Read in the data using fread.

library(data.table)
temp <- fread("Temperature.csv")

Extract all winter observations.

winter <- temp[Season == "winter"]
head(winter)
##           Sample     Date     DateNr dDay1 dDay2 dDay3 Station   Area 31UE_ED50
##           <char>    <int>     <char> <int> <int> <int>  <char> <char>     <num>
## 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
##        <num> <int> <int> <char>    <num>       <num> <num>
## 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  3.50

Extract all winter observations for zone NC.

winter_NC <- temp[Season == "winter" & Area == "NC"]
head(winter_NC)
##           Sample     Date     DateNr dDay1 dDay2 dDay3 Station   Area 31UE_ED50
##           <char>    <int>     <char> <int> <int> <int>  <char> <char>     <num>
## 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
##        <num> <int> <int> <char>    <num>       <num> <num>
## 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  0.44

Select only the columns Area, Season and Temperature.

area_seas_temp <- temp[, .(Area, Season, Temperature)]
head(area_seas_temp)
##      Area Season Temperature
##    <char> <char>       <num>
## 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        18.1

Select only the columns Area and Temperature but only for winter observations.

wint_areatemp <- temp[Season == "winter", .(Area, Temperature)]
head(wint_areatemp)
##      Area Temperature
##    <char>       <num>
## 1:     WZ         4.0
## 2:     WZ         6.0
## 3:     WZ         4.2
## 4:     WZ        -0.3
## 5:     WZ         3.9
## 6:     WZ         3.9

Find the total number of observations in winter.

wint_total <- temp[Season == "winter", .N]
wint_total
## [1] 1706

Calculate the mean temperature and mean salinity in winter (Note that there are missing values so will have to use na.rm = TRUE).

wintmeans_tempsal <- temp[Season == "winter", 
  .(mean_temp = mean(Temperature, na.rm = TRUE),
  mean_salinity = mean(Salinity, na.rm = TRUE))]
wintmeans_tempsal
##    mean_temp mean_salinity
##        <num>         <num>
## 1:   5.57162      29.15756

Find the number of observations per station in winter.

wint_obsperstation <- temp[Season == "winter", .N, by = Station]
head(wint_obsperstation)
##    Station     N
##     <char> <int>
## 1:    DANT    50
## 2:    DREI    52
## 3:      G6   101
## 4:    GROO    50
## 5:    HAMM    55
## 6:    HANS    56

Find the number of observations per station per season.

obsperstation_perseason <- temp[, .N, by = .(Station, Season)]
head(obsperstation_perseason)
##    Station Season     N
##     <char> <char> <int>
## 1:    DANT winter    50
## 2:    DANT spring    89
## 3:    DANT summer    89
## 4:    DANT autumn    72
## 5:    DREI winter    52
## 6:    DREI spring    92

Estimate average temperatures by month.

mean_tempbymonth <- temp[, .(mean_temp = mean(Temperature, na.rm = TRUE)), by = Month]
mean_tempbymonth
##     Month mean_temp
##     <int>     <num>
##  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  6.746339

Estimate average temperatures by month by area.

meantempbymonth_byarea <- temp[, .(mean_temp = mean(Temperature, na.rm = TRUE)), by = .(Month, Area)]
head(meantempbymonth_byarea)
##    Month   Area mean_temp
##    <int> <char>     <num>
## 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
## 6:     6     WZ 16.542222

Plot the output of the previous question using ggplot2 using the geom_line() geometry.

library(ggplot2)
ggplot(meantempbymonth_byarea, aes(x = Month, y = mean_temp, color = Area, group = Area)) +
  geom_line() +
  labs(x = "Month", y = "Mean Temp", color = "Area")