The following code was used to analyze temperature data from 31 locations on the Dutch coastline. We start by reading in the data:
Temperature = read.csv("Temperature.csv")Followed by calculating the means and standard deviations for temperature and salinity.
mean(Temperature$Temperature,na.rm=TRUE)## [1] 12.20743mean(Temperature$Salinity,na.rm=TRUE)## [1] 29.70221sd(Temperature$Temperature,na.rm=TRUE)## [1] 5.412521sd(Temperature$Salinity, na.rm=TRUE)## [1] 5.421593From a data analysis point, I noticed how similar the standard deviations are, despite the subject of measurement being quite different. As a layman in this field, I am going to assume this is a coincidence until told otherwise.
I then used the tapply function to determine the mean temperatures and salinities 1)as measured at each station 2)over a series of years. I’ll show you the code for each of them, but only the results for one (so you can see an example of what the output looks like).
tapply(Temperature$Temperature, Temperature$Station, mean, na.rm = TRUE)## DANT DREI G6 GROO HAMM HANS HUIB LODS
## 12.05908 12.77160 10.66570 12.25853 12.50000 13.54894 11.85353 12.61192
## MARS N02 N10 N20 N70 R03 R50 R70
## 12.39607 11.37433 12.45878 12.19753 12.14789 12.80000 13.94911 13.98022
## SOEL T004 T010 T100 T135 T175 T235 VLIS
## 13.21203 11.28425 12.37517 11.94766 11.75512 11.55431 11.32355 12.79292
## W02 W20 W70 WISS ZIJP ZUID
## 10.51829 11.87937 12.18243 12.45090 12.55904 11.83928tapply(Temperature$Temperature, Temperature$Year, mean, na.rm = TRUE)
tapply(Temperature$Salinity, Temperature$Station, mean, na.rm = TRUE)
tapply(Temperature$Salinity, Temperature$Year, mean, na.rm = TRUE)The reason why you did not need to see the output of the other three lines is that the aggregate code below will show you all four tapply functions, combined:
YearAndSite = aggregate(Temperature[,14:15],
list(Temperature$Year, Temperature$Station),
mean, na.rm=TRUE)We can then make the aggregate chart into its own csv file:
write.csv(YearAndSite, file = "YearAndSite.csv")Lastly, some quick analysis where the first code tell us how many entries per stations there are, the second tells us how many entries per year there are, and the third gives us a full breakdown based on both:
table(Temperature$Station)##
## DANT DREI G6 GROO HAMM HANS HUIB LODS MARS N02 N10 N20 N70 R03 R50 R70
## 300 293 278 296 295 309 296 294 296 402 665 266 268 161 106 106
## SOEL T004 T010 T100 T135 T175 T235 VLIS W02 W20 W70 WISS ZIJP ZUID
## 295 339 261 258 259 258 258 421 272 191 190 296 296 303table(Temperature$Year)##
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## 367 392 438 436 590 590 583 636 608 570 563 568 545 550 540 552table(Temperature$Station, Temperature$Year)##
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
## DANT 12 12 18 19 17 13 19 20 22 21 22 21 21 21
## DREI 13 13 13 13 19 21 20 20 21 20 20 20 20 20
## G6 12 16 20 17 21 24 23 28 23 21 13 12 12 12
## GROO 12 12 18 19 17 12 18 19 21 21 21 21 21 21
## HAMM 13 13 13 13 20 21 20 21 20 20 21 20 20 20
## HANS 13 16 13 13 21 24 19 19 18 18 29 29 20 19
## HUIB 12 11 18 19 17 12 19 19 21 21 21 22 21 21
## LODS 13 13 13 13 20 21 20 21 20 20 20 20 20 20
## MARS 12 12 18 18 16 12 19 19 22 21 21 21 21 21
## N02 12 17 20 17 27 29 31 40 37 29 24 27 23 23
## N10 47 45 40 46 53 56 43 52 45 43 36 37 30 31
## N20 12 12 11 11 18 18 18 18 18 18 19 19 19 19
## N70 12 11 11 11 18 18 18 18 18 19 18 20 20 19
## R03 0 5 7 7 13 17 17 23 20 9 8 7 7 7
## R50 0 5 7 7 10 7 7 7 7 7 7 7 7 7
## R70 0 5 7 7 10 7 7 7 7 7 7 7 7 7
## SOEL 13 13 13 12 20 22 20 21 20 20 20 20 20 20
## T004 12 14 20 16 26 27 27 33 28 20 19 19 19 20
## T010 12 10 11 10 18 18 18 18 18 18 18 18 18 19
## T100 12 10 10 10 18 18 18 18 18 18 18 18 18 18
## T135 12 10 10 10 18 18 18 18 18 18 17 19 18 19
## T175 12 10 10 10 18 18 18 18 18 18 18 18 18 18
## T235 12 10 10 10 18 18 18 18 18 18 18 18 18 18
## VLIS 14 18 21 24 35 36 22 23 22 22 30 31 30 32
## W02 12 17 20 17 21 23 23 28 23 16 12 12 12 12
## W20 12 12 11 11 12 12 12 12 12 13 12 12 12 12
## W70 12 12 11 11 12 12 12 12 12 12 12 12 12 12
## WISS 13 13 13 13 20 21 20 22 20 20 21 20 20 20
## ZIJP 13 13 13 13 20 21 20 23 20 20 20 20 20 20
## ZUID 11 12 18 19 17 14 19 21 21 22 21 21 21 22
##
## 2004 2005
## DANT 21 21
## DREI 20 20
## G6 12 12
## GROO 21 22
## HAMM 20 20
## HANS 19 19
## HUIB 21 21
## LODS 20 20
## MARS 21 22
## N02 22 24
## N10 29 32
## N20 17 19
## N70 18 19
## R03 7 7
## R50 7 7
## R70 7 7
## SOEL 20 21
## T004 19 20
## T010 18 19
## T100 18 18
## T135 18 18
## T175 18 18
## T235 18 18
## VLIS 31 30
## W02 12 12
## W20 12 12
## W70 12 12
## WISS 20 20
## ZIJP 20 20
## ZUID 22 22