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tm <- read.csv(tmittel_tag.csv", sep = ";", dec = ",", header = TRUE)

### str(tm)
###### 'data.frame':   5475 obs. of  2 variables:
###### $ Datum    : Factor w/ 21549 levels "1999-01-01",..: 1 2 3 4 5 6 7 8 9 10 ...
###### $ Tmittel  : num  -1.6 -1.8 -1.9 -1.5 -0.4 -2.2 -2.9 -0.3 2.5 1 ...
 	
### factor Datum in date umwandeln 
tm$Datum <- as.Date(tm$Datum)

### spalte Datum in Jahr, Monat und Tag auf
tm$Monat <- months(tm$Datum)
tm$Jahr <- format(tm$Datum, format = "%Y")
tm$Tag <- format(tm$Datum, format = "%d")

### Berechne Monatsmittelwerte nach Monat und Jahr 
T_mm <- aggregate(tm$Tmittel ~ tm$Monat + tm$Jahr, tm, FUN = mean) 

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tm$xMonat <- substr(tm$Datum, 1, 7)

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T_mm <- aggregate(Tmittel ~ Monat + Jahr, data=tm, FUN = mean)

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T_mm <- aggregate(Tmittel ~ xMonat, data=tm, FUN = mean)

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paste0(rep(1998:2001, each=4), c(".1F", ".2S", ".3H", ".4W"))

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 df <- data.frame(date = seq(from = as.Date("1991-01-01"),
                             to = as.Date("2009-12-31"),
                             by = "day"))
 df$vals <- rnorm(nrow(df))

 df$year <- format(df$date, "%Y")
 df$month <- format(df$date, "%m")
df$month <- as.integer(df$month)

jan <- subset(df, df$month==1)
feb <- subset(df, df$month==2)
dez <- subset(df, df$month==12)

##Bilde Monatsmittelwerte 
dez_mean <- aggregate(dez$vals ~ year, dez, FUN = mean)
jan_mean <- aggregate(jan$vals ~ year, jan, FUN = mean)
feb_mean <- aggregate(feb$vals ~ year, feb, FUN = mean)

colnames(dez_mean) <- c("year", "mittel")
colnames(jan_mean) <- c("year", "mittel")
colnames(feb_mean) <- c("year", "mittel")

## Wintersaison definiert
winter <- data.frame(dez_mean[1,], jan_mean[2,],feb_mean[2,])
mittel_winter <- mean(winter$mittel)


winter <- data.frame(name = character(0), len=numeric(0))
for (i in 1:length(feb_mean$year)){
   winter <- rbind(winter, data.frame(dez_mean[i,], jan_mean[i+1,],feb_mean[i+1,])) }
   
colnames(winter) <- c("year_d", "dez_mittel", "year_j", "jan_mittel", "year_f", "feb_mittel")

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paste0("Winter ", Jahr, "/", Jahr + 1)

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paste0("Winter ", Jahr - 1, "/", Jahr)

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df <- data.frame(date = seq(from = as.Date("1991-01-01"), to = as.Date("2009-12-31"), by = "day"))
set.seed(42)
df$vals <- rnorm(nrow(df))

df$year <- format(df$date, "%Y")
df$month <- format(df$date, "%m")
df$monthi <- as.integer(df$month)
df$ym <- sprintf("%s.%02d", df$year, df$monthi)

### wenn Du wirklich sowas machen willst, dann M <- split(df, df$month)
### ... aber in diesem Fall solltest Du das nicht wollen, sondern die Daten beisammen halten.
# jan <- subset(df, df$month==1)
# feb <- subset(df, df$month==2)
# dez <- subset(df, df$month==12)

##Bilde Monatsmittelwerte 
dfmean <- aggregate(vals ~ ym, data=df, FUN=mean)
dfmean <- within(dfmean, { monthi <- as.integer(substr(ym, 6, 7)); year <- as.integer(substr(ym, 1, 4)) })
# dez_mean <- aggregate(dez$vals ~ year, dez, FUN = mean)
# jan_mean <- aggregate(jan$vals ~ year, jan, FUN = mean)
# feb_mean <- aggregate(feb$vals ~ year, feb, FUN = mean)

S <- rep(c("1.Winter", "2.Frühling", "3.Sommer", "4.Herbst", "1.Winter"), c(2, 3, 3, 3, 1))
dfmean$saison <- S[dfmean$monthi]

dfmean$syear <- ifelse(dfmean$monthi==12, dfmean$year + 1, dfmean$year)
dfmean$ysaison <- sprintf("%d.%s", dfmean$syear, dfmean$saison)
dfmean[1:20,]

aggregate(vals ~ ysaison, data=dfmean, FUN=mean)