MMA

01-Import-Data.R

mma <- read.table("http://www.produnis.de/R/MMA.TXT",sep=";",header=T)
head(mma)
attach(mma)
# Erzeuge eine neue Spalte "time", in welcher Datum und Uhrzeit 
# im POSIXct-Format zusammengefasst werden.
mma$time <- as.POSIXct(paste(Datum,Uhrzeit),format="%Y-%m-%d %H:%M")
#Wandle falsch-numerisches in factor() um
mma$Action <- factor(mma$Action)
mma$Person <- factor(mma$Person)
# fertig
detach(mma)
head(mma)


get.data <- function(data,intervall) {
        # kompatibilitaetsgruende
   mma <- data
        # Startzeit und Endzeit
   start <- min(mma$time)
   end <- max(mma$time)
        # Ich erzeuge jetzt die erste Reihe des data.frames()
        # Intervall-Anfangszeitpunkt und Endpunkt
   x <- start
   y <- start + intervall

   new<-c(
      table(subset(data,data$time>x&data$time<y)$Qualif),
      table(subset(data,data$time>x&data$time<y)$Actiontxt),
      table(subset(data,data$time>x&data$time<y)$Kategorie)#,
      )
        # umwandeln als Datenframe, und Zeit hinzufügen 
   new <- as.data.frame(t(c(new,as.character(x))))
        # erhöhe Start, damit das nächste Intervall genommen wird
   start=start+intervall
   print("Looks good, calculating... this may take some time...")
        # erst jetzt fang ich mit der eigentlichen Schleife an
        # denn erst jetzt hab ich eine Reihe des neuen Frames
        # so dass rbind() jetzt funktioniert.
        # Die Schleife macht jetzt genau das selbe nochmal:
   while(start<end){
      x <- start
      y <- start + intervall
      new2<-c(
         table(subset(data,data$time>x&data$time<y)$Qualif),
         table(subset(data,data$time>x&data$time<y)$Actiontxt),
         table(subset(data,data$time>x&data$time<y)$Kategorie)#,
         )
                # Hier verbinde ich die neue Reihe mit dem Datenframe
      new2 <- as.data.frame(t(c(new2,as.character(x))))
      new <- rbind(new,new2)
      start=start+intervall
   }
   return(new)
}


new<-get.data(mma,3600)# Intervall = 3600sek = 1h

new<-get.data(mma,900)# (15 Minuten gleich 900 Sekunden)
#-------------------------------------------------------

x <- as.POSIXct("2005:10:04:04:00:00",format="%Y:%m:%d:%H:%M:%S") 
y <- x + 86400 # 86400Sekunden= 1day
mma$day <- 0
mma[(mma$time>x&mma$time<y),10]<-1
Tag1 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1day
mma[(mma$time>x&mma$time<y),10]<-2
Tag2 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1day
mma[(mma$time>x&mma$time<y),10]<-3
Tag3 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1day
mma[(mma$time>x&mma$time<y),10]<-4
Tag4 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1dtay
mma[(mma$time>x&mma$time<y),10]<-5
Tag5 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1day
mma[(mma$time>x&mma$time<y),10]<-6
Tag6 <- subset(mma,mma$time>x&mma$time<y)
x <- x + 86400 # 86400Sekunden= 1day
y <- x + 86400 # 86400Sekunden= 1day
mma[(mma$time>x&mma$time<y),10]<-7
Tag7 <- subset(mma,mma$time>x&mma$time<y)
rm(x,y)
head(Tag5)

get.per.day <- function(data,get.data,day){
  new.chir <- get.data(subset(data,data$Station=="Chirurgie"),900)
  new.chir$Station <- "Chirurgie"
  new.derm <- get.data(subset(data,data$Station=="Dermatologie"),900)
  new.derm$Station <- "Dermatologie"
  new <- rbind(new.chir, new.derm)
  new$day <- day
  names(new)[names(new)=="V47"] <- "Time"
  new$Time <- as.POSIXct(new$Time,format="%Y-%m-%d %H:%M")
  return(new)
}
get.per.day(Tag1,get.data,1)


Tag1.freq <- get.per.day(Tag1,get.data,1)
Tag2.freq <- get.per.day(Tag2,get.data,2)
Tag3.freq <- get.per.day(Tag3,get.data,3)
Tag4.freq <- get.per.day(Tag4,get.data,4)
Tag5.freq <- get.per.day(Tag5,get.data,5)
Tag6.freq <- get.per.day(Tag6,get.data,6)
Tag7.freq <- get.per.day(Tag7,get.data,7)
Tag1.freq$Time <-Tag1.freq[["Time"]]
Tag2.freq$Time <-Tag2.freq[["Time"]]-60
Tag3.freq$Time <-Tag3.freq[["Time"]]+60
Tag4.freq$Time <-Tag4.freq[["Time"]]+60
Tag5.freq$Time <-Tag5.freq[["Time"]]+60
Tag6.freq$Time <-Tag6.freq[["Time"]]+60
Tag7.freq$Time <-Tag7.freq[["Time"]]+60
head(Tag1.freq)


mma.freq <- rbind(Tag1.freq, Tag2.freq, Tag3.freq, Tag4.freq, Tag5.freq, Tag6.freq, Tag7.freq)
head(mma.freq)

20-Analyze.r

load(url("http://www.produnis.de/R/MMA.RData"))
library(ggplot2)


qplot(time,data=subset(mma,mma$Station=="Chirurgie"&mma$day==2),geom="bar",binwidth=500,fill=Kategorie)