Inflammation Data
Sanjay Fuloria
2023-12-01
Inflammation Dataset Analysis
This is based on the Carpentries lesson on R Programming
#Loading Data
read.csv(file = "data/inflammation-01.csv", header = FALSE)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## 1 0 0 1 3 1 2 4 7 8 3 3 3 10 5 7 4 7 7 12 18 6
## 2 0 1 2 1 2 1 3 2 2 6 10 11 5 9 4 4 7 16 8 6 18
## 3 0 1 1 3 3 2 6 2 5 9 5 7 4 5 4 15 5 11 9 10 19
## 4 0 0 2 0 4 2 2 1 6 7 10 7 9 13 8 8 15 10 10 7 17
## 5 0 1 1 3 3 1 3 5 2 4 4 7 6 5 3 10 8 10 6 17 9
## 6 0 0 1 2 2 4 2 1 6 4 7 6 6 9 9 15 4 16 18 12 12
## 7 0 0 2 2 4 2 2 5 5 8 6 5 11 9 4 13 5 12 10 6 9
## 8 0 0 1 2 3 1 2 3 5 3 7 8 8 5 10 9 15 11 18 19 20
## 9 0 0 0 3 1 5 6 5 5 8 2 4 11 12 10 11 9 10 17 11 6
## 10 0 1 1 2 1 3 5 3 5 8 6 8 12 5 13 6 13 8 16 8 18
## 11 0 1 0 0 4 3 3 5 5 4 5 8 7 10 13 3 7 13 15 18 8
## 12 0 1 0 0 3 4 2 7 8 5 2 8 11 5 5 8 14 11 6 11 9
## 13 0 0 2 1 4 3 6 4 6 7 9 9 3 11 6 12 4 17 13 15 13
## 14 0 0 0 0 1 3 1 6 6 5 5 6 3 6 13 3 10 13 9 16 15
## 15 0 1 2 1 1 1 4 1 5 2 3 3 10 7 13 5 7 17 6 9 12
## 16 0 1 1 0 1 2 4 3 6 4 7 5 5 7 5 10 7 8 18 17 9
## 17 0 0 0 0 2 3 6 5 7 4 3 2 10 7 9 11 12 5 12 9 13
## 18 0 0 0 1 2 1 4 3 6 7 4 2 12 6 12 4 14 7 8 14 13
## 19 0 0 2 1 2 5 4 2 7 8 4 7 11 9 8 11 15 17 11 12 7
## 20 0 1 2 0 1 4 3 2 2 7 3 3 12 13 11 13 6 5 9 16 9
## 21 0 1 1 3 1 4 4 1 8 2 2 3 12 12 10 15 13 6 5 5 18
## 22 0 0 2 3 2 3 2 6 3 8 7 4 6 6 9 5 12 12 8 5 12
## 23 0 0 0 3 4 5 1 7 7 8 2 5 12 4 10 14 5 5 17 13 16
## 24 0 1 1 1 1 3 3 2 6 3 9 7 8 8 4 13 7 14 11 15 14
## 25 0 1 1 1 2 3 5 3 6 3 7 10 3 8 12 4 12 9 15 5 17
## 26 0 0 2 1 3 3 2 7 4 4 3 8 12 9 12 9 5 16 8 17 7
## 27 0 0 1 2 4 2 2 3 5 7 10 5 5 12 3 13 4 13 7 15 9
## 28 0 0 1 1 1 5 1 5 2 2 4 10 4 8 14 6 15 6 12 15 15
## 29 0 0 2 2 3 4 6 3 7 6 4 5 8 4 7 7 6 11 12 19 20
## 30 0 0 0 1 4 4 6 3 8 6 4 10 12 3 3 6 8 7 17 16 14
## 31 0 1 1 0 3 2 4 6 8 6 2 3 11 3 14 14 12 8 8 16 13
## 32 0 0 2 3 3 4 5 3 6 7 10 5 10 13 14 3 8 10 9 9 19
## 33 0 1 2 2 2 3 6 6 6 7 6 3 11 12 13 15 15 10 14 11 11
## 34 0 0 2 1 3 5 6 7 5 8 9 3 12 10 12 4 12 9 13 10 10
## 35 0 0 1 2 4 1 5 5 2 3 4 8 8 12 5 15 9 17 7 19 14
## 36 0 0 0 3 1 3 6 4 3 4 8 3 4 8 3 11 5 7 10 5 15
## 37 0 1 2 2 2 5 5 1 4 6 3 6 5 9 6 7 4 7 16 7 16
## 38 0 1 1 2 3 1 5 1 2 2 5 7 6 6 5 10 6 7 17 13 15
## 39 0 1 0 3 2 4 1 1 5 9 10 7 12 10 9 15 12 13 13 6 19
## 40 0 1 1 3 1 1 5 5 3 7 2 2 3 12 4 6 8 15 16 16 15
## 41 0 0 0 2 2 1 3 4 5 5 6 5 5 12 13 5 7 5 11 15 18
## 42 0 0 1 3 3 1 2 1 8 9 2 8 10 3 8 6 10 13 11 17 19
## 43 0 1 1 3 4 5 2 1 3 7 9 6 10 5 8 15 11 12 15 6 12
## 44 0 0 1 3 1 4 3 6 7 8 5 7 11 3 6 11 6 10 6 19 18
## 45 0 1 1 3 3 4 4 6 3 4 9 9 7 6 8 15 12 15 6 11 6
## 46 0 1 2 2 4 3 1 4 8 9 5 10 10 3 4 6 7 11 16 6 14
## 47 0 0 2 3 4 5 4 6 2 9 7 4 9 10 8 11 16 12 15 17 19
## 48 0 1 1 3 1 4 6 2 8 2 10 3 11 9 13 15 5 15 6 10 10
## 49 0 0 1 3 2 5 1 2 7 6 6 3 12 9 4 14 4 6 12 9 12
## 50 0 0 1 2 3 4 5 7 5 4 10 5 12 12 5 4 7 9 18 16 16
## 51 0 1 2 1 1 3 5 3 6 3 10 10 11 10 13 10 13 6 6 14 5
## 52 0 1 2 2 3 5 2 4 5 6 8 3 5 4 3 15 15 12 16 7 20
## 53 0 0 0 2 4 4 5 3 3 3 10 4 4 4 14 11 15 13 10 14 11
## 54 0 0 2 1 1 4 4 7 2 9 4 10 12 7 6 6 11 12 9 15 15
## 55 0 1 2 1 1 4 5 4 4 5 9 7 10 3 13 13 8 9 17 16 16
## 56 0 0 1 3 2 3 6 4 5 7 2 4 11 11 3 8 8 16 5 13 16
## 57 0 1 1 2 2 5 1 7 4 2 5 5 4 6 6 4 16 11 14 16 14
## 58 0 1 1 1 4 1 6 4 6 3 6 5 6 4 14 13 13 9 12 19 9
## 59 0 0 0 1 4 5 6 3 8 7 9 10 8 6 5 12 15 5 10 5 8
## 60 0 0 1 0 3 2 5 4 8 2 9 3 3 10 12 9 14 11 13 8 6
## V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40
## 1 13 11 11 7 7 4 6 8 8 4 4 5 7 3 4 2 3 0 0
## 2 4 12 5 12 7 11 5 11 3 3 5 4 4 5 5 1 1 0 1
## 3 14 12 17 7 12 11 7 4 2 10 5 4 2 2 3 2 2 1 1
## 4 4 4 7 6 15 6 4 9 11 3 5 6 3 3 4 2 3 2 1
## 5 14 9 7 13 9 12 6 7 7 9 6 3 2 2 4 2 0 1 1
## 6 5 18 9 5 3 10 3 12 7 8 4 7 3 5 4 4 3 2 1
## 7 17 15 8 9 3 13 7 8 2 8 8 4 2 3 5 4 1 1 1
## 8 8 5 13 15 10 6 10 6 7 4 9 3 5 2 5 3 2 2 1
## 9 16 12 6 8 14 6 13 10 11 4 6 4 7 6 3 2 1 0 0
## 10 15 16 14 12 7 3 8 9 11 2 5 4 5 1 4 1 2 0 0
## 11 15 15 16 11 14 12 4 10 10 4 3 4 5 5 3 3 2 2 1
## 12 16 18 6 12 5 4 3 5 7 8 3 5 4 5 5 4 0 1 1
## 13 12 8 7 4 7 12 9 5 6 5 4 7 3 5 4 2 3 0 1
## 14 9 11 4 6 4 11 11 12 3 5 8 7 4 6 4 1 3 0 0
## 15 13 10 4 12 4 6 7 6 10 8 2 5 1 3 4 2 0 2 0
## 16 8 12 11 11 11 14 6 11 2 10 9 5 6 5 3 4 2 2 0
## 17 19 14 17 5 13 8 11 5 10 9 8 7 5 3 1 4 0 2 1
## 18 19 6 9 12 6 4 13 6 7 2 3 6 5 4 2 3 0 1 0
## 19 12 7 6 7 4 13 5 7 6 6 9 2 1 1 2 2 0 1 0
## 20 19 16 11 8 9 14 12 11 9 6 6 6 1 1 2 4 3 1 1
## 21 19 9 6 11 12 7 6 3 6 3 2 4 3 1 5 4 2 2 0
## 22 10 16 7 14 12 5 4 6 9 8 5 6 6 1 4 3 0 2 0
## 23 15 13 6 12 9 10 3 3 7 4 4 8 2 6 5 1 0 1 0
## 24 13 5 13 7 14 9 10 5 11 5 3 5 1 1 4 4 1 2 0
## 25 16 5 10 10 15 7 5 3 11 5 5 6 1 1 1 1 0 2 1
## 26 11 14 7 13 11 7 12 12 7 8 5 7 2 2 4 1 1 1 0
## 27 12 18 14 16 12 3 11 3 2 7 4 8 2 2 1 3 0 1 1
## 28 13 7 17 4 5 11 4 8 7 9 4 5 3 2 5 4 3 2 1
## 29 18 9 5 4 7 14 8 4 3 7 7 8 3 5 4 1 3 1 0
## 30 15 17 4 14 13 4 4 12 11 6 9 5 5 2 5 2 1 0 1
## 31 7 6 9 15 7 6 4 10 8 10 4 2 6 5 5 2 3 2 1
## 32 15 15 6 8 8 11 5 5 7 3 6 6 4 5 2 2 3 0 0
## 33 8 6 12 10 5 12 7 7 11 5 8 5 2 5 5 2 0 2 1
## 34 6 10 11 4 15 13 7 3 4 2 9 7 2 4 2 1 2 1 1
## 35 18 12 17 14 4 13 13 8 11 5 6 6 2 3 5 2 1 1 1
## 36 9 16 17 16 3 8 9 8 3 3 9 5 1 6 5 4 2 2 0
## 37 13 9 16 12 6 7 9 10 3 6 4 5 4 6 3 4 3 2 1
## 38 16 17 14 4 4 10 10 10 11 9 9 5 4 4 2 1 0 1 0
## 39 9 10 6 13 5 13 6 7 2 5 5 2 1 1 1 1 3 0 1
## 40 4 14 5 13 10 7 10 6 3 2 3 6 3 3 5 4 3 2 1
## 41 7 9 10 14 12 11 9 10 3 2 9 6 2 2 5 3 0 0 1
## 42 6 4 11 6 12 7 5 5 4 4 8 2 6 6 4 2 2 0 0
## 43 16 6 4 14 3 12 9 6 11 5 8 5 5 6 1 2 1 2 0
## 44 14 6 10 7 9 8 5 8 3 10 2 5 1 5 4 2 1 0 1
## 45 18 5 14 15 12 9 8 3 6 10 6 8 7 2 5 4 3 1 1
## 46 9 11 10 10 7 10 8 8 4 5 8 4 4 5 2 4 1 1 0
## 47 10 18 13 15 11 8 4 7 11 6 7 6 5 1 3 1 0 0 0
## 48 5 14 15 12 7 4 5 11 4 6 9 5 6 1 1 2 1 2 1
## 49 7 11 7 16 8 13 6 7 6 10 7 6 3 1 5 4 3 0 0
## 50 10 15 15 10 4 3 7 5 9 4 6 2 4 1 4 2 2 2 1
## 51 4 5 5 9 4 12 7 7 4 7 9 3 3 6 3 4 1 2 0
## 52 15 12 8 9 6 12 5 8 3 8 5 4 1 3 2 1 3 1 0
## 53 17 9 11 11 7 10 12 10 10 10 8 7 5 2 2 4 1 2 1
## 54 6 6 13 5 12 9 6 4 7 7 6 5 4 1 4 2 2 2 1
## 55 15 12 13 5 12 10 9 11 9 4 5 5 2 2 5 1 0 0 1
## 56 5 8 8 6 9 10 10 9 3 3 5 3 5 4 5 3 3 0 1
## 57 14 8 17 4 14 13 7 6 3 7 7 5 6 3 4 2 2 1 1
## 58 10 15 10 9 10 10 7 5 6 8 6 6 4 3 5 2 1 1 1
## 59 13 18 17 14 9 13 4 10 11 10 8 8 6 5 5 2 0 2 0
## 60 18 11 9 13 11 8 5 5 2 8 5 3 5 4 1 3 1 1 0#Loading a .csv2 format file
read.csv(file = "data/commadec.txt", sep = ";", dec = ",")## ID Gender Group BloodPressure Age Aneurisms_q1 Aneurisms_q2
## 1 Sub001 m Control 132 16.0 114 140
## 2 Sub002 m Treatment2 139 17.2 148 209
## 3 Sub003 m Treatment2 130 19.5 196 251
## 4 Sub004 f Treatment1 105 15.7 199 140
## 5 Sub005 m Treatment1 125 19.9 188 120
## 6 Sub006 M Treatment2 112 14.3 260 266
## 7 Sub007 f Control 173 17.7 135 98
## 8 Sub008 m Treatment2 108 19.8 216 238
## 9 Sub009 m Treatment2 131 19.4 117 215
## 10 Sub010 f Control 129 18.8 188 144
## 11 Sub011 m Treatment1 126 14.8 134 155
## 12 Sub012 f Treatment2 96 15.3 152 177
## 13 Sub013 f Control 77 16.5 112 220
## 14 Sub014 m Control 158 12.6 109 150
## 15 Sub015 m Control 81 14.3 146 140
## 16 Sub016 m Control 137 15.9 97 172
## 17 Sub017 f Treatment1 147 18.4 165 157
## 18 Sub018 m Treatment2 130 18.3 158 265
## 19 Sub019 m Treatment1 105 15.4 178 109
## 20 Sub020 F Treatment1 92 14.3 107 188
## 21 Sub021 f Control 111 12.7 174 160
## 22 Sub022 m Treatment1 122 15.4 97 110
## 23 Sub023 f Treatment2 97 17.2 187 239
## 24 Sub024 f Treatment2 118 17.3 188 191
## 25 Sub025 m Treatment1 82 16.7 114 199
## 26 Sub026 M Treatment1 123 19.6 115 160
## 27 Sub027 M Treatment2 126 15.0 128 249
## 28 Sub028 f Treatment1 94 16.1 112 230
## 29 Sub029 m Control 135 17.6 136 109
## 30 Sub030 f Control 108 18.6 103 148
## 31 Sub031 f Treatment1 133 18.3 132 151
## 32 Sub032 m Treatment1 108 16.7 118 154
## 33 Sub033 m Treatment2 122 12.5 166 176
## 34 Sub034 m Treatment1 134 14.3 152 105
## 35 Sub035 m Treatment1 145 19.7 191 148
## 36 Sub036 f Control 133 17.6 152 178
## 37 Sub037 f Treatment2 90 17.0 161 270
## 38 Sub038 m Treatment2 118 12.2 239 184
## 39 Sub039 M Treatment1 113 15.1 132 137
## 40 Sub040 m Treatment2 115 17.7 168 255
## 41 Sub041 f Treatment1 142 19.0 140 184
## 42 Sub042 m Treatment1 114 14.7 166 85
## 43 Sub043 m Control 139 15.2 141 160
## 44 Sub044 m Treatment1 90 15.3 161 168
## 45 Sub045 f Control 126 12.9 103 111
## 46 Sub046 f Treatment2 109 18.4 231 240
## 47 Sub047 M Control 125 18.1 192 141
## 48 Sub048 M Control 99 15.6 178 180
## 49 Sub049 m Control 122 19.5 167 123
## 50 Sub050 m Treatment1 111 13.5 135 150
## 51 Sub051 m Treatment2 109 13.5 150 166
## 52 Sub052 f Treatment1 134 13.7 192 80
## 53 Sub053 f Treatment1 113 18.7 153 153
## 54 Sub054 f Treatment2 105 12.2 205 264
## 55 Sub055 m Treatment2 125 16.9 117 194
## 56 Sub056 f Treatment2 123 19.5 199 119
## 57 Sub057 m Control 155 12.1 182 129
## 58 Sub058 m Treatment2 117 17.0 180 196
## 59 Sub059 m Treatment1 116 19.2 111 111
## 60 Sub060 f Control 133 14.7 101 98
## 61 Sub061 f Control 94 20.0 166 167
## 62 Sub062 f Treatment2 106 14.1 158 171
## 63 Sub063 f Treatment1 144 14.7 189 178
## 64 Sub064 M Treatment1 149 16.6 189 101
## 65 Sub065 f Treatment2 108 15.0 239 189
## 66 Sub066 m Treatment1 116 15.0 185 224
## 67 Sub067 f Treatment2 136 13.8 224 112
## 68 Sub068 f Control 98 14.8 104 139
## 69 Sub069 M Treatment2 148 19.1 222 199
## 70 Sub070 m Control 74 18.9 107 98
## 71 Sub071 m Treatment2 147 17.7 153 255
## 72 Sub072 m Control 116 17.4 118 165
## 73 Sub073 F Treatment1 133 15.5 102 184
## 74 Sub074 m Control 97 13.1 188 125
## 75 Sub075 m Treatment2 132 12.2 180 283
## 76 Sub076 f Treatment2 153 17.0 178 214
## 77 Sub077 M Treatment1 151 17.7 168 184
## 78 Sub078 M Treatment1 121 19.5 118 170
## 79 Sub079 M Treatment1 116 19.5 169 114
## 80 Sub080 f Control 104 12.8 156 138
## 81 Sub081 m Treatment2 111 17.6 232 211
## 82 Sub082 M Treatment1 62 17.7 188 108
## 83 Sub083 M Treatment2 124 14.2 169 168
## 84 Sub084 m Treatment2 124 19.2 241 233
## 85 Sub085 m Treatment2 109 16.0 65 207
## 86 Sub086 f Control 117 15.2 225 185
## 87 Sub087 f Control 90 17.6 104 116
## 88 Sub088 f Treatment1 158 17.6 179 158
## 89 Sub089 m Treatment1 113 15.1 103 140
## 90 Sub090 m Control 150 17.8 112 130
## 91 Sub091 f Treatment2 115 16.2 226 170
## 92 Sub092 m Treatment2 83 16.6 228 221
## 93 Sub093 F Control 116 19.1 209 142
## 94 Sub094 f Treatment1 141 17.2 153 104
## 95 Sub095 m Control 108 13.6 111 118
## 96 Sub096 m Control 102 14.6 148 132
## 97 Sub097 F Treatment2 90 19.6 141 196
## 98 Sub098 m Treatment1 133 17.0 193 112
## 99 Sub099 M Treatment2 83 16.2 130 226
## 100 Sub100 M Treatment1 122 18.4 126 157
## Aneurisms_q3 Aneurisms_q4
## 1 202 237
## 2 248 248
## 3 122 177
## 4 233 220
## 5 222 228
## 6 320 294
## 7 154 245
## 8 279 251
## 9 181 272
## 10 192 185
## 11 247 223
## 12 323 245
## 13 225 195
## 14 177 189
## 15 239 223
## 16 203 207
## 17 200 193
## 18 243 187
## 19 206 182
## 20 167 218
## 21 203 183
## 22 194 133
## 23 281 214
## 24 256 265
## 25 242 195
## 26 158 228
## 27 294 315
## 28 281 126
## 29 105 155
## 30 219 228
## 31 234 162
## 32 260 160
## 33 253 233
## 34 197 299
## 35 166 185
## 36 158 170
## 37 232 284
## 38 317 269
## 39 193 206
## 40 273 274
## 41 239 202
## 42 179 196
## 43 179 239
## 44 212 181
## 45 254 126
## 46 260 310
## 47 180 225
## 48 169 183
## 49 236 224
## 50 208 279
## 51 153 204
## 52 138 222
## 53 236 216
## 54 269 207
## 55 216 211
## 56 183 251
## 57 226 218
## 58 250 294
## 59 244 201
## 60 178 116
## 61 232 241
## 62 237 212
## 63 177 238
## 64 193 172
## 65 297 300
## 66 151 182
## 67 304 288
## 68 211 204
## 69 280 196
## 70 204 138
## 71 218 234
## 72 220 227
## 73 246 222
## 74 191 157
## 75 204 298
## 76 291 240
## 77 184 229
## 78 249 249
## 79 248 233
## 80 218 258
## 81 219 246
## 82 180 136
## 83 180 211
## 84 292 182
## 85 234 235
## 86 195 235
## 87 173 221
## 88 216 244
## 89 209 186
## 90 175 191
## 91 307 244
## 92 316 259
## 93 199 184
## 94 194 214
## 95 173 191
## 96 200 194
## 97 322 273
## 98 123 181
## 99 286 281
## 100 129 160read.csv2(file = "data/commadec.txt")## ID Gender Group BloodPressure Age Aneurisms_q1 Aneurisms_q2
## 1 Sub001 m Control 132 16.0 114 140
## 2 Sub002 m Treatment2 139 17.2 148 209
## 3 Sub003 m Treatment2 130 19.5 196 251
## 4 Sub004 f Treatment1 105 15.7 199 140
## 5 Sub005 m Treatment1 125 19.9 188 120
## 6 Sub006 M Treatment2 112 14.3 260 266
## 7 Sub007 f Control 173 17.7 135 98
## 8 Sub008 m Treatment2 108 19.8 216 238
## 9 Sub009 m Treatment2 131 19.4 117 215
## 10 Sub010 f Control 129 18.8 188 144
## 11 Sub011 m Treatment1 126 14.8 134 155
## 12 Sub012 f Treatment2 96 15.3 152 177
## 13 Sub013 f Control 77 16.5 112 220
## 14 Sub014 m Control 158 12.6 109 150
## 15 Sub015 m Control 81 14.3 146 140
## 16 Sub016 m Control 137 15.9 97 172
## 17 Sub017 f Treatment1 147 18.4 165 157
## 18 Sub018 m Treatment2 130 18.3 158 265
## 19 Sub019 m Treatment1 105 15.4 178 109
## 20 Sub020 F Treatment1 92 14.3 107 188
## 21 Sub021 f Control 111 12.7 174 160
## 22 Sub022 m Treatment1 122 15.4 97 110
## 23 Sub023 f Treatment2 97 17.2 187 239
## 24 Sub024 f Treatment2 118 17.3 188 191
## 25 Sub025 m Treatment1 82 16.7 114 199
## 26 Sub026 M Treatment1 123 19.6 115 160
## 27 Sub027 M Treatment2 126 15.0 128 249
## 28 Sub028 f Treatment1 94 16.1 112 230
## 29 Sub029 m Control 135 17.6 136 109
## 30 Sub030 f Control 108 18.6 103 148
## 31 Sub031 f Treatment1 133 18.3 132 151
## 32 Sub032 m Treatment1 108 16.7 118 154
## 33 Sub033 m Treatment2 122 12.5 166 176
## 34 Sub034 m Treatment1 134 14.3 152 105
## 35 Sub035 m Treatment1 145 19.7 191 148
## 36 Sub036 f Control 133 17.6 152 178
## 37 Sub037 f Treatment2 90 17.0 161 270
## 38 Sub038 m Treatment2 118 12.2 239 184
## 39 Sub039 M Treatment1 113 15.1 132 137
## 40 Sub040 m Treatment2 115 17.7 168 255
## 41 Sub041 f Treatment1 142 19.0 140 184
## 42 Sub042 m Treatment1 114 14.7 166 85
## 43 Sub043 m Control 139 15.2 141 160
## 44 Sub044 m Treatment1 90 15.3 161 168
## 45 Sub045 f Control 126 12.9 103 111
## 46 Sub046 f Treatment2 109 18.4 231 240
## 47 Sub047 M Control 125 18.1 192 141
## 48 Sub048 M Control 99 15.6 178 180
## 49 Sub049 m Control 122 19.5 167 123
## 50 Sub050 m Treatment1 111 13.5 135 150
## 51 Sub051 m Treatment2 109 13.5 150 166
## 52 Sub052 f Treatment1 134 13.7 192 80
## 53 Sub053 f Treatment1 113 18.7 153 153
## 54 Sub054 f Treatment2 105 12.2 205 264
## 55 Sub055 m Treatment2 125 16.9 117 194
## 56 Sub056 f Treatment2 123 19.5 199 119
## 57 Sub057 m Control 155 12.1 182 129
## 58 Sub058 m Treatment2 117 17.0 180 196
## 59 Sub059 m Treatment1 116 19.2 111 111
## 60 Sub060 f Control 133 14.7 101 98
## 61 Sub061 f Control 94 20.0 166 167
## 62 Sub062 f Treatment2 106 14.1 158 171
## 63 Sub063 f Treatment1 144 14.7 189 178
## 64 Sub064 M Treatment1 149 16.6 189 101
## 65 Sub065 f Treatment2 108 15.0 239 189
## 66 Sub066 m Treatment1 116 15.0 185 224
## 67 Sub067 f Treatment2 136 13.8 224 112
## 68 Sub068 f Control 98 14.8 104 139
## 69 Sub069 M Treatment2 148 19.1 222 199
## 70 Sub070 m Control 74 18.9 107 98
## 71 Sub071 m Treatment2 147 17.7 153 255
## 72 Sub072 m Control 116 17.4 118 165
## 73 Sub073 F Treatment1 133 15.5 102 184
## 74 Sub074 m Control 97 13.1 188 125
## 75 Sub075 m Treatment2 132 12.2 180 283
## 76 Sub076 f Treatment2 153 17.0 178 214
## 77 Sub077 M Treatment1 151 17.7 168 184
## 78 Sub078 M Treatment1 121 19.5 118 170
## 79 Sub079 M Treatment1 116 19.5 169 114
## 80 Sub080 f Control 104 12.8 156 138
## 81 Sub081 m Treatment2 111 17.6 232 211
## 82 Sub082 M Treatment1 62 17.7 188 108
## 83 Sub083 M Treatment2 124 14.2 169 168
## 84 Sub084 m Treatment2 124 19.2 241 233
## 85 Sub085 m Treatment2 109 16.0 65 207
## 86 Sub086 f Control 117 15.2 225 185
## 87 Sub087 f Control 90 17.6 104 116
## 88 Sub088 f Treatment1 158 17.6 179 158
## 89 Sub089 m Treatment1 113 15.1 103 140
## 90 Sub090 m Control 150 17.8 112 130
## 91 Sub091 f Treatment2 115 16.2 226 170
## 92 Sub092 m Treatment2 83 16.6 228 221
## 93 Sub093 F Control 116 19.1 209 142
## 94 Sub094 f Treatment1 141 17.2 153 104
## 95 Sub095 m Control 108 13.6 111 118
## 96 Sub096 m Control 102 14.6 148 132
## 97 Sub097 F Treatment2 90 19.6 141 196
## 98 Sub098 m Treatment1 133 17.0 193 112
## 99 Sub099 M Treatment2 83 16.2 130 226
## 100 Sub100 M Treatment1 122 18.4 126 157
## Aneurisms_q3 Aneurisms_q4
## 1 202 237
## 2 248 248
## 3 122 177
## 4 233 220
## 5 222 228
## 6 320 294
## 7 154 245
## 8 279 251
## 9 181 272
## 10 192 185
## 11 247 223
## 12 323 245
## 13 225 195
## 14 177 189
## 15 239 223
## 16 203 207
## 17 200 193
## 18 243 187
## 19 206 182
## 20 167 218
## 21 203 183
## 22 194 133
## 23 281 214
## 24 256 265
## 25 242 195
## 26 158 228
## 27 294 315
## 28 281 126
## 29 105 155
## 30 219 228
## 31 234 162
## 32 260 160
## 33 253 233
## 34 197 299
## 35 166 185
## 36 158 170
## 37 232 284
## 38 317 269
## 39 193 206
## 40 273 274
## 41 239 202
## 42 179 196
## 43 179 239
## 44 212 181
## 45 254 126
## 46 260 310
## 47 180 225
## 48 169 183
## 49 236 224
## 50 208 279
## 51 153 204
## 52 138 222
## 53 236 216
## 54 269 207
## 55 216 211
## 56 183 251
## 57 226 218
## 58 250 294
## 59 244 201
## 60 178 116
## 61 232 241
## 62 237 212
## 63 177 238
## 64 193 172
## 65 297 300
## 66 151 182
## 67 304 288
## 68 211 204
## 69 280 196
## 70 204 138
## 71 218 234
## 72 220 227
## 73 246 222
## 74 191 157
## 75 204 298
## 76 291 240
## 77 184 229
## 78 249 249
## 79 248 233
## 80 218 258
## 81 219 246
## 82 180 136
## 83 180 211
## 84 292 182
## 85 234 235
## 86 195 235
## 87 173 221
## 88 216 244
## 89 209 186
## 90 175 191
## 91 307 244
## 92 316 259
## 93 199 184
## 94 194 214
## 95 173 191
## 96 200 194
## 97 322 273
## 98 123 181
## 99 286 281
## 100 129 160#Assiging Variables
weight_kg <- 55
weight_kg## [1] 55#Weight in pounds
2.2 * weight_kg## [1] 121weight_kg <- 57.5
weight_kg## [1] 57.5weight_lb <- 2.2 * weight_kg
#Weight in kg
weight_kg## [1] 57.5#Weight in Pounds
weight_lb## [1] 126.5dat <- read.csv(file = "data/inflammation-01.csv", header = FALSE)
View(dat)
head(dat)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## 1 0 0 1 3 1 2 4 7 8 3 3 3 10 5 7 4 7 7 12 18 6
## 2 0 1 2 1 2 1 3 2 2 6 10 11 5 9 4 4 7 16 8 6 18
## 3 0 1 1 3 3 2 6 2 5 9 5 7 4 5 4 15 5 11 9 10 19
## 4 0 0 2 0 4 2 2 1 6 7 10 7 9 13 8 8 15 10 10 7 17
## 5 0 1 1 3 3 1 3 5 2 4 4 7 6 5 3 10 8 10 6 17 9
## 6 0 0 1 2 2 4 2 1 6 4 7 6 6 9 9 15 4 16 18 12 12
## V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40
## 1 13 11 11 7 7 4 6 8 8 4 4 5 7 3 4 2 3 0 0
## 2 4 12 5 12 7 11 5 11 3 3 5 4 4 5 5 1 1 0 1
## 3 14 12 17 7 12 11 7 4 2 10 5 4 2 2 3 2 2 1 1
## 4 4 4 7 6 15 6 4 9 11 3 5 6 3 3 4 2 3 2 1
## 5 14 9 7 13 9 12 6 7 7 9 6 3 2 2 4 2 0 1 1
## 6 5 18 9 5 3 10 3 12 7 8 4 7 3 5 4 4 3 2 1tail(dat)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## 55 0 1 2 1 1 4 5 4 4 5 9 7 10 3 13 13 8 9 17 16 16
## 56 0 0 1 3 2 3 6 4 5 7 2 4 11 11 3 8 8 16 5 13 16
## 57 0 1 1 2 2 5 1 7 4 2 5 5 4 6 6 4 16 11 14 16 14
## 58 0 1 1 1 4 1 6 4 6 3 6 5 6 4 14 13 13 9 12 19 9
## 59 0 0 0 1 4 5 6 3 8 7 9 10 8 6 5 12 15 5 10 5 8
## 60 0 0 1 0 3 2 5 4 8 2 9 3 3 10 12 9 14 11 13 8 6
## V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39 V40
## 55 15 12 13 5 12 10 9 11 9 4 5 5 2 2 5 1 0 0 1
## 56 5 8 8 6 9 10 10 9 3 3 5 3 5 4 5 3 3 0 1
## 57 14 8 17 4 14 13 7 6 3 7 7 5 6 3 4 2 2 1 1
## 58 10 15 10 9 10 10 7 5 6 8 6 6 4 3 5 2 1 1 1
## 59 13 18 17 14 9 13 4 10 11 10 8 8 6 5 5 2 0 2 0
## 60 18 11 9 13 11 8 5 5 2 8 5 3 5 4 1 3 1 1 0mass <- 47.5
mass## [1] 47.5age <- 122
age## [1] 122mass <- mass * 2.0
mass## [1] 95age <- age - 20
age## [1] 102#Manipulating Data
class(dat)## [1] "data.frame"dim(dat)## [1] 60 40dat[1,1]## [1] 0View(dat)
#Middle Value in dat, row 30, column 20
dat[30,20]## [1] 16#Picking columns 10 and 20 from rows 1,3, and 5
dat[c(1,3,5), c(10,20)]## V10 V20
## 1 3 18
## 3 9 10
## 5 4 17#Choosing contiguous rows
1:5## [1] 1 2 3 4 53:12## [1] 3 4 5 6 7 8 9 10 11 12#From the dataset 'dat', choose the first 4 rows and the first 10 columns
dat[1:4, 1:10]## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
## 1 0 0 1 3 1 2 4 7 8 3
## 2 0 1 2 1 2 1 3 2 2 6
## 3 0 1 1 3 3 2 6 2 5 9
## 4 0 0 2 0 4 2 2 1 6 7#From the dataset 'dat', choose the rows 5 to 10 and the first 10 columns
dat[5:10, 1:10]## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
## 5 0 1 1 3 3 1 3 5 2 4
## 6 0 0 1 2 2 4 2 1 6 4
## 7 0 0 2 2 4 2 2 5 5 8
## 8 0 0 1 2 3 1 2 3 5 3
## 9 0 0 0 3 1 5 6 5 5 8
## 10 0 1 1 2 1 3 5 3 5 8#All the columns from row 5
all_columns_for_row_5 <- dat[5,]
#All rows from columns 16-18
all_rows_from_columns_16to18 <- dat[,16:18]
dat$V5## [1] 1 2 3 4 3 2 4 3 1 1 4 3 4 1 1 1 2 2 2 1 1 2 4 1 2 3 4 1 3 4 3 3 2 3 4 1 2 3
## [39] 2 1 2 3 4 1 3 4 4 1 2 3 1 3 4 1 1 2 2 4 4 3dat[,'V5']## [1] 1 2 3 4 3 2 4 3 1 1 4 3 4 1 1 1 2 2 2 1 1 2 4 1 2 3 4 1 3 4 3 3 2 3 4 1 2 3
## [39] 2 1 2 3 4 1 3 4 4 1 2 3 1 3 4 1 1 2 2 4 4 3#First row, all the columns
patient_1 <- dat[1,]
#Maximum inflammation value for Patient 1
max(patient_1)## [1] 18min(patient_1)## [1] 0#Maximum inflmmation for Patient 2
patient_2 <- dat[2,]
max(patient_2)## [1] 18max(dat[2,])## [1] 18#Minimum inflammation on Day 7
min(dat[,7])## [1] 1#Mean inflammation on day 7
mean(dat[,7])## [1] 3.8#Median inflammation on day 7
median(dat[,7])## [1] 4#Standard Deviation of inflammation on Day 7
sd(dat[,7])## [1] 1.725187mean(dat[1,])## Warning in mean.default(dat[1, ]): argument is not numeric or logical:
## returning NA## [1] NAmean(as.numeric(dat[1,]))## [1] 5.45#Summarize Function
summary(dat[,1:4])## V1 V2 V3 V4
## Min. :0 Min. :0.00 Min. :0.000 Min. :0.00
## 1st Qu.:0 1st Qu.:0.00 1st Qu.:1.000 1st Qu.:1.00
## Median :0 Median :0.00 Median :1.000 Median :2.00
## Mean :0 Mean :0.45 Mean :1.117 Mean :1.75
## 3rd Qu.:0 3rd Qu.:1.00 3rd Qu.:2.000 3rd Qu.:3.00
## Max. :0 Max. :1.00 Max. :2.000 Max. :3.00#Maximum Inflammation for all patients
max_inflammation_for_all_patients <- apply(dat,1,max)
max_inflammation_for_all_patients## [1] 18 18 19 17 17 18 17 20 17 18 18 18 17 16 17 18 19 19 17 19 19 16 17 15 17
## [26] 17 18 17 20 17 16 19 15 15 19 17 16 17 19 16 18 19 16 19 18 16 19 15 16 18
## [51] 14 20 17 15 17 16 17 19 18 18#Average Inflammation for each day
avg_day_inflammation <- apply(dat, 2, mean)
avg_day_inflammation## V1 V2 V3 V4 V5 V6 V7
## 0.0000000 0.4500000 1.1166667 1.7500000 2.4333333 3.1500000 3.8000000
## V8 V9 V10 V11 V12 V13 V14
## 3.8833333 5.2333333 5.5166667 5.9500000 5.9000000 8.3500000 7.7333333
## V15 V16 V17 V18 V19 V20 V21
## 8.3666667 9.5000000 9.5833333 10.6333333 11.5666667 12.3500000 13.2500000
## V22 V23 V24 V25 V26 V27 V28
## 11.9666667 11.0333333 10.1666667 10.0000000 8.6666667 9.1500000 7.2500000
## V29 V30 V31 V32 V33 V34 V35
## 7.3333333 6.5833333 6.0666667 5.9500000 5.1166667 3.6000000 3.3000000
## V36 V37 V38 V39 V40
## 3.5666667 2.4833333 1.5000000 1.1333333 0.5666667avg_patient_inflammation <- apply(dat,1, mean)
avg_patient_inflammation## [1] 5.450 5.425 6.100 5.900 5.550 6.225 5.975 6.650 6.625 6.525 6.775 5.800
## [13] 6.225 5.750 5.225 6.300 6.550 5.700 5.850 6.550 5.775 5.825 6.175 6.100
## [25] 5.800 6.425 6.050 6.025 6.175 6.550 6.175 6.350 6.725 6.125 7.075 5.725
## [37] 5.925 6.150 6.075 5.750 5.975 5.725 6.300 5.900 6.750 5.925 7.225 6.150
## [49] 5.950 6.275 5.700 6.100 6.825 5.975 6.725 5.700 6.250 6.400 7.050 5.900#Subsetting Character/String Data
animal <- c("m","o","n","k","e","y")
#First three characters
animal[1:3]## [1] "m" "o" "n"#Last three characters
animal[4:6]## [1] "k" "e" "y"animal[4:1]## [1] "k" "n" "o" "m"animal[-1:-4]## [1] "e" "y"animal[5:6]## [1] "e" "y"#Use a subset of animal to create a new character vector that spells the word "eon" i.e.c("e","o","n")
animal[c(5,2,3)]## [1] "e" "o" "n"#Creating Functions
fahrenheit_to_celsius <- function(temp_F){
temp_C <- (temp_F - 32) * 5 / 9
return(temp_C)
}
#Freezing Point of Water
fahrenheit_to_celsius(32)## [1] 0#Boiling Point of Water
fahrenheit_to_celsius(212)## [1] 100celsius_to_kelvin <- function(temp_C){
temp_K <- temp_C + 273.15
return(temp_K)
}
#Freezing Point of Water in kelvin
celsius_to_kelvin(0)## [1] 273.15#Convert value from Fahrenheit to Kelvin
fahrenheit_to_kelvin <- function(temp_F){
temp_C <- fahrenheit_to_celsius(temp_F)
temp_K <- celsius_to_kelvin(temp_C)
return(temp_K)
}
#Freezing Point of Water in Kelvin
fahrenheit_to_kelvin(32.0)## [1] 273.15#Freezing Point of Water in Fahrenheit
celsius_to_kelvin(fahrenheit_to_celsius(32.0))## [1] 273.15#Content and Wrapper Exercise
best_practice <- c('Write', "programs", "for", "people", "not", "computers")
asterisk <- "****"
highlight <- function(content, wrapper){
answer <- c(wrapper, content, wrapper)
return(answer)
}
highlight(best_practice, asterisk)## [1] "****" "Write" "programs" "for" "people" "not"
## [7] "computers" "****"#Exercise 6
dry_principle <- c("Don't", "repeat", "yourself", "or", "others")
edges <- function(v) {
first <- v[1]
last <- v[length(v)]
answer <- c(first, last)
return(answer)
}
edges(dry_principle)## [1] "Don't" "others"input_1 <- 20
mySum <- function(input_1, input_2 =10) {
output <- input_1 + input_2
return(output)
}
mySum(input_1 =1,3)## [1] 4center <- function(data, midpoint){
new_data <- (data - mean(data)) + midpoint
return(new_data)
}
z <- c(0,0,0,0)
z## [1] 0 0 0 0center(z,3)## [1] 3 3 3 3#Centering our dat dataset from day 4 around 0
centered <- center(dat[,4],0)
head(centered)## [1] 1.25 -0.75 1.25 -1.75 1.25 0.25#Original Mean
mean(dat[,4])## [1] 1.75#Centered Mean
mean(centered)## [1] 0#Original Standard Deviation
sd(dat[,4])## [1] 1.067628#Centered Standard Deviation
sd(centered)## [1] 1.067628#Difference in standard deviations before and after
sd(dat[,4]) - sd(centered)## [1] 0#New Data Object and set one value in Column 4 to NA
datNA <- dat
datNA[10,4] <- NA
#Return all NA Values
center(datNA[,4],0)## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA#Modified Function
center <- function(data, midpoint){
#return a new vector containing the original data centered around the midpoint
#Example : center(c(1,2,3),0) => c(-1,0,1)
new_data <- (data - mean(data, na.rm = TRUE)) + midpoint
return(new_data)
}
center(datNA[,4],0)## [1] 1.2542373 -0.7457627 1.2542373 -1.7457627 1.2542373 0.2542373
## [7] 0.2542373 0.2542373 1.2542373 NA -1.7457627 -1.7457627
## [13] -0.7457627 -1.7457627 -0.7457627 -1.7457627 -1.7457627 -0.7457627
## [19] -0.7457627 -1.7457627 1.2542373 1.2542373 1.2542373 -0.7457627
## [25] -0.7457627 -0.7457627 0.2542373 -0.7457627 0.2542373 -0.7457627
## [31] -1.7457627 1.2542373 0.2542373 -0.7457627 0.2542373 1.2542373
## [37] 0.2542373 0.2542373 1.2542373 1.2542373 0.2542373 1.2542373
## [43] 1.2542373 1.2542373 1.2542373 0.2542373 1.2542373 1.2542373
## [49] 1.2542373 0.2542373 -0.7457627 0.2542373 0.2542373 -0.7457627
## [55] -0.7457627 1.2542373 0.2542373 -0.7457627 -0.7457627 -1.7457627#What happens if the center function encounters factor or character variable?
datNA[,1] <- as.numeric(datNA[,1])
datNA[,2] <- as.numeric(datNA[,2])
center(datNA[,1],0)## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [39] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0center(datNA[,2],0)## [1] -0.45 0.55 0.55 -0.45 0.55 -0.45 -0.45 -0.45 -0.45 0.55 0.55 0.55
## [13] -0.45 -0.45 0.55 0.55 -0.45 -0.45 -0.45 0.55 0.55 -0.45 -0.45 0.55
## [25] 0.55 -0.45 -0.45 -0.45 -0.45 -0.45 0.55 -0.45 0.55 -0.45 -0.45 -0.45
## [37] 0.55 0.55 0.55 0.55 -0.45 -0.45 0.55 -0.45 0.55 0.55 -0.45 0.55
## [49] -0.45 -0.45 0.55 0.55 -0.45 -0.45 0.55 -0.45 0.55 0.55 -0.45 -0.45#Function to create graphs
analyze <- function(filename) {
#Plots the average, min, max inflammation over time.
#Input is character string of a csv file.
dat <- read.csv(file = filename, header = FALSE)
avg_day_inflammation <- apply(dat,2,mean)
plot(avg_day_inflammation)
max_day_inflammation <- apply(dat,2,max)
plot(max_day_inflammation)
min_day_inflammation <- apply(dat,2,min)
plot(min_day_inflammation)
}
analyze("data/inflammation-01.csv")
analyze("data/inflammation-02.csv")
#Rescale function. If L and H are the lowest and the highets values in the original vector, then the replacement value v should be (v-L)/(H-L)
rescale <- function(v){
#Rescale a vector, v to lie in the range 0 to 1
L <- min(v)
H <- max(v)
result <- (v-L)/(H-L)
return(result)
}
rescale(c(3,4,5,6))## [1] 0.0000000 0.3333333 0.6666667 1.0000000Plots
\(\alpha = \dfrac{1}{(1- \beta)^2}\)