Capstone Project
Nate Jack
11/3/2022
Loading and Importing Covid Data
Covid19 <- read.csv("COVID19.csv")
Covid19## dates rate_NHAsian rate_Hispanic rate_NHBlack rate_NHWhite Cases
## 1 01/01/2020 0.1 0.0 0.0 0.0 0.1
## 2 01/02/2020 0.1 0.0 0.0 0.0 0.1
## 3 01/03/2020 0.1 0.0 0.0 0.0 0.1
## 4 01/04/2020 0.1 0.0 0.0 0.0 0.1
## 5 01/05/2020 0.1 0.0 0.0 0.0 0.1
## 6 01/06/2020 0.1 0.0 0.0 0.0 0.1
## 7 01/07/2020 0.1 0.0 0.0 0.0 0.1
## 8 01/08/2020 0.1 0.0 0.0 0.0 0.1
## 9 01/09/2020 0.1 0.0 0.0 0.0 0.1
## 10 01/10/2020 0.1 0.0 0.0 0.0 0.1
## 11 01/11/2020 0.1 0.0 0.0 0.0 0.1
## 12 01/12/2020 0.1 0.0 0.0 0.0 0.1
## 13 01/13/2020 0.1 0.0 0.0 0.0 0.1
## 14 01/14/2020 0.1 0.0 0.0 0.0 0.1
## 15 01/15/2020 0.1 0.0 0.0 0.0 0.1
## 16 01/16/2020 0.1 0.0 0.0 0.0 0.1
## 17 01/17/2020 0.1 0.0 0.0 0.0 0.1
## 18 01/18/2020 0.1 0.0 0.0 0.0 0.1
## 19 01/19/2020 0.1 0.0 0.0 0.0 0.1
## 20 01/20/2020 0.1 0.0 0.0 0.0 0.1
## 21 01/21/2020 0.1 0.0 0.0 0.0 0.1
## 22 01/22/2020 0.1 0.0 0.0 0.0 0.1
## 23 01/23/2020 0.1 0.0 0.0 0.0 0.1
## 24 01/24/2020 0.1 0.0 0.0 0.0 0.1
## 25 01/25/2020 0.1 0.0 0.0 0.0 0.1
## 26 01/26/2020 0.1 0.0 0.0 0.0 0.1
## 27 01/27/2020 0.1 0.0 0.0 0.0 0.1
## 28 01/28/2020 0.1 0.0 0.0 0.0 0.1
## 29 01/29/2020 0.1 0.0 0.0 0.0 0.1
## 30 01/30/2020 0.1 0.0 0.0 0.0 0.1
## 31 01/31/2020 0.1 0.0 0.0 0.0 0.1
## 32 02/01/2020 0.1 0.0 0.0 0.0 0.1
## 33 02/02/2020 0.1 0.0 0.0 0.0 0.1
## 34 02/03/2020 0.1 0.0 0.0 0.0 0.1
## 35 02/04/2020 0.1 0.0 0.0 0.0 0.1
## 36 02/05/2020 0.1 0.0 0.0 0.0 0.1
## 37 02/06/2020 0.1 0.0 0.0 0.0 0.1
## 38 02/07/2020 0.1 0.0 0.0 0.0 0.1
## 39 02/08/2020 0.1 0.0 0.0 0.0 0.1
## 40 02/09/2020 0.1 0.0 0.0 0.0 0.1
## 41 02/10/2020 0.1 0.0 0.0 0.0 0.1
## 42 02/11/2020 0.1 0.0 0.0 0.0 0.1
## 43 02/12/2020 0.1 0.0 0.0 0.0 0.1
## 44 02/13/2020 0.1 0.0 0.0 0.0 0.1
## 45 02/14/2020 0.1 0.0 0.0 0.0 0.1
## 46 02/15/2020 0.1 0.0 0.0 0.0 0.1
## 47 02/16/2020 0.1 0.0 0.0 0.0 0.1
## 48 02/17/2020 0.1 0.0 0.0 0.0 0.1
## 49 02/18/2020 0.1 0.0 0.0 0.0 0.1
## 50 02/19/2020 0.1 0.0 0.0 0.0 0.1
## 51 02/20/2020 0.1 0.0 0.0 0.0 0.1
## 52 02/21/2020 0.1 0.0 0.0 0.0 0.1
## 53 02/22/2020 0.1 0.0 0.0 0.0 0.1
## 54 02/23/2020 0.1 0.0 0.0 0.0 0.1
## 55 02/24/2020 0.1 0.0 0.0 0.0 0.1
## 56 02/25/2020 0.1 0.0 0.0 0.0 0.1
## 57 02/26/2020 0.1 0.0 0.0 0.0 0.1
## 58 02/27/2020 0.1 0.0 0.0 0.0 0.1
## 59 02/28/2020 0.1 0.0 0.0 0.0 0.1
## 60 03/01/2020 11.6 0.0 0.0 0.0 11.6
## 61 03/02/2020 0.1 0.0 0.0 0.0 0.1
## 62 03/03/2020 0.1 0.0 0.0 0.0 0.1
## 63 03/04/2020 0.1 0.0 0.0 0.0 0.1
## 64 03/05/2020 0.1 0.0 0.0 0.0 0.1
## 65 03/06/2020 0.1 0.0 0.0 0.0 0.1
## 66 03/07/2020 0.0 0.0 0.0 0.2 0.2
## 67 03/08/2020 0.0 0.0 0.0 0.2 0.2
## 68 03/09/2020 0.0 0.0 0.0 0.2 0.2
## 69 03/10/2020 0.0 0.0 0.0 0.2 0.2
## 70 03/11/2020 0.7 0.0 0.0 0.4 1.1
## 71 03/12/2020 0.7 0.0 0.5 0.4 1.6
## 72 03/13/2020 0.7 0.0 0.5 0.4 1.6
## 73 03/14/2020 0.7 0.0 0.5 0.2 1.4
## 74 03/15/2020 0.7 0.0 0.5 0.7 1.9
## 75 03/16/2020 0.7 0.0 0.5 1.1 2.3
## 76 03/17/2020 1.3 0.5 1.1 1.5 4.4
## 77 03/18/2020 0.7 0.5 2.7 1.7 5.6
## 78 03/19/2020 0.7 0.5 3.7 2.2 7.1
## 79 03/20/2020 0.7 1.0 8.0 2.8 12.5
## 80 03/21/2020 1.3 1.0 9.1 3.1 14.5
## 81 03/22/2020 1.3 2.0 10.2 3.9 17.4
## 82 03/23/2020 1.3 2.9 11.8 3.7 19.7
## 83 03/24/2020 0.7 2.9 12.8 3.9 20.3
## 84 03/25/2020 0.7 3.4 12.8 5.7 22.6
## 85 03/26/2020 1.3 4.4 13.4 6.1 25.2
## 86 03/27/2020 2.6 5.4 10.7 6.6 25.3
## 87 03/28/2020 2.0 5.9 13.4 7.4 28.7
## 88 03/29/2020 2.6 7.4 16.6 6.8 33.4
## 89 03/30/2020 2.6 7.4 20.3 8.5 38.8
## 90 03/31/2020 2.6 9.3 19.8 9.2 40.9
## 91 04/01/2020 2.6 10.3 20.3 8.5 41.7
## 92 04/02/2020 2.6 12.8 23.0 9.0 47.4
## 93 04/03/2020 3.9 14.2 23.5 9.0 50.6
## 94 04/04/2020 3.9 17.2 24.1 8.5 53.7
## 95 04/05/2020 3.9 16.2 21.4 9.6 51.1
## 96 04/06/2020 5.2 20.6 21.4 10.1 57.3
## 97 04/07/2020 7.2 23.1 28.9 10.9 70.1
## 98 04/08/2020 7.8 30.9 35.3 11.4 85.4
## 99 04/09/2020 8.5 32.4 34.8 11.6 87.3
## 100 04/10/2020 7.2 31.4 35.8 12.5 86.9
## 101 04/11/2020 9.1 32.9 35.8 12.7 90.5
## 102 04/12/2020 9.1 33.9 38.5 13.6 95.1
## 103 04/13/2020 9.1 34.8 38.5 13.8 96.2
## 104 04/14/2020 8.5 32.9 35.3 15.3 92.0
## 105 04/15/2020 9.1 31.4 32.1 16.0 88.6
## 106 04/16/2020 7.8 36.3 34.2 18.2 96.5
## 107 04/17/2020 8.5 41.7 37.4 17.9 105.5
## 108 04/18/2020 6.5 42.7 34.8 17.7 101.7
## 109 04/19/2020 6.5 45.6 32.6 16.4 101.1
## 110 04/20/2020 5.9 43.7 32.6 16.2 98.4
## 111 04/21/2020 5.2 48.1 35.8 15.5 104.6
## 112 04/22/2020 5.9 47.6 36.4 14.0 103.9
## 113 04/23/2020 9.8 44.7 33.7 11.8 100.0
## 114 04/24/2020 8.5 48.1 30.0 12.0 98.6
## 115 04/25/2020 9.8 48.6 35.3 12.9 106.6
## 116 04/26/2020 9.8 47.6 37.4 13.3 108.1
## 117 04/27/2020 10.4 51.5 35.8 13.3 111.0
## 118 04/28/2020 12.4 54.5 33.7 13.1 113.7
## 119 04/29/2020 11.7 54.5 34.8 13.8 114.8
## 120 04/30/2020 8.5 53.5 34.2 14.2 110.4
## 121 05/01/2020 7.8 51.0 35.8 15.3 109.9
## 122 05/02/2020 7.2 49.6 31.0 16.0 103.8
## 123 05/03/2020 9.1 51.5 30.0 16.0 106.6
## 124 05/04/2020 9.8 51.0 28.3 16.2 105.3
## 125 05/05/2020 9.8 51.0 27.8 15.1 103.7
## 126 05/06/2020 11.7 55.5 23.5 14.9 105.6
## 127 05/07/2020 11.1 56.4 21.9 14.4 103.8
## 128 05/08/2020 11.7 56.9 22.5 12.7 103.8
## 129 05/09/2020 11.7 56.9 22.5 11.6 102.7
## 130 05/10/2020 9.8 55.5 22.5 10.3 98.1
## 131 05/11/2020 8.5 54.0 26.2 9.6 98.3
## 132 05/12/2020 6.5 50.6 23.0 8.1 88.2
## 133 05/13/2020 3.3 44.7 20.9 8.1 77.0
## 134 05/14/2020 3.9 43.7 19.8 6.8 74.2
## 135 05/15/2020 3.3 40.7 18.7 7.0 69.7
## 136 05/16/2020 2.6 41.2 17.7 6.8 68.3
## 137 05/17/2020 2.0 40.7 16.0 6.6 65.3
## 138 05/18/2020 3.3 40.2 12.8 5.5 61.8
## 139 05/19/2020 4.6 39.8 12.3 6.1 62.8
## 140 05/20/2020 5.9 38.8 13.9 7.0 65.6
## 141 05/21/2020 6.5 38.3 17.1 7.7 69.6
## 142 05/22/2020 6.5 36.3 15.0 7.7 65.5
## 143 05/23/2020 8.5 31.4 15.0 7.0 61.9
## 144 05/24/2020 9.1 29.4 16.6 7.0 62.1
## 145 05/25/2020 7.8 30.4 15.0 6.8 60.0
## 146 05/26/2020 6.5 30.4 15.0 6.8 58.7
## 147 05/27/2020 5.9 29.4 14.4 5.0 54.7
## 148 05/28/2020 5.2 25.5 10.7 5.0 46.4
## 149 05/29/2020 5.2 25.0 9.1 3.7 43.0
## 150 05/30/2020 3.3 27.5 8.6 3.7 43.1
## 151 05/31/2020 2.6 25.5 7.0 3.9 39.0
## 152 06/01/2020 3.3 25.0 7.0 3.7 39.0
## 153 06/02/2020 3.3 24.5 6.4 3.5 37.7
## 154 06/03/2020 3.3 24.5 6.4 3.5 37.7
## 155 06/04/2020 3.3 28.5 7.0 2.6 41.4
## 156 06/05/2020 3.3 27.0 8.0 3.1 41.4
## 157 06/06/2020 3.3 27.0 8.6 3.1 42.0
## 158 06/07/2020 3.9 29.4 7.5 2.6 43.4
## 159 06/08/2020 2.6 26.0 8.6 2.8 40.0
## 160 06/09/2020 3.3 21.6 7.0 2.4 34.3
## 161 06/10/2020 3.3 21.6 5.3 2.0 32.2
## 162 06/11/2020 2.6 18.6 5.3 3.3 29.8
## 163 06/12/2020 3.3 20.6 4.8 2.8 31.5
## 164 06/13/2020 3.9 18.2 4.3 2.8 29.2
## 165 06/14/2020 3.3 16.7 3.7 3.1 26.8
## 166 06/15/2020 3.3 16.2 1.6 2.4 23.5
## 167 06/16/2020 2.0 18.2 2.7 2.6 25.5
## 168 06/17/2020 1.3 16.7 3.2 2.6 23.8
## 169 06/18/2020 1.3 17.7 3.2 1.5 23.7
## 170 06/19/2020 0.7 15.7 2.7 1.5 20.6
## 171 06/20/2020 0.7 17.7 2.1 1.5 22.0
## 172 06/21/2020 0.7 17.2 2.1 1.7 21.7
## 173 06/22/2020 0.7 16.2 2.7 2.6 22.2
## 174 06/23/2020 0.7 13.7 1.6 2.8 18.8
## 175 06/24/2020 1.3 11.8 1.6 2.8 17.5
## 176 06/25/2020 2.0 10.8 1.6 3.1 17.5
## 177 06/26/2020 2.0 10.3 1.6 2.8 16.7
## 178 06/27/2020 1.3 8.8 1.6 2.8 14.5
## 179 06/28/2020 1.3 9.3 2.1 2.4 15.1
## 180 06/29/2020 2.0 9.3 3.2 2.2 16.7
## 181 06/30/2020 2.0 11.8 3.2 2.0 19.0
## 182 07/01/2020 1.3 11.3 3.2 2.0 17.8
## 183 07/02/2020 0.7 10.8 2.7 2.2 16.4
## 184 07/03/2020 0.7 11.8 4.3 2.6 19.4
## 185 07/04/2020 2.0 11.3 4.8 2.8 20.9
## 186 07/05/2020 2.0 11.3 5.9 2.8 22.0
## 187 07/06/2020 2.0 11.3 4.3 2.4 20.0
## 188 07/07/2020 2.0 7.9 4.8 2.0 16.7
## 189 07/08/2020 2.0 8.8 4.8 2.2 17.8
## 190 07/09/2020 2.0 8.3 4.8 1.5 16.6
## 191 07/10/2020 2.0 9.3 3.7 1.5 16.5
## 192 07/11/2020 0.7 10.3 3.7 1.5 16.2
## 193 07/12/2020 0.7 9.8 2.7 2.0 15.2
## 194 07/13/2020 0.0 11.8 3.7 1.7 17.2
## 195 07/14/2020 0.7 13.7 4.8 1.7 20.9
## 196 07/15/2020 0.7 13.3 4.3 1.7 20.0
## 197 07/16/2020 0.7 14.2 5.3 1.7 21.9
## 198 07/17/2020 2.6 11.3 4.8 1.5 20.2
## 199 07/18/2020 2.6 11.8 5.9 2.0 22.3
## 200 07/19/2020 2.6 12.3 6.4 1.7 23.0
## 201 07/20/2020 3.3 11.3 7.0 2.0 23.6
## 202 07/21/2020 2.6 10.8 7.0 2.2 22.6
## 203 07/22/2020 2.6 11.8 7.0 1.7 23.1
## 204 07/23/2020 3.3 9.3 5.9 2.4 20.9
## 205 07/24/2020 1.3 10.8 6.4 2.8 21.3
## 206 07/25/2020 1.3 9.3 7.5 2.4 20.5
## 207 07/26/2020 1.3 9.3 7.0 2.4 20.0
## 208 07/27/2020 0.7 9.3 6.4 2.6 19.0
## 209 07/28/2020 0.7 8.8 5.9 3.3 18.7
## 210 07/29/2020 0.7 7.9 6.4 3.7 18.7
## 211 07/30/2020 0.7 9.3 8.0 3.3 21.3
## 212 07/31/2020 0.7 8.3 7.5 2.8 19.3
## 213 08/01/2020 0.7 9.3 4.8 2.8 17.6
## 214 08/02/2020 0.7 8.8 5.3 2.8 17.6
## 215 08/03/2020 0.7 7.9 4.8 2.8 16.2
## 216 08/04/2020 0.7 8.3 4.3 2.8 16.1
## 217 08/05/2020 2.0 9.8 4.3 2.4 18.5
## 218 08/06/2020 1.3 9.3 3.2 2.2 16.0
## 219 08/07/2020 2.6 9.3 3.7 2.6 18.2
## 220 08/08/2020 3.3 8.3 5.3 2.4 19.3
## 221 08/09/2020 3.3 8.8 5.9 2.2 20.2
## 222 08/10/2020 3.3 8.8 5.9 2.0 20.0
## 223 08/11/2020 3.3 8.3 5.3 1.1 18.0
## 224 08/12/2020 2.0 7.4 7.5 1.3 18.2
## 225 08/13/2020 2.0 7.4 7.5 1.3 18.2
## 226 08/14/2020 1.3 6.9 8.0 1.1 17.3
## 227 08/15/2020 0.7 6.4 6.4 0.9 14.4
## 228 08/16/2020 0.7 5.9 5.3 0.9 12.8
## 229 08/17/2020 0.7 4.9 4.8 1.3 11.7
## 230 08/18/2020 0.7 5.4 6.4 1.7 14.2
## 231 08/19/2020 0.7 5.4 5.3 2.2 13.6
## 232 08/20/2020 0.7 6.4 4.8 2.4 14.3
## 233 08/21/2020 0.0 6.9 4.8 2.0 13.7
## 234 08/22/2020 0.0 7.4 5.3 2.4 15.1
## 235 08/23/2020 0.0 6.9 4.8 2.8 14.5
## 236 08/24/2020 0.7 6.9 5.9 2.4 15.9
## 237 08/25/2020 1.3 6.4 4.8 2.2 14.7
## 238 08/26/2020 1.3 6.4 3.7 1.5 12.9
## 239 08/27/2020 1.3 5.9 5.3 1.5 14.0
## 240 08/28/2020 1.3 6.4 5.3 2.0 15.0
## 241 08/29/2020 1.3 8.3 5.3 2.0 16.9
## 242 08/30/2020 1.3 8.3 7.0 1.7 18.3
## 243 08/31/2020 0.7 7.9 7.5 1.7 17.8
## 244 09/01/2020 0.7 7.4 7.5 2.0 17.6
## 245 09/02/2020 1.3 8.3 8.0 2.4 20.0
## 246 09/03/2020 1.3 8.3 7.5 2.6 19.7
## 247 09/04/2020 1.3 7.4 9.1 2.4 20.2
## 248 09/05/2020 1.3 5.9 9.6 2.0 18.8
## 249 09/06/2020 1.3 5.4 8.6 2.0 17.3
## 250 09/07/2020 2.0 5.9 8.6 1.7 18.2
## 251 09/08/2020 2.0 6.4 8.6 1.3 18.3
## 252 09/09/2020 1.3 4.9 10.7 1.1 18.0
## 253 09/10/2020 1.3 3.4 10.2 0.7 15.6
## 254 09/11/2020 2.0 3.9 8.6 0.4 14.9
## 255 09/12/2020 2.0 3.4 7.5 0.7 13.6
## 256 09/13/2020 2.6 4.4 7.5 0.9 15.4
## 257 09/14/2020 2.0 4.9 6.4 1.1 14.4
## 258 09/15/2020 1.3 5.4 5.9 1.3 13.9
## 259 09/16/2020 2.0 5.4 3.2 1.1 11.7
## 260 09/17/2020 2.0 7.4 3.2 1.1 13.7
## 261 09/18/2020 1.3 7.4 2.1 1.7 12.5
## 262 09/19/2020 1.3 7.4 3.2 2.4 14.3
## 263 09/20/2020 0.7 6.9 2.7 2.2 12.5
## 264 09/21/2020 0.7 7.4 2.1 2.8 13.0
## 265 09/22/2020 0.7 8.3 2.7 3.1 14.8
## 266 09/23/2020 0.0 8.3 2.1 3.3 13.7
## 267 09/24/2020 0.0 7.9 1.6 3.7 13.2
## 268 09/25/2020 0.7 7.4 3.2 3.3 14.6
## 269 09/26/2020 0.7 7.9 2.1 3.5 14.2
## 270 09/27/2020 1.3 9.3 2.1 3.5 16.2
## 271 09/28/2020 1.3 8.8 3.7 3.1 16.9
## 272 09/29/2020 2.0 6.9 3.7 3.5 16.1
## 273 09/30/2020 2.0 6.4 4.8 3.7 16.9
## 274 10/01/2020 2.0 6.9 4.8 3.5 17.2
## 275 10/02/2020 1.3 9.3 3.7 3.3 17.6
## 276 10/03/2020 1.3 11.3 4.3 2.8 19.7
## 277 10/04/2020 1.3 9.8 4.3 2.6 18.0
## 278 10/05/2020 2.0 11.3 2.7 2.6 18.6
## 279 10/06/2020 1.3 11.3 2.1 2.0 16.7
## 280 10/07/2020 1.3 12.8 3.2 1.5 18.8
## 281 10/08/2020 2.0 12.3 3.7 1.3 19.3
## 282 10/09/2020 2.6 10.8 5.3 1.3 20.0
## 283 10/10/2020 2.6 8.8 5.9 0.9 18.2
## 284 10/11/2020 2.6 9.8 7.0 1.1 20.5
## 285 10/12/2020 2.6 9.3 8.0 1.1 21.0
## 286 10/13/2020 3.3 10.3 8.6 1.5 23.7
## 287 10/14/2020 3.3 10.8 7.0 1.7 22.8
## 288 10/15/2020 2.6 9.8 7.0 3.1 22.5
## 289 10/16/2020 2.0 8.8 4.8 3.3 18.9
## 290 10/17/2020 2.0 8.8 3.7 3.5 18.0
## 291 10/18/2020 1.3 8.8 3.7 3.7 17.5
## 292 10/19/2020 1.3 7.9 3.7 4.4 17.3
## 293 10/20/2020 0.7 8.8 3.7 3.9 17.1
## 294 10/21/2020 1.3 8.8 4.8 3.9 18.8
## 295 10/22/2020 1.3 10.8 5.9 3.5 21.5
## 296 10/23/2020 2.0 10.8 6.4 4.2 23.4
## 297 10/24/2020 2.0 10.8 6.4 3.9 23.1
## 298 10/25/2020 2.6 11.8 5.9 4.4 24.7
## 299 10/26/2020 2.0 12.3 5.3 3.9 23.5
## 300 10/27/2020 2.0 12.8 5.9 4.2 24.9
## 301 10/28/2020 2.0 14.7 4.8 4.8 26.3
## 302 10/29/2020 2.0 12.8 5.3 4.2 24.3
## 303 10/30/2020 2.0 14.7 5.9 3.7 26.3
## 304 10/31/2020 2.6 16.2 7.0 4.4 30.2
## 305 11/01/2020 2.0 17.2 7.5 3.9 30.6
## 306 11/02/2020 3.3 19.1 8.6 3.9 34.9
## 307 11/03/2020 3.3 19.6 8.0 4.4 35.3
## 308 11/04/2020 3.9 17.7 8.6 4.6 34.8
## 309 11/05/2020 5.2 19.6 7.5 5.0 37.3
## 310 11/06/2020 5.2 19.1 9.1 5.9 39.3
## 311 11/07/2020 4.6 20.6 9.6 5.2 40.0
## 312 11/08/2020 5.9 18.6 9.6 5.2 39.3
## 313 11/09/2020 5.2 19.6 10.2 5.5 40.5
## 314 11/10/2020 6.5 18.2 10.2 5.9 40.8
## 315 11/11/2020 6.5 22.1 11.8 6.6 47.0
## 316 11/12/2020 5.9 23.1 14.4 8.3 51.7
## 317 11/13/2020 5.2 24.0 12.8 8.3 50.3
## 318 11/14/2020 5.2 25.5 12.8 9.0 52.5
## 319 11/15/2020 5.9 27.0 12.8 9.8 55.5
## 320 11/16/2020 5.9 25.0 15.0 10.1 56.0
## 321 11/17/2020 4.6 25.5 17.1 10.7 57.9
## 322 11/18/2020 3.3 24.5 15.5 11.6 54.9
## 323 11/19/2020 5.2 23.1 16.6 10.9 55.8
## 324 11/20/2020 7.8 24.0 17.1 10.9 59.8
## 325 11/21/2020 9.1 21.6 17.7 10.7 59.1
## 326 11/22/2020 7.2 21.1 18.2 11.2 57.7
## 327 11/23/2020 7.2 21.6 17.1 13.8 59.7
## 328 11/24/2020 9.8 21.6 15.0 13.6 60.0
## 329 11/25/2020 10.4 21.1 15.5 12.9 59.9
## 330 11/26/2020 8.5 23.1 11.2 12.2 55.0
## 331 11/27/2020 7.2 25.0 12.3 13.1 57.6
## 332 11/28/2020 7.8 29.0 10.7 14.4 61.9
## 333 11/29/2020 8.5 28.5 10.2 13.6 60.8
## 334 11/30/2020 8.5 29.0 9.1 12.2 58.8
## 335 12/01/2020 5.9 31.4 11.8 13.6 62.7
## 336 12/02/2020 6.5 30.4 15.0 12.7 64.6
## 337 12/03/2020 5.9 30.9 16.6 14.9 68.3
## 338 12/04/2020 5.9 26.0 16.0 14.0 61.9
## 339 12/05/2020 3.9 22.6 19.3 12.9 58.7
## 340 12/06/2020 3.9 21.6 20.3 13.1 58.9
## 341 12/07/2020 4.6 19.6 19.8 12.0 56.0
## 342 12/08/2020 6.5 18.6 18.2 11.4 54.7
## 343 12/09/2020 5.2 19.1 17.1 12.9 54.3
## 344 12/10/2020 5.9 19.1 17.7 11.2 53.9
## 345 12/11/2020 5.9 21.1 18.7 11.6 57.3
## 346 12/12/2020 7.2 21.6 18.7 12.0 59.5
## 347 12/13/2020 7.2 26.0 20.3 12.7 66.2
## 348 12/14/2020 6.5 29.4 20.9 14.0 70.8
## 349 12/15/2020 7.8 29.0 24.1 13.3 74.2
## 350 12/16/2020 7.8 27.0 20.3 12.7 67.8
## 351 12/17/2020 7.2 27.5 18.2 13.1 66.0
## 352 12/18/2020 6.5 29.9 16.0 13.6 66.0
## 353 12/19/2020 5.2 29.4 15.0 12.7 62.3
## 354 12/20/2020 4.6 28.0 12.3 13.6 58.5
## 355 12/21/2020 3.9 26.5 16.6 12.2 59.2
## 356 12/22/2020 2.0 27.0 17.1 14.2 60.3
## 357 12/23/2020 2.6 27.0 19.8 13.3 62.7
## 358 12/24/2020 2.6 26.0 23.0 13.6 65.2
## 359 12/25/2020 2.0 22.1 23.0 12.2 59.3
## 360 12/26/2020 3.3 22.6 24.1 13.3 63.3
## 361 12/27/2020 3.3 23.6 23.5 11.8 62.2
## 362 12/28/2020 3.3 26.0 19.3 12.7 61.3
## 363 12/29/2020 3.3 26.0 16.0 11.4 56.7
## 364 12/30/2020 2.6 29.9 15.5 13.6 61.6
## 365 12/31/2020 4.6 29.4 12.8 12.2 59.0
## 366 01/01/2021 5.9 30.9 16.6 14.2 67.6
## 367 01/02/2021 5.9 30.4 15.0 14.7 66.0
## 368 01/03/2021 7.2 29.0 16.6 16.8 69.6
## 369 01/04/2021 7.2 30.4 19.8 18.2 75.6
## 370 01/05/2021 5.9 33.9 19.8 18.6 78.2
## 371 01/06/2021 6.5 30.9 18.7 17.7 73.8
## 372 01/07/2021 4.6 30.4 18.7 18.4 72.1
## 373 01/08/2021 3.3 31.4 18.2 17.3 70.2
## 374 01/09/2021 2.6 30.4 19.3 15.7 68.0
## 375 01/10/2021 2.6 30.9 19.8 14.9 68.2
## 376 01/11/2021 4.6 26.0 18.2 13.3 62.1
## 377 01/12/2021 5.2 24.5 18.2 12.5 60.4
## 378 01/13/2021 5.9 25.5 18.2 12.0 61.6
## 379 01/14/2021 5.9 28.5 18.7 13.1 66.2
## 380 01/15/2021 6.5 27.0 17.7 12.7 63.9
## 381 01/16/2021 5.9 29.4 16.0 14.0 65.3
## 382 01/17/2021 5.2 27.5 16.0 12.2 60.9
## 383 01/18/2021 3.3 28.5 13.4 12.9 58.1
## 384 01/19/2021 4.6 27.0 16.0 13.3 60.9
## 385 01/20/2021 3.3 24.0 17.1 13.1 57.5
## 386 01/21/2021 5.2 23.6 18.7 13.8 61.3
## 387 01/22/2021 6.5 24.5 17.1 14.2 62.3
## 388 01/23/2021 7.2 21.1 15.5 15.1 58.9
## 389 01/24/2021 6.5 20.6 13.4 15.3 55.8
## 390 01/25/2021 9.1 20.1 12.8 14.2 56.2
## 391 01/26/2021 7.8 19.6 9.6 13.6 50.6
## 392 01/27/2021 9.1 19.6 9.6 13.8 52.1
## 393 01/28/2021 7.2 16.7 9.6 11.8 45.3
## 394 01/29/2021 5.2 13.7 11.2 11.6 41.7
## 395 01/30/2021 5.2 14.2 11.8 9.8 41.0
## 396 01/31/2021 5.2 12.8 11.2 9.2 38.4
## 397 02/01/2021 2.6 12.8 12.8 8.5 36.7
## 398 02/02/2021 2.0 11.3 13.9 7.9 35.1
## 399 02/03/2021 0.7 14.7 12.8 7.2 35.4
## 400 02/04/2021 0.7 15.7 11.8 7.4 35.6
## 401 02/05/2021 1.3 18.6 10.2 7.9 38.0
## 402 02/06/2021 1.3 17.7 12.8 7.9 39.7
## 403 02/07/2021 2.0 18.2 13.9 8.3 42.4
## 404 02/08/2021 2.0 16.2 12.3 8.1 38.6
## 405 02/09/2021 2.0 14.2 10.2 8.1 34.5
## 406 02/10/2021 2.0 10.8 9.6 7.4 29.8
## 407 02/11/2021 2.0 8.8 9.6 6.3 26.7
## 408 02/12/2021 1.3 8.3 9.1 5.2 23.9
## 409 02/13/2021 2.0 8.8 7.0 4.8 22.6
## 410 02/14/2021 1.3 8.8 6.4 4.4 20.9
## 411 02/15/2021 1.3 8.8 7.0 4.2 21.3
## 412 02/16/2021 1.3 9.8 9.1 3.7 23.9
## 413 02/17/2021 2.0 11.8 10.7 4.2 28.7
## 414 02/18/2021 2.0 10.8 11.2 3.7 27.7
## 415 02/19/2021 2.6 10.3 10.7 3.5 27.1
## 416 02/20/2021 1.3 9.8 9.6 3.3 24.0
## 417 02/21/2021 1.3 9.3 11.8 3.5 25.9
## 418 02/22/2021 2.0 8.3 10.7 4.4 25.4
## 419 02/23/2021 2.0 6.9 9.6 4.4 22.9
## 420 02/24/2021 1.3 3.9 9.6 4.2 19.0
## 421 02/25/2021 1.3 4.4 9.1 4.6 19.4
## 422 02/26/2021 0.7 4.9 10.2 4.8 20.6
## 423 02/27/2021 0.7 5.4 12.3 5.0 23.4
## 424 02/28/2021 1.3 6.4 10.2 4.8 22.7
## 425 03/01/2021 0.7 7.9 10.7 4.2 23.5
## 426 03/02/2021 0.7 9.8 10.2 4.4 25.1
## 427 03/03/2021 0.7 10.3 8.6 4.2 23.8
## 428 03/04/2021 1.3 9.3 8.6 4.6 23.8
## 429 03/05/2021 1.3 7.4 7.5 3.7 19.9
## 430 03/06/2021 1.3 7.9 7.5 3.7 20.4
## 431 03/07/2021 0.7 7.4 7.5 3.7 19.3
## 432 03/08/2021 0.7 6.9 8.0 3.1 18.7
## 433 03/09/2021 0.7 5.4 8.0 2.6 16.7
## 434 03/10/2021 1.3 6.4 9.6 2.2 19.5
## 435 03/11/2021 1.3 7.9 11.8 1.5 22.5
## 436 03/12/2021 1.3 7.9 13.4 1.5 24.1
## 437 03/13/2021 1.3 6.4 12.8 1.1 21.6
## 438 03/14/2021 1.3 6.4 12.8 1.1 21.6
## 439 03/15/2021 1.3 7.4 12.8 1.3 22.8
## 440 03/16/2021 1.3 8.3 12.8 1.1 23.5
## 441 03/17/2021 0.7 7.9 13.9 1.1 23.6
## 442 03/18/2021 0.0 7.9 11.2 1.1 20.2
## 443 03/19/2021 0.0 7.4 10.7 1.3 19.4
## 444 03/20/2021 0.0 8.8 10.7 1.7 21.2
## 445 03/21/2021 0.0 8.3 12.3 2.8 23.4
## 446 03/22/2021 0.0 7.4 13.4 3.7 24.5
## 447 03/23/2021 2.0 7.4 13.9 4.2 27.5
## 448 03/24/2021 2.0 7.4 11.8 4.4 25.6
## 449 03/25/2021 2.6 7.9 11.2 4.6 26.3
## 450 03/26/2021 2.6 10.3 9.6 4.6 27.1
## 451 03/27/2021 3.3 8.8 9.1 4.2 25.4
## 452 03/28/2021 3.3 8.8 8.6 3.5 24.2
## 453 03/29/2021 3.3 10.3 7.5 2.6 23.7
## 454 03/30/2021 1.3 9.8 5.9 2.8 19.8
## 455 03/31/2021 1.3 10.3 5.3 3.7 20.6
## 456 04/01/2021 0.7 9.8 5.3 3.5 19.3
## 457 04/02/2021 1.3 7.9 8.0 3.7 20.9
## 458 04/03/2021 0.7 7.9 8.0 4.6 21.2
## 459 04/04/2021 0.7 7.9 8.6 4.2 21.4
## 460 04/05/2021 0.7 5.4 9.1 4.4 19.6
## 461 04/06/2021 1.3 5.4 11.8 3.9 22.4
## 462 04/07/2021 1.3 4.4 11.8 2.8 20.3
## 463 04/08/2021 1.3 3.9 11.2 2.6 19.0
## 464 04/09/2021 1.3 3.4 11.8 3.1 19.6
## 465 04/10/2021 1.3 4.4 10.7 2.6 19.0
## 466 04/11/2021 1.3 5.4 9.6 2.6 18.9
## 467 04/12/2021 1.3 7.4 9.1 2.6 20.4
## 468 04/13/2021 0.7 7.4 8.6 2.4 19.1
## 469 04/14/2021 0.7 7.4 9.6 2.6 20.3
## 470 04/15/2021 0.7 7.4 10.7 2.6 21.4
## 471 04/16/2021 0.0 7.4 8.0 1.7 17.1
## 472 04/17/2021 0.0 6.4 9.1 1.5 17.0
## 473 04/18/2021 0.0 6.9 9.1 1.5 17.5
## 474 04/19/2021 0.0 6.4 9.6 1.3 17.3
## 475 04/20/2021 0.0 7.4 8.6 1.5 17.5
## 476 04/21/2021 0.0 7.9 8.0 1.1 17.0
## 477 04/22/2021 0.0 6.9 8.0 1.3 16.2
## 478 04/23/2021 0.0 6.4 8.0 1.5 15.9
## 479 04/24/2021 0.0 5.9 8.0 1.3 15.2
## 480 04/25/2021 0.0 6.4 9.6 1.1 17.1
## 481 04/26/2021 0.0 4.9 9.6 1.1 15.6
## 482 04/27/2021 0.0 3.4 8.6 0.9 12.9
## 483 04/28/2021 0.0 3.4 9.6 1.3 14.3
## 484 04/29/2021 0.0 3.4 9.6 1.5 14.5
## 485 04/30/2021 0.0 3.9 10.2 1.3 15.4
## 486 05/01/2021 0.0 4.4 9.6 1.7 15.7
## 487 05/02/2021 0.0 3.4 8.0 2.0 13.4
## 488 05/03/2021 0.0 4.4 6.4 2.2 13.0
## 489 05/04/2021 0.0 3.9 6.4 2.8 13.1
## 490 05/05/2021 0.0 2.9 7.0 2.6 12.5
## 491 05/06/2021 0.0 4.4 5.9 2.2 12.5
## 492 05/07/2021 0.0 4.4 5.9 2.2 12.5
## 493 05/08/2021 0.0 4.4 6.4 1.7 12.5
## 494 05/09/2021 0.0 3.4 5.9 1.5 10.8
## 495 05/10/2021 0.0 2.5 6.4 1.7 10.6
## 496 05/11/2021 0.0 3.4 7.5 1.3 12.2
## 497 05/12/2021 0.0 3.4 4.8 2.0 10.2
## 498 05/13/2021 0.0 3.4 4.8 2.0 10.2
## 499 05/14/2021 0.0 3.4 4.8 2.0 10.2
## 500 05/15/2021 0.0 2.9 4.8 2.8 10.5
## 501 05/16/2021 0.0 3.4 4.3 3.1 10.8
## 502 05/17/2021 0.0 4.4 4.8 2.6 11.8
## 503 05/18/2021 0.0 3.4 4.3 2.2 9.9
## 504 05/19/2021 0.0 3.9 5.3 1.5 10.7
## 505 05/20/2021 0.0 2.9 4.8 1.7 9.4
## 506 05/21/2021 0.0 2.9 3.7 1.7 8.3
## 507 05/22/2021 0.0 2.9 2.7 0.9 6.5
## 508 05/23/2021 0.0 3.4 2.7 0.7 6.8
## 509 05/24/2021 0.0 3.4 1.6 0.4 5.4
## 510 05/25/2021 0.0 4.9 1.1 0.4 6.4
## 511 05/26/2021 0.0 5.4 0.0 0.2 5.6
## 512 05/27/2021 0.0 5.4 0.5 0.2 6.1
## 513 05/28/2021 0.0 4.9 2.1 0.2 7.2
## 514 05/29/2021 0.0 5.4 2.1 0.2 7.7
## 515 05/30/2021 0.0 4.4 2.1 0.2 6.7
## 516 05/31/2021 0.0 3.4 2.1 0.4 5.9
## 517 06/01/2021 0.0 2.5 2.7 0.4 5.6
## 518 06/02/2021 0.0 2.5 2.7 0.4 5.6
## 519 06/03/2021 0.0 2.0 2.7 0.2 4.9
## 520 06/04/2021 0.0 2.0 2.1 0.2 4.3
## 521 06/05/2021 0.0 1.5 2.1 0.2 3.8
## 522 06/06/2021 0.0 2.0 2.7 0.2 4.9
## 523 06/07/2021 0.0 2.9 2.7 0.2 5.8
## 524 06/08/2021 0.0 2.5 2.1 0.2 4.8
## 525 06/09/2021 0.0 2.9 2.1 0.4 5.4
## 526 06/10/2021 0.0 2.9 1.6 0.4 4.9
## 527 06/11/2021 0.0 2.9 0.5 0.4 3.8
## 528 06/12/2021 0.0 3.4 1.1 0.7 5.2
## 529 06/13/2021 0.0 2.9 0.5 0.7 4.1
## 530 06/14/2021 0.0 2.0 0.5 0.4 2.9
## 531 06/15/2021 0.0 2.5 0.5 0.4 3.4
## 532 06/16/2021 0.0 1.0 1.1 0.2 2.3
## 533 06/17/2021 0.0 1.0 1.1 0.2 2.3
## 534 06/18/2021 0.0 1.5 1.1 0.2 2.8
## 535 06/19/2021 0.0 1.0 0.5 0.0 1.5
## 536 06/20/2021 0.0 1.0 1.1 0.0 2.1
## 537 06/21/2021 0.0 1.5 1.1 0.0 2.6
## 538 06/22/2021 0.0 1.0 1.1 0.0 2.1
## 539 06/23/2021 0.0 1.0 0.5 0.0 1.5
## 540 06/24/2021 0.0 1.5 0.5 0.2 2.2
## 541 06/25/2021 0.0 1.0 0.5 0.2 1.7
## 542 06/26/2021 0.0 1.0 0.5 0.2 1.7
## 543 06/27/2021 0.0 1.0 0.0 0.2 1.2
## 544 06/28/2021 0.0 0.5 0.0 0.4 0.9
## 545 06/29/2021 0.0 1.0 0.0 0.4 1.4
## 546 06/30/2021 0.0 1.0 0.0 0.4 1.4
## 547 07/01/2021 0.0 0.5 0.0 0.4 0.9
## 548 07/02/2021 0.0 0.5 0.0 0.4 0.9
## 549 07/03/2021 0.0 0.5 0.0 0.4 0.9
## 550 07/04/2021 0.0 0.5 0.0 0.4 0.9
## 551 07/05/2021 0.0 0.5 0.5 0.2 1.2
## 552 07/06/2021 0.0 0.5 0.5 0.2 1.2
## 553 07/07/2021 0.0 1.0 0.5 0.2 1.7
## 554 07/08/2021 0.0 1.0 0.5 0.0 1.5
## 555 07/09/2021 0.0 1.0 1.1 0.0 2.1
## 556 07/10/2021 0.0 1.0 1.1 0.4 2.5
## 557 07/11/2021 0.0 1.0 1.6 0.7 3.3
## 558 07/12/2021 0.0 1.0 1.1 0.7 2.8
## 559 07/13/2021 0.0 1.5 1.1 0.9 3.5
## 560 07/14/2021 0.0 1.5 1.1 0.9 3.5
## 561 07/15/2021 0.0 2.0 2.7 1.1 5.8
## 562 07/16/2021 0.0 2.5 2.1 1.1 5.7
## 563 07/17/2021 0.0 2.5 2.1 0.7 5.3
## 564 07/18/2021 0.0 2.9 2.1 0.7 5.7
## 565 07/19/2021 0.0 2.9 2.1 0.7 5.7
## 566 07/20/2021 0.0 2.5 2.1 0.7 5.3
## 567 07/21/2021 0.0 2.0 2.7 0.9 5.6
## 568 07/22/2021 0.0 1.5 1.1 0.9 3.5
## 569 07/23/2021 0.0 1.0 1.6 0.9 3.5
## 570 07/24/2021 0.0 1.5 2.1 1.1 4.7
## 571 07/25/2021 0.0 1.0 2.7 0.9 4.6
## 572 07/26/2021 0.0 2.0 3.2 1.1 6.3
## 573 07/27/2021 0.0 2.0 3.2 1.1 6.3
## 574 07/28/2021 0.0 2.5 5.3 0.9 8.7
## 575 07/29/2021 0.7 2.5 6.4 1.5 11.1
## 576 07/30/2021 0.7 2.9 6.4 1.7 11.7
## 577 07/31/2021 0.7 2.9 6.4 1.7 11.7
## 578 08/01/2021 0.7 3.4 5.9 1.7 11.7
## 579 08/02/2021 0.7 2.5 7.5 2.2 12.9
## 580 08/03/2021 0.7 2.9 7.5 2.2 13.3
## 581 08/04/2021 0.7 2.5 5.3 2.8 11.3
## 582 08/05/2021 0.0 3.4 4.3 2.0 9.7
## 583 08/06/2021 0.0 3.4 5.9 2.4 11.7
## 584 08/07/2021 0.7 3.4 5.9 2.6 12.6
## 585 08/08/2021 0.7 2.9 7.5 3.1 14.2
## 586 08/09/2021 1.3 3.4 5.9 2.8 13.4
## 587 08/10/2021 1.3 2.5 5.9 3.1 12.8
## 588 08/11/2021 1.3 2.5 5.3 2.8 11.9
## 589 08/12/2021 1.3 2.5 5.9 3.3 13.0
## 590 08/13/2021 1.3 4.4 4.3 3.5 13.5
## 591 08/14/2021 0.7 4.4 3.7 3.7 12.5
## 592 08/15/2021 0.7 4.9 2.1 4.2 11.9
## 593 08/16/2021 0.0 6.4 2.1 4.2 12.7
## 594 08/17/2021 0.0 7.4 2.7 4.2 14.3
## 595 08/18/2021 0.0 7.9 3.7 4.2 15.8
## 596 08/19/2021 0.0 6.9 4.3 4.2 15.4
## 597 08/20/2021 0.0 4.4 3.7 3.9 12.0
## 598 08/21/2021 0.0 4.4 7.0 3.3 14.7
## 599 08/22/2021 0.0 4.9 7.5 3.1 15.5
## 600 08/23/2021 0.0 3.4 8.6 4.2 16.2
## 601 08/24/2021 0.7 3.4 9.1 4.4 17.6
## 602 08/25/2021 0.7 3.4 9.6 4.2 17.9
## 603 08/26/2021 0.7 3.4 9.1 4.6 17.8
## 604 08/27/2021 0.7 3.9 9.6 4.6 18.8
## 605 08/28/2021 0.7 5.9 8.0 4.8 19.4
## 606 08/29/2021 0.7 4.9 8.0 4.8 18.4
## 607 08/30/2021 0.7 5.4 6.4 4.4 16.9
## 608 08/31/2021 0.0 4.9 5.3 4.8 15.0
## 609 09/01/2021 0.0 4.4 4.3 5.7 14.4
## 610 09/02/2021 0.7 4.9 4.3 5.5 15.4
## 611 09/03/2021 0.7 4.4 3.7 5.0 13.8
## 612 09/04/2021 0.7 3.9 3.7 5.0 13.3
## 613 09/05/2021 0.7 3.9 2.7 4.6 11.9
## 614 09/06/2021 1.3 3.4 4.8 4.4 13.9
## 615 09/07/2021 1.3 3.9 4.8 3.7 13.7
## 616 09/08/2021 1.3 3.9 5.3 3.5 14.0
## 617 09/09/2021 0.7 3.9 7.5 2.8 14.9
## 618 09/10/2021 0.7 5.9 8.6 2.6 17.8
## 619 09/11/2021 0.7 3.9 8.6 2.6 15.8
## 620 09/12/2021 0.7 5.4 9.6 2.8 18.5
## 621 09/13/2021 0.0 5.4 8.0 2.8 16.2
## 622 09/14/2021 0.0 5.4 9.1 3.3 17.8
## 623 09/15/2021 0.0 5.4 8.6 3.1 17.1
## 624 09/16/2021 0.0 5.4 7.5 3.1 16.0
## 625 09/17/2021 0.0 3.9 7.0 3.7 14.6
## 626 09/18/2021 0.0 4.4 5.9 3.7 14.0
## 627 09/19/2021 0.0 2.9 5.9 3.7 12.5
## 628 09/20/2021 0.0 2.9 5.9 3.3 12.1
## 629 09/21/2021 0.0 2.9 5.3 2.8 11.0
## 630 09/22/2021 0.0 3.4 5.3 2.8 11.5
## 631 09/23/2021 0.0 3.4 5.9 3.7 13.0
## 632 09/24/2021 0.0 2.9 7.5 3.5 13.9
## 633 09/25/2021 0.0 2.9 7.5 3.5 13.9
## 634 09/26/2021 0.0 2.9 7.0 3.1 13.0
## 635 09/27/2021 0.0 3.4 8.6 2.6 14.6
## 636 09/28/2021 0.0 4.4 9.1 2.6 16.1
## 637 09/29/2021 0.0 3.9 9.6 2.2 15.7
## 638 09/30/2021 0.0 3.4 8.6 2.6 14.6
## 639 10/01/2021 0.0 3.9 7.0 2.8 13.7
## 640 10/02/2021 0.0 3.4 7.0 3.3 13.7
## 641 10/03/2021 0.0 3.4 7.5 3.7 14.6
## 642 10/04/2021 0.0 3.4 5.9 3.9 13.2
## 643 10/05/2021 0.0 2.9 5.3 3.5 11.7
## 644 10/06/2021 0.0 4.4 4.3 3.5 12.2
## 645 10/07/2021 0.0 6.4 3.7 2.4 12.5
## 646 10/08/2021 0.0 5.9 4.3 2.0 12.2
## 647 10/09/2021 0.0 6.4 3.7 1.7 11.8
## 648 10/10/2021 0.0 7.4 3.2 1.5 12.1
## 649 10/11/2021 0.0 6.9 4.3 1.3 12.5
## 650 10/12/2021 0.0 5.9 3.7 1.3 10.9
## 651 10/13/2021 0.0 4.9 4.3 1.1 10.3
## 652 10/14/2021 0.0 3.4 4.3 1.5 9.2
## 653 10/15/2021 0.0 4.9 3.7 1.7 10.3
## 654 10/16/2021 0.0 4.9 4.8 2.0 11.7
## 655 10/17/2021 0.0 4.4 4.3 1.7 10.4
## 656 10/18/2021 0.0 4.4 4.3 2.2 10.9
## 657 10/19/2021 0.0 5.4 4.3 2.4 12.1
## 658 10/20/2021 0.0 4.9 5.3 2.6 12.8
## 659 10/21/2021 0.0 4.9 4.8 2.0 11.7
## 660 10/22/2021 0.0 3.9 4.3 1.5 9.7
## 661 10/23/2021 0.0 3.4 3.7 1.3 8.4
## 662 10/24/2021 0.0 3.4 3.7 1.3 8.4
## 663 10/25/2021 0.0 3.4 2.7 1.5 7.6
## 664 10/26/2021 0.0 2.9 2.7 1.7 7.3
## 665 10/27/2021 0.0 3.4 2.1 1.7 7.2
## 666 10/28/2021 0.7 2.9 3.2 2.2 9.0
## 667 10/29/2021 1.3 2.9 3.7 2.2 10.1
## 668 10/30/2021 1.3 2.9 4.3 2.0 10.5
## 669 10/31/2021 1.3 2.5 4.3 2.6 10.7
## 670 11/01/2021 1.3 2.5 3.7 2.2 9.7
## 671 11/02/2021 1.3 1.5 3.7 2.2 8.7
## 672 11/03/2021 1.3 1.0 2.7 2.2 7.2
## 673 11/04/2021 0.7 2.0 1.6 2.4 6.7
## 674 11/05/2021 0.0 2.5 1.1 2.6 6.2
## 675 11/06/2021 0.0 2.9 0.5 2.8 6.2
## 676 11/07/2021 0.0 3.4 0.5 2.2 6.1
## 677 11/08/2021 0.0 2.9 1.1 2.4 6.4
## 678 11/09/2021 0.0 3.4 1.1 2.2 6.7
## 679 11/10/2021 0.0 3.4 1.1 2.0 6.5
## 680 11/11/2021 0.0 2.9 2.1 1.3 6.3
## 681 11/12/2021 0.0 2.5 2.7 1.5 6.7
## 682 11/13/2021 0.0 2.5 2.1 1.1 5.7
## 683 11/14/2021 0.0 2.9 2.7 1.7 7.3
## 684 11/15/2021 0.0 2.9 4.3 2.0 9.2
## 685 11/16/2021 0.0 2.5 4.8 2.0 9.3
## 686 11/17/2021 0.0 2.9 5.9 2.4 11.2
## 687 11/18/2021 0.0 2.9 5.3 3.9 12.1
## 688 11/19/2021 0.0 3.4 5.9 4.2 13.5
## 689 11/20/2021 0.0 3.4 6.4 4.6 14.4
## 690 11/21/2021 0.0 3.4 6.4 4.2 14.0
## 691 11/22/2021 0.0 3.4 5.3 4.2 12.9
## 692 11/23/2021 0.0 3.4 6.4 4.2 14.0
## 693 11/24/2021 0.0 3.4 5.9 3.9 13.2
## 694 11/25/2021 0.0 3.4 7.5 2.6 13.5
## 695 11/26/2021 0.0 2.9 8.0 2.6 13.5
## 696 11/27/2021 0.0 2.9 8.0 2.6 13.5
## 697 11/28/2021 0.0 2.9 7.5 3.1 13.5
## 698 11/29/2021 0.0 3.9 7.0 3.5 14.4
## 699 11/30/2021 0.7 4.9 5.9 4.4 15.9
## 700 12/01/2021 0.7 4.9 7.5 5.2 18.3
## 701 12/02/2021 1.3 4.9 7.0 5.9 19.1
## 702 12/03/2021 1.3 5.4 7.0 6.1 19.8
## 703 12/04/2021 3.3 4.9 7.0 5.7 20.9
## 704 12/05/2021 3.3 4.4 8.0 5.0 20.7
## 705 12/06/2021 3.9 5.9 8.0 4.8 22.6
## 706 12/07/2021 5.9 5.4 8.6 4.8 24.7
## 707 12/08/2021 5.9 6.4 8.0 3.7 24.0
## 708 12/09/2021 5.9 6.4 7.0 3.3 22.6
## 709 12/10/2021 6.5 6.4 6.4 2.8 22.1
## 710 12/11/2021 5.9 6.9 8.0 3.5 24.3
## 711 12/12/2021 6.5 8.3 8.0 3.9 26.7
## 712 12/13/2021 5.9 6.4 8.0 3.9 24.2
## 713 12/14/2021 3.9 8.3 9.1 3.9 25.2
## 714 12/15/2021 3.9 9.8 12.8 5.0 31.5
## 715 12/16/2021 3.3 11.3 13.9 4.8 33.3
## 716 12/17/2021 2.6 11.8 13.9 5.9 34.2
## 717 12/18/2021 2.6 12.8 15.0 6.1 36.5
## 718 12/19/2021 2.0 12.3 16.0 7.2 37.5
## 719 12/20/2021 4.6 12.8 17.7 7.9 43.0
## 720 12/21/2021 4.6 15.2 17.7 7.4 44.9
## 721 12/22/2021 5.9 14.2 17.1 7.9 45.1
## 722 12/23/2021 6.5 15.2 19.3 8.5 49.5
## 723 12/24/2021 6.5 17.7 23.5 10.1 57.8
## 724 12/25/2021 7.8 20.1 23.5 10.7 62.1
## 725 12/26/2021 8.5 24.5 26.7 12.2 71.9
## 726 12/27/2021 8.5 30.9 35.3 15.3 90.0
## 727 12/28/2021 10.4 33.4 43.9 19.7 107.4
## 728 12/29/2021 12.4 36.8 46.0 21.9 117.1
## 729 12/30/2021 15.7 42.2 49.7 23.8 131.4
## 730 12/31/2021 17.0 43.7 52.4 24.9 138.0
## 731 01/01/2022 16.3 43.2 54.6 27.3 141.4
## 732 01/02/2022 20.9 41.7 59.9 27.8 150.3
## 733 01/03/2022 21.5 39.8 58.8 25.1 145.2
## 734 01/04/2022 22.8 38.3 58.3 26.0 145.4
## 735 01/05/2022 22.2 39.3 63.1 26.0 150.6
## 736 01/06/2022 21.5 36.3 65.8 26.0 149.6
## 737 01/07/2022 20.9 39.3 66.3 24.7 151.2
## 738 01/08/2022 20.2 43.2 67.9 24.1 155.4
## 739 01/09/2022 18.9 43.2 64.7 22.3 149.1
## 740 01/10/2022 17.6 44.2 64.7 23.4 149.9
## 741 01/11/2022 16.3 43.7 60.4 22.3 142.7
## 742 01/12/2022 17.0 41.7 54.6 21.4 134.7
## 743 01/13/2022 14.4 40.7 52.4 20.3 127.8
## 744 01/14/2022 15.7 35.8 49.7 20.3 121.5
## 745 01/15/2022 17.0 30.9 46.5 19.5 113.9
## 746 01/16/2022 13.0 29.4 40.6 18.8 101.8
## 747 01/17/2022 13.0 25.5 35.3 16.2 90.0
## 748 01/18/2022 11.7 23.1 32.1 13.8 80.7
## 749 01/19/2022 10.4 21.1 30.5 12.9 74.9
## 750 01/20/2022 11.1 18.6 26.2 13.1 69.0
## 751 01/21/2022 10.4 17.2 23.5 11.8 62.9
## 752 01/22/2022 8.5 17.2 24.1 9.4 59.2
## 753 01/23/2022 9.1 15.2 24.6 9.6 58.5
## 754 01/24/2022 7.2 14.2 23.0 12.2 56.6
## 755 01/25/2022 7.2 12.3 21.9 11.4 52.8
## 756 01/26/2022 5.9 10.8 20.3 10.5 47.5
## 757 01/27/2022 5.9 9.8 17.7 10.1 43.5
## 758 01/28/2022 5.2 8.3 15.5 9.2 38.2
## 759 01/29/2022 6.5 7.9 14.4 9.6 38.4
## 760 01/30/2022 7.8 6.9 12.3 8.5 35.5
## 761 01/31/2022 8.5 5.4 9.6 5.9 29.4
## 762 02/01/2022 7.8 5.4 8.6 5.7 27.5
## 763 02/02/2022 9.1 5.4 7.5 5.5 27.5
## 764 02/03/2022 7.8 4.9 7.0 4.2 23.9
## 765 02/04/2022 7.2 4.9 7.0 4.2 23.3
## 766 02/05/2022 5.9 3.4 4.3 4.4 18.0
## 767 02/06/2022 4.6 3.4 5.3 4.6 17.9
## 768 02/07/2022 3.9 4.4 8.6 4.6 21.5
## 769 02/08/2022 3.3 4.9 9.1 3.5 20.8
## 770 02/09/2022 1.3 4.4 8.6 2.8 17.1
## 771 02/10/2022 1.3 4.9 10.7 2.6 19.5
## 772 02/11/2022 1.3 4.4 10.2 3.5 19.4
## 773 02/12/2022 0.7 3.9 9.6 3.1 17.3
## 774 02/13/2022 0.0 4.4 9.6 2.6 16.6
## 775 02/14/2022 0.7 4.4 6.4 3.1 14.6
## 776 02/15/2022 0.7 2.9 5.9 3.3 12.8
## 777 02/16/2022 0.7 2.5 5.3 3.9 12.4
## 778 02/17/2022 0.7 2.0 2.7 4.4 9.8
## 779 02/18/2022 0.7 2.0 2.1 2.8 7.6
## 780 02/19/2022 0.7 2.0 2.7 2.8 8.2
## 781 02/20/2022 0.7 1.5 1.6 3.7 7.5
## 782 02/21/2022 0.0 0.5 2.7 3.1 6.3
## 783 02/22/2022 0.0 1.0 2.1 3.7 6.8
## 784 02/23/2022 0.0 1.0 1.6 3.3 5.9
## 785 02/24/2022 0.0 0.5 1.6 3.1 5.2
## 786 02/25/2022 0.0 0.5 1.6 3.5 5.6
## 787 02/26/2022 0.0 0.5 1.6 3.7 5.8
## 788 02/27/2022 0.0 0.5 2.7 3.1 6.3
## 789 02/28/2022 0.0 0.5 1.6 3.5 5.6
## 790 03/01/2022 0.0 0.0 1.6 2.4 4.0
## 791 03/02/2022 0.0 0.0 2.1 2.8 4.9
## 792 03/03/2022 0.0 0.0 2.1 2.8 4.9
## 793 03/04/2022 0.0 0.0 2.7 2.4 5.1
## 794 03/05/2022 0.0 0.5 2.1 2.2 4.8
## 795 03/06/2022 0.0 0.5 1.1 2.0 3.6
## 796 03/07/2022 0.0 0.5 1.1 1.5 3.1
## 797 03/08/2022 0.0 1.0 1.6 2.0 4.6
## 798 03/09/2022 0.0 1.0 1.1 1.3 3.4
## 799 03/10/2022 0.0 1.0 1.6 1.1 3.7
## 800 03/11/2022 0.0 1.0 1.6 1.1 3.7
## 801 03/12/2022 0.0 0.5 2.1 1.1 3.7
## 802 03/13/2022 0.0 0.5 2.1 1.5 4.1
## 803 03/14/2022 0.0 0.5 2.1 1.7 4.3
## 804 03/15/2022 0.0 0.0 1.6 1.3 2.9
## 805 03/16/2022 0.0 0.0 1.6 1.3 2.9
## 806 03/17/2022 0.0 0.0 1.1 1.5 2.6
## 807 03/18/2022 0.0 0.5 0.5 1.5 2.5
## 808 03/19/2022 0.0 1.0 0.0 1.3 2.3
## 809 03/20/2022 0.0 1.0 0.5 0.9 2.4
## 810 03/21/2022 0.0 1.0 0.5 0.4 1.9
## 811 03/22/2022 0.0 1.0 0.5 0.9 2.4
## 812 03/23/2022 0.0 1.0 0.5 0.7 2.2
## 813 03/24/2022 0.0 1.0 0.5 0.4 1.9
## 814 03/25/2022 0.0 0.5 0.5 0.4 1.4
## 815 03/26/2022 0.0 0.0 1.1 0.7 1.8
## 816 03/27/2022 0.0 0.0 0.5 0.9 1.4
## 817 03/28/2022 0.0 0.0 1.1 1.1 2.2
## 818 03/29/2022 0.0 0.0 1.1 0.7 1.8
## 819 03/30/2022 0.0 0.0 1.1 0.9 2.0
## 820 03/31/2022 0.0 0.0 1.1 1.3 2.4
## 821 04/01/2022 0.0 0.5 1.6 1.7 3.8
## 822 04/02/2022 0.0 0.5 1.6 1.7 3.8
## 823 04/03/2022 0.0 1.0 1.6 2.2 4.8
## 824 04/04/2022 0.0 1.0 1.1 2.4 4.5
## 825 04/05/2022 0.0 1.0 1.1 2.4 4.5
## 826 04/06/2022 0.0 1.5 1.6 2.6 5.7
## 827 04/07/2022 0.0 1.5 1.6 2.2 5.3
## 828 04/08/2022 0.0 1.0 1.1 1.7 3.8
## 829 04/09/2022 0.0 1.5 1.1 2.0 4.6
## 830 04/10/2022 0.0 1.0 1.1 1.7 3.8
## 831 04/11/2022 0.0 1.5 2.1 2.2 5.8
## 832 04/12/2022 0.0 1.5 2.7 2.8 7.0
## 833 04/13/2022 0.0 2.0 2.7 2.8 7.5
## 834 04/14/2022 0.0 2.0 2.7 3.3 8.0
## 835 04/15/2022 0.0 2.5 3.7 3.5 9.7
## 836 04/16/2022 0.0 2.0 3.2 3.1 8.3
## 837 04/17/2022 0.0 2.5 3.2 2.8 8.5
## 838 04/18/2022 0.0 2.5 2.7 2.2 7.4
## 839 04/19/2022 0.0 2.9 3.2 1.5 7.6
## 840 04/20/2022 0.0 2.0 3.2 1.5 6.7
## 841 04/21/2022 0.0 2.5 3.7 1.7 7.9
## 842 04/22/2022 0.0 2.0 3.7 1.5 7.2
## 843 04/23/2022 0.0 2.5 3.7 2.2 8.4
## 844 04/24/2022 0.0 2.5 4.3 2.4 9.2
## 845 04/25/2022 0.0 2.0 4.3 2.4 8.7
## 846 04/26/2022 0.0 1.5 3.2 2.8 7.5
## 847 04/27/2022 0.0 1.5 2.7 3.5 7.7
## 848 04/28/2022 0.0 1.0 2.1 3.3 6.4
## 849 04/29/2022 0.0 1.0 1.6 3.9 6.5
## 850 04/30/2022 0.0 1.0 1.6 4.4 7.0
## 851 05/01/2022 0.0 1.0 1.6 4.8 7.4
## 852 05/02/2022 0.0 1.0 1.6 6.1 8.7
## 853 05/03/2022 0.0 2.0 1.6 7.0 10.6
## 854 05/04/2022 0.0 2.9 2.7 7.0 12.6
## 855 05/05/2022 0.0 3.4 6.4 7.2 17.0
## 856 05/06/2022 0.0 3.9 5.9 7.0 16.8
## 857 05/07/2022 0.0 3.4 7.0 6.8 17.2
## 858 05/08/2022 0.0 3.4 7.5 6.6 17.5
## 859 05/09/2022 0.0 3.9 8.0 6.1 18.0
## 860 05/10/2022 0.0 3.4 9.1 5.5 18.0
## 861 05/11/2022 0.0 3.4 9.6 6.1 19.1
## 862 05/12/2022 0.0 3.4 6.4 5.9 15.7
## 863 05/13/2022 0.0 2.9 7.5 6.6 17.0
## 864 05/14/2022 0.0 3.4 6.4 6.6 16.4
## 865 05/15/2022 0.0 3.4 6.4 7.0 16.8
## 866 05/16/2022 0.0 2.9 5.3 7.4 15.6
## 867 05/17/2022 0.0 2.5 4.8 8.3 15.6
## 868 05/18/2022 0.7 2.5 3.7 7.4 14.3
## 869 05/19/2022 0.7 2.0 3.7 7.9 14.3
## 870 05/20/2022 0.7 3.4 4.3 7.9 16.3
## 871 05/21/2022 1.3 3.4 4.3 8.7 17.7
## 872 05/22/2022 1.3 2.9 4.3 8.3 16.8
## 873 05/23/2022 1.3 2.9 6.4 7.9 18.5
## 874 05/24/2022 1.3 3.4 7.5 7.4 19.6
## 875 05/25/2022 0.7 2.9 9.6 8.1 21.3
## 876 05/26/2022 0.7 5.4 9.6 7.7 23.4
## 877 05/27/2022 0.7 4.4 11.2 7.2 23.5
## 878 05/28/2022 0.0 4.4 11.2 6.8 22.4
## 879 05/29/2022 0.0 4.4 12.8 7.7 24.9
## 880 05/30/2022 0.0 4.4 11.2 7.7 23.3
## 881 05/31/2022 0.0 3.9 11.8 7.4 23.1
## 882 06/01/2022 0.0 3.4 11.8 7.0 22.2
## 883 06/02/2022 0.0 1.5 12.3 7.4 21.2
## 884 06/03/2022 0.0 2.0 9.6 7.9 19.5
## 885 06/04/2022 0.0 2.0 9.6 7.4 19.0
## 886 06/05/2022 0.0 2.5 7.0 6.3 15.8
## 887 06/06/2022 0.0 2.5 8.6 5.5 16.6
## 888 06/07/2022 0.0 3.4 7.0 4.8 15.2
## 889 06/08/2022 0.0 3.9 5.9 4.8 14.6
## 890 06/09/2022 0.0 3.9 6.4 4.4 14.7
## 891 06/10/2022 0.0 3.4 7.0 4.2 14.6
## 892 06/11/2022 0.0 3.4 7.0 4.6 15.0
## 893 06/12/2022 0.0 3.9 7.5 4.2 15.6
## 894 06/13/2022 0.0 5.9 7.5 5.2 18.6
## 895 06/14/2022 0.0 5.4 8.6 5.9 19.9
## 896 06/15/2022 0.0 5.9 8.6 5.5 20.0
## 897 06/16/2022 0.0 5.9 7.5 6.6 20.0
## 898 06/17/2022 0.0 6.4 8.6 7.9 22.9
## 899 06/18/2022 0.0 5.9 10.2 7.0 23.1
## 900 06/19/2022 0.0 5.9 11.8 8.1 25.8
## 901 06/20/2022 0.0 4.4 10.2 7.9 22.5
## 902 06/21/2022 0.0 3.9 10.7 8.7 23.3
## 903 06/22/2022 0.0 3.9 10.7 9.4 24.0
## 904 06/23/2022 0.0 3.9 12.3 8.1 24.3
## 905 06/24/2022 0.0 3.9 11.8 7.2 22.9
## 906 06/25/2022 0.0 3.9 11.8 7.4 23.1
## 907 06/26/2022 0.0 3.4 10.2 7.7 21.3
## 908 06/27/2022 0.0 4.4 13.4 7.7 25.5
## 909 06/28/2022 0.7 4.4 12.8 7.7 25.6
## 910 06/29/2022 0.7 3.4 12.8 7.2 24.1
## 911 06/30/2022 0.7 3.4 10.7 8.5 23.3
## 912 07/01/2022 0.7 2.9 10.7 7.7 22.0
## 913 07/02/2022 0.7 2.9 9.6 7.4 20.6
## 914 07/03/2022 0.7 2.9 10.2 7.0 20.8
## 915 07/04/2022 0.7 1.5 8.6 6.8 17.6
## 916 07/05/2022 0.0 2.5 8.6 5.9 17.0
## 917 07/06/2022 0.0 4.4 8.6 7.0 20.0
## 918 07/07/2022 0.0 6.4 10.2 6.3 22.9
## 919 07/08/2022 0.0 6.9 9.6 7.0 23.5
## 920 07/09/2022 0.0 8.3 9.6 7.7 25.6
## 921 07/10/2022 0.0 8.8 9.6 7.2 25.6
## 922 07/11/2022 0.7 9.8 8.6 7.0 26.1
## 923 07/12/2022 0.7 9.3 8.6 7.4 26.0
## 924 07/13/2022 0.7 7.4 9.6 6.1 23.8
## 925 07/14/2022 0.7 4.9 8.0 6.3 19.9
## 926 07/15/2022 0.7 4.9 7.5 5.7 18.8
## 927 07/16/2022 0.7 3.4 7.5 4.8 16.4
## 928 07/17/2022 1.3 3.4 8.6 6.1 19.4
## 929 07/18/2022 0.7 2.5 8.0 7.4 18.6
## 930 07/19/2022 0.7 2.0 8.0 7.9 18.6
## 931 07/20/2022 0.7 3.4 7.5 7.9 19.5
## 932 07/21/2022 0.7 3.9 8.6 7.9 21.1
## 933 07/22/2022 0.7 3.9 9.1 7.7 21.4
## 934 07/23/2022 0.7 5.4 9.1 9.0 24.2
## 935 07/24/2022 0.0 4.9 8.6 7.9 21.4
## 936 07/25/2022 0.7 6.4 10.7 6.8 24.6
## 937 07/26/2022 1.3 7.4 10.7 6.1 25.5
## 938 07/27/2022 1.3 6.4 9.1 7.0 23.8
## 939 07/28/2022 1.3 6.4 10.2 7.9 25.8
## 940 07/29/2022 1.3 5.9 10.7 8.3 26.2
## 941 07/30/2022 1.3 4.4 10.7 7.9 24.3
## 942 07/31/2022 1.3 5.4 9.6 7.7 24.0
## 943 08/01/2022 0.7 4.9 8.0 7.7 21.3
## 944 08/02/2022 0.7 4.4 9.6 8.3 23.0
## 945 08/03/2022 1.3 5.4 10.2 8.3 25.2
## 946 08/04/2022 1.3 6.4 10.2 7.7 25.6
## 947 08/05/2022 1.3 7.4 10.2 8.5 27.4
## 948 08/06/2022 1.3 7.4 11.2 8.5 28.4
## 949 08/07/2022 1.3 7.4 11.8 9.6 30.1
## 950 08/08/2022 1.3 7.9 12.3 10.1 31.6
## 951 08/09/2022 1.3 9.3 9.1 8.7 28.4
## 952 08/10/2022 0.7 8.8 9.1 8.3 26.9
## 953 08/11/2022 0.7 7.9 9.1 8.7 26.4
## 954 08/12/2022 0.7 6.4 10.2 7.9 25.2
## 955 08/13/2022 0.7 8.3 8.6 7.9 25.5
## 956 08/14/2022 0.7 7.9 9.1 7.0 24.7
## 957 08/15/2022 0.7 7.4 8.0 6.1 22.2
## 958 08/16/2022 0.0 5.9 9.1 6.3 21.3
## 959 08/17/2022 0.0 6.9 10.7 5.5 23.1
## 960 08/18/2022 0.0 6.9 11.2 4.2 22.3
## 961 08/19/2022 0.0 6.9 10.2 4.8 21.9
## 962 08/20/2022 0.0 6.4 10.7 5.0 22.1
## 963 08/21/2022 0.0 5.4 10.2 5.7 21.3
## 964 08/22/2022 0.0 5.4 10.7 5.7 21.8
## 965 08/23/2022 0.0 4.9 10.2 6.3 21.4
## 966 08/24/2022 0.0 4.4 8.6 7.2 20.2
## 967 08/25/2022 0.0 3.9 6.4 8.3 18.6
## 968 08/26/2022 0.0 5.4 5.9 7.4 18.7
## 969 08/27/2022 0.0 4.4 6.4 7.0 17.8
## 970 08/28/2022 0.0 4.9 5.3 6.1 16.3
## 971 08/29/2022 0.0 3.9 4.3 6.3 14.5
## 972 08/30/2022 1.3 6.9 4.8 6.8 19.8
## 973 08/31/2022 1.3 6.9 3.7 7.0 18.9
## 974 09/01/2022 1.3 7.9 4.3 5.7 19.2
## 975 09/02/2022 1.3 7.4 4.3 6.1 19.1
## 976 09/03/2022 1.3 7.4 4.3 6.1 19.1
## 977 09/04/2022 1.3 8.3 4.8 6.3 20.7
## 978 09/05/2022 1.3 8.8 7.0 6.6 23.7
## 979 09/06/2022 0.0 5.9 7.0 5.7 18.6
## 980 09/07/2022 1.3 4.4 7.0 5.0 17.7
## 981 09/08/2022 1.3 3.4 7.5 6.3 18.5
## 982 09/09/2022 1.3 2.9 8.0 5.9 18.1
## 983 09/10/2022 1.3 2.5 7.5 6.1 17.4
## 984 09/11/2022 1.3 1.0 7.5 6.6 16.4
## 985 09/12/2022 2.6 0.5 7.5 6.3 16.9
## 986 09/13/2022 3.3 1.5 7.0 5.9 17.7
## 987 09/14/2022 2.6 2.0 7.5 5.5 17.6
## 988 09/15/2022 3.9 2.5 5.9 5.9 18.2
## 989 09/16/2022 3.9 2.5 4.3 6.1 16.8
## 990 09/17/2022 3.9 3.4 4.8 5.7 17.8
## 991 09/18/2022 4.6 3.4 5.3 5.5 18.8
## 992 09/19/2022 3.9 4.4 5.3 5.5 19.1
## 993 09/20/2022 3.3 4.9 4.8 6.1 19.1
## 994 09/21/2022 2.6 4.4 4.3 7.0 18.3
## 995 09/22/2022 1.3 3.9 4.8 5.9 15.9
## 996 09/23/2022 1.3 4.4 5.9 5.7 17.3
## 997 09/24/2022 2.6 3.4 4.8 5.5 16.3
## 998 09/25/2022 2.0 3.4 3.7 5.2 14.3
## 999 09/26/2022 1.3 2.9 2.7 5.5 12.4
## 1000 09/27/2022 2.0 2.5 4.3 5.5 14.3
## 1001 09/28/2022 2.0 2.5 4.8 5.2 14.5
## 1002 09/29/2022 2.0 2.9 5.3 5.0 15.2
## 1003 09/30/2022 2.0 2.5 4.8 5.9 15.2
## 1004 10/01/2022 0.7 2.5 4.8 5.7 13.7
## 1005 10/02/2022 0.7 2.5 4.8 5.7 13.7
## 1006 10/03/2022 0.7 2.5 4.8 5.5 13.5
## 1007 10/04/2022 0.7 2.5 3.7 5.2 12.1
## 1008 10/05/2022 0.7 2.5 3.2 5.0 11.4
## 1009 10/06/2022 0.7 2.9 2.7 4.8 11.1
## 1010 10/07/2022 0.7 3.4 2.1 4.6 10.8
## 1011 10/08/2022 0.7 3.9 2.1 4.8 11.5
## 1012 10/09/2022 0.7 3.9 2.7 4.6 11.9
## 1013 10/10/2022 0.7 3.4 1.6 5.2 10.9
## 1014 10/11/2022 0.0 2.5 1.6 5.2 9.3
## 1015 10/12/2022 1.3 2.5 3.2 5.7 12.7
## 1016 10/13/2022 2.6 1.5 4.3 6.6 15.0
## 1017 10/14/2022 2.6 1.5 5.3 6.6 16.0
## 1018 10/15/2022 2.6 1.0 5.9 7.2 16.7
## 1019 10/16/2022 2.6 1.0 7.0 7.9 18.5
## 1020 10/17/2022 2.6 1.5 7.5 7.7 19.3
## 1021 10/18/2022 3.3 1.5 8.6 7.2 20.6
## 1022 10/19/2022 2.0 2.0 8.6 6.3 18.9
## 1023 10/20/2022 0.7 3.4 7.5 5.2 16.8
## 1024 10/21/2022 1.3 2.5 7.0 5.2 16.0
## 1025 10/22/2022 2.0 2.5 6.4 4.8 15.7
## 1026 10/23/2022 2.0 2.9 5.9 4.4 15.2
## 1027 10/24/2022 2.0 2.5 6.4 4.8 15.7
## 1028 10/25/2022 1.3 2.5 4.8 4.6 13.2
## 1029 10/26/2022 1.3 2.0 3.2 4.4 10.9
## 1030 10/27/2022 1.3 0.5 2.7 4.2 8.7Loading libraries and Importing Data
setwd("~/Data 110 Folder")
CrashInc <- read.csv("CrashIncidents.csv")
CrashDriver <- read.csv("CrashDrivers.csv")
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library('colorspace')
library(dplyr)
library(leaflet)
library(ggpubr)
library(ggplot2)
library(psych)##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(corrplot)## corrplot 0.92 loadedlibrary(RColorBrewer)
library(dslabs)
library(highcharter)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use
##
## Attaching package: 'highcharter'
##
## The following object is masked from 'package:dslabs':
##
## starslibrary(dplyr)
library(sf)## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUElibrary(tidyverse)
library(leaflet)
library(pct)
library(mapview)Merge two datasets together
crashdf = merge(CrashInc, CrashDriver, by="Report.Number")Install calendar package and create a covid map for March 2020
# install.packages(calendR)
library(calendR)## Warning: package 'calendR' was built under R version 4.2.2## ~~ Package calendR
## Visit https://r-coder.com/ for R tutorials ~~# Data
Covid19$Cases## [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [13] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [25] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [37] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [49] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11.6
## [61] 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 1.1 1.6 1.6
## [73] 1.4 1.9 2.3 4.4 5.6 7.1 12.5 14.5 17.4 19.7 20.3 22.6
## [85] 25.2 25.3 28.7 33.4 38.8 40.9 41.7 47.4 50.6 53.7 51.1 57.3
## [97] 70.1 85.4 87.3 86.9 90.5 95.1 96.2 92.0 88.6 96.5 105.5 101.7
## [109] 101.1 98.4 104.6 103.9 100.0 98.6 106.6 108.1 111.0 113.7 114.8 110.4
## [121] 109.9 103.8 106.6 105.3 103.7 105.6 103.8 103.8 102.7 98.1 98.3 88.2
## [133] 77.0 74.2 69.7 68.3 65.3 61.8 62.8 65.6 69.6 65.5 61.9 62.1
## [145] 60.0 58.7 54.7 46.4 43.0 43.1 39.0 39.0 37.7 37.7 41.4 41.4
## [157] 42.0 43.4 40.0 34.3 32.2 29.8 31.5 29.2 26.8 23.5 25.5 23.8
## [169] 23.7 20.6 22.0 21.7 22.2 18.8 17.5 17.5 16.7 14.5 15.1 16.7
## [181] 19.0 17.8 16.4 19.4 20.9 22.0 20.0 16.7 17.8 16.6 16.5 16.2
## [193] 15.2 17.2 20.9 20.0 21.9 20.2 22.3 23.0 23.6 22.6 23.1 20.9
## [205] 21.3 20.5 20.0 19.0 18.7 18.7 21.3 19.3 17.6 17.6 16.2 16.1
## [217] 18.5 16.0 18.2 19.3 20.2 20.0 18.0 18.2 18.2 17.3 14.4 12.8
## [229] 11.7 14.2 13.6 14.3 13.7 15.1 14.5 15.9 14.7 12.9 14.0 15.0
## [241] 16.9 18.3 17.8 17.6 20.0 19.7 20.2 18.8 17.3 18.2 18.3 18.0
## [253] 15.6 14.9 13.6 15.4 14.4 13.9 11.7 13.7 12.5 14.3 12.5 13.0
## [265] 14.8 13.7 13.2 14.6 14.2 16.2 16.9 16.1 16.9 17.2 17.6 19.7
## [277] 18.0 18.6 16.7 18.8 19.3 20.0 18.2 20.5 21.0 23.7 22.8 22.5
## [289] 18.9 18.0 17.5 17.3 17.1 18.8 21.5 23.4 23.1 24.7 23.5 24.9
## [301] 26.3 24.3 26.3 30.2 30.6 34.9 35.3 34.8 37.3 39.3 40.0 39.3
## [313] 40.5 40.8 47.0 51.7 50.3 52.5 55.5 56.0 57.9 54.9 55.8 59.8
## [325] 59.1 57.7 59.7 60.0 59.9 55.0 57.6 61.9 60.8 58.8 62.7 64.6
## [337] 68.3 61.9 58.7 58.9 56.0 54.7 54.3 53.9 57.3 59.5 66.2 70.8
## [349] 74.2 67.8 66.0 66.0 62.3 58.5 59.2 60.3 62.7 65.2 59.3 63.3
## [361] 62.2 61.3 56.7 61.6 59.0 67.6 66.0 69.6 75.6 78.2 73.8 72.1
## [373] 70.2 68.0 68.2 62.1 60.4 61.6 66.2 63.9 65.3 60.9 58.1 60.9
## [385] 57.5 61.3 62.3 58.9 55.8 56.2 50.6 52.1 45.3 41.7 41.0 38.4
## [397] 36.7 35.1 35.4 35.6 38.0 39.7 42.4 38.6 34.5 29.8 26.7 23.9
## [409] 22.6 20.9 21.3 23.9 28.7 27.7 27.1 24.0 25.9 25.4 22.9 19.0
## [421] 19.4 20.6 23.4 22.7 23.5 25.1 23.8 23.8 19.9 20.4 19.3 18.7
## [433] 16.7 19.5 22.5 24.1 21.6 21.6 22.8 23.5 23.6 20.2 19.4 21.2
## [445] 23.4 24.5 27.5 25.6 26.3 27.1 25.4 24.2 23.7 19.8 20.6 19.3
## [457] 20.9 21.2 21.4 19.6 22.4 20.3 19.0 19.6 19.0 18.9 20.4 19.1
## [469] 20.3 21.4 17.1 17.0 17.5 17.3 17.5 17.0 16.2 15.9 15.2 17.1
## [481] 15.6 12.9 14.3 14.5 15.4 15.7 13.4 13.0 13.1 12.5 12.5 12.5
## [493] 12.5 10.8 10.6 12.2 10.2 10.2 10.2 10.5 10.8 11.8 9.9 10.7
## [505] 9.4 8.3 6.5 6.8 5.4 6.4 5.6 6.1 7.2 7.7 6.7 5.9
## [517] 5.6 5.6 4.9 4.3 3.8 4.9 5.8 4.8 5.4 4.9 3.8 5.2
## [529] 4.1 2.9 3.4 2.3 2.3 2.8 1.5 2.1 2.6 2.1 1.5 2.2
## [541] 1.7 1.7 1.2 0.9 1.4 1.4 0.9 0.9 0.9 0.9 1.2 1.2
## [553] 1.7 1.5 2.1 2.5 3.3 2.8 3.5 3.5 5.8 5.7 5.3 5.7
## [565] 5.7 5.3 5.6 3.5 3.5 4.7 4.6 6.3 6.3 8.7 11.1 11.7
## [577] 11.7 11.7 12.9 13.3 11.3 9.7 11.7 12.6 14.2 13.4 12.8 11.9
## [589] 13.0 13.5 12.5 11.9 12.7 14.3 15.8 15.4 12.0 14.7 15.5 16.2
## [601] 17.6 17.9 17.8 18.8 19.4 18.4 16.9 15.0 14.4 15.4 13.8 13.3
## [613] 11.9 13.9 13.7 14.0 14.9 17.8 15.8 18.5 16.2 17.8 17.1 16.0
## [625] 14.6 14.0 12.5 12.1 11.0 11.5 13.0 13.9 13.9 13.0 14.6 16.1
## [637] 15.7 14.6 13.7 13.7 14.6 13.2 11.7 12.2 12.5 12.2 11.8 12.1
## [649] 12.5 10.9 10.3 9.2 10.3 11.7 10.4 10.9 12.1 12.8 11.7 9.7
## [661] 8.4 8.4 7.6 7.3 7.2 9.0 10.1 10.5 10.7 9.7 8.7 7.2
## [673] 6.7 6.2 6.2 6.1 6.4 6.7 6.5 6.3 6.7 5.7 7.3 9.2
## [685] 9.3 11.2 12.1 13.5 14.4 14.0 12.9 14.0 13.2 13.5 13.5 13.5
## [697] 13.5 14.4 15.9 18.3 19.1 19.8 20.9 20.7 22.6 24.7 24.0 22.6
## [709] 22.1 24.3 26.7 24.2 25.2 31.5 33.3 34.2 36.5 37.5 43.0 44.9
## [721] 45.1 49.5 57.8 62.1 71.9 90.0 107.4 117.1 131.4 138.0 141.4 150.3
## [733] 145.2 145.4 150.6 149.6 151.2 155.4 149.1 149.9 142.7 134.7 127.8 121.5
## [745] 113.9 101.8 90.0 80.7 74.9 69.0 62.9 59.2 58.5 56.6 52.8 47.5
## [757] 43.5 38.2 38.4 35.5 29.4 27.5 27.5 23.9 23.3 18.0 17.9 21.5
## [769] 20.8 17.1 19.5 19.4 17.3 16.6 14.6 12.8 12.4 9.8 7.6 8.2
## [781] 7.5 6.3 6.8 5.9 5.2 5.6 5.8 6.3 5.6 4.0 4.9 4.9
## [793] 5.1 4.8 3.6 3.1 4.6 3.4 3.7 3.7 3.7 4.1 4.3 2.9
## [805] 2.9 2.6 2.5 2.3 2.4 1.9 2.4 2.2 1.9 1.4 1.8 1.4
## [817] 2.2 1.8 2.0 2.4 3.8 3.8 4.8 4.5 4.5 5.7 5.3 3.8
## [829] 4.6 3.8 5.8 7.0 7.5 8.0 9.7 8.3 8.5 7.4 7.6 6.7
## [841] 7.9 7.2 8.4 9.2 8.7 7.5 7.7 6.4 6.5 7.0 7.4 8.7
## [853] 10.6 12.6 17.0 16.8 17.2 17.5 18.0 18.0 19.1 15.7 17.0 16.4
## [865] 16.8 15.6 15.6 14.3 14.3 16.3 17.7 16.8 18.5 19.6 21.3 23.4
## [877] 23.5 22.4 24.9 23.3 23.1 22.2 21.2 19.5 19.0 15.8 16.6 15.2
## [889] 14.6 14.7 14.6 15.0 15.6 18.6 19.9 20.0 20.0 22.9 23.1 25.8
## [901] 22.5 23.3 24.0 24.3 22.9 23.1 21.3 25.5 25.6 24.1 23.3 22.0
## [913] 20.6 20.8 17.6 17.0 20.0 22.9 23.5 25.6 25.6 26.1 26.0 23.8
## [925] 19.9 18.8 16.4 19.4 18.6 18.6 19.5 21.1 21.4 24.2 21.4 24.6
## [937] 25.5 23.8 25.8 26.2 24.3 24.0 21.3 23.0 25.2 25.6 27.4 28.4
## [949] 30.1 31.6 28.4 26.9 26.4 25.2 25.5 24.7 22.2 21.3 23.1 22.3
## [961] 21.9 22.1 21.3 21.8 21.4 20.2 18.6 18.7 17.8 16.3 14.5 19.8
## [973] 18.9 19.2 19.1 19.1 20.7 23.7 18.6 17.7 18.5 18.1 17.4 16.4
## [985] 16.9 17.7 17.6 18.2 16.8 17.8 18.8 19.1 19.1 18.3 15.9 17.3
## [997] 16.3 14.3 12.4 14.3 14.5 15.2 15.2 13.7 13.7 13.5 12.1 11.4
## [1009] 11.1 10.8 11.5 11.9 10.9 9.3 12.7 15.0 16.0 16.7 18.5 19.3
## [1021] 20.6 18.9 16.8 16.0 15.7 15.2 15.7 13.2 10.9 8.7data4 <- runif(31,)
# Calendar
calendR(year = 2020,
month = 3,
special.days = data4,
gradient = TRUE,
low.col = "white",
special.col = "#8B0000",
legend.pos = "bottom")
Create a calendar for April 2020
# Data
Covid19$Cases## [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [13] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [25] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [37] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [49] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11.6
## [61] 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 1.1 1.6 1.6
## [73] 1.4 1.9 2.3 4.4 5.6 7.1 12.5 14.5 17.4 19.7 20.3 22.6
## [85] 25.2 25.3 28.7 33.4 38.8 40.9 41.7 47.4 50.6 53.7 51.1 57.3
## [97] 70.1 85.4 87.3 86.9 90.5 95.1 96.2 92.0 88.6 96.5 105.5 101.7
## [109] 101.1 98.4 104.6 103.9 100.0 98.6 106.6 108.1 111.0 113.7 114.8 110.4
## [121] 109.9 103.8 106.6 105.3 103.7 105.6 103.8 103.8 102.7 98.1 98.3 88.2
## [133] 77.0 74.2 69.7 68.3 65.3 61.8 62.8 65.6 69.6 65.5 61.9 62.1
## [145] 60.0 58.7 54.7 46.4 43.0 43.1 39.0 39.0 37.7 37.7 41.4 41.4
## [157] 42.0 43.4 40.0 34.3 32.2 29.8 31.5 29.2 26.8 23.5 25.5 23.8
## [169] 23.7 20.6 22.0 21.7 22.2 18.8 17.5 17.5 16.7 14.5 15.1 16.7
## [181] 19.0 17.8 16.4 19.4 20.9 22.0 20.0 16.7 17.8 16.6 16.5 16.2
## [193] 15.2 17.2 20.9 20.0 21.9 20.2 22.3 23.0 23.6 22.6 23.1 20.9
## [205] 21.3 20.5 20.0 19.0 18.7 18.7 21.3 19.3 17.6 17.6 16.2 16.1
## [217] 18.5 16.0 18.2 19.3 20.2 20.0 18.0 18.2 18.2 17.3 14.4 12.8
## [229] 11.7 14.2 13.6 14.3 13.7 15.1 14.5 15.9 14.7 12.9 14.0 15.0
## [241] 16.9 18.3 17.8 17.6 20.0 19.7 20.2 18.8 17.3 18.2 18.3 18.0
## [253] 15.6 14.9 13.6 15.4 14.4 13.9 11.7 13.7 12.5 14.3 12.5 13.0
## [265] 14.8 13.7 13.2 14.6 14.2 16.2 16.9 16.1 16.9 17.2 17.6 19.7
## [277] 18.0 18.6 16.7 18.8 19.3 20.0 18.2 20.5 21.0 23.7 22.8 22.5
## [289] 18.9 18.0 17.5 17.3 17.1 18.8 21.5 23.4 23.1 24.7 23.5 24.9
## [301] 26.3 24.3 26.3 30.2 30.6 34.9 35.3 34.8 37.3 39.3 40.0 39.3
## [313] 40.5 40.8 47.0 51.7 50.3 52.5 55.5 56.0 57.9 54.9 55.8 59.8
## [325] 59.1 57.7 59.7 60.0 59.9 55.0 57.6 61.9 60.8 58.8 62.7 64.6
## [337] 68.3 61.9 58.7 58.9 56.0 54.7 54.3 53.9 57.3 59.5 66.2 70.8
## [349] 74.2 67.8 66.0 66.0 62.3 58.5 59.2 60.3 62.7 65.2 59.3 63.3
## [361] 62.2 61.3 56.7 61.6 59.0 67.6 66.0 69.6 75.6 78.2 73.8 72.1
## [373] 70.2 68.0 68.2 62.1 60.4 61.6 66.2 63.9 65.3 60.9 58.1 60.9
## [385] 57.5 61.3 62.3 58.9 55.8 56.2 50.6 52.1 45.3 41.7 41.0 38.4
## [397] 36.7 35.1 35.4 35.6 38.0 39.7 42.4 38.6 34.5 29.8 26.7 23.9
## [409] 22.6 20.9 21.3 23.9 28.7 27.7 27.1 24.0 25.9 25.4 22.9 19.0
## [421] 19.4 20.6 23.4 22.7 23.5 25.1 23.8 23.8 19.9 20.4 19.3 18.7
## [433] 16.7 19.5 22.5 24.1 21.6 21.6 22.8 23.5 23.6 20.2 19.4 21.2
## [445] 23.4 24.5 27.5 25.6 26.3 27.1 25.4 24.2 23.7 19.8 20.6 19.3
## [457] 20.9 21.2 21.4 19.6 22.4 20.3 19.0 19.6 19.0 18.9 20.4 19.1
## [469] 20.3 21.4 17.1 17.0 17.5 17.3 17.5 17.0 16.2 15.9 15.2 17.1
## [481] 15.6 12.9 14.3 14.5 15.4 15.7 13.4 13.0 13.1 12.5 12.5 12.5
## [493] 12.5 10.8 10.6 12.2 10.2 10.2 10.2 10.5 10.8 11.8 9.9 10.7
## [505] 9.4 8.3 6.5 6.8 5.4 6.4 5.6 6.1 7.2 7.7 6.7 5.9
## [517] 5.6 5.6 4.9 4.3 3.8 4.9 5.8 4.8 5.4 4.9 3.8 5.2
## [529] 4.1 2.9 3.4 2.3 2.3 2.8 1.5 2.1 2.6 2.1 1.5 2.2
## [541] 1.7 1.7 1.2 0.9 1.4 1.4 0.9 0.9 0.9 0.9 1.2 1.2
## [553] 1.7 1.5 2.1 2.5 3.3 2.8 3.5 3.5 5.8 5.7 5.3 5.7
## [565] 5.7 5.3 5.6 3.5 3.5 4.7 4.6 6.3 6.3 8.7 11.1 11.7
## [577] 11.7 11.7 12.9 13.3 11.3 9.7 11.7 12.6 14.2 13.4 12.8 11.9
## [589] 13.0 13.5 12.5 11.9 12.7 14.3 15.8 15.4 12.0 14.7 15.5 16.2
## [601] 17.6 17.9 17.8 18.8 19.4 18.4 16.9 15.0 14.4 15.4 13.8 13.3
## [613] 11.9 13.9 13.7 14.0 14.9 17.8 15.8 18.5 16.2 17.8 17.1 16.0
## [625] 14.6 14.0 12.5 12.1 11.0 11.5 13.0 13.9 13.9 13.0 14.6 16.1
## [637] 15.7 14.6 13.7 13.7 14.6 13.2 11.7 12.2 12.5 12.2 11.8 12.1
## [649] 12.5 10.9 10.3 9.2 10.3 11.7 10.4 10.9 12.1 12.8 11.7 9.7
## [661] 8.4 8.4 7.6 7.3 7.2 9.0 10.1 10.5 10.7 9.7 8.7 7.2
## [673] 6.7 6.2 6.2 6.1 6.4 6.7 6.5 6.3 6.7 5.7 7.3 9.2
## [685] 9.3 11.2 12.1 13.5 14.4 14.0 12.9 14.0 13.2 13.5 13.5 13.5
## [697] 13.5 14.4 15.9 18.3 19.1 19.8 20.9 20.7 22.6 24.7 24.0 22.6
## [709] 22.1 24.3 26.7 24.2 25.2 31.5 33.3 34.2 36.5 37.5 43.0 44.9
## [721] 45.1 49.5 57.8 62.1 71.9 90.0 107.4 117.1 131.4 138.0 141.4 150.3
## [733] 145.2 145.4 150.6 149.6 151.2 155.4 149.1 149.9 142.7 134.7 127.8 121.5
## [745] 113.9 101.8 90.0 80.7 74.9 69.0 62.9 59.2 58.5 56.6 52.8 47.5
## [757] 43.5 38.2 38.4 35.5 29.4 27.5 27.5 23.9 23.3 18.0 17.9 21.5
## [769] 20.8 17.1 19.5 19.4 17.3 16.6 14.6 12.8 12.4 9.8 7.6 8.2
## [781] 7.5 6.3 6.8 5.9 5.2 5.6 5.8 6.3 5.6 4.0 4.9 4.9
## [793] 5.1 4.8 3.6 3.1 4.6 3.4 3.7 3.7 3.7 4.1 4.3 2.9
## [805] 2.9 2.6 2.5 2.3 2.4 1.9 2.4 2.2 1.9 1.4 1.8 1.4
## [817] 2.2 1.8 2.0 2.4 3.8 3.8 4.8 4.5 4.5 5.7 5.3 3.8
## [829] 4.6 3.8 5.8 7.0 7.5 8.0 9.7 8.3 8.5 7.4 7.6 6.7
## [841] 7.9 7.2 8.4 9.2 8.7 7.5 7.7 6.4 6.5 7.0 7.4 8.7
## [853] 10.6 12.6 17.0 16.8 17.2 17.5 18.0 18.0 19.1 15.7 17.0 16.4
## [865] 16.8 15.6 15.6 14.3 14.3 16.3 17.7 16.8 18.5 19.6 21.3 23.4
## [877] 23.5 22.4 24.9 23.3 23.1 22.2 21.2 19.5 19.0 15.8 16.6 15.2
## [889] 14.6 14.7 14.6 15.0 15.6 18.6 19.9 20.0 20.0 22.9 23.1 25.8
## [901] 22.5 23.3 24.0 24.3 22.9 23.1 21.3 25.5 25.6 24.1 23.3 22.0
## [913] 20.6 20.8 17.6 17.0 20.0 22.9 23.5 25.6 25.6 26.1 26.0 23.8
## [925] 19.9 18.8 16.4 19.4 18.6 18.6 19.5 21.1 21.4 24.2 21.4 24.6
## [937] 25.5 23.8 25.8 26.2 24.3 24.0 21.3 23.0 25.2 25.6 27.4 28.4
## [949] 30.1 31.6 28.4 26.9 26.4 25.2 25.5 24.7 22.2 21.3 23.1 22.3
## [961] 21.9 22.1 21.3 21.8 21.4 20.2 18.6 18.7 17.8 16.3 14.5 19.8
## [973] 18.9 19.2 19.1 19.1 20.7 23.7 18.6 17.7 18.5 18.1 17.4 16.4
## [985] 16.9 17.7 17.6 18.2 16.8 17.8 18.8 19.1 19.1 18.3 15.9 17.3
## [997] 16.3 14.3 12.4 14.3 14.5 15.2 15.2 13.7 13.7 13.5 12.1 11.4
## [1009] 11.1 10.8 11.5 11.9 10.9 9.3 12.7 15.0 16.0 16.7 18.5 19.3
## [1021] 20.6 18.9 16.8 16.0 15.7 15.2 15.7 13.2 10.9 8.7data3 <- runif(30,)
# Calendar
calendR(year = 2020,
month = 4,
special.days = data3,
gradient = TRUE,
low.col = "white",
special.col = "#8B1C62",
legend.pos = "bottom")
Create a map for February 2021
# Data
Covid19$Cases## [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [13] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [25] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [37] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [49] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11.6
## [61] 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 1.1 1.6 1.6
## [73] 1.4 1.9 2.3 4.4 5.6 7.1 12.5 14.5 17.4 19.7 20.3 22.6
## [85] 25.2 25.3 28.7 33.4 38.8 40.9 41.7 47.4 50.6 53.7 51.1 57.3
## [97] 70.1 85.4 87.3 86.9 90.5 95.1 96.2 92.0 88.6 96.5 105.5 101.7
## [109] 101.1 98.4 104.6 103.9 100.0 98.6 106.6 108.1 111.0 113.7 114.8 110.4
## [121] 109.9 103.8 106.6 105.3 103.7 105.6 103.8 103.8 102.7 98.1 98.3 88.2
## [133] 77.0 74.2 69.7 68.3 65.3 61.8 62.8 65.6 69.6 65.5 61.9 62.1
## [145] 60.0 58.7 54.7 46.4 43.0 43.1 39.0 39.0 37.7 37.7 41.4 41.4
## [157] 42.0 43.4 40.0 34.3 32.2 29.8 31.5 29.2 26.8 23.5 25.5 23.8
## [169] 23.7 20.6 22.0 21.7 22.2 18.8 17.5 17.5 16.7 14.5 15.1 16.7
## [181] 19.0 17.8 16.4 19.4 20.9 22.0 20.0 16.7 17.8 16.6 16.5 16.2
## [193] 15.2 17.2 20.9 20.0 21.9 20.2 22.3 23.0 23.6 22.6 23.1 20.9
## [205] 21.3 20.5 20.0 19.0 18.7 18.7 21.3 19.3 17.6 17.6 16.2 16.1
## [217] 18.5 16.0 18.2 19.3 20.2 20.0 18.0 18.2 18.2 17.3 14.4 12.8
## [229] 11.7 14.2 13.6 14.3 13.7 15.1 14.5 15.9 14.7 12.9 14.0 15.0
## [241] 16.9 18.3 17.8 17.6 20.0 19.7 20.2 18.8 17.3 18.2 18.3 18.0
## [253] 15.6 14.9 13.6 15.4 14.4 13.9 11.7 13.7 12.5 14.3 12.5 13.0
## [265] 14.8 13.7 13.2 14.6 14.2 16.2 16.9 16.1 16.9 17.2 17.6 19.7
## [277] 18.0 18.6 16.7 18.8 19.3 20.0 18.2 20.5 21.0 23.7 22.8 22.5
## [289] 18.9 18.0 17.5 17.3 17.1 18.8 21.5 23.4 23.1 24.7 23.5 24.9
## [301] 26.3 24.3 26.3 30.2 30.6 34.9 35.3 34.8 37.3 39.3 40.0 39.3
## [313] 40.5 40.8 47.0 51.7 50.3 52.5 55.5 56.0 57.9 54.9 55.8 59.8
## [325] 59.1 57.7 59.7 60.0 59.9 55.0 57.6 61.9 60.8 58.8 62.7 64.6
## [337] 68.3 61.9 58.7 58.9 56.0 54.7 54.3 53.9 57.3 59.5 66.2 70.8
## [349] 74.2 67.8 66.0 66.0 62.3 58.5 59.2 60.3 62.7 65.2 59.3 63.3
## [361] 62.2 61.3 56.7 61.6 59.0 67.6 66.0 69.6 75.6 78.2 73.8 72.1
## [373] 70.2 68.0 68.2 62.1 60.4 61.6 66.2 63.9 65.3 60.9 58.1 60.9
## [385] 57.5 61.3 62.3 58.9 55.8 56.2 50.6 52.1 45.3 41.7 41.0 38.4
## [397] 36.7 35.1 35.4 35.6 38.0 39.7 42.4 38.6 34.5 29.8 26.7 23.9
## [409] 22.6 20.9 21.3 23.9 28.7 27.7 27.1 24.0 25.9 25.4 22.9 19.0
## [421] 19.4 20.6 23.4 22.7 23.5 25.1 23.8 23.8 19.9 20.4 19.3 18.7
## [433] 16.7 19.5 22.5 24.1 21.6 21.6 22.8 23.5 23.6 20.2 19.4 21.2
## [445] 23.4 24.5 27.5 25.6 26.3 27.1 25.4 24.2 23.7 19.8 20.6 19.3
## [457] 20.9 21.2 21.4 19.6 22.4 20.3 19.0 19.6 19.0 18.9 20.4 19.1
## [469] 20.3 21.4 17.1 17.0 17.5 17.3 17.5 17.0 16.2 15.9 15.2 17.1
## [481] 15.6 12.9 14.3 14.5 15.4 15.7 13.4 13.0 13.1 12.5 12.5 12.5
## [493] 12.5 10.8 10.6 12.2 10.2 10.2 10.2 10.5 10.8 11.8 9.9 10.7
## [505] 9.4 8.3 6.5 6.8 5.4 6.4 5.6 6.1 7.2 7.7 6.7 5.9
## [517] 5.6 5.6 4.9 4.3 3.8 4.9 5.8 4.8 5.4 4.9 3.8 5.2
## [529] 4.1 2.9 3.4 2.3 2.3 2.8 1.5 2.1 2.6 2.1 1.5 2.2
## [541] 1.7 1.7 1.2 0.9 1.4 1.4 0.9 0.9 0.9 0.9 1.2 1.2
## [553] 1.7 1.5 2.1 2.5 3.3 2.8 3.5 3.5 5.8 5.7 5.3 5.7
## [565] 5.7 5.3 5.6 3.5 3.5 4.7 4.6 6.3 6.3 8.7 11.1 11.7
## [577] 11.7 11.7 12.9 13.3 11.3 9.7 11.7 12.6 14.2 13.4 12.8 11.9
## [589] 13.0 13.5 12.5 11.9 12.7 14.3 15.8 15.4 12.0 14.7 15.5 16.2
## [601] 17.6 17.9 17.8 18.8 19.4 18.4 16.9 15.0 14.4 15.4 13.8 13.3
## [613] 11.9 13.9 13.7 14.0 14.9 17.8 15.8 18.5 16.2 17.8 17.1 16.0
## [625] 14.6 14.0 12.5 12.1 11.0 11.5 13.0 13.9 13.9 13.0 14.6 16.1
## [637] 15.7 14.6 13.7 13.7 14.6 13.2 11.7 12.2 12.5 12.2 11.8 12.1
## [649] 12.5 10.9 10.3 9.2 10.3 11.7 10.4 10.9 12.1 12.8 11.7 9.7
## [661] 8.4 8.4 7.6 7.3 7.2 9.0 10.1 10.5 10.7 9.7 8.7 7.2
## [673] 6.7 6.2 6.2 6.1 6.4 6.7 6.5 6.3 6.7 5.7 7.3 9.2
## [685] 9.3 11.2 12.1 13.5 14.4 14.0 12.9 14.0 13.2 13.5 13.5 13.5
## [697] 13.5 14.4 15.9 18.3 19.1 19.8 20.9 20.7 22.6 24.7 24.0 22.6
## [709] 22.1 24.3 26.7 24.2 25.2 31.5 33.3 34.2 36.5 37.5 43.0 44.9
## [721] 45.1 49.5 57.8 62.1 71.9 90.0 107.4 117.1 131.4 138.0 141.4 150.3
## [733] 145.2 145.4 150.6 149.6 151.2 155.4 149.1 149.9 142.7 134.7 127.8 121.5
## [745] 113.9 101.8 90.0 80.7 74.9 69.0 62.9 59.2 58.5 56.6 52.8 47.5
## [757] 43.5 38.2 38.4 35.5 29.4 27.5 27.5 23.9 23.3 18.0 17.9 21.5
## [769] 20.8 17.1 19.5 19.4 17.3 16.6 14.6 12.8 12.4 9.8 7.6 8.2
## [781] 7.5 6.3 6.8 5.9 5.2 5.6 5.8 6.3 5.6 4.0 4.9 4.9
## [793] 5.1 4.8 3.6 3.1 4.6 3.4 3.7 3.7 3.7 4.1 4.3 2.9
## [805] 2.9 2.6 2.5 2.3 2.4 1.9 2.4 2.2 1.9 1.4 1.8 1.4
## [817] 2.2 1.8 2.0 2.4 3.8 3.8 4.8 4.5 4.5 5.7 5.3 3.8
## [829] 4.6 3.8 5.8 7.0 7.5 8.0 9.7 8.3 8.5 7.4 7.6 6.7
## [841] 7.9 7.2 8.4 9.2 8.7 7.5 7.7 6.4 6.5 7.0 7.4 8.7
## [853] 10.6 12.6 17.0 16.8 17.2 17.5 18.0 18.0 19.1 15.7 17.0 16.4
## [865] 16.8 15.6 15.6 14.3 14.3 16.3 17.7 16.8 18.5 19.6 21.3 23.4
## [877] 23.5 22.4 24.9 23.3 23.1 22.2 21.2 19.5 19.0 15.8 16.6 15.2
## [889] 14.6 14.7 14.6 15.0 15.6 18.6 19.9 20.0 20.0 22.9 23.1 25.8
## [901] 22.5 23.3 24.0 24.3 22.9 23.1 21.3 25.5 25.6 24.1 23.3 22.0
## [913] 20.6 20.8 17.6 17.0 20.0 22.9 23.5 25.6 25.6 26.1 26.0 23.8
## [925] 19.9 18.8 16.4 19.4 18.6 18.6 19.5 21.1 21.4 24.2 21.4 24.6
## [937] 25.5 23.8 25.8 26.2 24.3 24.0 21.3 23.0 25.2 25.6 27.4 28.4
## [949] 30.1 31.6 28.4 26.9 26.4 25.2 25.5 24.7 22.2 21.3 23.1 22.3
## [961] 21.9 22.1 21.3 21.8 21.4 20.2 18.6 18.7 17.8 16.3 14.5 19.8
## [973] 18.9 19.2 19.1 19.1 20.7 23.7 18.6 17.7 18.5 18.1 17.4 16.4
## [985] 16.9 17.7 17.6 18.2 16.8 17.8 18.8 19.1 19.1 18.3 15.9 17.3
## [997] 16.3 14.3 12.4 14.3 14.5 15.2 15.2 13.7 13.7 13.5 12.1 11.4
## [1009] 11.1 10.8 11.5 11.9 10.9 9.3 12.7 15.0 16.0 16.7 18.5 19.3
## [1021] 20.6 18.9 16.8 16.0 15.7 15.2 15.7 13.2 10.9 8.7data4 <- runif(28,)
# Calendar
calendR(year = 2021,
month = 2,
special.days = data4,
gradient = TRUE,
low.col = "white",
special.col = "#008B00",
legend.pos = "bottom")
Create a calendar of 2021
# install.packages(calendR)
library(calendR)
# Data
set.seed(2)
covid1 <- rnorm(365)
# Calendar
calendR(year = 2021,
special.days = covid1,
gradient = TRUE,
low.col = "white",
special.col = "#473C8B",
legend.pos = "right",
legend.title = "Title")
Create a calendar of 2022
# install.packages(calendR)
library(calendR)
# Data
set.seed(2)
covid1 <- rnorm(365)
# Calendar
calendR(year = 2022,
special.days = covid1,
gradient = TRUE,
low.col = "white",
special.col = "#68228B")
legend.pos = "right"
legend.title = "Covid Cases"Create a calendar of October 2022
# install.packages(calendR)
library(calendR)
# Data
Covid19$Cases## [1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [13] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [25] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [37] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## [49] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11.6
## [61] 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 1.1 1.6 1.6
## [73] 1.4 1.9 2.3 4.4 5.6 7.1 12.5 14.5 17.4 19.7 20.3 22.6
## [85] 25.2 25.3 28.7 33.4 38.8 40.9 41.7 47.4 50.6 53.7 51.1 57.3
## [97] 70.1 85.4 87.3 86.9 90.5 95.1 96.2 92.0 88.6 96.5 105.5 101.7
## [109] 101.1 98.4 104.6 103.9 100.0 98.6 106.6 108.1 111.0 113.7 114.8 110.4
## [121] 109.9 103.8 106.6 105.3 103.7 105.6 103.8 103.8 102.7 98.1 98.3 88.2
## [133] 77.0 74.2 69.7 68.3 65.3 61.8 62.8 65.6 69.6 65.5 61.9 62.1
## [145] 60.0 58.7 54.7 46.4 43.0 43.1 39.0 39.0 37.7 37.7 41.4 41.4
## [157] 42.0 43.4 40.0 34.3 32.2 29.8 31.5 29.2 26.8 23.5 25.5 23.8
## [169] 23.7 20.6 22.0 21.7 22.2 18.8 17.5 17.5 16.7 14.5 15.1 16.7
## [181] 19.0 17.8 16.4 19.4 20.9 22.0 20.0 16.7 17.8 16.6 16.5 16.2
## [193] 15.2 17.2 20.9 20.0 21.9 20.2 22.3 23.0 23.6 22.6 23.1 20.9
## [205] 21.3 20.5 20.0 19.0 18.7 18.7 21.3 19.3 17.6 17.6 16.2 16.1
## [217] 18.5 16.0 18.2 19.3 20.2 20.0 18.0 18.2 18.2 17.3 14.4 12.8
## [229] 11.7 14.2 13.6 14.3 13.7 15.1 14.5 15.9 14.7 12.9 14.0 15.0
## [241] 16.9 18.3 17.8 17.6 20.0 19.7 20.2 18.8 17.3 18.2 18.3 18.0
## [253] 15.6 14.9 13.6 15.4 14.4 13.9 11.7 13.7 12.5 14.3 12.5 13.0
## [265] 14.8 13.7 13.2 14.6 14.2 16.2 16.9 16.1 16.9 17.2 17.6 19.7
## [277] 18.0 18.6 16.7 18.8 19.3 20.0 18.2 20.5 21.0 23.7 22.8 22.5
## [289] 18.9 18.0 17.5 17.3 17.1 18.8 21.5 23.4 23.1 24.7 23.5 24.9
## [301] 26.3 24.3 26.3 30.2 30.6 34.9 35.3 34.8 37.3 39.3 40.0 39.3
## [313] 40.5 40.8 47.0 51.7 50.3 52.5 55.5 56.0 57.9 54.9 55.8 59.8
## [325] 59.1 57.7 59.7 60.0 59.9 55.0 57.6 61.9 60.8 58.8 62.7 64.6
## [337] 68.3 61.9 58.7 58.9 56.0 54.7 54.3 53.9 57.3 59.5 66.2 70.8
## [349] 74.2 67.8 66.0 66.0 62.3 58.5 59.2 60.3 62.7 65.2 59.3 63.3
## [361] 62.2 61.3 56.7 61.6 59.0 67.6 66.0 69.6 75.6 78.2 73.8 72.1
## [373] 70.2 68.0 68.2 62.1 60.4 61.6 66.2 63.9 65.3 60.9 58.1 60.9
## [385] 57.5 61.3 62.3 58.9 55.8 56.2 50.6 52.1 45.3 41.7 41.0 38.4
## [397] 36.7 35.1 35.4 35.6 38.0 39.7 42.4 38.6 34.5 29.8 26.7 23.9
## [409] 22.6 20.9 21.3 23.9 28.7 27.7 27.1 24.0 25.9 25.4 22.9 19.0
## [421] 19.4 20.6 23.4 22.7 23.5 25.1 23.8 23.8 19.9 20.4 19.3 18.7
## [433] 16.7 19.5 22.5 24.1 21.6 21.6 22.8 23.5 23.6 20.2 19.4 21.2
## [445] 23.4 24.5 27.5 25.6 26.3 27.1 25.4 24.2 23.7 19.8 20.6 19.3
## [457] 20.9 21.2 21.4 19.6 22.4 20.3 19.0 19.6 19.0 18.9 20.4 19.1
## [469] 20.3 21.4 17.1 17.0 17.5 17.3 17.5 17.0 16.2 15.9 15.2 17.1
## [481] 15.6 12.9 14.3 14.5 15.4 15.7 13.4 13.0 13.1 12.5 12.5 12.5
## [493] 12.5 10.8 10.6 12.2 10.2 10.2 10.2 10.5 10.8 11.8 9.9 10.7
## [505] 9.4 8.3 6.5 6.8 5.4 6.4 5.6 6.1 7.2 7.7 6.7 5.9
## [517] 5.6 5.6 4.9 4.3 3.8 4.9 5.8 4.8 5.4 4.9 3.8 5.2
## [529] 4.1 2.9 3.4 2.3 2.3 2.8 1.5 2.1 2.6 2.1 1.5 2.2
## [541] 1.7 1.7 1.2 0.9 1.4 1.4 0.9 0.9 0.9 0.9 1.2 1.2
## [553] 1.7 1.5 2.1 2.5 3.3 2.8 3.5 3.5 5.8 5.7 5.3 5.7
## [565] 5.7 5.3 5.6 3.5 3.5 4.7 4.6 6.3 6.3 8.7 11.1 11.7
## [577] 11.7 11.7 12.9 13.3 11.3 9.7 11.7 12.6 14.2 13.4 12.8 11.9
## [589] 13.0 13.5 12.5 11.9 12.7 14.3 15.8 15.4 12.0 14.7 15.5 16.2
## [601] 17.6 17.9 17.8 18.8 19.4 18.4 16.9 15.0 14.4 15.4 13.8 13.3
## [613] 11.9 13.9 13.7 14.0 14.9 17.8 15.8 18.5 16.2 17.8 17.1 16.0
## [625] 14.6 14.0 12.5 12.1 11.0 11.5 13.0 13.9 13.9 13.0 14.6 16.1
## [637] 15.7 14.6 13.7 13.7 14.6 13.2 11.7 12.2 12.5 12.2 11.8 12.1
## [649] 12.5 10.9 10.3 9.2 10.3 11.7 10.4 10.9 12.1 12.8 11.7 9.7
## [661] 8.4 8.4 7.6 7.3 7.2 9.0 10.1 10.5 10.7 9.7 8.7 7.2
## [673] 6.7 6.2 6.2 6.1 6.4 6.7 6.5 6.3 6.7 5.7 7.3 9.2
## [685] 9.3 11.2 12.1 13.5 14.4 14.0 12.9 14.0 13.2 13.5 13.5 13.5
## [697] 13.5 14.4 15.9 18.3 19.1 19.8 20.9 20.7 22.6 24.7 24.0 22.6
## [709] 22.1 24.3 26.7 24.2 25.2 31.5 33.3 34.2 36.5 37.5 43.0 44.9
## [721] 45.1 49.5 57.8 62.1 71.9 90.0 107.4 117.1 131.4 138.0 141.4 150.3
## [733] 145.2 145.4 150.6 149.6 151.2 155.4 149.1 149.9 142.7 134.7 127.8 121.5
## [745] 113.9 101.8 90.0 80.7 74.9 69.0 62.9 59.2 58.5 56.6 52.8 47.5
## [757] 43.5 38.2 38.4 35.5 29.4 27.5 27.5 23.9 23.3 18.0 17.9 21.5
## [769] 20.8 17.1 19.5 19.4 17.3 16.6 14.6 12.8 12.4 9.8 7.6 8.2
## [781] 7.5 6.3 6.8 5.9 5.2 5.6 5.8 6.3 5.6 4.0 4.9 4.9
## [793] 5.1 4.8 3.6 3.1 4.6 3.4 3.7 3.7 3.7 4.1 4.3 2.9
## [805] 2.9 2.6 2.5 2.3 2.4 1.9 2.4 2.2 1.9 1.4 1.8 1.4
## [817] 2.2 1.8 2.0 2.4 3.8 3.8 4.8 4.5 4.5 5.7 5.3 3.8
## [829] 4.6 3.8 5.8 7.0 7.5 8.0 9.7 8.3 8.5 7.4 7.6 6.7
## [841] 7.9 7.2 8.4 9.2 8.7 7.5 7.7 6.4 6.5 7.0 7.4 8.7
## [853] 10.6 12.6 17.0 16.8 17.2 17.5 18.0 18.0 19.1 15.7 17.0 16.4
## [865] 16.8 15.6 15.6 14.3 14.3 16.3 17.7 16.8 18.5 19.6 21.3 23.4
## [877] 23.5 22.4 24.9 23.3 23.1 22.2 21.2 19.5 19.0 15.8 16.6 15.2
## [889] 14.6 14.7 14.6 15.0 15.6 18.6 19.9 20.0 20.0 22.9 23.1 25.8
## [901] 22.5 23.3 24.0 24.3 22.9 23.1 21.3 25.5 25.6 24.1 23.3 22.0
## [913] 20.6 20.8 17.6 17.0 20.0 22.9 23.5 25.6 25.6 26.1 26.0 23.8
## [925] 19.9 18.8 16.4 19.4 18.6 18.6 19.5 21.1 21.4 24.2 21.4 24.6
## [937] 25.5 23.8 25.8 26.2 24.3 24.0 21.3 23.0 25.2 25.6 27.4 28.4
## [949] 30.1 31.6 28.4 26.9 26.4 25.2 25.5 24.7 22.2 21.3 23.1 22.3
## [961] 21.9 22.1 21.3 21.8 21.4 20.2 18.6 18.7 17.8 16.3 14.5 19.8
## [973] 18.9 19.2 19.1 19.1 20.7 23.7 18.6 17.7 18.5 18.1 17.4 16.4
## [985] 16.9 17.7 17.6 18.2 16.8 17.8 18.8 19.1 19.1 18.3 15.9 17.3
## [997] 16.3 14.3 12.4 14.3 14.5 15.2 15.2 13.7 13.7 13.5 12.1 11.4
## [1009] 11.1 10.8 11.5 11.9 10.9 9.3 12.7 15.0 16.0 16.7 18.5 19.3
## [1021] 20.6 18.9 16.8 16.0 15.7 15.2 15.7 13.2 10.9 8.7data3 <- runif(31,)
# Calendar
calendR(year = 2022,
month = 10,
special.days = data3,
gradient = TRUE,
low.col = "white",
special.col = "#8B1C62",
legend.pos = "bottom")
Hypothesis
H0 : There is no association between the physical environment and the severity or incidence of road crashes
HA : There is an association between the physical environment and the severity or incidence of road crashes
To assess the validity of the null hypothesis, I will perform a number of hypothesis tests to test the association of key variables. For each variable, I will identify a specific null and alternative hypothesis. The variables of interest are:
- Crash Time
- INTERSECTION
- LIGHTING
- ROAD CONDITION
- WEATHER CONDITION
For CRASH TIME and INTERSECTION, I can make reasonable assumptions about the expected proportions and will use the Chi-squared goodness of fit test calculated as:
X2=∑(Obs−Exp)2/Exp
For LIGHTING CONDITION, ROAD CONDITION, and WEATHER CONDITION, the population proportions are not available and cannot be reasonably estimated. Data isn’t available for lighting, road and weather conditions other than what is reported in the dataset. For this reason, I will use the Chi-squared test of association calculated as:
X2=∑(Oij−Eij)2/Eij
Time of Day - Hypothesis Testing
From the summary statistics, we can start to make an estimation that a road crash is more likely to be recorded at certain times of day. This estimation will be examined with the Chi-squared goodness of fit test to test if the recorded observations “fit” the model of expected outcomes.
H0: Road crashes are evenly distributed throughout the day
HA: Road crashes are not evenly distributed throughout the day
hist(crashdf$Time, prob = TRUE, main = "Vehicle Collisions Throughout the Day",col = "lightblue")
x <- seq(min(crashdf$Time), max(crashdf$Time), length = 40)
f <- dnorm(x, mean = mean(crashdf$Time), sd = sd(crashdf$Time))
lines(x, f, col = "red", lwd = 2)
### Based on the visualization, we can see that there are certain times that accidents are much higher than others. We can accept the null hypothesis based on the normal line curve.
library(knitr)Summary Statistics for light
table(crashdf$Light.x) %>% prop.table() %>% round(3) %>% kable(col.names = c("Light Condition", "Proportion"))| Light Condition | Proportion |
|---|---|
| DARK – UNKNOWN LIGHTING | 0.009 |
| DARK LIGHTS ON | 0.225 |
| DARK NO LIGHTS | 0.029 |
| DAWN | 0.020 |
| DAYLIGHT | 0.680 |
| DUSK | 0.023 |
| N/A | 0.008 |
| OTHER | 0.002 |
| UNKNOWN | 0.004 |
Create data for barplot
my_table <- table(CrashDriver$Light)Bar plot of accidents in different types of lighting
barp <- barplot(my_table, col = rainbow(3), ylim = c(0, 120000), las=2)
text(barp, my_table + 0.5, labels = my_table)
Lighting Conditions - Hypothesis Testing
Lighting is a key consideration of visibility and therefore safety while driving. Headlights and streetlights are an attempt to address the risks of driving in the dark. But how does lighting affect the severity of a road crash? I will used the Chi-square test of association to examine this question.
H0: There is no association between lighting conditions and crash severity
HA: There is an association between lighting conditions and crash severity
qchisq(p = .05, df = 1, lower.tail = FALSE)## [1] 3.841459chisq.test(table(CrashDriver$Injury.Severity, CrashDriver$Light))## Warning in chisq.test(table(CrashDriver$Injury.Severity, CrashDriver$Light)):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(CrashDriver$Injury.Severity, CrashDriver$Light)
## X-squared = 255.22, df = 32, p-value < 2.2e-16We can reject the null hypothesis that there is no association between lighting condition and crash severity on the basis of the critical value and the p-value. We can use a visualisation to see what patterns may be occurring here and guide us on what further investigations might be necessary.
library(ggplot2)ggplot(CrashDriver, aes(fill=Light, y= Latitude, x=ACRS.Report.Type, )) +
geom_bar(Light='stack', stat='identity')+
theme(axis.text.x = element_text(angle = 90)) ## Warning: Ignoring unknown parameters: Light
Road Conditions - Hypothesis Testing
A road’s surface affects the ability of a vehicle to drive as expected. Crucially for crashes, a road that is slippery for any reason is likely to inhibit a driver’s ability to steer and brake safely. To examine the relationship between road condition and the severity of crash, I will used the Chi-squared test of association.
H0: There is no association between the bad condition road conditions and the severity of a crash
HA: There is an association between the bad conditions and the severity of a crash
Road Conditions - Summary Statistics
table(CrashDriver$Surface.Condition) %>% prop.table() %>% round(3) %>% kable(col.names = c("Road Condition", "Proportion"))| Road Condition | Proportion |
|---|---|
| 0.093 | |
| DRY | 0.697 |
| ICE | 0.007 |
| MUD, DIRT, GRAVEL | 0.000 |
| N/A | 0.025 |
| OIL | 0.000 |
| OTHER | 0.001 |
| SAND | 0.000 |
| SLUSH | 0.001 |
| SNOW | 0.006 |
| UNKNOWN | 0.003 |
| WATER(STANDING/MOVING) | 0.000 |
| WET | 0.166 |
chisq.test(table(CrashDriver$Injury.Severity, CrashDriver$Surface.Condition))## Warning in chisq.test(table(CrashDriver$Injury.Severity,
## CrashDriver$Surface.Condition)): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(CrashDriver$Injury.Severity, CrashDriver$Surface.Condition)
## X-squared = 1742.4, df = 48, p-value < 2.2e-16ggplot(CrashDriver, aes(fill=Injury.Severity, y= Latitude, x=Surface.Condition, )) +
geom_bar(Injury.Severity='stack', stat='identity')+
theme(axis.text.x = element_text(angle = 90))## Warning: Ignoring unknown parameters: Injury.Severity
### We can accept the null hypothesis. The visualisation above demonstrate that crashes which occur on a road in good condition are more likely to have severe consequences.
Weather Conditions - Hypothesis Testing
Similar to lighting and road conditions, weather conditions can impact the ability of drivers to complete their journey safely. Using the Chi-square test of association, I will examine whether this is reflected in the severity of crashes observed.
H0: There is no association between reported weather conditions and the severity of a crash
HA: There is an association between reported weather conditions and the severity of a crash
table(CrashDriver$Weather) %>% prop.table() %>% round(3) %>% kable(col.names = c("Weather", "Proportion"))| Weather | Proportion |
|---|---|
| BLOWING SAND, SOIL, DIRT | 0.000 |
| BLOWING SNOW | 0.001 |
| CLEAR | 0.674 |
| CLOUDY | 0.103 |
| FOGGY | 0.004 |
| N/A | 0.078 |
| OTHER | 0.002 |
| RAINING | 0.120 |
| SEVERE WINDS | 0.001 |
| SLEET | 0.001 |
| SNOW | 0.010 |
| UNKNOWN | 0.004 |
| WINTRY MIX | 0.003 |
Table for weather to make a bar plot
my_table1 <- table(CrashDriver$Weather)Bar plot of crashes in certain types of weather
barp1 <- barplot(my_table1, col = rainbow(3), ylim = c(0, 120000), las=2)
text(barp1, my_table1 + 0.5, labels = my_table)
Stack bar plot of weather’s effect on injury severity
ggplot(CrashDriver, aes(fill=Injury.Severity, y= Latitude, x=Weather, )) +
geom_bar(Injury.Severity='stack', stat='identity')+
theme(axis.text.x = element_text(angle = 90))## Warning: Ignoring unknown parameters: Injury.Severity
chisq.test(table(CrashDriver$Injury.Severity, CrashDriver$Weather))## Warning in chisq.test(table(CrashDriver$Injury.Severity, CrashDriver$Weather)):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(CrashDriver$Injury.Severity, CrashDriver$Weather)
## X-squared = 196.68, df = 48, p-value < 2.2e-16With an X squared value of 196.68 and a p-value of < 2.2e-16, we can confidently reject the null hypothesis, having satisfied both X2>X2crit and p<σ. The chart below provides some ideas for further analysis.
MD_Geo = CrashDriverMap of Collisions within Montgomery County
MD_Geo%>%
select(Longitude, Latitude, Report.Number, Collision.Type, Traffic.Control, Vehicle.Damage.Extent, Injury.Severity, ACRS.Report.Type) %>%
leaflet() %>%
setView(lng = -77.2030633, lat = 39.1373815, zoom = 7) %>%
addTiles() %>%
addMarkers(clusterOptions = markerClusterOptions(),
~Longitude, ~Latitude,
label = ~paste(
"Report Number :", Report.Number,
"Collision Type :", Collision.Type,
"Traffic Control :", Traffic.Control,
"Vehicle Damage :", Vehicle.Damage.Extent,
"Injury Severity :", Injury.Severity,
"ACRS Report.Type :", Report.Number))