GEOG 5680 Module 11
Ben Williamson
2024-06-13
This exercise will use the dataset Temperature.csv, a file with sea temperature and salinity recorded from 31 stations over 15 years.The stations are located in one of 10 areas.
Step one: Read in the data
library(data.table)
temp <- fread("Temperature.csv")
dim(temp)## [1] 8528 16class(temp)## [1] "data.table" "data.frame"Step two: Extract all winter observations
temp_winter <- temp[Season == "winter"]
head(temp_winter)## Sample Date DateNr dDay1 dDay2 dDay3 Station Area 31UE_ED50
## 1: DANT.19900110 19900110 10/1/90 7 9 9 DANT WZ 681379.6
## 2: DANT.19900206 19900206 6/2/90 34 36 36 DANT WZ 681379.6
## 3: DANT.19901212 19901212 12/12/90 343 345 345 DANT WZ 681379.6
## 4: DANT.19910116 19910116 1/16/1991 378 380 15 DANT WZ 681379.6
## 5: DANT.19910226 19910226 2/26/1991 419 421 56 DANT WZ 681379.6
## 6: DANT.19911219 19911219 12/19/1991 715 717 352 DANT WZ 681379.6
## 31UN_ED50 Year Month Season Salinity Temperature CHLFa
## 1: 5920571 1990 1 winter 29.19 4.0 1.30
## 2: 5920571 1990 2 winter 27.37 6.0 NA
## 3: 5920571 1990 12 winter 31.50 4.2 60.50
## 4: 5920571 1991 1 winter 20.83 -0.3 2.30
## 5: 5920571 1991 2 winter 28.06 3.9 3.52
## 6: 5920571 1991 12 winter 25.31 3.9 3.50Step three: Extract all winter observations for zone NC
temp_winter_NC <- temp[Season == "winter" & Area == "NC"]
head(temp_winter_NC)## Sample Date DateNr dDay1 dDay2 dDay3 Station Area 31UE_ED50
## 1: T100.19900103 19900103 3/1/90 0 2 2 T100 NC 587650.2
## 2: T100.19900205 19900205 5/2/90 33 35 35 T100 NC 587650.2
## 3: T100.19901218 19901218 12/18/1990 349 351 351 T100 NC 587650.2
## 4: T100.19910116 19910116 1/16/1991 378 380 15 T100 NC 587650.2
## 5: T100.19910205 19910205 5/2/91 398 400 35 T100 NC 587650.2
## 6: T100.19911211 19911211 11/12/91 707 709 344 T100 NC 587650.2
## 31UN_ED50 Year Month Season Salinity Temperature CHLFa
## 1: 6001110 1990 1 winter 34.82 8.5 0.30
## 2: 6001110 1990 2 winter NA NA NA
## 3: 6001110 1990 12 winter 34.80 9.2 0.40
## 4: 6001110 1991 1 winter 34.86 6.1 0.68
## 5: 6001110 1991 2 winter 34.53 5.2 0.34
## 6: 6001110 1991 12 winter 34.79 9.7 0.44Step four: Select only the columns Area, Season and Temperature
temp_selected_area_season_temp <- temp[, .(Area, Season, Temperature)]
head(temp_selected_area_season_temp)## Area Season Temperature
## 1: WZ winter 4.0
## 2: WZ winter 6.0
## 3: WZ spring 7.3
## 4: WZ spring 8.2
## 5: WZ spring 17.4
## 6: WZ summer 18.1Step five: Select only the columns Area and Temperature but only for winter observations
temp_selected_winter <- temp[Season == "winter", .(Area, Temperature)]
head(temp_selected_winter)## Area Temperature
## 1: WZ 4.0
## 2: WZ 6.0
## 3: WZ 4.2
## 4: WZ -0.3
## 5: WZ 3.9
## 6: WZ 3.9Step six: Find the total number of observations in winter
winter_observations <- temp[Season == "winter", .N]
winter_observations## [1] 1706Step seven: Calculate the mean temperature and mean salinity in winter (Note that there are missing values so will have to use na.rm = TRUE)
winter_mean_temp <- temp[Season == "winter", .(mean_temp = mean(Temperature, na.rm = TRUE))]
winter_mean_temp## mean_temp
## 1: 5.57162winter_mean_salinity <- temp[Season == "winter", .(mean_salinity = mean(Salinity, na.rm = TRUE))]
winter_mean_salinity## mean_salinity
## 1: 29.15756Step eight: Find the number of observations per station in winter
winter_observations_per_station <- temp[Season == "winter", .N, by = Station]
winter_observations_per_station## Station N
## 1: DANT 50
## 2: DREI 52
## 3: G6 101
## 4: GROO 50
## 5: HAMM 55
## 6: HANS 56
## 7: HUIB 50
## 8: LODS 54
## 9: MARS 49
## 10: N02 115
## 11: N10 131
## 12: N20 50
## 13: N70 50
## 14: R03 32
## 15: SOEL 50
## 16: T004 97
## 17: T010 45
## 18: T100 45
## 19: T135 46
## 20: T175 45
## 21: T235 45
## 22: VLIS 84
## 23: W02 99
## 24: W20 47
## 25: W70 47
## 26: WISS 55
## 27: ZIJP 54
## 28: ZUID 52
## Station NStep nine: Find the number of observations per station per season
observations_per_station_per_season <- temp[, .N, by = .(Station, Season)]
observations_per_station_per_season## Station Season N
## 1: DANT winter 50
## 2: DANT spring 89
## 3: DANT summer 89
## 4: DANT autumn 72
## 5: DREI winter 52
## ---
## 114: ZIJP autumn 61
## 115: ZUID winter 52
## 116: ZUID spring 89
## 117: ZUID summer 89
## 118: ZUID autumn 73Step ten: Estimate average temperatures by month
mean_temp_by_month <- temp[, .(mean_temp = mean(Temperature, na.rm = TRUE)), by = Month]
mean_temp_by_month## Month mean_temp
## 1: 1 5.174210
## 2: 2 4.737400
## 3: 3 6.125961
## 4: 4 8.702035
## 5: 5 12.293479
## 6: 6 15.659933
## 7: 7 18.077343
## 8: 8 19.388355
## 9: 9 16.995974
## 10: 10 13.619670
## 11: 11 9.848891
## 12: 12 6.746339Step eleven: Estimate average temperatures by month by area
mean_temp_by_month_by_area <- temp[, .(mean_temp = mean(Temperature, na.rm = TRUE)), by = .(Month, Area)]
mean_temp_by_month_by_area## Month Area mean_temp
## 1: 1 WZ 3.377826
## 2: 2 WZ 3.925800
## 3: 3 WZ 5.818481
## 4: 4 WZ 9.270805
## 5: 5 WZ 13.398191
## ---
## 116: 1 NC 6.789808
## 117: 2 NC 5.682581
## 118: 3 NC 5.837500
## 119: 11 NC 10.978269
## 120: 12 NC 8.716957Step twelve: Plot the output of the previous question using ggplot2 using the geom_line() geometry
library(ggplot2)
ggplot(mean_temp_by_month_by_area, aes(x = Month, y = mean_temp, color = Area, group = Area)) +
geom_line() +
labs(x = "Month", y = "Mean Temperature", color = "Area") +
theme_minimal()