I will use the code from Module 4 Exercise to produce this R
Markdown example
The file Temperature.csv contains temperature and
salinity observations made at 31 locations along the
Dutch coastline over 16 years. Sampling frequency was 0–4 times per
month, depending on the season. Read in the file:
temp <- read.csv("Temperature.csv")
str(temp)
## 'data.frame': 8528 obs. of 16 variables:
## $ Sample : chr "DANT.19900110" "DANT.19900206" "DANT.19900308" "DANT.19900404" ...
## $ Date : int 19900110 19900206 19900308 19900404 19900509 19900620 19900718 19900815 19900919 19901017 ...
## $ DateNr : chr "10/1/90" "6/2/90" "8/3/90" "4/4/90" ...
## $ 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 : chr "DANT" "DANT" "DANT" "DANT" ...
## $ Area : chr "WZ" "WZ" "WZ" "WZ" ...
## $ 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 : chr "winter" "winter" "spring" "spring" ...
## $ 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 ...
names(temp)
## [1] "Sample" "Date" "DateNr" "dDay1" "dDay2"
## [6] "dDay3" "Station" "Area" "X31UE_ED50" "X31UN_ED50"
## [11] "Year" "Month" "Season" "Salinity" "Temperature"
## [16] "CHLFa"
I first estimate the mean and standard deviation for temperature and
salinity across all samples.
mean(temp$Temperature, na.rm = TRUE)
## [1] 12.20743
mean(temp$Salinity, na.rm = TRUE)
## [1] 29.70221
sd(temp$Temperature, na.rm = TRUE)
## [1] 5.412521
sd(temp$Salinity, na.rm = TRUE)
## [1] 5.421593
I then use tapply() estimate the average values of temperature and
salinity across a) all sites and b) all years
tapply(temp$Temperature, temp$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 11.83928
tapply(temp$Salinity, temp$Station, mean, na.rm = TRUE)
## DANT DREI G6 GROO HAMM HANS HUIB LODS
## 28.94468 29.83813 30.59610 14.57571 31.35738 18.29124 29.52631 30.73000
## MARS N02 N10 N20 N70 R03 R50 R70
## 28.03457 28.78695 30.45223 31.93275 34.94147 30.54978 33.49888 33.67944
## SOEL T004 T010 T100 T135 T175 T235 VLIS
## 18.09477 32.08114 32.72095 34.56746 34.67667 34.75135 34.87812 29.55476
## W02 W20 W70 WISS ZIJP ZUID
## 32.05992 33.31554 35.02006 31.75164 29.39230 28.83997
tapply(temp$Temperature, temp$Year, mean, na.rm = TRUE)
## 1990 1991 1992 1993 1994 1995 1996 1997
## 11.99146 11.00885 11.55799 11.45385 12.07944 12.32080 10.72728 12.33687
## 1998 1999 2000 2001 2002 2003 2004 2005
## 12.13386 13.10577 12.61539 12.74162 13.05500 12.69017 12.56788 12.56112
tapply(temp$Salinity, temp$Year, mean, na.rm = TRUE)
## 1990 1991 1992 1993 1994 1995 1996 1997
## 30.89873 30.88625 30.27116 29.52839 29.07707 28.94954 30.66670 30.54525
## 1998 1999 2000 2001 2002 2003 2004 2005
## 29.55748 29.03291 28.99534 28.22390 28.89323 29.60946 30.45629 30.35355
I then use aggregate() to estimate the average monthly values of
temperature and salinity by year across all sites to make a time
series
aggregate(temp[,15], list(temp$Year, temp$Month), mean, na.rm = TRUE)
## Group.1 Group.2 x
## 1 1990 1 6.313889
## 2 1991 1 5.210000
## 3 1992 1 5.858140
## 4 1993 1 4.965217
## 5 1994 1 5.691111
## 6 1995 1 5.778000
## 7 1996 1 3.645238
## 8 1997 1 2.146857
## 9 1998 1 6.685926
## 10 1999 1 5.717778
## 11 2000 1 5.622963
## 12 2001 1 4.754375
## 13 2002 1 4.427778
## 14 2003 1 4.848485
## 15 2004 1 5.558636
## 16 2005 1 5.877692
## 17 1990 2 6.475000
## 18 1991 2 3.576923
## 19 1992 2 5.040625
## 20 1993 2 5.231111
## 21 1994 2 4.814706
## 22 1995 2 6.400000
## 23 1996 2 1.751351
## 24 1997 2 2.793600
## 25 1998 2 5.023438
## 26 1999 2 4.280000
## 27 2000 2 5.635517
## 28 2001 2 5.717826
## 29 2002 2 7.634286
## 30 2003 2 3.864545
## 31 2004 2 5.739286
## 32 2005 2 4.670455
## 33 1990 3 8.192857
## 34 1991 3 5.286207
## 35 1992 3 6.525806
## 36 1993 3 5.521622
## 37 1994 3 5.815686
## 38 1995 3 6.800000
## 39 1996 3 2.950000
## 40 1997 3 6.747551
## 41 1998 3 6.965862
## 42 1999 3 6.684898
## 43 2000 3 6.984773
## 44 2001 3 5.268108
## 45 2002 3 7.487778
## 46 2003 3 6.042927
## 47 2004 3 5.784407
## 48 2005 3 5.051000
## 49 1990 4 9.153333
## 50 1991 4 8.365714
## 51 1992 4 8.030769
## 52 1993 4 8.584615
## 53 1994 4 7.742857
## 54 1995 4 8.386364
## 55 1996 4 6.392683
## 56 1997 4 8.507045
## 57 1998 4 8.965862
## 58 1999 4 10.036364
## 59 2000 4 8.857907
## 60 2001 4 8.117222
## 61 2002 4 9.589038
## 62 2003 4 9.197736
## 63 2004 4 9.695370
## 64 2005 4 9.044444
## 65 1990 5 14.357576
## 66 1991 5 10.185000
## 67 1992 5 12.074324
## 68 1993 5 13.170270
## 69 1994 5 11.987097
## 70 1995 5 11.572549
## 71 1996 5 10.101429
## 72 1997 5 12.223214
## 73 1998 5 13.181455
## 74 1999 5 13.028776
## 75 2000 5 13.255000
## 76 2001 5 12.031867
## 77 2002 5 12.416441
## 78 2003 5 13.620857
## 79 2004 5 12.478364
## 80 2005 5 11.883061
## 81 1990 6 16.277273
## 82 1991 6 13.059259
## 83 1992 6 16.887838
## 84 1993 6 16.024390
## 85 1994 6 14.637255
## 86 1995 6 14.632609
## 87 1996 6 14.996364
## 88 1997 6 15.570200
## 89 1998 6 16.557660
## 90 1999 6 16.019091
## 91 2000 6 15.611111
## 92 2001 6 15.330877
## 93 2002 6 16.343036
## 94 2003 6 18.874643
## 95 2004 6 15.784259
## 96 2005 6 15.061846
## 97 1990 7 17.423333
## 98 1991 7 17.724324
## 99 1992 7 18.745000
## 100 1993 7 17.142857
## 101 1994 7 19.314035
## 102 1995 7 18.335849
## 103 1996 7 16.509091
## 104 1997 7 18.483594
## 105 1998 7 17.108049
## 106 1999 7 18.442623
## 107 2000 7 16.645918
## 108 2001 7 19.071818
## 109 2002 7 17.872241
## 110 2003 7 19.644000
## 111 2004 7 17.292632
## 112 2005 7 18.799111
## 113 1990 8 18.976667
## 114 1991 8 19.550000
## 115 1992 8 18.905000
## 116 1993 8 17.376316
## 117 1994 8 19.903065
## 118 1995 8 20.385000
## 119 1996 8 18.390741
## 120 1997 8 20.919815
## 121 1998 8 18.749200
## 122 1999 8 19.174603
## 123 2000 8 18.756102
## 124 2001 8 19.292273
## 125 2002 8 20.403500
## 126 2003 8 21.038269
## 127 2004 8 19.780185
## 128 2005 8 17.692105
## 129 1990 9 16.831250
## 130 1991 9 17.896000
## 131 1992 9 15.958621
## 132 1993 9 15.660526
## 133 1994 9 15.753846
## 134 1995 9 17.243902
## 135 1996 9 15.953191
## 136 1997 9 17.910833
## 137 1998 9 15.998444
## 138 1999 9 18.999268
## 139 2000 9 17.424048
## 140 2001 9 15.645429
## 141 2002 9 18.297895
## 142 2003 9 17.982400
## 143 2004 9 16.174694
## 144 2005 9 17.883455
## 145 1990 10 13.851724
## 146 1991 10 13.647727
## 147 1992 10 13.095135
## 148 1993 10 13.648485
## 149 1994 10 12.884375
## 150 1995 10 15.133333
## 151 1996 10 13.725000
## 152 1997 10 13.920000
## 153 1998 10 11.752941
## 154 1999 10 13.080385
## 155 2000 10 13.697297
## 156 2001 10 14.866410
## 157 2002 10 13.262703
## 158 2003 10 12.246765
## 159 2004 10 12.790000
## 160 2005 10 14.498125
## 161 1990 11 10.345161
## 162 1991 11 8.400000
## 163 1992 11 8.900833
## 164 1993 11 8.580000
## 165 1994 11 11.212195
## 166 1995 11 11.479487
## 167 1996 11 9.386410
## 168 1997 11 10.031200
## 169 1998 11 9.037097
## 170 1999 11 7.207857
## 171 2000 11 9.189630
## 172 2001 11 10.069130
## 173 2002 11 10.565357
## 174 2003 11 9.401000
## 175 2004 11 10.301786
## 176 2005 11 11.562759
## 177 1990 12 6.766667
## 178 1991 12 6.556757
## 179 1992 12 7.576977
## 180 1993 12 5.532258
## 181 1994 12 9.012821
## 182 1995 12 5.417143
## 183 1996 12 6.581818
## 184 1997 12 6.399259
## 185 1998 12 6.080741
## 186 1999 12 5.637200
## 187 2000 12 7.550952
## 188 2001 12 7.821786
## 189 2002 12 5.246552
## 190 2003 12 6.464762
## 191 2004 12 7.444643
## 192 2005 12 7.111481
aggregate(temp[,14], list(temp$Year, temp$Month), mean, na.rm = TRUE)
## Group.1 Group.2 x
## 1 1990 1 30.27000
## 2 1991 1 29.77267
## 3 1992 1 29.99535
## 4 1993 1 28.19087
## 5 1994 1 27.31000
## 6 1995 1 27.97180
## 7 1996 1 30.66524
## 8 1997 1 31.24865
## 9 1998 1 30.24886
## 10 1999 1 28.94469
## 11 2000 1 28.49000
## 12 2001 1 29.25000
## 13 2002 1 28.19519
## 14 2003 1 28.65606
## 15 2004 1 28.64591
## 16 2005 1 30.72360
## 17 1990 2 27.94625
## 18 1991 2 30.51923
## 19 1992 2 30.47500
## 20 1993 2 27.25280
## 21 1994 2 28.97088
## 22 1995 2 25.15593
## 23 1996 2 30.01189
## 24 1997 2 28.73720
## 25 1998 2 30.55347
## 26 1999 2 26.74842
## 27 2000 2 28.62345
## 28 2001 2 26.99696
## 29 2002 2 26.19952
## 30 2003 2 26.75606
## 31 2004 2 25.41143
## 32 2005 2 29.20818
## 33 1990 3 30.22250
## 34 1991 3 30.27966
## 35 1992 3 30.16500
## 36 1993 3 28.32056
## 37 1994 3 27.95980
## 38 1995 3 28.45118
## 39 1996 3 30.35333
## 40 1997 3 30.33224
## 41 1998 3 26.51448
## 42 1999 3 27.82719
## 43 2000 3 26.74886
## 44 2001 3 27.01111
## 45 2002 3 27.08886
## 46 2003 3 27.52780
## 47 2004 3 29.26576
## 48 2005 3 29.38240
## 49 1990 4 30.80133
## 50 1991 4 30.84543
## 51 1992 4 29.69103
## 52 1993 4 29.49949
## 53 1994 4 27.25381
## 54 1995 4 28.28159
## 55 1996 4 29.99561
## 56 1997 4 29.89523
## 57 1998 4 30.20926
## 58 1999 4 26.46606
## 59 2000 4 27.78628
## 60 2001 4 25.79417
## 61 2002 4 29.27327
## 62 2003 4 29.42528
## 63 2004 4 30.29222
## 64 2005 4 29.66933
## 65 1990 5 30.71281
## 66 1991 5 29.63042
## 67 1992 5 29.55811
## 68 1993 5 30.34541
## 69 1994 5 29.02492
## 70 1995 5 29.05375
## 71 1996 5 30.96757
## 72 1997 5 29.87750
## 73 1998 5 30.07436
## 74 1999 5 28.90551
## 75 2000 5 29.11696
## 76 2001 5 27.69520
## 77 2002 5 28.68169
## 78 2003 5 27.61743
## 79 2004 5 30.51545
## 80 2005 5 29.39306
## 81 1990 6 31.01000
## 82 1991 6 31.08407
## 83 1992 6 30.35611
## 84 1993 6 30.54561
## 85 1994 6 28.83816
## 86 1995 6 28.32043
## 87 1996 6 30.93800
## 88 1997 6 30.86431
## 89 1998 6 29.16064
## 90 1999 6 29.79455
## 91 2000 6 29.80463
## 92 2001 6 28.86246
## 93 2002 6 29.54439
## 94 2003 6 27.59964
## 95 2004 6 30.69167
## 96 2005 6 30.86169
## 97 1990 7 31.43033
## 98 1991 7 30.83892
## 99 1992 7 30.59800
## 100 1993 7 30.94595
## 101 1994 7 30.38172
## 102 1995 7 29.41472
## 103 1996 7 31.25352
## 104 1997 7 30.05484
## 105 1998 7 29.86098
## 106 1999 7 30.31197
## 107 2000 7 29.52940
## 108 2001 7 29.14891
## 109 2002 7 30.07845
## 110 2003 7 31.02720
## 111 2004 7 31.00930
## 112 2005 7 30.15822
## 113 1990 8 31.99467
## 114 1991 8 31.43059
## 115 1992 8 30.80000
## 116 1993 8 30.53184
## 117 1994 8 30.16051
## 118 1995 8 29.85034
## 119 1996 8 31.21519
## 120 1997 8 30.58132
## 121 1998 8 30.79800
## 122 1999 8 30.69317
## 123 2000 8 29.97169
## 124 2001 8 30.04818
## 125 2002 8 29.98167
## 126 2003 8 31.83269
## 127 2004 8 31.41574
## 128 2005 8 30.64439
## 129 1990 9 30.41125
## 130 1991 9 31.36160
## 131 1992 9 31.25241
## 132 1993 9 28.51892
## 133 1994 9 30.61487
## 134 1995 9 29.36300
## 135 1996 9 30.43723
## 136 1997 9 30.03083
## 137 1998 9 30.19549
## 138 1999 9 30.03122
## 139 2000 9 29.35476
## 140 2001 9 27.70486
## 141 2002 9 29.12079
## 142 2003 9 31.38060
## 143 2004 9 31.03040
## 144 2005 9 31.44145
## 145 1990 10 31.91690
## 146 1991 10 31.58044
## 147 1992 10 31.80270
## 148 1993 10 30.09182
## 149 1994 10 30.50844
## 150 1995 10 30.29039
## 151 1996 10 31.50750
## 152 1997 10 31.62538
## 153 1998 10 26.48765
## 154 1999 10 29.21885
## 155 2000 10 29.77703
## 156 2001 10 28.18974
## 157 2002 10 27.88711
## 158 2003 10 30.37000
## 159 2004 10 31.61171
## 160 2005 10 30.68656
## 161 1990 11 31.53484
## 162 1991 11 31.11848
## 163 1992 11 28.24167
## 164 1993 11 29.79150
## 165 1994 11 28.81049
## 166 1995 11 30.25692
## 167 1996 11 30.36205
## 168 1997 11 31.85209
## 169 1998 11 27.67930
## 170 1999 11 28.35071
## 171 2000 11 29.72741
## 172 2001 11 27.49217
## 173 2002 11 28.56679
## 174 2003 11 31.03300
## 175 2004 11 31.16250
## 176 2005 11 31.25586
## 177 1990 12 31.10160
## 178 1991 12 31.42028
## 179 1992 12 29.92356
## 180 1993 12 28.58935
## 181 1994 12 28.97487
## 182 1995 12 29.41657
## 183 1996 12 29.53676
## 184 1997 12 31.20455
## 185 1998 12 29.48020
## 186 1999 12 28.57640
## 187 2000 12 27.88381
## 188 2001 12 28.03893
## 189 2002 12 29.41345
## 190 2003 12 29.75091
## 191 2004 12 30.50429
## 192 2005 12 30.53444
I then use the table() function to determine the number of
observations:
table(temp$Station)
##
## DANT DREI G6 GROO HAMM HANS HUIB LODS MARS N02 N10 N20 N70 R03 R50 R70
## 300 293 278 296 295 309 296 294 296 402 665 266 268 161 106 106
## SOEL T004 T010 T100 T135 T175 T235 VLIS W02 W20 W70 WISS ZIJP ZUID
## 295 339 261 258 259 258 258 421 272 191 190 296 296 303
table(temp$Year)
##
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## 367 392 438 436 590 590 583 636 608 570 563 568 545 550 540 552
table(temp$Station, temp$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
That’s it for Module 6 Exercise