require(plyr)
## Loading required package: plyr
require(doBy)
## Loading required package: doBy
## Loading required package: survival
## Loading required package: splines
## Loading required package: MASS
require(ggplot2)
## Loading required package: ggplot2
reproanalysis<-read.csv('ReproAnalysis.csv', header=T)
print(reproanalysis)
## X Date Site Temp Pop Brooders Gaping Percent Dead Closed
## 1 1 2014-05-02 Fidalgo 10.0 N 0 0 0.000 NA NA
## 2 2 2014-05-16 Fidalgo 9.0 N 0 53 0.000 0 46
## 3 3 2014-05-24 Fidalgo 10.0 N 0 24 0.000 0 86
## 4 4 2014-05-30 Fidalgo 10.0 N 0 46 0.000 0 50
## 5 5 2014-06-06 Fidalgo 10.0 N 1 59 1.695 2 36
## 6 6 2014-06-13 Fidalgo 7.0 N 0 68 0.000 1 27
## 7 7 2014-06-20 Fidalgo 8.0 N 0 76 0.000 0 34
## 8 8 2014-06-27 Fidalgo 9.0 N 1 87 1.149 0 6
## 9 9 2014-07-04 Fidalgo 9.0 N 1 82 1.220 0 13
## 10 10 2014-07-11 Fidalgo 16.0 N 0 78 0.000 0 17
## 11 11 2014-07-18 Fidalgo 10.0 N 0 68 0.000 0 40
## 12 12 2014-07-25 Fidalgo 11.0 N 0 55 0.000 0 45
## 13 13 2014-08-01 Fidalgo 10.0 N 0 28 0.000 0 68
## 14 14 2014-08-08 Fidalgo 11.0 N 5 89 5.618 0 10
## 15 15 2014-04-30 Manchester 9.5 N 0 0 0.000 NA NA
## 16 16 2014-05-14 Manchester 9.0 N 0 31 0.000 0 28
## 17 17 2014-05-21 Manchester 10.0 N 0 40 0.000 3 48
## 18 18 2014-05-28 Manchester 13.0 N 0 54 0.000 15 28
## 19 19 2014-06-04 Manchester 8.0 N 0 43 0.000 15 32
## 20 20 2014-06-11 Manchester 9.0 N 0 41 0.000 8 12
## 21 21 2014-06-18 Manchester 7.0 N 0 73 0.000 2 12
## 22 22 2014-06-25 Manchester 10.0 N 1 53 1.887 23 19
## 23 23 2014-07-02 Manchester 10.0 N 0 40 0.000 20 18
## 24 24 2014-07-09 Manchester 11.0 N 0 45 0.000 12 2
## 25 25 2014-07-16 Manchester 10.0 N 0 61 0.000 6 8
## 26 26 2014-07-23 Manchester 8.0 N 0 60 0.000 10 9
## 27 27 2014-07-30 Manchester 11.0 N 0 45 0.000 12 7
## 28 28 2014-08-06 Manchester 18.0 N 0 45 0.000 4 5
## 29 29 2014-05-01 Oyster Bay 11.0 N 0 NA 0.000 NA NA
## 30 30 2014-05-15 Oyster Bay 13.0 N 0 46 0.000 7 14
## 31 31 2014-05-22 Oyster Bay 13.0 N 0 3 0.000 93 5
## 32 32 2014-05-29 Oyster Bay 15.0 N 2 51 3.922 6 10
## 33 33 2014-06-05 Oyster Bay 13.0 N 1 52 1.923 7 8
## 34 34 2014-06-12 Oyster Bay 15.0 N 2 48 4.167 8 1
## 35 35 2014-06-19 Oyster Bay 16.0 N 3 54 5.556 8 1
## 36 36 2014-06-26 Oyster Bay 14.0 N 7 156 4.487 26 2
## 37 37 2014-07-03 Oyster Bay 15.0 N 0 49 0.000 0 1
## 38 38 2014-07-10 Oyster Bay 16.0 N 8 73 10.959 3 2
## 39 39 2014-07-17 Oyster Bay 16.0 N 0 48 0.000 1 1
## 40 40 2014-07-24 Oyster Bay 15.0 N 3 68 4.412 0 10
## 41 41 2014-07-31 Oyster Bay 17.0 N 1 50 2.000 0 0
## 42 42 2014-08-07 Oyster Bay 15.0 N 3 71 4.225 0 7
## 43 43 2014-05-02 Fidalgo 10.0 H 0 NA 0.000 NA NA
## 44 44 2014-05-16 Fidalgo 9.0 H 0 48 0.000 0 52
## 45 45 2014-05-24 Fidalgo 10.0 H 0 53 0.000 0 30
## 46 46 2014-05-30 Fidalgo 10.0 H 0 50 0.000 1 40
## 47 47 2014-06-06 Fidalgo 10.0 H 0 58 0.000 0 33
## 48 48 2014-06-13 Fidalgo 7.0 H 0 72 0.000 0 27
## 49 49 2014-06-20 Fidalgo 8.0 H 0 77 0.000 0 10
## 50 50 2014-06-27 Fidalgo 9.0 H 0 65 0.000 0 28
## 51 51 2014-07-04 Fidalgo 9.0 H 1 88 1.136 0 9
## 52 52 2014-07-11 Fidalgo 16.0 H 0 70 0.000 0 28
## 53 53 2014-07-18 Fidalgo 10.0 H 2 32 6.250 0 50
## 54 54 2014-07-25 Fidalgo 11.0 H 2 50 4.000 0 36
## 55 55 2014-08-01 Fidalgo 10.0 H 0 84 0.000 0 2
## 56 56 2014-08-08 Fidalgo 11.0 H 3 67 4.478 0 15
## 57 57 2014-04-30 Manchester 9.5 H 0 NA 0.000 NA NA
## 58 58 2014-05-14 Manchester 9.0 H 0 25 0.000 0 72
## 59 59 2014-05-21 Manchester 10.0 H 0 16 0.000 2 66
## 60 60 2014-05-28 Manchester 13.0 H 0 27 0.000 0 58
## 61 61 2014-06-04 Manchester 8.0 H 0 55 0.000 11 23
## 62 62 2014-06-11 Manchester 9.0 H 0 63 0.000 2 31
## 63 63 2014-06-18 Manchester 7.0 H 0 63 0.000 4 12
## 64 64 2014-06-25 Manchester 10.0 H 1 52 1.923 7 33
## 65 65 2014-07-02 Manchester 10.0 H 0 52 0.000 20 8
## 66 66 2014-07-09 Manchester 11.0 H 1 60 1.667 5 21
## 67 67 2014-07-16 Manchester 10.0 H 1 56 1.786 8 17
## 68 68 2014-07-23 Manchester 8.0 H 1 67 1.493 7 9
## 69 69 2014-07-30 Manchester 11.0 H 1 45 2.222 9 11
## 70 70 2014-08-06 Manchester 18.0 H 2 77 2.597 12 5
## 71 71 2014-05-01 Oyster Bay 11.0 H 0 NA 0.000 NA NA
## 72 72 2014-05-15 Oyster Bay 13.0 H 0 49 0.000 7 33
## 73 73 2014-05-22 Oyster Bay 13.0 H 0 47 0.000 51 9
## 74 74 2014-05-29 Oyster Bay 15.0 H 1 80 1.250 7 2
## 75 75 2014-06-05 Oyster Bay 13.0 H 2 79 2.532 7 3
## 76 76 2014-06-12 Oyster Bay 15.0 H 1 86 1.163 8 1
## 77 77 2014-06-19 Oyster Bay 16.0 H 0 70 0.000 9 7
## 78 78 2014-06-26 Oyster Bay 14.0 H 1 92 1.087 4 1
## 79 79 2014-07-03 Oyster Bay 15.0 H 3 66 4.545 4 4
## 80 80 2014-07-10 Oyster Bay 16.0 H 6 83 7.229 3 3
## 81 81 2014-07-17 Oyster Bay 16.0 H 5 68 7.353 6 1
## 82 82 2014-07-24 Oyster Bay 15.0 H 4 72 5.556 4 6
## 83 83 2014-07-31 Oyster Bay 17.0 H 1 59 1.695 2 8
## 84 84 2014-08-07 Oyster Bay 15.0 H 2 80 2.500 0 6
## 85 85 2014-05-02 Fidalgo 10.0 S 0 NA 0.000 NA NA
## 86 86 2014-05-16 Fidalgo 9.0 S 0 55 0.000 0 NA
## 87 87 2014-05-24 Fidalgo 10.0 S 0 77 0.000 0 NA
## 88 88 2014-05-30 Fidalgo 10.0 S 0 77 0.000 1 55
## 89 89 2014-06-06 Fidalgo 10.0 S 0 53 0.000 1 26
## 90 90 2014-06-13 Fidalgo 7.0 S 7 83 8.434 1 39
## 91 91 2014-06-20 Fidalgo 8.0 S 1 83 1.205 0 8
## 92 92 2014-06-27 Fidalgo 9.0 S 3 87 3.448 0 4
## 93 93 2014-07-04 Fidalgo 9.0 S 6 71 8.451 0 21
## 94 94 2014-07-11 Fidalgo 16.0 S 11 81 13.580 0 11
## 95 95 2014-07-18 Fidalgo 10.0 S 0 67 0.000 0 26
## 96 96 2014-07-25 Fidalgo 11.0 S 6 72 8.333 0 32
## 97 97 2014-08-01 Fidalgo 10.0 S 3 45 6.667 0 49
## 98 98 2014-08-08 Fidalgo 11.0 S 2 70 2.857 0 23
## 99 99 2014-04-30 Manchester 9.5 S 0 NA 0.000 NA 43
## 100 100 2014-05-14 Manchester 9.0 S 0 43 0.000 0 37
## 101 101 2014-05-21 Manchester 10.0 S 0 76 0.000 3 6
## 102 102 2014-05-28 Manchester 13.0 S 0 43 0.000 9 3
## 103 103 2014-06-04 Manchester 8.0 S 0 60 0.000 3 28
## 104 104 2014-06-11 Manchester 9.0 S 0 49 0.000 12 1
## 105 105 2014-06-18 Manchester 7.0 S 1 55 1.818 10 10
## 106 106 2014-06-25 Manchester 10.0 S 0 50 0.000 2 13
## 107 107 2014-07-02 Manchester 10.0 S 0 31 0.000 4 2
## 108 108 2014-07-09 Manchester 11.0 S 0 55 0.000 22 6
## 109 109 2014-07-16 Manchester 10.0 S 3 59 5.085 12 48
## 110 110 2014-07-23 Manchester 8.0 S 2 59 3.390 1 3
## 111 111 2014-07-30 Manchester 11.0 S 1 69 1.449 10 14
## 112 112 2014-08-06 Manchester 18.0 S 4 50 8.000 11 15
## 113 113 2014-05-01 Oyster Bay 11.0 S 0 NA 0.000 NA 1
## 114 114 2014-05-15 Oyster Bay 13.0 S 0 59 0.000 8 11
## 115 115 2014-05-22 Oyster Bay 13.0 S 0 2 0.000 68 12
## 116 116 2014-05-29 Oyster Bay 15.0 S 5 74 6.757 9 2
## 117 117 2014-06-05 Oyster Bay 13.0 S 2 60 3.333 12 30
## 118 118 2014-06-12 Oyster Bay 15.0 S 3 63 4.762 11 6
## 119 119 2014-06-19 Oyster Bay 16.0 S 11 80 13.750 12 7
## 120 120 2014-06-26 Oyster Bay 14.0 S 11 85 12.941 10 25
## 121 121 2014-07-03 Oyster Bay 15.0 S 9 78 11.538 0 10
## 122 122 2014-07-10 Oyster Bay 16.0 S 10 82 12.195 0 2
## 123 123 2014-07-17 Oyster Bay 16.0 S 0 75 0.000 1 38
## 124 124 2014-07-24 Oyster Bay 15.0 S 8 75 10.667 4 7
## 125 125 2014-07-31 Oyster Bay 17.0 S 0 70 0.000 0 1
## 126 126 2014-08-07 Oyster Bay 15.0 S 10 80 12.500 0 22
## Total Tide arcsinbrooders prop arcsinprop
## 1 100.00 -0.84 0.000 NA 0.0000
## 2 99.00 -2.27 0.000 0.00000 0.0000
## 3 110.00 1.12 0.000 0.00000 0.0000
## 4 96.00 -1.46 0.000 0.00000 0.0000
## 5 98.69 3.13 1.571 0.01695 0.1306
## 6 96.00 -2.81 0.000 0.00000 0.0000
## 7 110.00 2.14 0.000 0.00000 0.0000
## 8 94.15 -1.42 1.571 0.01149 0.1074
## 9 96.22 2.58 1.571 0.01220 0.1107
## 10 95.00 -2.57 0.000 0.00000 0.0000
## 11 108.00 2.05 0.000 0.00000 0.0000
## 12 100.00 -0.78 0.000 0.00000 0.0000
## 13 96.00 2.39 0.000 0.00000 0.0000
## 14 104.62 -1.66 0.000 0.05618 0.2393
## 15 90.00 -1.50 0.000 NA 0.0000
## 16 59.00 -1.63 0.000 0.00000 0.0000
## 17 91.00 0.87 0.000 0.00000 0.0000
## 18 97.00 -1.83 0.000 0.00000 0.0000
## 19 90.00 1.70 0.000 0.00000 0.0000
## 20 61.00 -1.75 0.000 0.00000 0.0000
## 21 87.00 0.19 0.000 0.00000 0.0000
## 22 96.89 -1.49 1.571 0.01887 0.1378
## 23 78.00 0.94 0.000 0.00000 0.0000
## 24 59.00 -1.12 0.000 0.00000 0.0000
## 25 75.00 -0.28 0.000 0.00000 0.0000
## 26 79.00 -0.60 0.000 0.00000 0.0000
## 27 64.00 0.86 0.000 0.00000 0.0000
## 28 54.00 -0.14 0.000 0.00000 0.0000
## 29 100.00 -1.53 0.000 NA 0.0000
## 30 67.00 -2.43 0.000 0.00000 0.0000
## 31 101.00 2.31 0.000 0.00000 0.0000
## 32 67.00 -2.00 0.000 0.03922 0.1993
## 33 67.00 2.97 1.571 0.01923 0.1391
## 34 61.17 -2.47 0.000 0.04167 0.2056
## 35 68.56 1.93 0.000 0.05556 0.2379
## 36 182.00 -1.72 0.000 0.04487 0.2134
## 37 50.00 2.14 0.000 0.00000 0.0000
## 38 88.96 -2.19 0.000 0.10959 0.3374
## 39 50.00 1.42 0.000 0.00000 0.0000
## 40 82.41 -0.85 0.000 0.04412 0.2116
## 41 52.00 1.84 1.571 0.02000 0.1419
## 42 82.23 -1.05 0.000 0.04225 0.2070
## 43 94.00 -0.84 0.000 NA 0.0000
## 44 100.00 -2.27 0.000 0.00000 0.0000
## 45 83.00 1.12 0.000 0.00000 0.0000
## 46 91.00 -1.46 0.000 0.00000 0.0000
## 47 91.00 3.13 0.000 0.00000 0.0000
## 48 99.00 -2.81 0.000 0.00000 0.0000
## 49 87.00 2.14 0.000 0.00000 0.0000
## 50 93.00 -1.42 0.000 0.00000 0.0000
## 51 98.14 2.58 1.571 0.01136 0.1068
## 52 98.00 -2.57 0.000 0.00000 0.0000
## 53 88.25 2.05 0.000 0.06250 0.2527
## 54 90.00 -0.78 0.000 0.04000 0.2014
## 55 86.00 2.39 0.000 0.00000 0.0000
## 56 86.48 -1.66 0.000 0.04478 0.2132
## 57 89.00 -1.50 0.000 NA 0.0000
## 58 97.00 -1.63 0.000 0.00000 0.0000
## 59 84.00 0.87 0.000 0.00000 0.0000
## 60 85.00 -1.83 0.000 0.00000 0.0000
## 61 89.00 1.70 0.000 0.00000 0.0000
## 62 96.00 -1.75 0.000 0.00000 0.0000
## 63 79.00 0.19 0.000 0.00000 0.0000
## 64 93.92 -1.49 1.571 0.01923 0.1391
## 65 80.00 0.94 0.000 0.00000 0.0000
## 66 87.67 -1.12 1.571 0.01667 0.1295
## 67 82.79 -0.28 1.571 0.01786 0.1340
## 68 84.49 -0.60 1.571 0.01493 0.1225
## 69 67.22 0.86 1.571 0.02222 0.1496
## 70 96.60 -0.14 0.000 0.02597 0.1619
## 71 101.00 -1.53 0.000 NA 0.0000
## 72 89.00 -2.43 0.000 0.00000 0.0000
## 73 107.00 2.31 0.000 0.00000 0.0000
## 74 89.00 -2.00 1.571 0.01250 0.1120
## 75 89.00 2.97 0.000 0.02532 0.1598
## 76 96.16 -2.47 1.571 0.01163 0.1080
## 77 86.00 1.93 0.000 0.00000 0.0000
## 78 98.09 -1.72 1.571 0.01087 0.1044
## 79 78.55 2.14 0.000 0.04545 0.2148
## 80 96.23 -2.19 0.000 0.07229 0.2722
## 81 82.35 1.42 0.000 0.07353 0.2746
## 82 87.56 -0.85 0.000 0.05556 0.2379
## 83 70.69 1.84 1.571 0.01695 0.1306
## 84 88.50 -1.05 0.000 0.02500 0.1588
## 85 94.00 -0.84 0.000 NA 0.0000
## 86 74.00 -2.27 0.000 0.00000 0.0000
## 87 93.00 1.12 0.000 0.00000 0.0000
## 88 133.00 -1.46 0.000 0.00000 0.0000
## 89 80.00 3.13 0.000 0.00000 0.0000
## 90 131.43 -2.81 0.000 0.08434 0.2947
## 91 91.00 2.14 1.571 0.01205 0.1100
## 92 94.45 -1.42 0.000 0.03448 0.1868
## 93 92.00 2.58 0.000 0.08451 0.2950
## 94 92.00 -2.57 0.000 0.13580 0.3774
## 95 93.00 2.05 0.000 0.00000 0.0000
## 96 104.00 -0.78 0.000 0.08333 0.2928
## 97 94.00 2.39 0.000 0.06667 0.2612
## 98 93.00 -1.66 0.000 0.02857 0.1698
## 99 43.00 -1.50 0.000 NA 0.0000
## 100 80.00 -1.63 0.000 0.00000 0.0000
## 101 85.00 0.87 0.000 0.00000 0.0000
## 102 55.00 -1.83 0.000 0.00000 0.0000
## 103 91.00 1.70 0.000 0.00000 0.0000
## 104 62.00 -1.75 0.000 0.00000 0.0000
## 105 76.82 0.19 1.571 0.01818 0.1353
## 106 65.00 -1.49 0.000 0.00000 0.0000
## 107 37.00 0.94 0.000 0.00000 0.0000
## 108 83.00 -1.12 0.000 0.00000 0.0000
## 109 124.08 -0.28 0.000 0.05085 0.2274
## 110 66.39 -0.60 0.000 0.03390 0.1852
## 111 94.45 0.86 1.571 0.01449 0.1207
## 112 84.00 -0.14 0.000 0.08000 0.2868
## 113 1.00 -1.53 0.000 NA 0.0000
## 114 78.00 -2.43 0.000 0.00000 0.0000
## 115 82.00 2.31 0.000 0.00000 0.0000
## 116 91.76 -2.00 0.000 0.06757 0.2630
## 117 105.33 2.97 0.000 0.03333 0.1836
## 118 84.76 -2.47 0.000 0.04762 0.2200
## 119 112.75 1.93 0.000 0.13750 0.3799
## 120 132.94 -1.72 0.000 0.12941 0.3680
## 121 99.54 2.14 0.000 0.11538 0.3466
## 122 96.20 -2.19 0.000 0.12195 0.3567
## 123 114.00 1.42 0.000 0.00000 0.0000
## 124 96.67 -0.85 0.000 0.10667 0.3327
## 125 71.00 1.84 0.000 0.00000 0.0000
## 126 114.50 -1.05 0.000 0.12500 0.3614
#First we will find the time in days between the threshold temp and the peak brooding
peakbrood<-ddply(reproanalysis,.(Site,Pop),subset,Brooders==max(Brooders, na.rm=T))
print(peakbrood)
## X Date Site Temp Pop Brooders Gaping Percent Dead Closed
## 1 56 2014-08-08 Fidalgo 11 H 3 67 4.478 0 15
## 2 14 2014-08-08 Fidalgo 11 N 5 89 5.618 0 10
## 3 94 2014-07-11 Fidalgo 16 S 11 81 13.580 0 11
## 4 70 2014-08-06 Manchester 18 H 2 77 2.597 12 5
## 5 22 2014-06-25 Manchester 10 N 1 53 1.887 23 19
## 6 112 2014-08-06 Manchester 18 S 4 50 8.000 11 15
## 7 80 2014-07-10 Oyster Bay 16 H 6 83 7.229 3 3
## 8 38 2014-07-10 Oyster Bay 16 N 8 73 10.959 3 2
## 9 119 2014-06-19 Oyster Bay 16 S 11 80 13.750 12 7
## 10 120 2014-06-26 Oyster Bay 14 S 11 85 12.941 10 25
## Total Tide arcsinbrooders prop arcsinprop
## 1 86.48 -1.66 0.000 0.04478 0.2132
## 2 104.62 -1.66 0.000 0.05618 0.2393
## 3 92.00 -2.57 0.000 0.13580 0.3774
## 4 96.60 -0.14 0.000 0.02597 0.1619
## 5 96.89 -1.49 1.571 0.01887 0.1378
## 6 84.00 -0.14 0.000 0.08000 0.2868
## 7 96.23 -2.19 0.000 0.07229 0.2722
## 8 88.96 -2.19 0.000 0.10959 0.3374
## 9 112.75 1.93 0.000 0.13750 0.3799
## 10 132.94 -1.72 0.000 0.12941 0.3680
#find the dates with maximum number of brooders
oysthresh<-ddply(oysmintemp,.(Date),subset,min_temp>=12.5)
#create a list of temps to find those above threshold temp for minimum daily temps at each site
print(oysthresh)
## Date min_temp
## 1 2013-08-18 17.38
## 2 2013-08-19 17.38
## 3 2013-08-20 17.66
## 4 2013-08-21 17.76
## 5 2013-08-22 17.66
## 6 2013-08-23 17.76
## 7 2013-08-24 17.57
## 8 2013-08-25 17.48
## 9 2013-08-26 17.38
## 10 2013-08-27 17.09
## 11 2013-08-28 17.09
## 12 2013-08-29 17.00
## 13 2013-08-30 16.90
## 14 2013-08-31 17.09
## 15 2013-09-01 17.19
## 16 2013-09-02 17.19
## 17 2013-09-03 17.28
## 18 2013-09-04 17.48
## 19 2013-09-05 17.57
## 20 2013-09-06 17.38
## 21 2013-09-07 17.28
## 22 2013-09-08 17.48
## 23 2013-09-09 17.48
## 24 2013-09-10 17.28
## 25 2013-09-11 17.19
## 26 2013-09-12 17.09
## 27 2013-09-13 17.19
## 28 2013-09-14 17.09
## 29 2013-09-15 17.19
## 30 2013-09-16 17.00
## 31 2013-09-17 17.00
## 32 2013-09-18 16.81
## 33 2013-09-19 16.90
## 34 2013-09-20 16.71
## 35 2013-09-21 16.71
## 36 2013-09-22 16.33
## 37 2013-09-23 16.14
## 38 2013-09-24 15.66
## 39 2013-09-25 15.47
## 40 2013-09-26 15.38
## 41 2013-09-27 15.28
## 42 2013-09-28 15.19
## 43 2013-09-29 14.80
## 44 2013-09-30 14.42
## 45 2013-10-01 13.94
## 46 2013-10-02 14.13
## 47 2013-10-03 13.94
## 48 2013-10-04 13.94
## 49 2013-10-05 14.13
## 50 2013-10-06 13.94
## 51 2013-10-07 14.04
## 52 2013-10-10 12.59
## 53 2013-10-11 13.46
## 54 2013-10-12 13.46
## 55 2013-10-13 12.88
## 56 2013-10-14 13.17
## 57 2013-10-15 12.98
## 58 2013-10-16 13.08
## 59 2013-10-17 13.17
## 60 2013-10-18 12.79
## 61 2013-10-19 12.59
## 62 2013-10-23 12.59
## 63 2014-05-14 12.69
## 64 2014-05-15 12.59
## 65 2014-05-16 12.69
## 66 2014-05-17 13.08
## 67 2014-05-18 13.27
## 68 2014-05-19 13.17
## 69 2014-05-20 13.27
## 70 2014-05-21 13.27
## 71 2014-05-22 13.46
## 72 2014-05-23 13.17
## 73 2014-05-24 13.56
## 74 2014-05-25 13.46
## 75 2014-05-26 13.56
## 76 2014-05-27 13.56
## 77 2014-05-28 13.46
## 78 2014-05-29 13.65
## 79 2014-05-30 13.65
## 80 2014-05-31 13.85
## 81 2014-06-01 13.75
## 82 2014-06-02 14.04
## 83 2014-06-03 14.04
## 84 2014-06-04 14.04
## 85 2014-06-05 14.23
## 86 2014-06-06 13.94
## 87 2014-06-07 14.13
## 88 2014-06-08 14.04
## 89 2014-06-09 13.94
## 90 2014-06-10 14.61
## 91 2014-06-11 13.94
## 92 2014-06-12 14.61
## 93 2014-06-13 14.90
## 94 2014-06-14 14.80
## 95 2014-06-15 14.71
## 96 2014-06-16 14.42
## 97 2014-06-17 14.42
## 98 2014-06-18 14.42
## 99 2014-06-19 14.52
## 100 2014-06-20 14.52
## 101 2014-06-21 15.00
## 102 2014-06-22 14.90
## 103 2014-06-23 14.80
## 104 2014-06-24 14.80
## 105 2014-06-25 15.28
## 106 2014-06-26 15.38
## 107 2014-06-27 15.47
## 108 2014-06-28 15.66
## 109 2014-06-29 15.57
## 110 2014-06-30 15.57
## 111 2014-07-01 15.66
## 112 2014-07-02 15.57
## 113 2014-07-03 15.86
## 114 2014-07-04 16.14
## 115 2014-07-05 15.76
## 116 2014-07-06 15.66
## 117 2014-07-07 16.24
## 118 2014-07-08 16.24
## 119 2014-07-09 16.24
## 120 2014-07-10 16.52
## 121 2014-07-11 17.19
## 122 2014-07-12 17.19
## 123 2014-07-13 17.28
## 124 2014-07-14 17.48
## 125 2014-07-15 17.19
## 126 2014-07-16 17.66
## 127 2014-07-17 17.48
## 128 2014-07-18 17.48
## 129 2014-07-19 17.19
## 130 2014-07-20 17.19
## 131 2014-07-21 17.28
## 132 2014-07-22 17.09
## 133 2014-07-23 17.00
## 134 2014-07-24 17.00
## 135 2014-07-25 16.90
## 136 2014-07-26 17.00
## 137 2014-07-27 17.09
## 138 2014-07-28 17.19
## 139 2014-07-29 17.38
## 140 2014-07-30 17.57
## 141 2014-07-31 17.76
## 142 2014-08-01 17.86
## 143 2014-08-02 17.76
## 144 2014-08-03 17.86
## 145 2014-08-04 17.48
## 146 2014-08-05 17.57
## 147 2014-08-06 17.76
## 148 2014-08-07 17.76
## 149 2014-08-08 17.95
## 150 2014-08-09 18.05
## 151 2014-08-10 18.24
## 152 2014-08-11 18.43
## 153 2014-08-12 18.05
## 154 2014-08-13 18.05
## 155 2014-08-14 18.05
## 156 2014-08-15 17.95
## 157 2014-08-16 17.86
## 158 2014-08-17 17.76
## 159 2014-08-18 17.66
## 160 2014-08-19 17.38
## 161 2014-08-20 17.86
## 162 2014-08-21 17.66
## 163 2014-08-22 17.76
## 164 2014-08-23 17.76
## 165 2014-08-24 17.66
## 166 2014-08-25 17.76
## 167 2014-08-26 17.76
## 168 2014-08-27 17.76
## 169 2014-08-28 17.86
## 170 2014-08-29 18.05
## 171 2014-08-30 17.76
## 172 2014-08-31 17.48
## 173 2014-09-01 17.48
## 174 2014-09-02 17.28
## 175 2014-09-03 17.19
## 176 2014-09-04 17.09
## 177 2014-09-05 17.38
## 178 2014-09-06 17.28
## 179 2014-09-07 17.38
## 180 2014-09-08 17.28
## 181 2014-09-09 17.28
## 182 2014-09-10 17.19
## 183 2014-09-11 17.09
## 184 2014-09-12 16.90
## 185 2014-09-13 16.81
## 186 2014-09-14 16.62
## 187 2014-09-15 16.62
## 188 2014-09-16 16.62
## 189 2014-09-17 16.71
## 190 2014-09-18 16.52
## 191 2014-09-19 16.62
fidthresh<-ddply(fidmintemp,.(Date),subset,min_temp>=12.5)
View(fidthresh)
manthresh<-ddply(manmintemp,.(Date),subset,min_temp>=12.5)
View(manthresh)
peakbrood$Date<-as.Date(peakbrood$Date)
oysthresh$Date<-as.Date(oysthresh$Date)
fidthresh$Date<-as.Date(fidthresh$Date)
manthresh$Date<-as.Date(manthresh$Date)
#make sure everything works as a Date in R after producing all the threshold temp data
d<-c("2014-06-03","2014-06-08","2014-05-14")
#dates visually confirmed for threshold temps
p<-c("Fidalgo","Manchester","Oyster Bay")
thresholddate<-data.frame(p,d)
thresholddate$d<-as.Date(thresholddate$d)
peakthresh<-merge(peakbrood,thresholddate,by.x="Site",by.y="p",all=F)
print(peakthresh)
## Site X Date Temp Pop Brooders Gaping Percent Dead Closed
## 1 Fidalgo 56 2014-08-08 11 H 3 67 4.478 0 15
## 2 Fidalgo 14 2014-08-08 11 N 5 89 5.618 0 10
## 3 Fidalgo 94 2014-07-11 16 S 11 81 13.580 0 11
## 4 Manchester 70 2014-08-06 18 H 2 77 2.597 12 5
## 5 Manchester 22 2014-06-25 10 N 1 53 1.887 23 19
## 6 Manchester 112 2014-08-06 18 S 4 50 8.000 11 15
## 7 Oyster Bay 80 2014-07-10 16 H 6 83 7.229 3 3
## 8 Oyster Bay 38 2014-07-10 16 N 8 73 10.959 3 2
## 9 Oyster Bay 119 2014-06-19 16 S 11 80 13.750 12 7
## 10 Oyster Bay 120 2014-06-26 14 S 11 85 12.941 10 25
## Total Tide arcsinbrooders prop arcsinprop d
## 1 86.48 -1.66 0.000 0.04478 0.2132 2014-06-03
## 2 104.62 -1.66 0.000 0.05618 0.2393 2014-06-03
## 3 92.00 -2.57 0.000 0.13580 0.3774 2014-06-03
## 4 96.60 -0.14 0.000 0.02597 0.1619 2014-06-08
## 5 96.89 -1.49 1.571 0.01887 0.1378 2014-06-08
## 6 84.00 -0.14 0.000 0.08000 0.2868 2014-06-08
## 7 96.23 -2.19 0.000 0.07229 0.2722 2014-05-14
## 8 88.96 -2.19 0.000 0.10959 0.3374 2014-05-14
## 9 112.75 1.93 0.000 0.13750 0.3799 2014-05-14
## 10 132.94 -1.72 0.000 0.12941 0.3680 2014-05-14
#create a data frame that compares dates for threshold and peak spawning dates
peakthresh$time_to_peak<-difftime(peakthresh$Date,peakthresh$d,units="days")
#finds the difference in days between threshold temp and peak spawning
print(peakthresh)
## Site X Date Temp Pop Brooders Gaping Percent Dead Closed
## 1 Fidalgo 56 2014-08-08 11 H 3 67 4.478 0 15
## 2 Fidalgo 14 2014-08-08 11 N 5 89 5.618 0 10
## 3 Fidalgo 94 2014-07-11 16 S 11 81 13.580 0 11
## 4 Manchester 70 2014-08-06 18 H 2 77 2.597 12 5
## 5 Manchester 22 2014-06-25 10 N 1 53 1.887 23 19
## 6 Manchester 112 2014-08-06 18 S 4 50 8.000 11 15
## 7 Oyster Bay 80 2014-07-10 16 H 6 83 7.229 3 3
## 8 Oyster Bay 38 2014-07-10 16 N 8 73 10.959 3 2
## 9 Oyster Bay 119 2014-06-19 16 S 11 80 13.750 12 7
## 10 Oyster Bay 120 2014-06-26 14 S 11 85 12.941 10 25
## Total Tide arcsinbrooders prop arcsinprop d time_to_peak
## 1 86.48 -1.66 0.000 0.04478 0.2132 2014-06-03 66 days
## 2 104.62 -1.66 0.000 0.05618 0.2393 2014-06-03 66 days
## 3 92.00 -2.57 0.000 0.13580 0.3774 2014-06-03 38 days
## 4 96.60 -0.14 0.000 0.02597 0.1619 2014-06-08 59 days
## 5 96.89 -1.49 1.571 0.01887 0.1378 2014-06-08 17 days
## 6 84.00 -0.14 0.000 0.08000 0.2868 2014-06-08 59 days
## 7 96.23 -2.19 0.000 0.07229 0.2722 2014-05-14 57 days
## 8 88.96 -2.19 0.000 0.10959 0.3374 2014-05-14 57 days
## 9 112.75 1.93 0.000 0.13750 0.3799 2014-05-14 36 days
## 10 132.94 -1.72 0.000 0.12941 0.3680 2014-05-14 43 days
peakthresh2<-peakthresh[c("Date","Site","Pop","Brooders","d","time_to_peak")]
#subsets df to only relevant information
peakthresh2<-rename(peakthresh2,c('Date'='Peak_Date',"Site"="Site","Pop"="Pop","Brooders"="Brooders","d"="Threshold Date","time_to_peak"="Days_to_Peak"))
#renames df columns to more meaningful names
print(peakthresh2)
## Peak_Date Site Pop Brooders Threshold Date Days_to_Peak
## 1 2014-08-08 Fidalgo H 3 2014-06-03 66 days
## 2 2014-08-08 Fidalgo N 5 2014-06-03 66 days
## 3 2014-07-11 Fidalgo S 11 2014-06-03 38 days
## 4 2014-08-06 Manchester H 2 2014-06-08 59 days
## 5 2014-06-25 Manchester N 1 2014-06-08 17 days
## 6 2014-08-06 Manchester S 4 2014-06-08 59 days
## 7 2014-07-10 Oyster Bay H 6 2014-05-14 57 days
## 8 2014-07-10 Oyster Bay N 8 2014-05-14 57 days
## 9 2014-06-19 Oyster Bay S 11 2014-05-14 36 days
## 10 2014-06-26 Oyster Bay S 11 2014-05-14 43 days
#next we want to find the degree days from minimum winter temp to spawning peak
#looking at previously generated temp graphs we decided that 8 was minimum winter temp
#we have to visually confirm when the temps continually increase from 8 to spawning
oysdd<-ddply(oysmintemp,.(Date),subset,min_temp>=8)
#subsets minimum temp data to find dates with temps above 8 C.
oysdd<-oysmintemp[c(oysmintemp$Date>="2014-03-06"),]
#after visually confirming the initial temp date we then subset the data from this point on
print(oysdd)
## Date min_temp
## 201 2014-03-06 8.082
## 202 2014-03-07 8.282
## 203 2014-03-08 8.382
## 204 2014-03-09 8.382
## 205 2014-03-10 8.382
## 206 2014-03-11 8.879
## 207 2014-03-12 8.779
## 208 2014-03-13 8.779
## 209 2014-03-14 8.680
## 210 2014-03-15 8.779
## 211 2014-03-16 8.879
## 212 2014-03-17 8.779
## 213 2014-03-18 8.779
## 214 2014-03-19 8.879
## 215 2014-03-20 8.680
## 216 2014-03-21 8.779
## 217 2014-03-22 8.879
## 218 2014-03-23 8.879
## 219 2014-03-24 8.978
## 220 2014-03-25 8.978
## 221 2014-03-26 9.176
## 222 2014-03-27 9.176
## 223 2014-03-28 9.275
## 224 2014-03-29 9.275
## 225 2014-03-30 9.176
## 226 2014-03-31 9.275
## 227 2014-04-01 9.275
## 228 2014-04-02 9.373
## 229 2014-04-03 9.571
## 230 2014-04-04 9.571
## 231 2014-04-05 9.669
## 232 2014-04-06 9.571
## 233 2014-04-07 9.669
## 234 2014-04-08 9.669
## 235 2014-04-09 9.866
## 236 2014-04-10 9.866
## 237 2014-04-11 10.063
## 238 2014-04-12 10.161
## 239 2014-04-13 10.455
## 240 2014-04-14 10.553
## 241 2014-04-15 10.748
## 242 2014-04-16 10.944
## 243 2014-04-17 10.748
## 244 2014-04-18 10.651
## 245 2014-04-19 10.651
## 246 2014-04-20 10.651
## 247 2014-04-21 10.553
## 248 2014-04-22 10.553
## 249 2014-04-23 10.651
## 250 2014-04-24 10.651
## 251 2014-04-25 10.846
## 252 2014-04-26 10.748
## 253 2014-04-27 10.748
## 254 2014-04-28 10.846
## 255 2014-04-29 10.944
## 256 2014-04-30 11.139
## 257 2014-05-01 11.236
## 258 2014-05-02 11.431
## 259 2014-05-03 12.013
## 260 2014-05-04 11.819
## 261 2014-05-05 11.722
## 262 2014-05-06 11.722
## 263 2014-05-07 11.625
## 264 2014-05-08 11.625
## 265 2014-05-09 11.625
## 266 2014-05-10 11.819
## 267 2014-05-11 11.916
## 268 2014-05-12 12.013
## 269 2014-05-13 12.013
## 270 2014-05-14 12.690
## 271 2014-05-15 12.594
## 272 2014-05-16 12.690
## 273 2014-05-17 13.076
## 274 2014-05-18 13.269
## 275 2014-05-19 13.173
## 276 2014-05-20 13.269
## 277 2014-05-21 13.269
## 278 2014-05-22 13.461
## 279 2014-05-23 13.173
## 280 2014-05-24 13.558
## 281 2014-05-25 13.461
## 282 2014-05-26 13.558
## 283 2014-05-27 13.558
## 284 2014-05-28 13.461
## 285 2014-05-29 13.654
## 286 2014-05-30 13.654
## 287 2014-05-31 13.846
## 288 2014-06-01 13.750
## 289 2014-06-02 14.038
## 290 2014-06-03 14.038
## 291 2014-06-04 14.038
## 292 2014-06-05 14.230
## 293 2014-06-06 13.942
## 294 2014-06-07 14.134
## 295 2014-06-08 14.038
## 296 2014-06-09 13.942
## 297 2014-06-10 14.613
## 298 2014-06-11 13.942
## 299 2014-06-12 14.613
## 300 2014-06-13 14.900
## 301 2014-06-14 14.804
## 302 2014-06-15 14.709
## 303 2014-06-16 14.421
## 304 2014-06-17 14.421
## 305 2014-06-18 14.421
## 306 2014-06-19 14.517
## 307 2014-06-20 14.517
## 308 2014-06-21 14.996
## 309 2014-06-22 14.900
## 310 2014-06-23 14.804
## 311 2014-06-24 14.804
## 312 2014-06-25 15.282
## 313 2014-06-26 15.378
## 314 2014-06-27 15.473
## 315 2014-06-28 15.664
## 316 2014-06-29 15.569
## 317 2014-06-30 15.569
## 318 2014-07-01 15.664
## 319 2014-07-02 15.569
## 320 2014-07-03 15.855
## 321 2014-07-04 16.141
## 322 2014-07-05 15.760
## 323 2014-07-06 15.664
## 324 2014-07-07 16.237
## 325 2014-07-08 16.237
## 326 2014-07-09 16.237
## 327 2014-07-10 16.523
## 328 2014-07-11 17.189
## 329 2014-07-12 17.189
## 330 2014-07-13 17.284
## 331 2014-07-14 17.475
## 332 2014-07-15 17.189
## 333 2014-07-16 17.665
## 334 2014-07-17 17.475
## 335 2014-07-18 17.475
## 336 2014-07-19 17.189
## 337 2014-07-20 17.189
## 338 2014-07-21 17.284
## 339 2014-07-22 17.094
## 340 2014-07-23 16.999
## 341 2014-07-24 16.999
## 342 2014-07-25 16.903
## 343 2014-07-26 16.999
## 344 2014-07-27 17.094
## 345 2014-07-28 17.189
## 346 2014-07-29 17.379
## 347 2014-07-30 17.570
## 348 2014-07-31 17.760
## 349 2014-08-01 17.855
## 350 2014-08-02 17.760
## 351 2014-08-03 17.855
## 352 2014-08-04 17.475
## 353 2014-08-05 17.570
## 354 2014-08-06 17.760
## 355 2014-08-07 17.760
## 356 2014-08-08 17.950
## 357 2014-08-09 18.045
## 358 2014-08-10 18.236
## 359 2014-08-11 18.426
## 360 2014-08-12 18.045
## 361 2014-08-13 18.045
## 362 2014-08-14 18.045
## 363 2014-08-15 17.950
## 364 2014-08-16 17.855
## 365 2014-08-17 17.760
## 366 2014-08-18 17.665
## 367 2014-08-19 17.379
## 368 2014-08-20 17.855
## 369 2014-08-21 17.665
## 370 2014-08-22 17.760
## 371 2014-08-23 17.760
## 372 2014-08-24 17.665
## 373 2014-08-25 17.760
## 374 2014-08-26 17.760
## 375 2014-08-27 17.760
## 376 2014-08-28 17.855
## 377 2014-08-29 18.045
## 378 2014-08-30 17.760
## 379 2014-08-31 17.475
## 380 2014-09-01 17.475
## 381 2014-09-02 17.284
## 382 2014-09-03 17.189
## 383 2014-09-04 17.094
## 384 2014-09-05 17.379
## 385 2014-09-06 17.284
## 386 2014-09-07 17.379
## 387 2014-09-08 17.284
## 388 2014-09-09 17.284
## 389 2014-09-10 17.189
## 390 2014-09-11 17.094
## 391 2014-09-12 16.903
## 392 2014-09-13 16.808
## 393 2014-09-14 16.618
## 394 2014-09-15 16.618
## 395 2014-09-16 16.618
## 396 2014-09-17 16.713
## 397 2014-09-18 16.523
## 398 2014-09-19 16.618
#we have to subset temp data to just the time frame between 8C beginning and peak spawn for each pop at each site
#luckily two pops at each site had the same spawn time data so we use that
oyshndd<-oysdd[c(oysdd$Date<="2014-07-10"),]
oyssdd<-oysdd[c(oysdd$Date<="2014-06-19"),]
print(oyshndd)
## Date min_temp
## 201 2014-03-06 8.082
## 202 2014-03-07 8.282
## 203 2014-03-08 8.382
## 204 2014-03-09 8.382
## 205 2014-03-10 8.382
## 206 2014-03-11 8.879
## 207 2014-03-12 8.779
## 208 2014-03-13 8.779
## 209 2014-03-14 8.680
## 210 2014-03-15 8.779
## 211 2014-03-16 8.879
## 212 2014-03-17 8.779
## 213 2014-03-18 8.779
## 214 2014-03-19 8.879
## 215 2014-03-20 8.680
## 216 2014-03-21 8.779
## 217 2014-03-22 8.879
## 218 2014-03-23 8.879
## 219 2014-03-24 8.978
## 220 2014-03-25 8.978
## 221 2014-03-26 9.176
## 222 2014-03-27 9.176
## 223 2014-03-28 9.275
## 224 2014-03-29 9.275
## 225 2014-03-30 9.176
## 226 2014-03-31 9.275
## 227 2014-04-01 9.275
## 228 2014-04-02 9.373
## 229 2014-04-03 9.571
## 230 2014-04-04 9.571
## 231 2014-04-05 9.669
## 232 2014-04-06 9.571
## 233 2014-04-07 9.669
## 234 2014-04-08 9.669
## 235 2014-04-09 9.866
## 236 2014-04-10 9.866
## 237 2014-04-11 10.063
## 238 2014-04-12 10.161
## 239 2014-04-13 10.455
## 240 2014-04-14 10.553
## 241 2014-04-15 10.748
## 242 2014-04-16 10.944
## 243 2014-04-17 10.748
## 244 2014-04-18 10.651
## 245 2014-04-19 10.651
## 246 2014-04-20 10.651
## 247 2014-04-21 10.553
## 248 2014-04-22 10.553
## 249 2014-04-23 10.651
## 250 2014-04-24 10.651
## 251 2014-04-25 10.846
## 252 2014-04-26 10.748
## 253 2014-04-27 10.748
## 254 2014-04-28 10.846
## 255 2014-04-29 10.944
## 256 2014-04-30 11.139
## 257 2014-05-01 11.236
## 258 2014-05-02 11.431
## 259 2014-05-03 12.013
## 260 2014-05-04 11.819
## 261 2014-05-05 11.722
## 262 2014-05-06 11.722
## 263 2014-05-07 11.625
## 264 2014-05-08 11.625
## 265 2014-05-09 11.625
## 266 2014-05-10 11.819
## 267 2014-05-11 11.916
## 268 2014-05-12 12.013
## 269 2014-05-13 12.013
## 270 2014-05-14 12.690
## 271 2014-05-15 12.594
## 272 2014-05-16 12.690
## 273 2014-05-17 13.076
## 274 2014-05-18 13.269
## 275 2014-05-19 13.173
## 276 2014-05-20 13.269
## 277 2014-05-21 13.269
## 278 2014-05-22 13.461
## 279 2014-05-23 13.173
## 280 2014-05-24 13.558
## 281 2014-05-25 13.461
## 282 2014-05-26 13.558
## 283 2014-05-27 13.558
## 284 2014-05-28 13.461
## 285 2014-05-29 13.654
## 286 2014-05-30 13.654
## 287 2014-05-31 13.846
## 288 2014-06-01 13.750
## 289 2014-06-02 14.038
## 290 2014-06-03 14.038
## 291 2014-06-04 14.038
## 292 2014-06-05 14.230
## 293 2014-06-06 13.942
## 294 2014-06-07 14.134
## 295 2014-06-08 14.038
## 296 2014-06-09 13.942
## 297 2014-06-10 14.613
## 298 2014-06-11 13.942
## 299 2014-06-12 14.613
## 300 2014-06-13 14.900
## 301 2014-06-14 14.804
## 302 2014-06-15 14.709
## 303 2014-06-16 14.421
## 304 2014-06-17 14.421
## 305 2014-06-18 14.421
## 306 2014-06-19 14.517
## 307 2014-06-20 14.517
## 308 2014-06-21 14.996
## 309 2014-06-22 14.900
## 310 2014-06-23 14.804
## 311 2014-06-24 14.804
## 312 2014-06-25 15.282
## 313 2014-06-26 15.378
## 314 2014-06-27 15.473
## 315 2014-06-28 15.664
## 316 2014-06-29 15.569
## 317 2014-06-30 15.569
## 318 2014-07-01 15.664
## 319 2014-07-02 15.569
## 320 2014-07-03 15.855
## 321 2014-07-04 16.141
## 322 2014-07-05 15.760
## 323 2014-07-06 15.664
## 324 2014-07-07 16.237
## 325 2014-07-08 16.237
## 326 2014-07-09 16.237
## 327 2014-07-10 16.523
print(oyssdd)
## Date min_temp
## 201 2014-03-06 8.082
## 202 2014-03-07 8.282
## 203 2014-03-08 8.382
## 204 2014-03-09 8.382
## 205 2014-03-10 8.382
## 206 2014-03-11 8.879
## 207 2014-03-12 8.779
## 208 2014-03-13 8.779
## 209 2014-03-14 8.680
## 210 2014-03-15 8.779
## 211 2014-03-16 8.879
## 212 2014-03-17 8.779
## 213 2014-03-18 8.779
## 214 2014-03-19 8.879
## 215 2014-03-20 8.680
## 216 2014-03-21 8.779
## 217 2014-03-22 8.879
## 218 2014-03-23 8.879
## 219 2014-03-24 8.978
## 220 2014-03-25 8.978
## 221 2014-03-26 9.176
## 222 2014-03-27 9.176
## 223 2014-03-28 9.275
## 224 2014-03-29 9.275
## 225 2014-03-30 9.176
## 226 2014-03-31 9.275
## 227 2014-04-01 9.275
## 228 2014-04-02 9.373
## 229 2014-04-03 9.571
## 230 2014-04-04 9.571
## 231 2014-04-05 9.669
## 232 2014-04-06 9.571
## 233 2014-04-07 9.669
## 234 2014-04-08 9.669
## 235 2014-04-09 9.866
## 236 2014-04-10 9.866
## 237 2014-04-11 10.063
## 238 2014-04-12 10.161
## 239 2014-04-13 10.455
## 240 2014-04-14 10.553
## 241 2014-04-15 10.748
## 242 2014-04-16 10.944
## 243 2014-04-17 10.748
## 244 2014-04-18 10.651
## 245 2014-04-19 10.651
## 246 2014-04-20 10.651
## 247 2014-04-21 10.553
## 248 2014-04-22 10.553
## 249 2014-04-23 10.651
## 250 2014-04-24 10.651
## 251 2014-04-25 10.846
## 252 2014-04-26 10.748
## 253 2014-04-27 10.748
## 254 2014-04-28 10.846
## 255 2014-04-29 10.944
## 256 2014-04-30 11.139
## 257 2014-05-01 11.236
## 258 2014-05-02 11.431
## 259 2014-05-03 12.013
## 260 2014-05-04 11.819
## 261 2014-05-05 11.722
## 262 2014-05-06 11.722
## 263 2014-05-07 11.625
## 264 2014-05-08 11.625
## 265 2014-05-09 11.625
## 266 2014-05-10 11.819
## 267 2014-05-11 11.916
## 268 2014-05-12 12.013
## 269 2014-05-13 12.013
## 270 2014-05-14 12.690
## 271 2014-05-15 12.594
## 272 2014-05-16 12.690
## 273 2014-05-17 13.076
## 274 2014-05-18 13.269
## 275 2014-05-19 13.173
## 276 2014-05-20 13.269
## 277 2014-05-21 13.269
## 278 2014-05-22 13.461
## 279 2014-05-23 13.173
## 280 2014-05-24 13.558
## 281 2014-05-25 13.461
## 282 2014-05-26 13.558
## 283 2014-05-27 13.558
## 284 2014-05-28 13.461
## 285 2014-05-29 13.654
## 286 2014-05-30 13.654
## 287 2014-05-31 13.846
## 288 2014-06-01 13.750
## 289 2014-06-02 14.038
## 290 2014-06-03 14.038
## 291 2014-06-04 14.038
## 292 2014-06-05 14.230
## 293 2014-06-06 13.942
## 294 2014-06-07 14.134
## 295 2014-06-08 14.038
## 296 2014-06-09 13.942
## 297 2014-06-10 14.613
## 298 2014-06-11 13.942
## 299 2014-06-12 14.613
## 300 2014-06-13 14.900
## 301 2014-06-14 14.804
## 302 2014-06-15 14.709
## 303 2014-06-16 14.421
## 304 2014-06-17 14.421
## 305 2014-06-18 14.421
## 306 2014-06-19 14.517
#once these subsets are created we need to create a column of the difference between the 8 C minimum
#and the daily minimum temp for each subsets
oyshndd$tempdiff<-oyshndd$min_temp-8
oyssdd$tempdiff<-oyssdd$min_temp-8
#use this temp diff column to create the degree days between 8C minimum and the peak threshold
colSums(oyshndd[,-1])
## min_temp tempdiff
## 1529 513
colSums(oyssdd[,-1])
## min_temp tempdiff
## 1202.2 354.2
#we generate this same info for all pops at all sites
fiddd<-ddply(fidmintemp,.(Date),subset,min_temp>=8)
fiddd<-fidmintemp[c(fidmintemp$Date>="2014-03-06"),]
View(fiddd)
fidhndd<-fiddd[c(fiddd$Date<="2014-08-08"),]
fidsdd<-fiddd[c(fiddd$Date<="2014-07-11"),]
View(fidhndd)
View(fidsdd)
fidhndd$tempdiff<-fidhndd$min_temp-8
fidsdd$tempdiff<-fidsdd$min_temp-8
colSums(fidhndd[,-1])
## min_temp tempdiff
## 1697.7 449.7
colSums(fidsdd[,-1])
## min_temp tempdiff
## 1328.6 304.6
mandd<-ddply(manmintemp,.(Date),subset,min_temp>=8)
mandd<-manmintemp[c(manmintemp$Date>="2014-03-06"),]
View(mandd)
manhsdd<-mandd[c(mandd$Date<="2014-08-06"),]
manndd<-mandd[c(mandd$Date<="2014-06-25"),]
View(manhsdd)
View(manndd)
manhsdd$tempdiff<-manhsdd$min_temp-8
manndd$tempdiff<-manndd$min_temp-8
colSums(manhsdd[,-1])
## min_temp tempdiff
## 1723.7 491.7
colSums(manndd[,-1])
## min_temp tempdiff
## 1141.6 245.6
#due to how R works its easier to just copy these numbers and create a data frame to merge with the peak threshold info
DegreeDays<-c("512.999","512.999","354.156","453.021","453.021","307.894","377.561","175.322","377.561")
Pop<-c("H","N","S")
Site<-c("Oyster Bay","Oyster Bay","Oyster Bay","Fidalgo","Fidalgo","Fidalgo","Manchester","Manchester","Manchester")
Degree<-data.frame(Site,Pop,DegreeDays)
#onces the Degree data frame is created it can be merged with the peakthresh2 data frame to show degree days and time to peak in the same table
peakthresh3<-merge(peakthresh2,Degree,by.x=c("Site","Pop"),by.y=c("Site","Pop"),all=T)
print(peakthresh3)
## Site Pop Peak_Date Brooders Threshold Date Days_to_Peak
## 1 Fidalgo H 2014-08-08 3 2014-06-03 66 days
## 2 Fidalgo N 2014-08-08 5 2014-06-03 66 days
## 3 Fidalgo S 2014-07-11 11 2014-06-03 38 days
## 4 Manchester H 2014-08-06 2 2014-06-08 59 days
## 5 Manchester N 2014-06-25 1 2014-06-08 17 days
## 6 Manchester S 2014-08-06 4 2014-06-08 59 days
## 7 Oyster Bay H 2014-07-10 6 2014-05-14 57 days
## 8 Oyster Bay N 2014-07-10 8 2014-05-14 57 days
## 9 Oyster Bay S 2014-06-19 11 2014-05-14 36 days
## 10 Oyster Bay S 2014-06-26 11 2014-05-14 43 days
## DegreeDays
## 1 453.021
## 2 453.021
## 3 307.894
## 4 377.561
## 5 175.322
## 6 377.561
## 7 512.999
## 8 512.999
## 9 354.156
## 10 354.156
#now we need to make a graph because nothing is good unless its a graph
#first we merge the three longest time frame tempdiff to create a data frame that works with ggplot2
of<-merge(oyshndd,fidhndd,by="Date",all=T,incomparables="0")
dddf<-merge(of,manhsdd,by="Date",all=T,incomparables="0")
#we need to clean up the NAs produced so that these can be graphed in ggplot2
dddf[is.na(dddf)]<-0
#Now we rename the columns to meaningful titles
dddf<-rename(dddf,c('Date'='Date','min_temp.x'='oysmin','tempdiff.x'='oystempdiff','min_temp.y'='fidmin','tempdiff.y'='fidtempdiff','min_temp'='manmin','tempdiff'='mantempdiff'))
#check the data frame to make sure that everything aligns to the X axis dates of interest with the right tempdiff numbers
print(dddf)
## Date oysmin oystempdiff fidmin fidtempdiff manmin mantempdiff
## 1 2014-03-06 8.082 0.082 7.079 -0.921 8.082 0.0820
## 2 2014-03-07 8.282 0.282 7.280 -0.720 8.082 0.0820
## 3 2014-03-08 8.382 0.382 7.582 -0.418 8.082 0.0820
## 4 2014-03-09 8.382 0.382 7.381 -0.619 8.282 0.2820
## 5 2014-03-10 8.382 0.382 7.582 -0.418 8.282 0.2820
## 6 2014-03-11 8.879 0.879 7.782 -0.218 8.382 0.3820
## 7 2014-03-12 8.779 0.779 8.082 0.082 8.680 0.6800
## 8 2014-03-13 8.779 0.779 8.581 0.581 8.879 0.8790
## 9 2014-03-14 8.680 0.680 7.782 -0.218 8.680 0.6800
## 10 2014-03-15 8.779 0.779 7.782 -0.218 8.581 0.5810
## 11 2014-03-16 8.879 0.879 7.983 -0.017 8.481 0.4810
## 12 2014-03-17 8.779 0.779 7.682 -0.318 8.481 0.4810
## 13 2014-03-18 8.779 0.779 7.682 -0.318 8.581 0.5810
## 14 2014-03-19 8.879 0.879 7.882 -0.118 8.481 0.4810
## 15 2014-03-20 8.680 0.680 7.782 -0.218 8.382 0.3820
## 16 2014-03-21 8.779 0.779 8.182 0.182 8.481 0.4810
## 17 2014-03-22 8.879 0.879 7.983 -0.017 8.481 0.4810
## 18 2014-03-23 8.879 0.879 7.983 -0.017 8.581 0.5810
## 19 2014-03-24 8.978 0.978 8.182 0.182 8.779 0.7790
## 20 2014-03-25 8.978 0.978 8.082 0.082 8.879 0.8790
## 21 2014-03-26 9.176 1.176 7.882 -0.118 8.581 0.5810
## 22 2014-03-27 9.176 1.176 8.082 0.082 8.581 0.5810
## 23 2014-03-28 9.275 1.275 8.282 0.282 8.581 0.5810
## 24 2014-03-29 9.275 1.275 8.082 0.082 8.431 0.4315
## 25 2014-03-30 9.176 1.176 8.282 0.282 8.382 0.3820
## 26 2014-03-31 9.275 1.275 7.983 -0.017 8.481 0.4810
## 27 2014-04-01 9.275 1.275 8.581 0.581 8.581 0.5810
## 28 2014-04-02 9.373 1.373 8.481 0.481 8.680 0.6800
## 29 2014-04-03 9.571 1.571 8.481 0.481 8.680 0.6800
## 30 2014-04-04 9.571 1.571 8.282 0.282 8.581 0.5810
## 31 2014-04-05 9.669 1.669 8.382 0.382 8.680 0.6800
## 32 2014-04-06 9.571 1.571 8.382 0.382 8.779 0.7790
## 33 2014-04-07 9.669 1.669 8.680 0.680 8.978 0.9780
## 34 2014-04-08 9.669 1.669 8.879 0.879 9.127 1.1265
## 35 2014-04-09 9.866 1.866 8.680 0.680 9.176 1.1760
## 36 2014-04-10 9.866 1.866 8.779 0.779 9.669 1.6690
## 37 2014-04-11 10.063 2.063 9.077 1.077 10.161 2.1610
## 38 2014-04-12 10.161 2.161 9.176 1.176 10.602 2.6020
## 39 2014-04-13 10.455 2.455 9.373 1.373 10.357 2.3570
## 40 2014-04-14 10.553 2.553 9.669 1.669 10.161 2.1610
## 41 2014-04-15 10.748 2.748 9.176 1.176 10.161 2.1610
## 42 2014-04-16 10.944 2.944 9.275 1.275 9.768 1.7680
## 43 2014-04-17 10.748 2.748 9.472 1.472 9.275 1.2750
## 44 2014-04-18 10.651 2.651 9.077 1.077 9.472 1.4720
## 45 2014-04-19 10.651 2.651 9.275 1.275 9.275 1.2750
## 46 2014-04-20 10.651 2.651 8.978 0.978 9.275 1.2750
## 47 2014-04-21 10.553 2.553 9.275 1.275 9.275 1.2750
## 48 2014-04-22 10.553 2.553 9.176 1.176 9.373 1.3730
## 49 2014-04-23 10.651 2.651 9.571 1.571 9.472 1.4720
## 50 2014-04-24 10.651 2.651 9.275 1.275 9.472 1.4720
## 51 2014-04-25 10.846 2.846 9.472 1.472 9.472 1.4720
## 52 2014-04-26 10.748 2.748 9.472 1.472 9.373 1.3730
## 53 2014-04-27 10.748 2.748 9.275 1.275 9.176 1.1760
## 54 2014-04-28 10.846 2.846 9.373 1.373 9.275 1.2750
## 55 2014-04-29 10.944 2.944 10.161 2.161 9.275 1.2750
## 56 2014-04-30 11.139 3.139 10.357 2.357 9.275 1.2750
## 57 2014-05-01 11.236 3.236 10.259 2.259 9.768 1.7680
## 58 2014-05-02 11.431 3.431 10.161 2.161 9.965 1.9650
## 59 2014-05-03 12.013 4.013 9.866 1.866 9.669 1.6690
## 60 2014-05-04 11.819 3.819 9.965 1.965 9.669 1.6690
## 61 2014-05-05 11.722 3.722 9.965 1.965 9.768 1.7680
## 62 2014-05-06 11.722 3.722 10.063 2.063 9.866 1.8660
## 63 2014-05-07 11.625 3.625 10.357 2.357 10.161 2.1610
## 64 2014-05-08 11.625 3.625 10.259 2.259 9.965 1.9650
## 65 2014-05-09 11.625 3.625 9.965 1.965 10.063 2.0630
## 66 2014-05-10 11.819 3.819 10.259 2.259 9.866 1.8660
## 67 2014-05-11 11.916 3.916 10.553 2.553 9.965 1.9650
## 68 2014-05-12 12.013 4.013 11.041 3.041 10.357 2.3570
## 69 2014-05-13 12.013 4.013 12.304 4.304 11.285 3.2850
## 70 2014-05-14 12.690 4.690 12.594 4.594 11.819 3.8190
## 71 2014-05-15 12.594 4.594 11.916 3.916 11.625 3.6250
## 72 2014-05-16 12.690 4.690 11.041 3.041 11.041 3.0410
## 73 2014-05-17 13.076 5.076 11.041 3.041 10.504 2.5040
## 74 2014-05-18 13.269 5.269 11.139 3.139 10.553 2.5530
## 75 2014-05-19 13.173 5.173 10.944 2.944 10.602 2.6020
## 76 2014-05-20 13.269 5.269 11.041 3.041 10.651 2.6510
## 77 2014-05-21 13.269 5.269 10.748 2.748 10.944 2.9440
## 78 2014-05-22 13.461 5.461 11.139 3.139 11.674 3.6735
## 79 2014-05-23 13.173 5.173 10.846 2.846 11.916 3.9160
## 80 2014-05-24 13.558 5.558 10.748 2.748 11.431 3.4310
## 81 2014-05-25 13.461 5.461 11.041 3.041 11.625 3.6250
## 82 2014-05-26 13.558 5.558 10.846 2.846 11.479 3.4795
## 83 2014-05-27 13.558 5.558 10.748 2.748 11.041 3.0410
## 84 2014-05-28 13.461 5.461 11.528 3.528 11.236 3.2360
## 85 2014-05-29 13.654 5.654 10.944 2.944 11.041 3.0410
## 86 2014-05-30 13.654 5.654 11.041 3.041 10.846 2.8460
## 87 2014-05-31 13.846 5.846 11.916 3.916 11.139 3.1390
## 88 2014-06-01 13.750 5.750 11.722 3.722 11.236 3.2360
## 89 2014-06-02 14.038 6.038 11.819 3.819 11.334 3.3340
## 90 2014-06-03 14.038 6.038 13.173 5.173 11.528 3.5280
## 91 2014-06-04 14.038 6.038 12.883 4.883 11.528 3.5280
## 92 2014-06-05 14.230 6.230 13.558 5.558 11.625 3.6250
## 93 2014-06-06 13.942 5.942 14.613 6.613 12.013 4.0130
## 94 2014-06-07 14.134 6.134 15.091 7.091 12.690 4.6900
## 95 2014-06-08 14.038 6.038 13.461 5.461 13.750 5.7500
## 96 2014-06-09 13.942 5.942 13.365 5.365 14.182 6.1820
## 97 2014-06-10 14.613 6.613 12.690 4.690 13.654 5.6540
## 98 2014-06-11 13.942 5.942 12.594 4.594 13.558 5.5580
## 99 2014-06-12 14.613 6.613 12.013 4.013 12.980 4.9800
## 100 2014-06-13 14.900 6.900 11.431 3.431 12.449 4.4490
## 101 2014-06-14 14.804 6.804 11.139 3.139 12.013 4.0130
## 102 2014-06-15 14.709 6.709 11.041 3.041 11.674 3.6735
## 103 2014-06-16 14.421 6.421 10.944 2.944 11.722 3.7220
## 104 2014-06-17 14.421 6.421 10.846 2.846 11.819 3.8190
## 105 2014-06-18 14.421 6.421 10.748 2.748 11.722 3.7220
## 106 2014-06-19 14.517 6.517 10.651 2.651 11.819 3.8190
## 107 2014-06-20 14.517 6.517 10.944 2.944 12.013 4.0130
## 108 2014-06-21 14.996 6.996 11.334 3.334 12.013 4.0130
## 109 2014-06-22 14.900 6.900 12.110 4.110 12.110 4.1100
## 110 2014-06-23 14.804 6.804 12.110 4.110 12.883 4.8830
## 111 2014-06-24 14.804 6.804 11.528 3.528 13.173 5.1730
## 112 2014-06-25 15.282 7.282 12.013 4.013 13.076 5.0760
## 113 2014-06-26 15.378 7.378 13.076 5.076 12.931 4.9315
## 114 2014-06-27 15.473 7.473 12.401 4.401 12.497 4.4970
## 115 2014-06-28 15.664 7.664 11.916 3.916 12.304 4.3040
## 116 2014-06-29 15.569 7.569 11.819 3.819 12.304 4.3040
## 117 2014-06-30 15.569 7.569 11.722 3.722 12.207 4.2070
## 118 2014-07-01 15.664 7.664 12.110 4.110 12.594 4.5940
## 119 2014-07-02 15.569 7.569 11.625 3.625 13.125 5.1245
## 120 2014-07-03 15.855 7.855 11.334 3.334 13.269 5.2690
## 121 2014-07-04 16.141 8.141 11.722 3.722 13.990 5.9900
## 122 2014-07-05 15.760 7.760 12.690 4.690 14.517 6.5170
## 123 2014-07-06 15.664 7.664 13.076 5.076 14.996 6.9960
## 124 2014-07-07 16.237 8.237 13.654 5.654 15.569 7.5690
## 125 2014-07-08 16.237 8.237 14.613 6.613 15.617 7.6165
## 126 2014-07-09 16.237 8.237 15.473 7.473 15.808 7.8075
## 127 2014-07-10 16.523 8.523 16.427 8.427 15.282 7.2820
## 128 2014-07-11 0.000 0.000 16.332 8.332 14.517 6.5170
## 129 2014-07-12 0.000 0.000 14.613 6.613 14.421 6.4210
## 130 2014-07-13 0.000 0.000 13.269 5.269 14.038 6.0380
## 131 2014-07-14 0.000 0.000 12.690 4.690 13.750 5.7500
## 132 2014-07-15 0.000 0.000 12.497 4.497 13.654 5.6540
## 133 2014-07-16 0.000 0.000 12.401 4.401 13.654 5.6540
## 134 2014-07-17 0.000 0.000 12.110 4.110 13.846 5.8460
## 135 2014-07-18 0.000 0.000 12.110 4.110 13.750 5.7500
## 136 2014-07-19 0.000 0.000 12.207 4.207 13.365 5.3650
## 137 2014-07-20 0.000 0.000 12.401 4.401 13.173 5.1730
## 138 2014-07-21 0.000 0.000 12.594 4.594 13.461 5.4610
## 139 2014-07-22 0.000 0.000 12.980 4.980 13.365 5.3650
## 140 2014-07-23 0.000 0.000 12.594 4.594 13.173 5.1730
## 141 2014-07-24 0.000 0.000 11.916 3.916 13.125 5.1245
## 142 2014-07-25 0.000 0.000 11.916 3.916 13.076 5.0760
## 143 2014-07-26 0.000 0.000 12.401 4.401 13.269 5.2690
## 144 2014-07-27 0.000 0.000 12.110 4.110 13.461 5.4610
## 145 2014-07-28 0.000 0.000 12.401 4.401 13.558 5.5580
## 146 2014-07-29 0.000 0.000 12.883 4.883 13.750 5.7500
## 147 2014-07-30 0.000 0.000 12.013 4.013 13.942 5.9420
## 148 2014-07-31 0.000 0.000 12.980 4.980 14.325 6.3250
## 149 2014-08-01 0.000 0.000 13.654 5.654 14.469 6.4690
## 150 2014-08-02 0.000 0.000 14.804 6.804 14.613 6.6130
## 151 2014-08-03 0.000 0.000 15.664 7.664 14.709 6.7090
## 152 2014-08-04 0.000 0.000 15.855 7.855 14.709 6.7090
## 153 2014-08-05 0.000 0.000 15.473 7.473 14.996 6.9960
## 154 2014-08-06 0.000 0.000 14.996 6.996 14.900 6.9000
## 155 2014-08-07 0.000 0.000 14.134 6.134 0.000 0.0000
## 156 2014-08-08 0.000 0.000 13.461 5.461 0.000 0.0000
#using ggplot and cumsum(cumulativesum) we can create cumulative lines of the tempdiffs
#we have to manually add points to the line through annotate to show the threshold temps and peak brooding for each pop
ggplot(dddf)+
geom_line(aes(x=Date,y=cumsum(dddf$oystempdiff)),color="orange",size=2)+
geom_line(aes(x=Date,y=cumsum(dddf$fidtempdiff)),color="purple",size=2)+
geom_line(aes(x=Date,y=cumsum(dddf$mantempdif)),color="red",size=2)+
annotate("point",x=as.Date("2014-06-03",'%Y-%m-%d'),y=133,size=5,color='red',pch=15)+
annotate("point",x=as.Date("2014-05-14",'%Y-%m-%d'),y=143,size=5,color='red',pch=15)+
annotate("point",x=as.Date("2014-06-08",'%Y-%m-%d'),y=113,size=5,color='red',pch=15)+
annotate("point",x=as.Date("2014-08-08",'%Y-%m-%d'),y=460,size=10,color='blue',pch=13)+
annotate("point",x=as.Date("2014-08-06",'%Y-%m-%d'),y=383,size=10,color='blue',pch=13)+
annotate("point",x=as.Date("2014-07-10",'%Y-%m-%d'),y=520,size=10,color='blue',pch=13)+
annotate("point",x=as.Date("2014-08-08",'%Y-%m-%d'),y=453.021,size=10,color='purple',pch=13)+
annotate("point",x=as.Date("2014-06-25",'%Y-%m-%d'),y=175.322,size=10,color='purple',pch=13)+
annotate("point",x=as.Date("2014-07-10",'%Y-%m-%d'),y=512.999,size=10,color='purple',pch=13)+
annotate("point",x=as.Date("2014-07-11",'%Y-%m-%d'),y=307.894,size=10,color='orange',pch=13)+
annotate("point",x=as.Date("2014-08-06",'%Y-%m-%d'),y=377.561,size=10,color='orange',pch=13)+
annotate("point",x=as.Date("2014-06-19",'%Y-%m-%d'),y=354.156,size=10,color='orange',pch=13)+
theme_bw()+
labs(title="Degree Days compared between Sites and Populations",x="Date",y="Cumulative Degrees over 8 C Minimum")
#each red square represents the date when the threshold 12.5 C spawning temp was reached
#the orange, purple, and red lines are Oyster Bay, Fidalgo, and Manchester Sites respectfull
#the orange, blue, and purple crosshairs are peak brooding for Oyster Bay, Dabob, and Fidalgo pops at each site respectfully
#