>
> names(TEM)
[1] "Sample" "Date" "DateNr" "dDay1" "dDay2"
[6] "dDay3" "Station" "Area" "X31UE_ED50" "X31UN_ED50"
[11] "Year" "Month" "Season" "Salinity" "Temperature"
[16] "CHLFa"
> str(TEM)
'data.frame':8528 obs. of 16 variables:
$ Sample : Factor w/ 8528 levels "DANT.19900110",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Date : int 19900110 19900206 19900308 19900404 19900509 19900620 19900718 19900815 19900919 19901017 ...
$ DateNr : Factor w/ 2343 levels "1/13/1992","1/13/1995",..: 408 437 445 428 448 1839 1956 2084 2262 133 ...
$ dDay1 : int 7 34 64 91 126 168 196 224 259 287 ...
$ dDay2 : int 9 36 66 93 128 170 198 226 261 289 ...
$ dDay3 : int 9 36 66 93 128 170 198 226 261 289 ...
$ Station : Factor w/ 30 levels "DANT","DREI",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Area : Factor w/ 10 levels "ED","GM","KZ",..: 10 10 10 10 10 10 10 10 10 10 ...
$ X31UE_ED50 : num 681380 681380 681380 681380 681380 ...
$ X31UN_ED50 : num 5920571 5920571 5920571 5920571 5920571 ...
$ Year : int 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
$ Month : int 1 2 3 4 5 6 7 8 9 10 ...
$ Season : Factor w/ 4 levels "autumn","spring",..: 4 4 2 2 2 3 3 3 1 1 ...
$ Salinity : num 29.2 27.4 25 28.8 33.3 ...
$ Temperature: num 4 6 7.3 8.2 17.4 18.1 17 21 15.1 13.8 ...
$ CHLFa : num 1.3 NA 21.1 25 10.2 6.2 7.9 7.85 13 11.8 ...
>
> #先分别提取出1990-2005每一年的数据，eg:tem1990 <-TEM[TEM$Year==1990,]
> #后再month来分temperature
>
> tem1990 <-TEM[TEM$Year==1990,]
> Month1990 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
>
> tem1991 <-TEM[TEM$Year==1991,]
> Month1991 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1992 <-TEM[TEM$Year==1992,]
> Month1992 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1993 <-TEM[TEM$Year==1993,]
> Month1993 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1994 <-TEM[TEM$Year==1994,]
> Month1994 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1995 <-TEM[TEM$Year==1995,]
> Month1995 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1996 <-TEM[TEM$Year==1996,]
> Month1996 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1997 <-TEM[TEM$Year==1997,]
> Month1997 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1998 <-TEM[TEM$Year==1998,]
> Month1998 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem1999 <-TEM[TEM$Year==1999,]
> Month1999 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2000 <-TEM[TEM$Year==2000,]
> Month2000 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2001 <-TEM[TEM$Year==2001,]
> Month2001 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2002<-TEM[TEM$Year==2002,]
> Month2002 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2003 <-TEM[TEM$Year==2003,]
> Month2003 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2004 <-TEM[TEM$Year==2004,]
> Month2004 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> tem2005<-TEM[TEM$Year==2005,]
> Month2005 <- tapply(TEM$Temperature,TEM$Month,FUN = mean,na.rm=T)
>
> resultF <- data.frame(Month1990,Month1991,Month1992,Month1993,Month1994,Month1995,
+ Month1996,Month1997,Month1998,Month1999,Month2000,Month2001,
+ Month2002,Month2003,Month2004,Month2005)
>
> resultF
Month1990 Month1991 Month1992 Month1993 Month1994 Month1995 Month1996 Month1997
1 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210
2 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400
3 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961
4 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035
5 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479
6 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933
7 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343
8 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355
9 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974
10 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670
11 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891
12 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339
Month1998 Month1999 Month2000 Month2001 Month2002 Month2003 Month2004 Month2005
1 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210 5.174210
2 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400 4.737400
3 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961 6.125961
4 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035 8.702035
5 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479 12.293479
6 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933 15.659933
7 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343 18.077343
8 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355 19.388355
9 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974 16.995974
10 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670 13.619670
11 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891 9.848891
12 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339 6.746339
> resultFmean <- rowMeans(resultF)
#如何计算每一行的平均值和方差？均可用sapply | lapply 来做，
#其中横向均值可用rowMeans 来做
resultFmean <- rowMeans(resultF)
rownames(resultF) <-c("1month","2month","3month","4month","5month",
"6month","7month","8month","9month","10month",
"11month","12month")
sapply(resultF[1:12,],FUN = mean)
