

  
  metadata<-generate_metadata(fileinfo, nrow(data), filename, metainfilename)
  proper_session     <- find_gaps_assign_session(data$datetime)
  metadata$sessionid <- proper_session 
  data<-cbind(data, metadata)
  
  return(data)
}


library(ggplot2)
ggplot(data, aes(datetime, 1))+geom_point()




session<-rep(1:nrow(xxx), times=xxx$length)

data$session<-session

data$gap<-c(x, NA)
data[anaction,c("datetime", "flow", "gap", "session")]

data[1:23,c("datetime","action", "flow", "gap", "session")]


dat


data[1:10,]

# *****************************************************************************
#  process NPM
# *****************************************************************************

filepath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0001_ABP01_S01_150306.abp"
filename<-"BIKE0001_ABP01_S01_150306.abp"

filepath<-"X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/ABPM data/BIKE0001_ABP01_S01_150627.abp"
filename<-"BIKE0001_ABP01_S01_150627.abp"

fileinfo<-unlist(stringr::str_split(filename, "_"))
fileinfo<-fileinfo[-length(fileinfo)] # we don't need date
fileinfo<-c(fileinfo, "columbiaBike")
metainfilename<-TRUE

process_abp<-function(filepath, filename, fileinfo, metainfilename){
  #filepath<-X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/ABPM data/BIKE0001_ABP01_S01_150627.abp"
  #filepath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0001_ABP01_S01_150306.abp"
  rl<-readLines(file(filepath,encoding="UTF-16LE"), warn=FALSE)
  endOfIntro<-max(grep("Unknown line", rl))+1
  endOfFile<-grep("XML", rl)-1
  
  
  ID <- read.csv(filepath, 
                 fileEncoding="UTF-16LE", 
                 header=F, 
                 nrows=endOfIntro, 
                 sep=" ", 
                 stringsAsFactors=F,
                 blank.lines.skip = FALSE) # I kept blank lines so that we have consistency with file
  #visit <- ID[1,1]      # assumes that visit number is entered for first name
  studyid <- ID[1,2]    # assumes that study id is entered for last name
  #session <- ID[2,1]    # assumes that session id is entered for patient id 
  
  cntobs <- as.numeric(ID[endOfIntro,1]) # this is not always in the same place, sometimes it's row 8 and sometimes row 9
  data <- read.csv(filepath, 
                   fileEncoding="UTF-16LE", 
                   header=F, 
                   skip=endOfIntro, 
                   nrows=cntobs,
                   blank.lines.skip = FALSE) # load BP data 
  
  data <- data[,1:7]
  names(data) <- c("hour", "minute", "systolic_bp", "mean_arterial_p", "diastolic_bp", "heart_rate", "event_code")
  
  timestart <-endOfIntro+cntobs
  
  DS <- read.csv(filepath, 
                 fileEncoding="UTF-16LE", 
                 header=F, 
                 skip=timestart, 
                 nrows= cntobs,
                 blank.lines.skip = FALSE)
  
  names(DS) <- c( "month", "day","year", "code")
  data$studyid <- studyid
  
  data <- cbind(data, DS)
  
  
  # addZero is function to pad in helper functions
  data[,c("hour", "minute", "month", "day")]<-
    lapply(data[,c("hour", "minute", "month", "day")],addZero)
  
  
  data$event_code<-as.character(data$event_code)
  data$event_code[data$event_code==""]<-NA
  
  data$datetime <- paste("20", data$year, "-", data$month,"-", data$day," ", data$hour,":", data$minute,":00", sep="")
  #BP$datetime <- ymd_hm(BP$datetime), uses lubridate
  #BP$file <- basename(file)
  data%<>% dplyr::select(datetime, which(!names(data)%in%c("datetime","day", "month", "year", "hour", "minute")))
  
  metadata<-generate_metadata(fileinfo, nrow(data), filename, metainfilename)
  proper_session     <- find_gaps_assign_session(data$datetime)
  metadata$sessionid <- proper_session 
  
  data<-cbind(data, metadata)
  
  return(data)
  
}

"X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/ABPM data/BIKE0001_ABP_S01_150626.csv"



# *****************************************************************************
#  alreadyuploaded
# *****************************************************************************


already_uploaded("mpm", "BIKE0002_MPM01_S99_BK0001_150306.csv")

# *****************************************************************************
#  alreadyuploaded
# *****************************************************************************

# old version 
# filepath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0001_MAE01_S01_150306.csv"
# filename <- "BIKE0001_MAE01_S01_150306.csv"


# new version
#filepath<-"X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/MicroAeth Data/LTest1 - 104/AE51-S0-104-0903/BIKE0001_MAE04_S04_150623.csv"
#filename<-"BIKE0001_MAE04_S04_150623.csv"
#filepath<-"X:/projects/columbia_bike/data/client_data/20150710_prepilotdata/PrePilot_01/MicroAeth Data/Ltest1 - 636/AE51-S4-636_20150622-165100.csv"


fileinfo<-unlist(stringr::str_split(filename, "_"))
fileinfo<-fileinfo[-length(fileinfo)] # we don't need date
fileinfo<-c(fileinfo, "columbiaBike")
metainfilename<-TRUE


process_microaeth<-function(filepath, filename, fileinfo,metainfilename){
  
  headinfo<-read.csv(filepath,as.is=T, nrow=30, header=FALSE)
  
  # this is the old format
  if(headinfo$V1[1] == "AethLabs"){
    
    manufacturer<-headinfo[1,1]
    
    vals<-sapply(trim(strsplit(headinfo[2:5,1], "=")), "[[", 2)
    
    deviceid<-vals[1]
    softwareV<-vals[2]
    flowrate<-vals[3]
    freq<-vals[4]
    startdate    <- NA
    starttime    <- NA
    origdtform   <- NA
    origtmform   <- NA
    flowunit     <- NA
    pcbtempunit  <- NA
    batteryunit  <- NA
    bcunit       <- NA
    
    dateformat<- "%m/%d/%y"
    toskip <- 7
  }
  
  if(headinfo$V1[1] == "Sep = "){
    
    manufacturer <- headinfo[3,1]
    
    vals<-sapply(trim(strsplit(headinfo[4:15,1], "=")), "[[", 2)
    
    deviceid     <- vals[1]
    softwareV    <- vals[2]
    flowrate     <- vals[3]
    freq         <- vals[4]
    startdate    <- vals[5]
    starttime    <- vals[6]
    origdtform   <- vals[7]
    origtmform   <- vals[8]
    flowunit     <- vals[9]
    pcbtempunit  <- vals[10]
    batteryunit  <- vals[11]
    bcunit       <- vals[12]
    
    
    dateformat<-"%Y/%m/%d"
    toskip <- 16
    
  }
  

  
  colNames<-names(read.csv(filepath, as.is=T, nrow=1, skip=toskip))
  #BC should be last column
  colNames<-tolower(colNames[1:which(colNames=="BC")])
  data<-read.csv(filepath, as.is=T, skip=toskip+2, header=FALSE)
  
  data%<>%dplyr::select(1:length(colNames)) # don't comment
  
  names(data)<-colNames
  
  names(data)[grep("date.yyyy.mm.dd.", names(data))]<-"date"
  names(data)[names(data)=="temp"]<-"pcbtemp"
  names(data)[names(data)=="pcb.temp"]<-"pcbtemp"
  
  data%<>%dplyr::mutate(time=addZero(time,8), date=paste(as.Date(date,dateformat), time))%>%
    dplyr::select(-time)%>%
    dplyr::rename(datetime=date)
  
  
  data$hdr_deviceid <-deviceid
  data$hdr_appv     <- softwareV
  data$hdr_flowrate <- flowrate
  data$hdr_timebase <- freq
  data$hdr_startdate<-startdate
  data$hdr_starttime <- starttime
  data$hdr_origdateform<- origdtform
  data$hdr_origtimeform<-origtmform
  data$hdr_flowunit<-flowunit
  data$hdr_pcbtempunit<-pcbtempunit
  data$hdr_batteryunit <- batteryunit
  data$hdr_bcunit<-bcunit
    
  
  metadata<-generate_metadata(fileinfo, nrow(data), filename, metainfilename)
  data<-cbind(data, metadata)
  
  return(data)
  
  
}

upload_postgres<-function(tablename, data){
  rows<-nrow(data)
  #tablename<-"mae"
  writeLines(paste("About to upload", rows, "rows to" , tablename))
  
  postgresqlWriteTableAlt(.connection$con, tablename, data, append=TRUE, row.names=FALSE)
  
  msg<-paste("Completed upload of", rows, "rows to" , tablename)
  writeLines(msg)
  
}


# *****************************************************************************
#  GPS
# *****************************************************************************
filepath<-"X:/projects/columbia_bike/notes/issues_w_app/PrePilot_01/Hexoskin data/BIKE0001_HXI94_S01_150617.csv"
filename<-"BIKE0001_HXI94_S01_150617.csv"

filepath<-"X:/projects/columbia_bike/bikeStats/bikeApp/sample_data/BIKE0001_HXI01_S01A_150306.csv"

fileinfo<-unlist(stringr::str_split(filename, "_"))
fileinfo<-fileinfo[-length(fileinfo)] # we don't need date
fileinfo<-c(fileinfo, "columbiaBike")
metainfilename<-TRUE


process_hexoskin<-function(filepath, filename, fileinfo,metainfilename){
  
  
  #filepath<-"X:/projects/columbia_bike/data/client_data/20141001_sample_data_files/hexoskin/record-55556.csv"
  data<-read.csv(filepath,as.is=T, check.names=FALSE)
  
  # names are there but followed by a space and junk
  m<-regexpr("[^ ]*", names(data), perl=TRUE)
  names(data)<-regmatches(names(data), m)
  
  data$timestamp<-format(as.POSIXct((data$timestamp)/256, origin = "1970-01-01"), usetz=FALSE)
  names(data)[names(data)=="timestamp"]<-"datetime"
  
  if(!"sleep_position"%in%names(data)) data$sleep_position <- NA
  
  # put them in order
  data <- select(data, datetime, breathing_rate, heart_rate,   minute_ventilation,
                 cadence, sleep_position, activity)
  
  
  metadata<-generate_metadata(fileinfo, nrow(data), filename, metainfilename)
  data<-cbind(data, metadata)
  
  return(data)
  
}




dat<-process_hexoskin(filepath, filename, fileinfo, metainfilename)
upload_postgres("hxi", dat)



# *****************************************************************************
#  comparing
# *****************************************************************************
library(dplyr)
con<-src_postgres(dbname="columbiaBike", host="localhost",
                  password="spatial", 
                  port=5433, 
                  user="postgres")

x<-collect(tbl(con, "mpm"))
x<-mutate(x, hr = format(datetime, "%Y-%m-%d %H"),
          sessionid = as.numeric(sessionid))

x2<-group_by(x, hr, subjectid, sessionid) %>% 
  summarize(avgtemp = mean(temperature, na.rm=T), cnt=sum(!is.na(temperature)))




library(sensorDataImport)

get_connection("columbiaBike","spatial",  host="localhost",
               port=5433, user="postgres")

micropem <- get_sensor_data("mpm",
                            do_aggregate     = TRUE,
                            aggregation_unit = "1 hour",
                            xtravars         = NULL,
                            summarize_vars   = c("temperature", "flow", "neph_rhcorrect"),
                            grouping_vars    = c("subjectid", "sessionid"))

micropem <- mutate(micropem, hr = format(begin, "%Y-%m-%d %H"))

head(micropem)


xx<-merge(x2, micropem, by=c("hr", "subjectid", "sessionid"))
plot(xx$avgtemp, xx$temperature_avg)
plot(xx$cnt, xx$temperature_cnt)
abline(0,1)


micropem[5,]
head(x2[,c("hr", "subjectid", "sessionid")])
head(micropem[,c("hr", "subjectid", "sessionid")])

# *****************************************************************************
#  comparing
# *****************************************************************************

knitr::knit("D:/work/r_packages/sensorDataImport/vignettes/how-to.Rmd", "D:/work/r_packages/sensorDataImport.wiki/Examples.md")