Visualizations
Syukur Halim, NIM :220605220014, Dosen Pembimbing Prof. Dr. Suhartono, M.Kom., Magister Informatika UIN Maulana Malik Ibrahim Malang, Indonesia
2023-11-07
birthweight <- read.csv("birthweight.csv")mir <- read.csv("miRNA.csv", row.names = 1)experiment <- merge(birthweight, mir)experiment## ID birth.date location length birthweight head.circumference
## 1 1107 1/25/1967 General 52 3.23 36
## 2 697 2/6/1967 Silver Hill 48 3.03 35
## 3 1683 2/14/1967 Silver Hill 53 3.35 33
## 4 27 3/9/1967 Silver Hill 53 3.55 37
## 5 1522 3/13/1967 Memorial 50 2.74 33
## 6 569 3/23/1967 Memorial 50 2.51 35
## 7 365 4/23/1967 Memorial 52 3.53 37
## 8 808 5/5/1967 Silver Hill 48 2.92 33
## 9 1369 6/4/1967 Silver Hill 49 3.18 34
## 10 1023 6/7/1967 Memorial 52 3.00 35
## 11 822 6/14/1967 Memorial 50 3.42 35
## 12 1272 6/20/1967 Memorial 53 2.75 32
## 13 1262 6/25/1967 Silver Hill 53 3.19 34
## 14 575 7/12/1967 Memorial 50 2.78 30
## 15 1016 7/13/1967 Silver Hill 53 4.32 36
## 16 792 9/7/1967 Memorial 53 3.64 38
## 17 820 10/7/1967 General 52 3.77 34
## 18 752 10/19/1967 General 49 3.32 36
## 19 619 11/1/1967 Memorial 52 3.41 33
## 20 1764 12/7/1967 Silver Hill 58 4.57 39
## 21 1081 12/14/1967 Silver Hill 54 3.63 38
## 22 516 1/8/1968 Silver Hill 47 2.66 33
## 23 272 1/10/1968 Memorial 52 3.86 36
## 24 321 1/21/1968 Silver Hill 48 3.11 33
## 25 1636 2/2/1968 Silver Hill 51 3.93 38
## 26 1360 2/16/1968 General 56 4.55 34
## 27 1388 2/22/1968 Memorial 51 3.14 33
## 28 1363 4/2/1968 General 48 2.37 30
## 29 1058 4/24/1968 Silver Hill 53 3.15 34
## 30 755 4/25/1968 Memorial 53 3.20 33
## 31 462 6/19/1968 Silver Hill 58 4.10 39
## 32 300 7/18/1968 Silver Hill 46 2.05 32
## 33 1088 7/24/1968 General 51 3.27 36
## 34 57 8/12/1968 Memorial 51 3.32 38
## 35 553 8/17/1968 Silver Hill 54 3.94 37
## 36 1191 9/7/1968 General 53 3.65 33
## 37 431 9/16/1968 Silver Hill 48 1.92 30
## 38 1313 9/27/1968 Silver Hill 43 2.65 32
## 39 1600 10/9/1968 General 53 2.90 34
## 40 532 10/25/1968 General 53 3.59 34
## 41 223 12/11/1968 General 50 3.87 33
## 42 1187 12/19/1968 Silver Hill 53 4.07 38
## 43 1107 1/25/1967 General 52 3.23 36
## 44 697 2/6/1967 Silver Hill 48 3.03 35
## 45 1683 2/14/1967 Silver Hill 53 3.35 33
## 46 27 3/9/1967 Silver Hill 53 3.55 37
## 47 1522 3/13/1967 Memorial 50 2.74 33
## 48 569 3/23/1967 Memorial 50 2.51 35
## 49 365 4/23/1967 Memorial 52 3.53 37
## 50 808 5/5/1967 Silver Hill 48 2.92 33
## 51 1369 6/4/1967 Silver Hill 49 3.18 34
## 52 1023 6/7/1967 Memorial 52 3.00 35
## 53 822 6/14/1967 Memorial 50 3.42 35
## 54 1272 6/20/1967 Memorial 53 2.75 32
## 55 1262 6/25/1967 Silver Hill 53 3.19 34
## 56 575 7/12/1967 Memorial 50 2.78 30
## 57 1016 7/13/1967 Silver Hill 53 4.32 36
## 58 792 9/7/1967 Memorial 53 3.64 38
## 59 820 10/7/1967 General 52 3.77 34
## 60 752 10/19/1967 General 49 3.32 36
## 61 619 11/1/1967 Memorial 52 3.41 33
## 62 1764 12/7/1967 Silver Hill 58 4.57 39
## 63 1081 12/14/1967 Silver Hill 54 3.63 38
## 64 516 1/8/1968 Silver Hill 47 2.66 33
## 65 272 1/10/1968 Memorial 52 3.86 36
## 66 321 1/21/1968 Silver Hill 48 3.11 33
## 67 1636 2/2/1968 Silver Hill 51 3.93 38
## 68 1360 2/16/1968 General 56 4.55 34
## 69 1388 2/22/1968 Memorial 51 3.14 33
## 70 1363 4/2/1968 General 48 2.37 30
## 71 1058 4/24/1968 Silver Hill 53 3.15 34
## 72 755 4/25/1968 Memorial 53 3.20 33
## 73 462 6/19/1968 Silver Hill 58 4.10 39
## 74 300 7/18/1968 Silver Hill 46 2.05 32
## 75 1088 7/24/1968 General 51 3.27 36
## 76 57 8/12/1968 Memorial 51 3.32 38
## 77 553 8/17/1968 Silver Hill 54 3.94 37
## 78 1191 9/7/1968 General 53 3.65 33
## 79 431 9/16/1968 Silver Hill 48 1.92 30
## 80 1313 9/27/1968 Silver Hill 43 2.65 32
## 81 1600 10/9/1968 General 53 2.90 34
## 82 532 10/25/1968 General 53 3.59 34
## 83 223 12/11/1968 General 50 3.87 33
## 84 1187 12/19/1968 Silver Hill 53 4.07 38
## 85 1107 1/25/1967 General 52 3.23 36
## 86 697 2/6/1967 Silver Hill 48 3.03 35
## 87 1683 2/14/1967 Silver Hill 53 3.35 33
## 88 27 3/9/1967 Silver Hill 53 3.55 37
## 89 1522 3/13/1967 Memorial 50 2.74 33
## 90 569 3/23/1967 Memorial 50 2.51 35
## 91 365 4/23/1967 Memorial 52 3.53 37
## 92 808 5/5/1967 Silver Hill 48 2.92 33
## 93 1369 6/4/1967 Silver Hill 49 3.18 34
## 94 1023 6/7/1967 Memorial 52 3.00 35
## 95 822 6/14/1967 Memorial 50 3.42 35
## 96 1272 6/20/1967 Memorial 53 2.75 32
## 97 1262 6/25/1967 Silver Hill 53 3.19 34
## 98 575 7/12/1967 Memorial 50 2.78 30
## 99 1016 7/13/1967 Silver Hill 53 4.32 36
## 100 792 9/7/1967 Memorial 53 3.64 38
## 101 820 10/7/1967 General 52 3.77 34
## 102 752 10/19/1967 General 49 3.32 36
## 103 619 11/1/1967 Memorial 52 3.41 33
## 104 1764 12/7/1967 Silver Hill 58 4.57 39
## 105 1081 12/14/1967 Silver Hill 54 3.63 38
## 106 516 1/8/1968 Silver Hill 47 2.66 33
## 107 272 1/10/1968 Memorial 52 3.86 36
## 108 321 1/21/1968 Silver Hill 48 3.11 33
## 109 1636 2/2/1968 Silver Hill 51 3.93 38
## 110 1360 2/16/1968 General 56 4.55 34
## 111 1388 2/22/1968 Memorial 51 3.14 33
## 112 1363 4/2/1968 General 48 2.37 30
## 113 1058 4/24/1968 Silver Hill 53 3.15 34
## 114 755 4/25/1968 Memorial 53 3.20 33
## 115 462 6/19/1968 Silver Hill 58 4.10 39
## 116 300 7/18/1968 Silver Hill 46 2.05 32
## 117 1088 7/24/1968 General 51 3.27 36
## 118 57 8/12/1968 Memorial 51 3.32 38
## 119 553 8/17/1968 Silver Hill 54 3.94 37
## 120 1191 9/7/1968 General 53 3.65 33
## 121 431 9/16/1968 Silver Hill 48 1.92 30
## 122 1313 9/27/1968 Silver Hill 43 2.65 32
## 123 1600 10/9/1968 General 53 2.90 34
## 124 532 10/25/1968 General 53 3.59 34
## 125 223 12/11/1968 General 50 3.87 33
## 126 1187 12/19/1968 Silver Hill 53 4.07 38
## 127 1107 1/25/1967 General 52 3.23 36
## 128 697 2/6/1967 Silver Hill 48 3.03 35
## 129 1683 2/14/1967 Silver Hill 53 3.35 33
## 130 27 3/9/1967 Silver Hill 53 3.55 37
## 131 1522 3/13/1967 Memorial 50 2.74 33
## 132 569 3/23/1967 Memorial 50 2.51 35
## 133 365 4/23/1967 Memorial 52 3.53 37
## 134 808 5/5/1967 Silver Hill 48 2.92 33
## 135 1369 6/4/1967 Silver Hill 49 3.18 34
## 136 1023 6/7/1967 Memorial 52 3.00 35
## 137 822 6/14/1967 Memorial 50 3.42 35
## 138 1272 6/20/1967 Memorial 53 2.75 32
## 139 1262 6/25/1967 Silver Hill 53 3.19 34
## 140 575 7/12/1967 Memorial 50 2.78 30
## 141 1016 7/13/1967 Silver Hill 53 4.32 36
## 142 792 9/7/1967 Memorial 53 3.64 38
## 143 820 10/7/1967 General 52 3.77 34
## 144 752 10/19/1967 General 49 3.32 36
## 145 619 11/1/1967 Memorial 52 3.41 33
## 146 1764 12/7/1967 Silver Hill 58 4.57 39
## 147 1081 12/14/1967 Silver Hill 54 3.63 38
## 148 516 1/8/1968 Silver Hill 47 2.66 33
## 149 272 1/10/1968 Memorial 52 3.86 36
## 150 321 1/21/1968 Silver Hill 48 3.11 33
## 151 1636 2/2/1968 Silver Hill 51 3.93 38
## 152 1360 2/16/1968 General 56 4.55 34
## 153 1388 2/22/1968 Memorial 51 3.14 33
## 154 1363 4/2/1968 General 48 2.37 30
## 155 1058 4/24/1968 Silver Hill 53 3.15 34
## 156 755 4/25/1968 Memorial 53 3.20 33
## 157 462 6/19/1968 Silver Hill 58 4.10 39
## 158 300 7/18/1968 Silver Hill 46 2.05 32
## 159 1088 7/24/1968 General 51 3.27 36
## 160 57 8/12/1968 Memorial 51 3.32 38
## 161 553 8/17/1968 Silver Hill 54 3.94 37
## 162 1191 9/7/1968 General 53 3.65 33
## 163 431 9/16/1968 Silver Hill 48 1.92 30
## 164 1313 9/27/1968 Silver Hill 43 2.65 32
## 165 1600 10/9/1968 General 53 2.90 34
## 166 532 10/25/1968 General 53 3.59 34
## 167 223 12/11/1968 General 50 3.87 33
## 168 1187 12/19/1968 Silver Hill 53 4.07 38
## weeks.gestation smoker maternal.age maternal.cigarettes maternal.height
## 1 38 no 31 0 164
## 2 39 no 27 0 162
## 3 41 no 27 0 164
## 4 41 yes 37 25 161
## 5 39 yes 21 17 156
## 6 39 yes 22 7 159
## 7 40 yes 26 25 170
## 8 34 no 26 0 167
## 9 38 yes 31 25 162
## 10 38 yes 30 12 165
## 11 38 no 20 0 157
## 12 40 yes 37 50 168
## 13 41 yes 27 35 163
## 14 37 yes 19 7 165
## 15 40 no 19 0 171
## 16 40 yes 20 2 170
## 17 40 no 24 0 157
## 18 40 yes 27 12 152
## 19 39 yes 23 25 181
## 20 41 yes 32 12 173
## 21 38 no 18 0 172
## 22 35 yes 20 35 170
## 23 39 yes 30 25 170
## 24 37 no 28 0 158
## 25 38 no 29 0 165
## 26 44 no 20 0 162
## 27 41 yes 22 7 160
## 28 37 yes 20 7 163
## 29 40 no 29 0 167
## 30 41 no 21 0 155
## 31 41 no 35 0 172
## 32 35 yes 41 7 166
## 33 40 no 24 0 168
## 34 39 yes 23 17 157
## 35 42 no 24 0 175
## 36 42 no 21 0 165
## 37 33 yes 20 7 161
## 38 33 no 24 0 149
## 39 39 no 19 0 165
## 40 40 yes 31 12 163
## 41 45 yes 28 25 163
## 42 44 no 20 0 174
## 43 38 no 31 0 164
## 44 39 no 27 0 162
## 45 41 no 27 0 164
## 46 41 yes 37 25 161
## 47 39 yes 21 17 156
## 48 39 yes 22 7 159
## 49 40 yes 26 25 170
## 50 34 no 26 0 167
## 51 38 yes 31 25 162
## 52 38 yes 30 12 165
## 53 38 no 20 0 157
## 54 40 yes 37 50 168
## 55 41 yes 27 35 163
## 56 37 yes 19 7 165
## 57 40 no 19 0 171
## 58 40 yes 20 2 170
## 59 40 no 24 0 157
## 60 40 yes 27 12 152
## 61 39 yes 23 25 181
## 62 41 yes 32 12 173
## 63 38 no 18 0 172
## 64 35 yes 20 35 170
## 65 39 yes 30 25 170
## 66 37 no 28 0 158
## 67 38 no 29 0 165
## 68 44 no 20 0 162
## 69 41 yes 22 7 160
## 70 37 yes 20 7 163
## 71 40 no 29 0 167
## 72 41 no 21 0 155
## 73 41 no 35 0 172
## 74 35 yes 41 7 166
## 75 40 no 24 0 168
## 76 39 yes 23 17 157
## 77 42 no 24 0 175
## 78 42 no 21 0 165
## 79 33 yes 20 7 161
## 80 33 no 24 0 149
## 81 39 no 19 0 165
## 82 40 yes 31 12 163
## 83 45 yes 28 25 163
## 84 44 no 20 0 174
## 85 38 no 31 0 164
## 86 39 no 27 0 162
## 87 41 no 27 0 164
## 88 41 yes 37 25 161
## 89 39 yes 21 17 156
## 90 39 yes 22 7 159
## 91 40 yes 26 25 170
## 92 34 no 26 0 167
## 93 38 yes 31 25 162
## 94 38 yes 30 12 165
## 95 38 no 20 0 157
## 96 40 yes 37 50 168
## 97 41 yes 27 35 163
## 98 37 yes 19 7 165
## 99 40 no 19 0 171
## 100 40 yes 20 2 170
## 101 40 no 24 0 157
## 102 40 yes 27 12 152
## 103 39 yes 23 25 181
## 104 41 yes 32 12 173
## 105 38 no 18 0 172
## 106 35 yes 20 35 170
## 107 39 yes 30 25 170
## 108 37 no 28 0 158
## 109 38 no 29 0 165
## 110 44 no 20 0 162
## 111 41 yes 22 7 160
## 112 37 yes 20 7 163
## 113 40 no 29 0 167
## 114 41 no 21 0 155
## 115 41 no 35 0 172
## 116 35 yes 41 7 166
## 117 40 no 24 0 168
## 118 39 yes 23 17 157
## 119 42 no 24 0 175
## 120 42 no 21 0 165
## 121 33 yes 20 7 161
## 122 33 no 24 0 149
## 123 39 no 19 0 165
## 124 40 yes 31 12 163
## 125 45 yes 28 25 163
## 126 44 no 20 0 174
## 127 38 no 31 0 164
## 128 39 no 27 0 162
## 129 41 no 27 0 164
## 130 41 yes 37 25 161
## 131 39 yes 21 17 156
## 132 39 yes 22 7 159
## 133 40 yes 26 25 170
## 134 34 no 26 0 167
## 135 38 yes 31 25 162
## 136 38 yes 30 12 165
## 137 38 no 20 0 157
## 138 40 yes 37 50 168
## 139 41 yes 27 35 163
## 140 37 yes 19 7 165
## 141 40 no 19 0 171
## 142 40 yes 20 2 170
## 143 40 no 24 0 157
## 144 40 yes 27 12 152
## 145 39 yes 23 25 181
## 146 41 yes 32 12 173
## 147 38 no 18 0 172
## 148 35 yes 20 35 170
## 149 39 yes 30 25 170
## 150 37 no 28 0 158
## 151 38 no 29 0 165
## 152 44 no 20 0 162
## 153 41 yes 22 7 160
## 154 37 yes 20 7 163
## 155 40 no 29 0 167
## 156 41 no 21 0 155
## 157 41 no 35 0 172
## 158 35 yes 41 7 166
## 159 40 no 24 0 168
## 160 39 yes 23 17 157
## 161 42 no 24 0 175
## 162 42 no 21 0 165
## 163 33 yes 20 7 161
## 164 33 no 24 0 149
## 165 39 no 19 0 165
## 166 40 yes 31 12 163
## 167 45 yes 28 25 163
## 168 44 no 20 0 174
## maternal.prepregnant.weight paternal.age paternal.education
## 1 57 NA NA
## 2 62 27 14
## 3 62 37 14
## 4 66 46 NA
## 5 53 24 12
## 6 52 23 14
## 7 62 30 10
## 8 64 25 12
## 9 57 32 16
## 10 64 38 14
## 11 48 22 14
## 12 61 31 16
## 13 51 31 16
## 14 60 20 14
## 15 62 19 12
## 16 59 24 12
## 17 50 31 16
## 18 48 37 12
## 19 69 23 16
## 20 70 38 14
## 21 50 20 12
## 22 57 23 12
## 23 78 40 16
## 24 54 39 10
## 25 61 NA NA
## 26 57 23 10
## 27 53 24 16
## 28 47 20 10
## 29 60 30 16
## 30 55 25 14
## 31 58 31 16
## 32 57 37 14
## 33 53 29 16
## 34 48 NA NA
## 35 66 30 12
## 36 61 21 10
## 37 50 20 10
## 38 45 26 16
## 39 57 NA NA
## 40 49 41 12
## 41 54 30 16
## 42 68 26 14
## 43 57 NA NA
## 44 62 27 14
## 45 62 37 14
## 46 66 46 NA
## 47 53 24 12
## 48 52 23 14
## 49 62 30 10
## 50 64 25 12
## 51 57 32 16
## 52 64 38 14
## 53 48 22 14
## 54 61 31 16
## 55 51 31 16
## 56 60 20 14
## 57 62 19 12
## 58 59 24 12
## 59 50 31 16
## 60 48 37 12
## 61 69 23 16
## 62 70 38 14
## 63 50 20 12
## 64 57 23 12
## 65 78 40 16
## 66 54 39 10
## 67 61 NA NA
## 68 57 23 10
## 69 53 24 16
## 70 47 20 10
## 71 60 30 16
## 72 55 25 14
## 73 58 31 16
## 74 57 37 14
## 75 53 29 16
## 76 48 NA NA
## 77 66 30 12
## 78 61 21 10
## 79 50 20 10
## 80 45 26 16
## 81 57 NA NA
## 82 49 41 12
## 83 54 30 16
## 84 68 26 14
## 85 57 NA NA
## 86 62 27 14
## 87 62 37 14
## 88 66 46 NA
## 89 53 24 12
## 90 52 23 14
## 91 62 30 10
## 92 64 25 12
## 93 57 32 16
## 94 64 38 14
## 95 48 22 14
## 96 61 31 16
## 97 51 31 16
## 98 60 20 14
## 99 62 19 12
## 100 59 24 12
## 101 50 31 16
## 102 48 37 12
## 103 69 23 16
## 104 70 38 14
## 105 50 20 12
## 106 57 23 12
## 107 78 40 16
## 108 54 39 10
## 109 61 NA NA
## 110 57 23 10
## 111 53 24 16
## 112 47 20 10
## 113 60 30 16
## 114 55 25 14
## 115 58 31 16
## 116 57 37 14
## 117 53 29 16
## 118 48 NA NA
## 119 66 30 12
## 120 61 21 10
## 121 50 20 10
## 122 45 26 16
## 123 57 NA NA
## 124 49 41 12
## 125 54 30 16
## 126 68 26 14
## 127 57 NA NA
## 128 62 27 14
## 129 62 37 14
## 130 66 46 NA
## 131 53 24 12
## 132 52 23 14
## 133 62 30 10
## 134 64 25 12
## 135 57 32 16
## 136 64 38 14
## 137 48 22 14
## 138 61 31 16
## 139 51 31 16
## 140 60 20 14
## 141 62 19 12
## 142 59 24 12
## 143 50 31 16
## 144 48 37 12
## 145 69 23 16
## 146 70 38 14
## 147 50 20 12
## 148 57 23 12
## 149 78 40 16
## 150 54 39 10
## 151 61 NA NA
## 152 57 23 10
## 153 53 24 16
## 154 47 20 10
## 155 60 30 16
## 156 55 25 14
## 157 58 31 16
## 158 57 37 14
## 159 53 29 16
## 160 48 NA NA
## 161 66 30 12
## 162 61 21 10
## 163 50 20 10
## 164 45 26 16
## 165 57 NA NA
## 166 49 41 12
## 167 54 30 16
## 168 68 26 14
## paternal.cigarettes paternal.height low.birthweight geriatric.pregnancy
## 1 NA NA 0 FALSE
## 2 0 178 0 FALSE
## 3 0 170 0 FALSE
## 4 0 175 0 TRUE
## 5 7 179 0 FALSE
## 6 25 NA 1 FALSE
## 7 25 181 0 FALSE
## 8 25 175 0 FALSE
## 9 50 194 0 FALSE
## 10 50 180 0 FALSE
## 11 0 179 0 FALSE
## 12 0 173 0 TRUE
## 13 25 185 0 FALSE
## 14 0 183 0 FALSE
## 15 0 183 0 FALSE
## 16 12 185 0 FALSE
## 17 0 173 0 FALSE
## 18 25 170 0 FALSE
## 19 2 181 0 FALSE
## 20 25 180 0 FALSE
## 21 7 172 0 FALSE
## 22 50 186 1 FALSE
## 23 50 178 0 FALSE
## 24 0 171 0 FALSE
## 25 NA NA 0 FALSE
## 26 35 179 0 FALSE
## 27 12 176 0 FALSE
## 28 35 185 1 FALSE
## 29 NA 182 0 FALSE
## 30 25 183 0 FALSE
## 31 25 185 0 TRUE
## 32 25 173 1 TRUE
## 33 0 181 0 FALSE
## 34 NA NA 0 FALSE
## 35 0 184 0 FALSE
## 36 25 185 0 FALSE
## 37 35 180 1 FALSE
## 38 0 169 1 FALSE
## 39 NA NA 0 FALSE
## 40 50 191 0 FALSE
## 41 0 183 0 FALSE
## 42 25 189 0 FALSE
## 43 NA NA 0 FALSE
## 44 0 178 0 FALSE
## 45 0 170 0 FALSE
## 46 0 175 0 TRUE
## 47 7 179 0 FALSE
## 48 25 NA 1 FALSE
## 49 25 181 0 FALSE
## 50 25 175 0 FALSE
## 51 50 194 0 FALSE
## 52 50 180 0 FALSE
## 53 0 179 0 FALSE
## 54 0 173 0 TRUE
## 55 25 185 0 FALSE
## 56 0 183 0 FALSE
## 57 0 183 0 FALSE
## 58 12 185 0 FALSE
## 59 0 173 0 FALSE
## 60 25 170 0 FALSE
## 61 2 181 0 FALSE
## 62 25 180 0 FALSE
## 63 7 172 0 FALSE
## 64 50 186 1 FALSE
## 65 50 178 0 FALSE
## 66 0 171 0 FALSE
## 67 NA NA 0 FALSE
## 68 35 179 0 FALSE
## 69 12 176 0 FALSE
## 70 35 185 1 FALSE
## 71 NA 182 0 FALSE
## 72 25 183 0 FALSE
## 73 25 185 0 TRUE
## 74 25 173 1 TRUE
## 75 0 181 0 FALSE
## 76 NA NA 0 FALSE
## 77 0 184 0 FALSE
## 78 25 185 0 FALSE
## 79 35 180 1 FALSE
## 80 0 169 1 FALSE
## 81 NA NA 0 FALSE
## 82 50 191 0 FALSE
## 83 0 183 0 FALSE
## 84 25 189 0 FALSE
## 85 NA NA 0 FALSE
## 86 0 178 0 FALSE
## 87 0 170 0 FALSE
## 88 0 175 0 TRUE
## 89 7 179 0 FALSE
## 90 25 NA 1 FALSE
## 91 25 181 0 FALSE
## 92 25 175 0 FALSE
## 93 50 194 0 FALSE
## 94 50 180 0 FALSE
## 95 0 179 0 FALSE
## 96 0 173 0 TRUE
## 97 25 185 0 FALSE
## 98 0 183 0 FALSE
## 99 0 183 0 FALSE
## 100 12 185 0 FALSE
## 101 0 173 0 FALSE
## 102 25 170 0 FALSE
## 103 2 181 0 FALSE
## 104 25 180 0 FALSE
## 105 7 172 0 FALSE
## 106 50 186 1 FALSE
## 107 50 178 0 FALSE
## 108 0 171 0 FALSE
## 109 NA NA 0 FALSE
## 110 35 179 0 FALSE
## 111 12 176 0 FALSE
## 112 35 185 1 FALSE
## 113 NA 182 0 FALSE
## 114 25 183 0 FALSE
## 115 25 185 0 TRUE
## 116 25 173 1 TRUE
## 117 0 181 0 FALSE
## 118 NA NA 0 FALSE
## 119 0 184 0 FALSE
## 120 25 185 0 FALSE
## 121 35 180 1 FALSE
## 122 0 169 1 FALSE
## 123 NA NA 0 FALSE
## 124 50 191 0 FALSE
## 125 0 183 0 FALSE
## 126 25 189 0 FALSE
## 127 NA NA 0 FALSE
## 128 0 178 0 FALSE
## 129 0 170 0 FALSE
## 130 0 175 0 TRUE
## 131 7 179 0 FALSE
## 132 25 NA 1 FALSE
## 133 25 181 0 FALSE
## 134 25 175 0 FALSE
## 135 50 194 0 FALSE
## 136 50 180 0 FALSE
## 137 0 179 0 FALSE
## 138 0 173 0 TRUE
## 139 25 185 0 FALSE
## 140 0 183 0 FALSE
## 141 0 183 0 FALSE
## 142 12 185 0 FALSE
## 143 0 173 0 FALSE
## 144 25 170 0 FALSE
## 145 2 181 0 FALSE
## 146 25 180 0 FALSE
## 147 7 172 0 FALSE
## 148 50 186 1 FALSE
## 149 50 178 0 FALSE
## 150 0 171 0 FALSE
## 151 NA NA 0 FALSE
## 152 35 179 0 FALSE
## 153 12 176 0 FALSE
## 154 35 185 1 FALSE
## 155 NA 182 0 FALSE
## 156 25 183 0 FALSE
## 157 25 185 0 TRUE
## 158 25 173 1 TRUE
## 159 0 181 0 FALSE
## 160 NA NA 0 FALSE
## 161 0 184 0 FALSE
## 162 25 185 0 FALSE
## 163 35 180 1 FALSE
## 164 0 169 1 FALSE
## 165 NA NA 0 FALSE
## 166 50 191 0 FALSE
## 167 0 183 0 FALSE
## 168 25 189 0 FALSE
## sample.27 sample.1522 sample.569 sample.365 sample.1369 sample.1023
## 1 46 56 47 54 56 59
## 2 46 56 47 54 56 59
## 3 46 56 47 54 56 59
## 4 46 56 47 54 56 59
## 5 46 56 47 54 56 59
## 6 46 56 47 54 56 59
## 7 46 56 47 54 56 59
## 8 46 56 47 54 56 59
## 9 46 56 47 54 56 59
## 10 46 56 47 54 56 59
## 11 46 56 47 54 56 59
## 12 46 56 47 54 56 59
## 13 46 56 47 54 56 59
## 14 46 56 47 54 56 59
## 15 46 56 47 54 56 59
## 16 46 56 47 54 56 59
## 17 46 56 47 54 56 59
## 18 46 56 47 54 56 59
## 19 46 56 47 54 56 59
## 20 46 56 47 54 56 59
## 21 46 56 47 54 56 59
## 22 46 56 47 54 56 59
## 23 46 56 47 54 56 59
## 24 46 56 47 54 56 59
## 25 46 56 47 54 56 59
## 26 46 56 47 54 56 59
## 27 46 56 47 54 56 59
## 28 46 56 47 54 56 59
## 29 46 56 47 54 56 59
## 30 46 56 47 54 56 59
## 31 46 56 47 54 56 59
## 32 46 56 47 54 56 59
## 33 46 56 47 54 56 59
## 34 46 56 47 54 56 59
## 35 46 56 47 54 56 59
## 36 46 56 47 54 56 59
## 37 46 56 47 54 56 59
## 38 46 56 47 54 56 59
## 39 46 56 47 54 56 59
## 40 46 56 47 54 56 59
## 41 46 56 47 54 56 59
## 42 46 56 47 54 56 59
## 43 52 43 40 35 59 47
## 44 52 43 40 35 59 47
## 45 52 43 40 35 59 47
## 46 52 43 40 35 59 47
## 47 52 43 40 35 59 47
## 48 52 43 40 35 59 47
## 49 52 43 40 35 59 47
## 50 52 43 40 35 59 47
## 51 52 43 40 35 59 47
## 52 52 43 40 35 59 47
## 53 52 43 40 35 59 47
## 54 52 43 40 35 59 47
## 55 52 43 40 35 59 47
## 56 52 43 40 35 59 47
## 57 52 43 40 35 59 47
## 58 52 43 40 35 59 47
## 59 52 43 40 35 59 47
## 60 52 43 40 35 59 47
## 61 52 43 40 35 59 47
## 62 52 43 40 35 59 47
## 63 52 43 40 35 59 47
## 64 52 43 40 35 59 47
## 65 52 43 40 35 59 47
## 66 52 43 40 35 59 47
## 67 52 43 40 35 59 47
## 68 52 43 40 35 59 47
## 69 52 43 40 35 59 47
## 70 52 43 40 35 59 47
## 71 52 43 40 35 59 47
## 72 52 43 40 35 59 47
## 73 52 43 40 35 59 47
## 74 52 43 40 35 59 47
## 75 52 43 40 35 59 47
## 76 52 43 40 35 59 47
## 77 52 43 40 35 59 47
## 78 52 43 40 35 59 47
## 79 52 43 40 35 59 47
## 80 52 43 40 35 59 47
## 81 52 43 40 35 59 47
## 82 52 43 40 35 59 47
## 83 52 43 40 35 59 47
## 84 52 43 40 35 59 47
## 85 98 97 87 96 84 96
## 86 98 97 87 96 84 96
## 87 98 97 87 96 84 96
## 88 98 97 87 96 84 96
## 89 98 97 87 96 84 96
## 90 98 97 87 96 84 96
## 91 98 97 87 96 84 96
## 92 98 97 87 96 84 96
## 93 98 97 87 96 84 96
## 94 98 97 87 96 84 96
## 95 98 97 87 96 84 96
## 96 98 97 87 96 84 96
## 97 98 97 87 96 84 96
## 98 98 97 87 96 84 96
## 99 98 97 87 96 84 96
## 100 98 97 87 96 84 96
## 101 98 97 87 96 84 96
## 102 98 97 87 96 84 96
## 103 98 97 87 96 84 96
## 104 98 97 87 96 84 96
## 105 98 97 87 96 84 96
## 106 98 97 87 96 84 96
## 107 98 97 87 96 84 96
## 108 98 97 87 96 84 96
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## 73 46 39 57 55 46 58
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## 168 63 60 50 42 67 43Visualizations
The ggplot2 library is an extremely popular visualization package that provides an interface for extremely fine control over graphics for plotting. It is used by a number of of other popular packages in their built-in plotting functions. It provides a “grammar of graphics” that is quite useful to know.
A note about accessibility:
The default colors automatically selected by ggplot2 are not very user-friendly. Colors are chosen by sampling evenly spaced hues on the color wheel. Because of this behavior, all of the colors have similar intensity, which means that they do not work well when printed in gray-scale, and may be difficult to distinguish for users with atypical color vision. There are many resources for selecting color palettes online. Here are just a few:
- Viridis
- ColorBrewer
- colorBlindness Guide
- Digital Accessibility
- Blog entry
In this documentation we will be using four palettes generated using the viridis library.
library(ggplot2)
library(viridis)## Loading required package: viridisLite?viridis## Help on topic 'viridis' was found in the following packages:
##
## Package Library
## viridis /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
## viridisLite /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
##
##
## Using the first match ...locations.palette <- viridis(3)
smoking.palette <- inferno(2, begin = 0.5, direction = -1)
years.palette <- mako(2, begin = 0.4, end = 0.9, direction = -1)
genes.palette <- plasma(4)As we go through this plotting section, we will pause several times to allow you to explore. Don’t limit yourself to the visualizations included! Experiment with manipulating each of the elements of the plots to accheive interesting and informative graphics.
8.1 The ggplot() function
The basic function of the ggplot2 library is ggplot().
?ggplotIt is capable of taking a lot of arguments and options, but requires only two: an object (typically a data frame) containing the data, and a list of “aesthetic mappings” that tell R which values to use for the axes, colors, and other graphical elements of the plot.
ggplot(data = experiment, mapping = aes(x = birthweight))
Alone, this produces an empty plot. The ggplot() function by itself creates the blank canvas upon which the plot will be drawn. The plot elements are added to this canvas in layers called “geoms.”
##8.2 The geom
There are over 30 geoms in the ggplot2 library, each of which accepts a particular set of aesthetic mappings. The geoms inherit the mapping specified in the original ggplot() function call, and additional layer-specific aesthetics may be specified within the geom. Let’s start with one of the simplest geoms, the histogram.
The geom_histogram() function requires, at a minimum, that a value be provided for x.
ggplot(data = experiment, mapping = aes(x = birthweight)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Setting the “binwidth” parameter within the geom_histogram() call changes the appearance of the plot and eliminates the message.
ggplot(data = experiment, mapping = aes(x = birthweight)) +
geom_histogram(binwidth = 1)
ggplot(data = experiment, mapping = aes(x = birthweight)) +
geom_histogram(binwidth = 0.25)
8.3 Adding color (and fill) to geoms
The color (for lines and points) and fill (for areas, like bars) of a geom can add another layer of information to the plot.
ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +
geom_histogram(binwidth = 0.25) +
scale_fill_manual(values = locations.palette) 
Here the total height of the bar is equal to the number of births at each weight, and the fill denotes the hospital at which the birth occured.
##8.4 Creating faceted plots Sometimes, it may be more clear graphically to create multiple sub-plots or “facets” based on categorical values in the data. The facet_wrap() and facet_grid() functions allow the user to break the data down into multiple plots by one or two categorical variables, respectively.
'ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +
geom_histogram(binwidth = 0.25) +
scale_fill_manual(values = locations.palette) +
facet_wrap(~year, nrow = 2)'## [1] "ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +\n geom_histogram(binwidth = 0.25) +\n scale_fill_manual(values = locations.palette) +\n facet_wrap(~year, nrow = 2)"getwd()## [1] "/Users/syukurhalim/Documents/KULIAH S2/SEMESTER 2/Bioinformatika/PENGANTAR BIOINFORMATIKA (PUBLISH 1)"
8.5 Add and modify labels
Often, the aesthetic names (column names) are uninformative, or unattractive. This may not make much difference when making exploratory figures, but in a report or publication, it is important to have a greater degree of control over the plot title, axis labels, and so on. The labs() function offers the option to set the following labels:
- any aesthetic that has been set: in this case, x and fill
- title: main title of the plot
- subtitle: displayed below the title
- caption: displayed at the bottom right of the plot by default
- tag: label that appears at the top left of the plot by default (e.g. 1A)
- alt, alt_insight: alt text for the plot (used by screen readers)
'ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +
geom_histogram(binwidth = 0.25) +
scale_fill_manual(values = locations.palette) +
facet_wrap(~year, nrow = 2) +
labs(x = "birth weight (kg)",
fill = "Hospital",
tag = "fig. 1",
caption = "Birth weights by year, color-coded by delivery location.",
alt = "Pair of histograms displaying the distribution of birth weights of infants born at General Hospital, Memorial Hospital, and Silver Hill Medical Center in 1967 and 1968.")'## [1] "ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +\n geom_histogram(binwidth = 0.25) +\n scale_fill_manual(values = locations.palette) +\n facet_wrap(~year, nrow = 2) +\n labs(x = \"birth weight (kg)\",\n fill = \"Hospital\",\n tag = \"fig. 1\",\n caption = \"Birth weights by year, color-coded by delivery location.\",\n alt = \"Pair of histograms displaying the distribution of birth weights of infants born at General Hospital, Memorial Hospital, and Silver Hill Medical Center in 1967 and 1968.\")"
8.6 Making global changes to the plot’s appearance using themes
The “theme” of a ggplot object controls the graphical elements that are not mapped onto the data. This includes things like the font size and alignment of axis labels and the color of the plot background. There are a number of pre-set themes offering a range of plot styles.
In addition, the theme() function offers access to these graphical elements independently. Using it, you can change the angle of the text on the x axis, the placement of the legend, and many other things. Here, we will use it to remove the legend title.
'ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +
geom_histogram(binwidth = 0.25) +
scale_fill_manual(values = locations.palette) +
facet_grid(location~year) +
labs(x = "weight (kg)", y = "births") +
theme_bw() +
theme(legend.title = element_blank())'## [1] "ggplot(data = experiment, mapping = aes(x = birthweight, fill = location)) +\n geom_histogram(binwidth = 0.25) +\n scale_fill_manual(values = locations.palette) +\n facet_grid(location~year) +\n labs(x = \"weight (kg)\", y = \"births\") +\n theme_bw() +\n theme(legend.title = element_blank())"
An interesting alternative to geom_histogram() is geom_density().
ggplot(data = experiment, mapping = aes(x = birthweight, fill = smoker)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = smoking.palette) +
labs(x = "birth weight (kg)", fill = "Maternal smoking in pregnancy") +
theme_bw()
The alpha channel of a color determines its opacity. In this case, we have placed alpha = 0.5 outside of the aes() function call, because the opacity is not being mapped to any characteristic of the data.
The examples above use color in a discrete fashion. In the scatter plot below, the color of each point represents a birth weight (continuous variable).
'experiment %>%
filter(smoker == TRUE) %>%
ggplot(mapping = aes(x = maternal.cigarettes,
y = weeks.gestation,
color = birthweight)) +
geom_point() +
labs(x = "Maternal cigarettes / day", y = "Gestational age at birth (weeks)", color = "Birth weight (kg)") +
scale_color_viridis(option = "inferno", begin = 0.4) +
theme_bw()'## [1] "experiment %>%\n filter(smoker == TRUE) %>%\n ggplot(mapping = aes(x = maternal.cigarettes,\n y = weeks.gestation,\n color = birthweight)) +\n geom_point() +\n labs(x = \"Maternal cigarettes / day\", y = \"Gestational age at birth (weeks)\", color = \"Birth weight (kg)\") +\n scale_color_viridis(option = \"inferno\", begin = 0.4) +\n theme_bw()"
Notice that it is not necessary to specify a color palette; viridis has a built-in function for interacting with ggplot objects.
8.7 Change the labels for categorical values
All of the elements of the plot, not just the height of bars and position of points, convey information. The labels on a categorical axis should be meaningful.
'experiment %>%
ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +
geom_boxplot() +
scale_fill_manual(values = smoking.palette) +
theme_bw()'## [1] "experiment %>%\n ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +\n geom_boxplot() +\n scale_fill_manual(values = smoking.palette) +\n theme_bw()"
The bar chart above displays the values “TRUE” and “FALSE” on the x-axis. This is fine for an exploratory plot, but in a report or publication, it might be more informative to replace them with “smoker” and “non-smoker.” The scale_x_discrete() function allows us to do just that.
'experiment %>%
ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +
geom_boxplot() +
scale_fill_manual(values = smoking.palette) +
scale_x_discrete(labels = c("non-smoker", "smoker")) +
guides(fill = "none") +
labs(y = "birth weight (kg)", x = "maternal cigarette use in pregnancy") +
theme_bw()'## [1] "experiment %>%\n ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +\n geom_boxplot() +\n scale_fill_manual(values = smoking.palette) +\n scale_x_discrete(labels = c(\"non-smoker\", \"smoker\")) +\n guides(fill = \"none\") +\n labs(y = \"birth weight (kg)\", x = \"maternal cigarette use in pregnancy\") +\n theme_bw()"
## 8.8 Change the direction
of axes to improve readability
If the category names are very long, it may be necessary to change the direction of the axis labels.
'experiment %>%
ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +
geom_boxplot() +
scale_fill_manual(values = smoking.palette) +
scale_x_discrete(labels = c("non-smoking in pregnancy", "cigarette smoking in pregnancy")) +
guides(fill = "none") +
labs(y = "birth weight (kg)") +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))'## [1] "experiment %>%\n ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +\n geom_boxplot() +\n scale_fill_manual(values = smoking.palette) +\n scale_x_discrete(labels = c(\"non-smoking in pregnancy\", \"cigarette smoking in pregnancy\")) +\n guides(fill = \"none\") +\n labs(y = \"birth weight (kg)\") +\n theme_bw() +\n theme(axis.title.x = element_blank(),\n axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))"
Or even the direction of the axes themselves.
'experiment %>%
ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +
geom_boxplot() +
scale_fill_manual(values = smoking.palette) +
scale_x_discrete(labels = c("non-smoking in pregnancy", "cigarette smoking in pregnancy")) +
guides(fill = "none") +
labs(y = "birth weight (kg)") +
coord_flip() +
theme_bw() +
theme(axis.title.y = element_blank())'## [1] "experiment %>%\n ggplot(mapping = aes(x = smoker, y = birthweight, fill = smoker)) +\n geom_boxplot() +\n scale_fill_manual(values = smoking.palette) +\n scale_x_discrete(labels = c(\"non-smoking in pregnancy\", \"cigarette smoking in pregnancy\")) +\n guides(fill = \"none\") +\n labs(y = \"birth weight (kg)\") +\n coord_flip() +\n theme_bw() +\n theme(axis.title.y = element_blank())"
##8.9 The pivot_longer() function revisited
In the previous section on Tidyverse, pivot_longer() was only briefly discussed. This is because the easiest way to explain why changing the shape of a data set to create fewer, longer columns is useful is to demonstrate.
'experiment %>%
pivot_longer(cols = c(22:25),
names_to = "gene.id",
values_to = "expression") %>%
ggplot(mapping = aes(x = smoker, y = expression, fill = smoker)) +
geom_violin() +
scale_fill_manual(values = smoking.palette) +
scale_x_discrete(labels = c("non-smoker", "smoker")) +
facet_wrap(~ gene.id, nrow = 2) +
guides(fill = "none") +
labs(x = "Maternal tobacco use in pregnancy", y = "miRNA expression") +
theme_bw()'## [1] "experiment %>%\n pivot_longer(cols = c(22:25),\n names_to = \"gene.id\",\n values_to = \"expression\") %>%\n ggplot(mapping = aes(x = smoker, y = expression, fill = smoker)) +\n geom_violin() +\n scale_fill_manual(values = smoking.palette) +\n scale_x_discrete(labels = c(\"non-smoker\", \"smoker\")) +\n facet_wrap(~ gene.id, nrow = 2) +\n guides(fill = \"none\") +\n labs(x = \"Maternal tobacco use in pregnancy\", y = \"miRNA expression\") +\n theme_bw()"
After pivoting the miRNA data
into a single column called expression, that data is available for
ggplot to map that onto an aesthetic property.
##8.10 Use shape and line type to convey meaning
Color is not the only aesthetic available for mapping to the data. Shape and line type are also good candidates for displaying categorical (or discrete) values.
'experiment %>%
pivot_longer(cols = c(22:25),
names_to = "gene.id",
values_to = "expression") %>%
ggplot(mapping = aes(x = maternal.cigarettes, y = expression, color = location, shape = low.birthweight)) +
geom_point() +
scale_color_manual(values = locations.palette) +
scale_shape_manual(values = c(16, 17), labels = c("< 2.72 kg", ">= 2.72 kg")) +
facet_wrap(~ gene.id, nrow = 2) +
labs(x = "Maternal cigarettes / day", y = "miRNA expression") +
theme_bw() +
theme(legend.title = element_blank())'## [1] "experiment %>%\n pivot_longer(cols = c(22:25),\n names_to = \"gene.id\",\n values_to = \"expression\") %>%\n ggplot(mapping = aes(x = maternal.cigarettes, y = expression, color = location, shape = low.birthweight)) +\n geom_point() +\n scale_color_manual(values = locations.palette) +\n scale_shape_manual(values = c(16, 17), labels = c(\"< 2.72 kg\", \">= 2.72 kg\")) +\n facet_wrap(~ gene.id, nrow = 2) +\n labs(x = \"Maternal cigarettes / day\", y = \"miRNA expression\") +\n theme_bw() +\n theme(legend.title = element_blank())"There are 25 shapes available for plotting. You can see them all here.
'experiment %>%
ggplot(mapping = aes(x = weeks.gestation, y = birthweight, linetype = smoker)) +
geom_quantile(quantiles = c(0.25, 0.5, 0.75), color = "black") +
labs(x = "gestational age at birth", y = "birth weight (kg)", linetype = "geriatric preganancy") +
theme_bw()'## [1] "experiment %>%\n ggplot(mapping = aes(x = weeks.gestation, y = birthweight, linetype = smoker)) +\n geom_quantile(quantiles = c(0.25, 0.5, 0.75), color = \"black\") +\n labs(x = \"gestational age at birth\", y = \"birth weight (kg)\", linetype = \"geriatric preganancy\") +\n theme_bw()"
##8.11 Focus in on one
portion of the plot
We can zoom in to the the lower left hand corner of the plot using coord_cartesian().
'experiment %>%
ggplot(mapping = aes(x = weeks.gestation, y = birthweight, linetype = smoker)) +
geom_quantile(quantiles = c(0.25, 0.5, 0.75), color = "black") +
labs(x = "gestational age at birth", y = "birth weight (kg)", linetype = "geriatric preganancy") +
coord_cartesian(xlim = c(33, 35), ylim = c(1.5, 3.5)) +
theme_bw()'## [1] "experiment %>%\n ggplot(mapping = aes(x = weeks.gestation, y = birthweight, linetype = smoker)) +\n geom_quantile(quantiles = c(0.25, 0.5, 0.75), color = \"black\") +\n labs(x = \"gestational age at birth\", y = \"birth weight (kg)\", linetype = \"geriatric preganancy\") +\n coord_cartesian(xlim = c(33, 35), ylim = c(1.5, 3.5)) +\n theme_bw()"
##8.12 Layer multiple geoms
It is not necessary to make a single geom convey all of the information a plot must communicate. Instead, ggplot2 offers users the ability to layer geoms together. As long as they use the same axes, geoms may share a plot.
ggplot(experiment, mapping = aes(x = weeks.gestation,
y = birthweight,
color = smoker)) +
geom_point() +
geom_smooth(alpha = 0.2) +
labs(x = "Gestational age at birth (weeks)",
y = "Birth weight (kg)",
color = "Maternal tobacco use",
caption = "Birthweight increases with gestational age for infants born to both\nsmokers and non-smokers.") +
scale_color_manual(values = smoking.palette) +
theme_bw() +
theme(plot.caption = element_text(hjust = 0))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
'experiment %>%
group_by(year, smoker) %>%
summarise(mean.birthweight = mean(birthweight),
sd.birthweight = sd(birthweight)) %>%
ggplot(mapping = aes(x = as.factor(year), y = mean.birthweight, fill = smoker)) +
geom_col(position = position_dodge2(preserve = "single")) +
geom_errorbar(mapping = aes(ymin = mean.birthweight - sd.birthweight,
ymax = mean.birthweight + sd.birthweight),
position = position_dodge2(preserve = "single")) +
scale_fill_manual(values = smoking.palette) +
labs(x = "birth year", y = "birth weight (kg)", fill = "maternal smoking") +
theme_bw()'## [1] "experiment %>%\n group_by(year, smoker) %>%\n summarise(mean.birthweight = mean(birthweight),\n sd.birthweight = sd(birthweight)) %>%\n ggplot(mapping = aes(x = as.factor(year), y = mean.birthweight, fill = smoker)) +\n geom_col(position = position_dodge2(preserve = \"single\")) +\n geom_errorbar(mapping = aes(ymin = mean.birthweight - sd.birthweight,\n ymax = mean.birthweight + sd.birthweight),\n position = position_dodge2(preserve = \"single\")) +\n scale_fill_manual(values = smoking.palette) +\n labs(x = \"birth year\", y = \"birth weight (kg)\", fill = \"maternal smoking\") +\n theme_bw()"
Refrensi :