homework_eight
Isaac Buabeng
2025-03-26
Acknowlegements and Data Use Permissions
The dataset used for this homework was focused on the number of active daily covid-19 cases recorded in 2020 across Ghana and was sourced from google kaggle, with subsequent source permissions from the Ministry of Health, Ghana
loading in the necessary libraries
library(ggplot2) # for graphics
library(MASS) # for maximum likelihood estimation## Warning: package 'MASS' was built under R version 4.4.3library(skimr) # for inspecting our dataload in the dataset and inspect it
data <- read.table("Ghana_Covid19_DailyActive.csv",header=TRUE, sep=",", fill=TRUE, quote="")
str(data) # checking the structure of the dataframe## 'data.frame': 233 obs. of 8 variables:
## $ confirmed : int 2 2 2 1 2 2 5 3 3 3 ...
## $ recovered : int 0 0 0 0 0 0 0 2 0 0 ...
## $ death : int 0 0 0 0 0 0 0 1 1 0 ...
## $ date : chr "03/12/2020" "03/13/2020" "03/14/2020" "03/17/2020" ...
## $ cumulative_confirmed: int 2 4 6 7 9 11 16 19 22 25 ...
## $ cumulative_recovered: int 0 0 0 0 0 0 0 2 2 2 ...
## $ cumulative_death : int 0 0 0 0 0 0 0 1 2 2 ...
## $ active_cases : int 2 4 6 7 9 11 16 16 18 21 ...skim(data) # inspecting the dataframe| Name | data |
| Number of rows | 233 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| date | 0 | 1 | 10 | 10 | 0 | 233 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| confirmed | 0 | 1 | 221.75 | 241.39 | 1 | 59 | 131 | 295 | 1513 | ▇▂▁▁▁ |
| recovered | 0 | 1 | 216.94 | 441.66 | 0 | 32 | 97 | 221 | 5526 | ▇▁▁▁▁ |
| death | 0 | 1 | 1.39 | 2.57 | 0 | 0 | 0 | 2 | 15 | ▇▁▁▁▁ |
| cumulative_confirmed | 0 | 1 | 28642.17 | 19231.47 | 2 | 8070 | 35501 | 46626 | 51667 | ▅▂▂▁▇ |
| cumulative_recovered | 0 | 1 | 26223.44 | 20028.78 | 0 | 2947 | 32096 | 45757 | 50547 | ▆▂▁▂▇ |
| cumulative_death | 0 | 1 | 174.39 | 124.10 | 0 | 36 | 182 | 301 | 323 | ▆▂▂▁▇ |
| active_cases | 0 | 1 | 2244.34 | 2066.14 | 2 | 524 | 1384 | 3782 | 8585 | ▇▂▃▁▁ |
summary(data) # inspecting the dataframe## confirmed recovered death date
## Min. : 1.0 Min. : 0.0 Min. : 0.000 Length:233
## 1st Qu.: 59.0 1st Qu.: 32.0 1st Qu.: 0.000 Class :character
## Median : 131.0 Median : 97.0 Median : 0.000 Mode :character
## Mean : 221.7 Mean : 216.9 Mean : 1.386
## 3rd Qu.: 295.0 3rd Qu.: 221.0 3rd Qu.: 2.000
## Max. :1513.0 Max. :5526.0 Max. :15.000
## cumulative_confirmed cumulative_recovered cumulative_death active_cases
## Min. : 2 Min. : 0 Min. : 0.0 Min. : 2
## 1st Qu.: 8070 1st Qu.: 2947 1st Qu.: 36.0 1st Qu.: 524
## Median :35501 Median :32096 Median :182.0 Median :1384
## Mean :28642 Mean :26223 Mean :174.4 Mean :2244
## 3rd Qu.:46626 3rd Qu.:45757 3rd Qu.:301.0 3rd Qu.:3782
## Max. :51667 Max. :50547 Max. :323.0 Max. :8585print(data)## confirmed recovered death date cumulative_confirmed
## 1 2 0 0 03/12/2020 2
## 2 2 0 0 03/13/2020 4
## 3 2 0 0 03/14/2020 6
## 4 1 0 0 03/17/2020 7
## 5 2 0 0 03/18/2020 9
## 6 2 0 0 03/19/2020 11
## 7 5 0 0 03/20/2020 16
## 8 3 2 1 03/21/2020 19
## 9 3 0 1 03/22/2020 22
## 10 3 0 0 03/23/2020 25
## 11 26 0 0 03/24/2020 51
## 12 15 0 0 03/25/2020 66
## 13 64 0 1 03/26/2020 130
## 14 5 0 1 03/27/2020 135
## 15 6 0 1 03/28/2020 141
## 16 11 0 0 03/29/2020 152
## 17 9 1 0 03/31/2020 161
## 18 34 0 0 04/01/2020 195
## 19 9 0 0 04/02/2020 204
## 20 1 0 0 04/03/2020 205
## 21 9 0 0 04/04/2020 214
## 22 73 0 0 04/06/2020 287
## 23 26 0 0 04/07/2020 313
## 24 65 1 1 04/09/2020 378
## 25 30 0 2 04/10/2020 408
## 26 158 0 0 04/11/2020 566
## 27 70 79 0 04/14/2020 636
## 28 5 0 0 04/15/2020 641
## 29 193 16 1 04/18/2020 834
## 30 208 0 0 04/19/2020 1042
## 31 237 35 1 04/22/2020 1279
## 32 271 21 1 04/25/2020 1550
## 33 121 33 5 04/27/2020 1671
## 34 403 24 1 04/28/2020 2074
## 35 95 17 1 05/01/2020 2169
## 36 550 65 0 05/02/2020 2719
## 37 372 9 0 05/04/2020 3091
## 38 921 20 0 05/07/2020 4012
## 39 251 55 4 05/08/2020 4263
## 40 437 116 0 05/10/2020 4700
## 41 427 0 0 05/11/2020 5127
## 42 281 20 2 05/12/2020 5408
## 43 122 160 0 05/13/2020 5530
## 44 108 786 4 05/14/2020 5638
## 45 97 294 1 05/15/2020 5735
## 46 183 0 2 05/17/2020 5918
## 47 178 19 0 05/18/2020 6096
## 48 173 125 0 05/19/2020 6269
## 49 217 53 0 05/20/2020 6486
## 50 131 27 0 05/21/2020 6617
## 51 66 20 1 05/22/2020 6683
## 52 125 72 0 05/23/2020 6808
## 53 156 27 0 05/24/2020 6964
## 54 153 220 2 05/25/2020 7117
## 55 186 95 0 05/26/2020 7303
## 56 313 9 0 05/27/2020 7616
## 57 152 119 1 05/28/2020 7768
## 58 113 301 1 05/29/2020 7881
## 59 189 106 0 05/30/2020 8070
## 60 227 39 2 05/31/2020 8297
## 61 251 146 0 06/01/2020 8548
## 62 337 57 0 06/02/2020 8885
## 63 283 268 4 06/03/2020 9168
## 64 294 90 2 06/04/2020 9462
## 65 176 89 0 06/05/2020 9638
## 66 272 9 4 06/06/2020 9910
## 67 291 110 0 06/07/2020 10201
## 68 157 69 0 06/08/2020 10358
## 69 498 97 0 06/09/2020 10856
## 70 262 58 0 06/10/2020 11118
## 71 304 177 3 06/11/2020 11422
## 72 295 102 3 06/12/2020 11717
## 73 247 0 0 06/13/2020 11964
## 74 229 68 4 06/14/2020 12193
## 75 397 84 8 06/15/2020 12590
## 76 339 58 0 06/16/2020 12929
## 77 274 80 4 06/17/2020 13203
## 78 514 5526 15 06/18/2020 13717
## 79 290 399 0 06/19/2020 14007
## 80 147 0 0 06/20/2020 14154
## 81 414 434 10 06/21/2020 14568
## 82 445 171 0 06/22/2020 15013
## 83 460 353 0 06/23/2020 15473
## 84 361 324 8 06/24/2020 15834
## 85 597 502 0 06/25/2020 16431
## 86 311 463 9 06/26/2020 16742
## 87 609 274 0 06/27/2020 17351
## 88 390 274 0 06/28/2020 17741
## 89 393 282 5 06/29/2020 18134
## 90 1254 780 0 07/01/2020 19388
## 91 697 540 5 07/02/2020 20085
## 92 992 1200 7 07/03/2020 21077
## 93 891 1086 0 07/04/2020 21968
## 94 854 408 0 07/05/2020 22822
## 95 641 1058 0 07/06/2020 23463
## 96 371 590 6 07/07/2020 23834
## 97 414 619 0 07/08/2020 24248
## 98 270 356 4 07/09/2020 24518
## 99 470 880 0 07/10/2020 24988
## 100 264 330 0 07/11/2020 25252
## 101 178 114 0 07/12/2020 25430
## 102 695 759 0 07/13/2020 26125
## 103 447 645 5 07/14/2020 26572
## 104 488 129 1 07/15/2020 27060
## 105 607 205 3 07/16/2020 27667
## 106 763 1652 5 07/17/2020 28430
## 107 559 430 0 07/18/2020 28989
## 108 683 759 0 07/19/2020 29672
## 109 694 597 0 07/20/2020 30366
## 110 691 1114 8 07/21/2020 31057
## 111 794 637 0 07/22/2020 31851
## 112 586 489 0 07/23/2020 32437
## 113 532 567 7 07/24/2020 32969
## 114 655 307 0 07/25/2020 33624
## 115 782 820 0 07/26/2020 34406
## 116 736 665 7 07/27/2020 35142
## 117 359 810 7 07/28/2020 35501
## 118 1513 1269 0 07/30/2020 37014
## 119 798 948 9 07/31/2020 37812
## 120 689 469 0 08/01/2020 38501
## 121 574 781 8 08/02/2020 39075
## 122 567 821 0 08/03/2020 39642
## 123 455 254 7 08/04/2020 40097
## 124 436 1064 0 08/05/2020 40533
## 125 470 628 9 08/06/2020 41003
## 126 209 397 0 08/07/2020 41212
## 127 192 328 0 08/08/2020 41404
## 128 168 265 8 08/09/2020 41572
## 129 153 175 0 08/10/2020 41725
## 130 122 223 0 08/11/2020 41847
## 131 216 275 8 08/12/2020 42063
## 132 147 154 0 08/13/2020 42210
## 133 322 215 0 08/14/2020 42532
## 134 121 205 8 08/15/2020 42653
## 135 340 229 9 08/16/2020 42993
## 136 83 167 8 08/17/2020 43076
## 137 184 313 5 08/18/2020 43260
## 138 65 132 0 08/19/2020 43325
## 139 180 124 0 08/20/2020 43505
## 140 117 163 2 08/21/2020 43622
## 141 95 148 7 08/22/2020 43717
## 142 52 205 0 08/23/2020 43769
## 143 72 198 0 08/24/2020 43841
## 144 108 146 0 08/25/2020 43949
## 145 169 189 0 08/26/2020 44118
## 146 87 196 6 08/27/2020 44205
## 147 255 344 0 08/29/2020 44460
## 148 157 232 0 08/30/2020 44617
## 149 41 125 0 08/31/2020 44658
## 150 55 99 4 09/01/2020 44713
## 151 64 116 3 09/02/2020 44777
## 152 92 108 0 09/03/2020 44869
## 153 143 97 0 09/04/2020 45012
## 154 176 144 0 09/05/2020 45188
## 155 125 146 0 09/06/2020 45313
## 156 75 79 2 09/07/2020 45388
## 157 46 75 1 09/08/2020 45434
## 158 59 129 3 09/09/2020 45493
## 159 79 170 3 09/10/2020 45572
## 160 29 38 2 09/11/2020 45601
## 161 54 118 0 09/12/2020 45655
## 162 59 99 0 09/13/2020 45714
## 163 46 77 1 09/14/2020 45760
## 164 97 56 0 09/15/2020 45857
## 165 20 52 2 09/16/2020 45877
## 166 127 72 0 09/17/2020 46004
## 167 58 105 0 09/18/2020 46062
## 168 54 32 0 09/19/2020 46116
## 169 37 9 2 09/20/2020 46153
## 170 69 118 0 09/21/2020 46222
## 171 131 160 0 09/22/2020 46353
## 172 34 41 0 09/23/2020 46387
## 173 57 28 0 09/24/2020 46444
## 174 38 5 2 09/25/2020 46482
## 175 144 106 0 09/26/2020 46626
## 176 30 185 0 09/27/2020 46656
## 177 38 3 0 09/28/2020 46694
## 178 74 19 0 09/29/2020 46768
## 179 35 42 2 09/30/2020 46803
## 180 26 54 0 10/01/2020 46829
## 181 76 163 3 10/03/2020 46905
## 182 42 36 0 10/04/2020 46947
## 183 17 51 0 10/05/2020 46964
## 184 23 68 0 10/06/2020 46987
## 185 18 20 0 10/07/2020 47005
## 186 25 26 2 10/08/2020 47030
## 187 67 28 0 10/09/2020 47097
## 188 29 17 2 10/10/2020 47126
## 189 25 21 0 10/11/2020 47151
## 190 22 37 0 10/12/2020 47173
## 191 26 14 0 10/13/2020 47199
## 192 33 37 0 10/14/2020 47232
## 193 78 40 0 10/15/2020 47310
## 194 62 46 0 10/16/2020 47372
## 195 89 88 2 10/17/2020 47461
## 196 77 37 0 10/18/2020 47538
## 197 63 35 2 10/19/2020 47601
## 198 48 32 0 10/20/2020 47649
## 199 41 31 2 10/21/2020 47690
## 200 55 32 0 10/22/2020 47745
## 201 30 52 0 10/23/2020 47775
## 202 216 165 4 10/26/2020 47991
## 203 64 33 0 10/27/2020 48055
## 204 69 46 0 10/28/2020 48124
## 205 76 45 0 10/29/2020 48200
## 206 311 112 0 10/31/2020 48511
## 207 132 74 0 11/01/2020 48643
## 208 145 75 0 11/02/2020 48788
## 209 116 90 0 11/03/2020 48904
## 210 100 55 0 11/04/2020 49004
## 211 198 60 0 11/05/2020 49202
## 212 100 117 0 11/06/2020 49302
## 213 270 102 0 11/07/2020 49572
## 214 236 74 0 11/08/2020 49808
## 215 149 77 0 11/09/2020 49957
## 216 61 83 0 11/10/2020 50018
## 217 105 149 2 11/11/2020 50123
## 218 81 232 1 11/12/2020 50204
## 219 172 66 0 11/13/2020 50376
## 220 81 84 0 11/14/2020 50457
## 221 102 172 0 11/15/2020 50559
## 222 72 221 0 11/16/2020 50631
## 223 86 178 0 11/17/2020 50717
## 224 157 124 0 11/18/2020 50874
## 225 67 194 0 11/19/2020 50941
## 226 153 232 0 11/20/2020 51094
## 227 90 198 0 11/21/2020 51184
## 228 41 98 0 11/22/2020 51225
## 229 49 78 0 11/23/2020 51274
## 230 105 93 0 11/24/2020 51379
## 231 97 60 0 11/25/2020 51476
## 232 93 92 0 11/26/2020 51569
## 233 98 97 0 11/27/2020 51667
## cumulative_recovered cumulative_death active_cases
## 1 0 0 2
## 2 0 0 4
## 3 0 0 6
## 4 0 0 7
## 5 0 0 9
## 6 0 0 11
## 7 0 0 16
## 8 2 1 16
## 9 2 2 18
## 10 2 2 21
## 11 2 2 47
## 12 2 2 62
## 13 2 3 125
## 14 2 4 129
## 15 2 5 134
## 16 2 5 145
## 17 3 5 153
## 18 3 5 187
## 19 3 5 196
## 20 3 5 197
## 21 3 5 206
## 22 3 5 279
## 23 3 5 305
## 24 4 6 368
## 25 4 8 396
## 26 4 8 554
## 27 83 8 545
## 28 83 8 550
## 29 99 9 726
## 30 99 9 934
## 31 134 10 1135
## 32 155 11 1384
## 33 188 16 1467
## 34 212 17 1845
## 35 229 18 1922
## 36 294 18 2407
## 37 303 18 2770
## 38 323 18 3671
## 39 378 22 3863
## 40 494 22 4184
## 41 494 22 4611
## 42 514 24 4870
## 43 674 24 4832
## 44 1460 28 4150
## 45 1754 29 3952
## 46 1754 31 4133
## 47 1773 31 4292
## 48 1898 31 4340
## 49 1951 31 4504
## 50 1978 31 4608
## 51 1998 32 4653
## 52 2070 32 4706
## 53 2097 32 4835
## 54 2317 34 4766
## 55 2412 34 4857
## 56 2421 34 5161
## 57 2540 35 5193
## 58 2841 36 5004
## 59 2947 36 5087
## 60 2986 38 5273
## 61 3132 38 5378
## 62 3189 38 5658
## 63 3457 42 5669
## 64 3547 44 5871
## 65 3636 44 5958
## 66 3645 48 6217
## 67 3755 48 6398
## 68 3824 48 6486
## 69 3921 48 6887
## 70 3979 48 7091
## 71 4156 51 7215
## 72 4258 54 7405
## 73 4258 54 7652
## 74 4326 58 7809
## 75 4410 66 8114
## 76 4468 66 8395
## 77 4548 70 8585
## 78 10074 85 3558
## 79 10473 85 3449
## 80 10473 85 3596
## 81 10907 95 3566
## 82 11078 95 3840
## 83 11431 95 3947
## 84 11755 103 3976
## 85 12257 103 4071
## 86 12720 112 3910
## 87 12994 112 4245
## 88 13268 112 4361
## 89 13550 117 4467
## 90 14330 117 4941
## 91 14870 122 5093
## 92 16070 129 4878
## 93 17156 129 4683
## 94 17564 129 5129
## 95 18622 129 4712
## 96 19212 135 4487
## 97 19831 135 4282
## 98 20187 139 4192
## 99 21067 139 3782
## 100 21397 139 3716
## 101 21511 139 3780
## 102 22270 139 3716
## 103 22915 144 3513
## 104 23044 145 3871
## 105 23249 148 4270
## 106 24901 153 3376
## 107 25331 153 3505
## 108 26090 153 3429
## 109 26687 153 3526
## 110 27801 161 3095
## 111 28438 161 3252
## 112 28927 161 3349
## 113 29494 168 3307
## 114 29801 168 3655
## 115 30621 168 3617
## 116 31286 175 3681
## 117 32096 182 3223
## 118 33365 182 3467
## 119 34313 191 3308
## 120 34782 191 3528
## 121 35563 199 3313
## 122 36384 199 3059
## 123 36638 206 3253
## 124 37702 206 2625
## 125 38330 215 2458
## 126 38727 215 2270
## 127 39055 215 2134
## 128 39320 223 2029
## 129 39495 223 2007
## 130 39718 223 1906
## 131 39993 231 1839
## 132 40147 231 1832
## 133 40362 231 1939
## 134 40567 239 1847
## 135 40796 248 1949
## 136 40963 256 1857
## 137 41276 261 1723
## 138 41408 261 1656
## 139 41532 261 1712
## 140 41695 263 1664
## 141 41843 270 1604
## 142 42048 270 1451
## 143 42246 270 1325
## 144 42392 270 1287
## 145 42581 270 1267
## 146 42777 276 1152
## 147 43121 276 1063
## 148 43353 276 988
## 149 43478 276 904
## 150 43577 280 856
## 151 43693 283 801
## 152 43801 283 785
## 153 43898 283 831
## 154 44042 283 863
## 155 44188 283 842
## 156 44267 285 836
## 157 44342 286 806
## 158 44471 289 733
## 159 44641 292 639
## 160 44679 294 628
## 161 44797 294 564
## 162 44896 294 524
## 163 44973 295 492
## 164 45029 295 533
## 165 45081 297 499
## 166 45153 297 554
## 167 45258 297 507
## 168 45290 297 529
## 169 45299 299 555
## 170 45417 299 506
## 171 45577 299 477
## 172 45618 299 470
## 173 45646 299 499
## 174 45651 301 530
## 175 45757 301 568
## 176 45942 301 413
## 177 45945 301 448
## 178 45964 301 503
## 179 46006 303 494
## 180 46060 303 466
## 181 46223 306 376
## 182 46259 306 382
## 183 46310 306 348
## 184 46378 306 303
## 185 46398 306 301
## 186 46424 308 298
## 187 46452 308 337
## 188 46469 310 347
## 189 46490 310 351
## 190 46527 310 336
## 191 46541 310 348
## 192 46578 310 344
## 193 46618 310 382
## 194 46664 310 398
## 195 46752 312 397
## 196 46789 312 437
## 197 46824 314 463
## 198 46856 314 479
## 199 46887 316 487
## 200 46919 316 510
## 201 46971 316 488
## 202 47136 320 535
## 203 47169 320 566
## 204 47215 320 589
## 205 47260 320 620
## 206 47372 320 819
## 207 47446 320 877
## 208 47521 320 947
## 209 47611 320 973
## 210 47666 320 1018
## 211 47726 320 1156
## 212 47843 320 1139
## 213 47945 320 1307
## 214 48019 320 1469
## 215 48096 320 1541
## 216 48179 320 1519
## 217 48328 322 1473
## 218 48560 323 1321
## 219 48626 323 1427
## 220 48710 323 1424
## 221 48882 323 1354
## 222 49103 323 1205
## 223 49281 323 1113
## 224 49405 323 1146
## 225 49599 323 1019
## 226 49831 323 940
## 227 50029 323 832
## 228 50127 323 775
## 229 50205 323 746
## 230 50298 323 758
## 231 50358 323 795
## 232 50450 323 796
## 233 50547 323 797Cleaning up the dataset
# checking for NAs
is.na(data)## confirmed recovered death date cumulative_confirmed
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE FALSE FALSE
## [8,] FALSE FALSE FALSE FALSE FALSE
## [9,] FALSE FALSE FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE FALSE FALSE
## [20,] FALSE FALSE FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE FALSE FALSE
## [22,] FALSE FALSE FALSE FALSE FALSE
## [23,] FALSE FALSE FALSE FALSE FALSE
## [24,] FALSE FALSE FALSE FALSE FALSE
## [25,] FALSE FALSE FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE FALSE FALSE
## [36,] FALSE FALSE FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE FALSE FALSE
## [38,] FALSE FALSE FALSE FALSE FALSE
## [39,] FALSE FALSE FALSE FALSE FALSE
## [40,] FALSE FALSE FALSE FALSE FALSE
## [41,] FALSE FALSE FALSE FALSE FALSE
## [42,] FALSE FALSE FALSE FALSE FALSE
## [43,] FALSE FALSE FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE FALSE FALSE
## [47,] FALSE FALSE FALSE FALSE FALSE
## [48,] FALSE FALSE FALSE FALSE FALSE
## [49,] FALSE FALSE FALSE FALSE FALSE
## [50,] FALSE FALSE FALSE FALSE FALSE
## [51,] FALSE FALSE FALSE FALSE FALSE
## [52,] FALSE FALSE FALSE FALSE FALSE
## [53,] FALSE FALSE FALSE FALSE FALSE
## [54,] FALSE FALSE FALSE FALSE FALSE
## [55,] FALSE FALSE FALSE FALSE FALSE
## [56,] FALSE FALSE FALSE FALSE FALSE
## [57,] FALSE FALSE FALSE FALSE FALSE
## [58,] FALSE FALSE FALSE FALSE FALSE
## [59,] FALSE FALSE FALSE FALSE FALSE
## [60,] FALSE FALSE FALSE FALSE FALSE
## [61,] FALSE FALSE FALSE FALSE FALSE
## [62,] FALSE FALSE FALSE FALSE FALSE
## [63,] FALSE FALSE FALSE FALSE FALSE
## [64,] FALSE FALSE FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE FALSE FALSE
## [67,] FALSE FALSE FALSE FALSE FALSE
## [68,] FALSE FALSE FALSE FALSE FALSE
## [69,] FALSE FALSE FALSE FALSE FALSE
## [70,] FALSE FALSE FALSE FALSE FALSE
## [71,] FALSE FALSE FALSE FALSE FALSE
## [72,] FALSE FALSE FALSE FALSE FALSE
## [73,] FALSE FALSE FALSE FALSE FALSE
## [74,] FALSE FALSE FALSE FALSE FALSE
## [75,] FALSE FALSE FALSE FALSE FALSE
## [76,] FALSE FALSE FALSE FALSE FALSE
## [77,] FALSE FALSE FALSE FALSE FALSE
## [78,] FALSE FALSE FALSE FALSE FALSE
## [79,] FALSE FALSE FALSE FALSE FALSE
## [80,] FALSE FALSE FALSE FALSE FALSE
## [81,] FALSE FALSE FALSE FALSE FALSE
## [82,] FALSE FALSE FALSE FALSE FALSE
## [83,] FALSE FALSE FALSE FALSE FALSE
## [84,] FALSE FALSE FALSE FALSE FALSE
## [85,] FALSE FALSE FALSE FALSE FALSE
## [86,] FALSE FALSE FALSE FALSE FALSE
## [87,] FALSE FALSE FALSE FALSE FALSE
## [88,] FALSE FALSE FALSE FALSE FALSE
## [89,] FALSE FALSE FALSE FALSE FALSE
## [90,] FALSE FALSE FALSE FALSE FALSE
## [91,] FALSE FALSE FALSE FALSE FALSE
## [92,] FALSE FALSE FALSE FALSE FALSE
## [93,] FALSE FALSE FALSE FALSE FALSE
## [94,] FALSE FALSE FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE FALSE FALSE
## [97,] FALSE FALSE FALSE FALSE FALSE
## [98,] FALSE FALSE FALSE FALSE FALSE
## [99,] FALSE FALSE FALSE FALSE FALSE
## [100,] FALSE FALSE FALSE FALSE FALSE
## [101,] FALSE FALSE FALSE FALSE FALSE
## [102,] FALSE FALSE FALSE FALSE FALSE
## [103,] FALSE FALSE FALSE FALSE FALSE
## [104,] FALSE FALSE FALSE FALSE FALSE
## [105,] FALSE FALSE FALSE FALSE FALSE
## [106,] FALSE FALSE FALSE FALSE FALSE
## [107,] FALSE FALSE FALSE FALSE FALSE
## [108,] FALSE FALSE FALSE FALSE FALSE
## [109,] FALSE FALSE FALSE FALSE FALSE
## [110,] FALSE FALSE FALSE FALSE FALSE
## [111,] FALSE FALSE FALSE FALSE FALSE
## [112,] FALSE FALSE FALSE FALSE FALSE
## [113,] FALSE FALSE FALSE FALSE FALSE
## [114,] FALSE FALSE FALSE FALSE FALSE
## [115,] FALSE FALSE FALSE FALSE FALSE
## [116,] FALSE FALSE FALSE FALSE FALSE
## [117,] FALSE FALSE FALSE FALSE FALSE
## [118,] FALSE FALSE FALSE FALSE FALSE
## [119,] FALSE FALSE FALSE FALSE FALSE
## [120,] FALSE FALSE FALSE FALSE FALSE
## [121,] FALSE FALSE FALSE FALSE FALSE
## [122,] FALSE FALSE FALSE FALSE FALSE
## [123,] FALSE FALSE FALSE FALSE FALSE
## [124,] FALSE FALSE FALSE FALSE FALSE
## [125,] FALSE FALSE FALSE FALSE FALSE
## [126,] FALSE FALSE FALSE FALSE FALSE
## [127,] FALSE FALSE FALSE FALSE FALSE
## [128,] FALSE FALSE FALSE FALSE FALSE
## [129,] FALSE FALSE FALSE FALSE FALSE
## [130,] FALSE FALSE FALSE FALSE FALSE
## [131,] FALSE FALSE FALSE FALSE FALSE
## [132,] FALSE FALSE FALSE FALSE FALSE
## [133,] FALSE FALSE FALSE FALSE FALSE
## [134,] FALSE FALSE FALSE FALSE FALSE
## [135,] FALSE FALSE FALSE FALSE FALSE
## [136,] FALSE FALSE FALSE FALSE FALSE
## [137,] FALSE FALSE FALSE FALSE FALSE
## [138,] FALSE FALSE FALSE FALSE FALSE
## [139,] FALSE FALSE FALSE FALSE FALSE
## [140,] FALSE FALSE FALSE FALSE FALSE
## [141,] FALSE FALSE FALSE FALSE FALSE
## [142,] FALSE FALSE FALSE FALSE FALSE
## [143,] FALSE FALSE FALSE FALSE FALSE
## [144,] FALSE FALSE FALSE FALSE FALSE
## [145,] FALSE FALSE FALSE FALSE FALSE
## [146,] FALSE FALSE FALSE FALSE FALSE
## [147,] FALSE FALSE FALSE FALSE FALSE
## [148,] FALSE FALSE FALSE FALSE FALSE
## [149,] FALSE FALSE FALSE FALSE FALSE
## [150,] FALSE FALSE FALSE FALSE FALSE
## [151,] FALSE FALSE FALSE FALSE FALSE
## [152,] FALSE FALSE FALSE FALSE FALSE
## [153,] FALSE FALSE FALSE FALSE FALSE
## [154,] FALSE FALSE FALSE FALSE FALSE
## [155,] FALSE FALSE FALSE FALSE FALSE
## [156,] FALSE FALSE FALSE FALSE FALSE
## [157,] FALSE FALSE FALSE FALSE FALSE
## [158,] FALSE FALSE FALSE FALSE FALSE
## [159,] FALSE FALSE FALSE FALSE FALSE
## [160,] FALSE FALSE FALSE FALSE FALSE
## [161,] FALSE FALSE FALSE FALSE FALSE
## [162,] FALSE FALSE FALSE FALSE FALSE
## [163,] FALSE FALSE FALSE FALSE FALSE
## [164,] FALSE FALSE FALSE FALSE FALSE
## [165,] FALSE FALSE FALSE FALSE FALSE
## [166,] FALSE FALSE FALSE FALSE FALSE
## [167,] FALSE FALSE FALSE FALSE FALSE
## [168,] FALSE FALSE FALSE FALSE FALSE
## [169,] FALSE FALSE FALSE FALSE FALSE
## [170,] FALSE FALSE FALSE FALSE FALSE
## [171,] FALSE FALSE FALSE FALSE FALSE
## [172,] FALSE FALSE FALSE FALSE FALSE
## [173,] FALSE FALSE FALSE FALSE FALSE
## [174,] FALSE FALSE FALSE FALSE FALSE
## [175,] FALSE FALSE FALSE FALSE FALSE
## [176,] FALSE FALSE FALSE FALSE FALSE
## [177,] FALSE FALSE FALSE FALSE FALSE
## [178,] FALSE FALSE FALSE FALSE FALSE
## [179,] FALSE FALSE FALSE FALSE FALSE
## [180,] FALSE FALSE FALSE FALSE FALSE
## [181,] FALSE FALSE FALSE FALSE FALSE
## [182,] FALSE FALSE FALSE FALSE FALSE
## [183,] FALSE FALSE FALSE FALSE FALSE
## [184,] FALSE FALSE FALSE FALSE FALSE
## [185,] FALSE FALSE FALSE FALSE FALSE
## [186,] FALSE FALSE FALSE FALSE FALSE
## [187,] FALSE FALSE FALSE FALSE FALSE
## [188,] FALSE FALSE FALSE FALSE FALSE
## [189,] FALSE FALSE FALSE FALSE FALSE
## [190,] FALSE FALSE FALSE FALSE FALSE
## [191,] FALSE FALSE FALSE FALSE FALSE
## [192,] FALSE FALSE FALSE FALSE FALSE
## [193,] FALSE FALSE FALSE FALSE FALSE
## [194,] FALSE FALSE FALSE FALSE FALSE
## [195,] FALSE FALSE FALSE FALSE FALSE
## [196,] FALSE FALSE FALSE FALSE FALSE
## [197,] FALSE FALSE FALSE FALSE FALSE
## [198,] FALSE FALSE FALSE FALSE FALSE
## [199,] FALSE FALSE FALSE FALSE FALSE
## [200,] FALSE FALSE FALSE FALSE FALSE
## [201,] FALSE FALSE FALSE FALSE FALSE
## [202,] FALSE FALSE FALSE FALSE FALSE
## [203,] FALSE FALSE FALSE FALSE FALSE
## [204,] FALSE FALSE FALSE FALSE FALSE
## [205,] FALSE FALSE FALSE FALSE FALSE
## [206,] FALSE FALSE FALSE FALSE FALSE
## [207,] FALSE FALSE FALSE FALSE FALSE
## [208,] FALSE FALSE FALSE FALSE FALSE
## [209,] FALSE FALSE FALSE FALSE FALSE
## [210,] FALSE FALSE FALSE FALSE FALSE
## [211,] FALSE FALSE FALSE FALSE FALSE
## [212,] FALSE FALSE FALSE FALSE FALSE
## [213,] FALSE FALSE FALSE FALSE FALSE
## [214,] FALSE FALSE FALSE FALSE FALSE
## [215,] FALSE FALSE FALSE FALSE FALSE
## [216,] FALSE FALSE FALSE FALSE FALSE
## [217,] FALSE FALSE FALSE FALSE FALSE
## [218,] FALSE FALSE FALSE FALSE FALSE
## [219,] FALSE FALSE FALSE FALSE FALSE
## [220,] FALSE FALSE FALSE FALSE FALSE
## [221,] FALSE FALSE FALSE FALSE FALSE
## [222,] FALSE FALSE FALSE FALSE FALSE
## [223,] FALSE FALSE FALSE FALSE FALSE
## [224,] FALSE FALSE FALSE FALSE FALSE
## [225,] FALSE FALSE FALSE FALSE FALSE
## [226,] FALSE FALSE FALSE FALSE FALSE
## [227,] FALSE FALSE FALSE FALSE FALSE
## [228,] FALSE FALSE FALSE FALSE FALSE
## [229,] FALSE FALSE FALSE FALSE FALSE
## [230,] FALSE FALSE FALSE FALSE FALSE
## [231,] FALSE FALSE FALSE FALSE FALSE
## [232,] FALSE FALSE FALSE FALSE FALSE
## [233,] FALSE FALSE FALSE FALSE FALSE
## cumulative_recovered cumulative_death active_cases
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE
## [8,] FALSE FALSE FALSE
## [9,] FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE
## [20,] FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE
## [22,] FALSE FALSE FALSE
## [23,] FALSE FALSE FALSE
## [24,] FALSE FALSE FALSE
## [25,] FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE
## [36,] FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE
## [38,] FALSE FALSE FALSE
## [39,] FALSE FALSE FALSE
## [40,] FALSE FALSE FALSE
## [41,] FALSE FALSE FALSE
## [42,] FALSE FALSE FALSE
## [43,] FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE
## [47,] FALSE FALSE FALSE
## [48,] FALSE FALSE FALSE
## [49,] FALSE FALSE FALSE
## [50,] FALSE FALSE FALSE
## [51,] FALSE FALSE FALSE
## [52,] FALSE FALSE FALSE
## [53,] FALSE FALSE FALSE
## [54,] FALSE FALSE FALSE
## [55,] FALSE FALSE FALSE
## [56,] FALSE FALSE FALSE
## [57,] FALSE FALSE FALSE
## [58,] FALSE FALSE FALSE
## [59,] FALSE FALSE FALSE
## [60,] FALSE FALSE FALSE
## [61,] FALSE FALSE FALSE
## [62,] FALSE FALSE FALSE
## [63,] FALSE FALSE FALSE
## [64,] FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE
## [67,] FALSE FALSE FALSE
## [68,] FALSE FALSE FALSE
## [69,] FALSE FALSE FALSE
## [70,] FALSE FALSE FALSE
## [71,] FALSE FALSE FALSE
## [72,] FALSE FALSE FALSE
## [73,] FALSE FALSE FALSE
## [74,] FALSE FALSE FALSE
## [75,] FALSE FALSE FALSE
## [76,] FALSE FALSE FALSE
## [77,] FALSE FALSE FALSE
## [78,] FALSE FALSE FALSE
## [79,] FALSE FALSE FALSE
## [80,] FALSE FALSE FALSE
## [81,] FALSE FALSE FALSE
## [82,] FALSE FALSE FALSE
## [83,] FALSE FALSE FALSE
## [84,] FALSE FALSE FALSE
## [85,] FALSE FALSE FALSE
## [86,] FALSE FALSE FALSE
## [87,] FALSE FALSE FALSE
## [88,] FALSE FALSE FALSE
## [89,] FALSE FALSE FALSE
## [90,] FALSE FALSE FALSE
## [91,] FALSE FALSE FALSE
## [92,] FALSE FALSE FALSE
## [93,] FALSE FALSE FALSE
## [94,] FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE
## [97,] FALSE FALSE FALSE
## [98,] FALSE FALSE FALSE
## [99,] FALSE FALSE FALSE
## [100,] FALSE FALSE FALSE
## [101,] FALSE FALSE FALSE
## [102,] FALSE FALSE FALSE
## [103,] FALSE FALSE FALSE
## [104,] FALSE FALSE FALSE
## [105,] FALSE FALSE FALSE
## [106,] FALSE FALSE FALSE
## [107,] FALSE FALSE FALSE
## [108,] FALSE FALSE FALSE
## [109,] FALSE FALSE FALSE
## [110,] FALSE FALSE FALSE
## [111,] FALSE FALSE FALSE
## [112,] FALSE FALSE FALSE
## [113,] FALSE FALSE FALSE
## [114,] FALSE FALSE FALSE
## [115,] FALSE FALSE FALSE
## [116,] FALSE FALSE FALSE
## [117,] FALSE FALSE FALSE
## [118,] FALSE FALSE FALSE
## [119,] FALSE FALSE FALSE
## [120,] FALSE FALSE FALSE
## [121,] FALSE FALSE FALSE
## [122,] FALSE FALSE FALSE
## [123,] FALSE FALSE FALSE
## [124,] FALSE FALSE FALSE
## [125,] FALSE FALSE FALSE
## [126,] FALSE FALSE FALSE
## [127,] FALSE FALSE FALSE
## [128,] FALSE FALSE FALSE
## [129,] FALSE FALSE FALSE
## [130,] FALSE FALSE FALSE
## [131,] FALSE FALSE FALSE
## [132,] FALSE FALSE FALSE
## [133,] FALSE FALSE FALSE
## [134,] FALSE FALSE FALSE
## [135,] FALSE FALSE FALSE
## [136,] FALSE FALSE FALSE
## [137,] FALSE FALSE FALSE
## [138,] FALSE FALSE FALSE
## [139,] FALSE FALSE FALSE
## [140,] FALSE FALSE FALSE
## [141,] FALSE FALSE FALSE
## [142,] FALSE FALSE FALSE
## [143,] FALSE FALSE FALSE
## [144,] FALSE FALSE FALSE
## [145,] FALSE FALSE FALSE
## [146,] FALSE FALSE FALSE
## [147,] FALSE FALSE FALSE
## [148,] FALSE FALSE FALSE
## [149,] FALSE FALSE FALSE
## [150,] FALSE FALSE FALSE
## [151,] FALSE FALSE FALSE
## [152,] FALSE FALSE FALSE
## [153,] FALSE FALSE FALSE
## [154,] FALSE FALSE FALSE
## [155,] FALSE FALSE FALSE
## [156,] FALSE FALSE FALSE
## [157,] FALSE FALSE FALSE
## [158,] FALSE FALSE FALSE
## [159,] FALSE FALSE FALSE
## [160,] FALSE FALSE FALSE
## [161,] FALSE FALSE FALSE
## [162,] FALSE FALSE FALSE
## [163,] FALSE FALSE FALSE
## [164,] FALSE FALSE FALSE
## [165,] FALSE FALSE FALSE
## [166,] FALSE FALSE FALSE
## [167,] FALSE FALSE FALSE
## [168,] FALSE FALSE FALSE
## [169,] FALSE FALSE FALSE
## [170,] FALSE FALSE FALSE
## [171,] FALSE FALSE FALSE
## [172,] FALSE FALSE FALSE
## [173,] FALSE FALSE FALSE
## [174,] FALSE FALSE FALSE
## [175,] FALSE FALSE FALSE
## [176,] FALSE FALSE FALSE
## [177,] FALSE FALSE FALSE
## [178,] FALSE FALSE FALSE
## [179,] FALSE FALSE FALSE
## [180,] FALSE FALSE FALSE
## [181,] FALSE FALSE FALSE
## [182,] FALSE FALSE FALSE
## [183,] FALSE FALSE FALSE
## [184,] FALSE FALSE FALSE
## [185,] FALSE FALSE FALSE
## [186,] FALSE FALSE FALSE
## [187,] FALSE FALSE FALSE
## [188,] FALSE FALSE FALSE
## [189,] FALSE FALSE FALSE
## [190,] FALSE FALSE FALSE
## [191,] FALSE FALSE FALSE
## [192,] FALSE FALSE FALSE
## [193,] FALSE FALSE FALSE
## [194,] FALSE FALSE FALSE
## [195,] FALSE FALSE FALSE
## [196,] FALSE FALSE FALSE
## [197,] FALSE FALSE FALSE
## [198,] FALSE FALSE FALSE
## [199,] FALSE FALSE FALSE
## [200,] FALSE FALSE FALSE
## [201,] FALSE FALSE FALSE
## [202,] FALSE FALSE FALSE
## [203,] FALSE FALSE FALSE
## [204,] FALSE FALSE FALSE
## [205,] FALSE FALSE FALSE
## [206,] FALSE FALSE FALSE
## [207,] FALSE FALSE FALSE
## [208,] FALSE FALSE FALSE
## [209,] FALSE FALSE FALSE
## [210,] FALSE FALSE FALSE
## [211,] FALSE FALSE FALSE
## [212,] FALSE FALSE FALSE
## [213,] FALSE FALSE FALSE
## [214,] FALSE FALSE FALSE
## [215,] FALSE FALSE FALSE
## [216,] FALSE FALSE FALSE
## [217,] FALSE FALSE FALSE
## [218,] FALSE FALSE FALSE
## [219,] FALSE FALSE FALSE
## [220,] FALSE FALSE FALSE
## [221,] FALSE FALSE FALSE
## [222,] FALSE FALSE FALSE
## [223,] FALSE FALSE FALSE
## [224,] FALSE FALSE FALSE
## [225,] FALSE FALSE FALSE
## [226,] FALSE FALSE FALSE
## [227,] FALSE FALSE FALSE
## [228,] FALSE FALSE FALSE
## [229,] FALSE FALSE FALSE
## [230,] FALSE FALSE FALSE
## [231,] FALSE FALSE FALSE
## [232,] FALSE FALSE FALSE
## [233,] FALSE FALSE FALSE#no Nas reported but I will go ahead and omit them anyways
data <- na.omit(data)
skim(data) # checking again| Name | data |
| Number of rows | 233 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| date | 0 | 1 | 10 | 10 | 0 | 233 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| confirmed | 0 | 1 | 221.75 | 241.39 | 1 | 59 | 131 | 295 | 1513 | ▇▂▁▁▁ |
| recovered | 0 | 1 | 216.94 | 441.66 | 0 | 32 | 97 | 221 | 5526 | ▇▁▁▁▁ |
| death | 0 | 1 | 1.39 | 2.57 | 0 | 0 | 0 | 2 | 15 | ▇▁▁▁▁ |
| cumulative_confirmed | 0 | 1 | 28642.17 | 19231.47 | 2 | 8070 | 35501 | 46626 | 51667 | ▅▂▂▁▇ |
| cumulative_recovered | 0 | 1 | 26223.44 | 20028.78 | 0 | 2947 | 32096 | 45757 | 50547 | ▆▂▁▂▇ |
| cumulative_death | 0 | 1 | 174.39 | 124.10 | 0 | 36 | 182 | 301 | 323 | ▆▂▂▁▇ |
| active_cases | 0 | 1 | 2244.34 | 2066.14 | 2 | 524 | 1384 | 3782 | 8585 | ▇▂▃▁▁ |
subsetting our variables of interest
# so I am interested in the total number of cases recorded across the year so I will subset the variable for active cases and inspect it
data$myVar1 <- data$active_cases
str(data$myVar1)## int [1:233] 2 4 6 7 9 11 16 16 18 21 ...summary(data$myVar1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 524 1384 2244 3782 8585Plotting the histograms of the longitudes of the different facilities across the country
p1 <- ggplot(data=data, aes(x=myVar1, y=..density..)) +
geom_histogram(color="grey60",fill="cornsilk",size=0.2) ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.print(p1) #clear bimodal peaks in the number of actives cases recorded showing on the graph. This can be correlated to the seasons of the year and the laxing of rules surrounding covid.## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
adding the emperical density curve
p1 <- p1 + geom_density(linetype="dotted",size=0.75)
print(p1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Get maximum likelihood parameters for normal
normPars <- fitdistr(data$myVar1,"normal")
print(normPars)## mean sd
## 2244.34335 2061.70604
## ( 135.06685) ( 95.50669)str(normPars)## List of 5
## $ estimate: Named num [1:2] 2244 2062
## ..- attr(*, "names")= chr [1:2] "mean" "sd"
## $ sd : Named num [1:2] 135.1 95.5
## ..- attr(*, "names")= chr [1:2] "mean" "sd"
## $ vcov : num [1:2, 1:2] 18243 0 0 9122
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "mean" "sd"
## .. ..$ : chr [1:2] "mean" "sd"
## $ n : int 233
## $ loglik : num -2109
## - attr(*, "class")= chr "fitdistr"normPars$estimate["mean"] ## mean
## 2244.343Plot normal probability density
meanML <- normPars$estimate["mean"]
sdML <- normPars$estimate["sd"]
xval <- seq(0,max(data$myVar1),len=length(data$myVar1))
stat <- stat_function(aes(x = xval, y = ..y..), fun = dnorm, colour="red", n = length(data$myVar1), args = list(mean = meanML, sd = sdML))
p1 + stat## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot exponential probability density
expoPars <- fitdistr(data$myVar1,"exponential")
rateML <- expoPars$estimate["rate"]
stat2 <- stat_function(aes(x = xval, y = ..y..), fun = dexp, colour="blue", n = length(data$myVar1), args = list(rate=rateML))
p1 + stat + stat2## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot uniform probability density
stat3 <- stat_function(aes(x = xval, y = ..y..), fun = dunif, colour="darkgreen", n = length(data$myVar1), args = list(min=min(data$myVar1), max=max(data$myVar1)))
p1 + stat + stat2 + stat3## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot gamma probability density
# received an earlier error while trying to do this estimation, will try log transforming the data before fitting it.
gammaPars <- fitdistr(log(data$myVar1),"gamma")## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs producedshapeML <- gammaPars$estimate["shape"]
rateML <- gammaPars$estimate["rate"]
stat4 <- stat_function(aes(x = xval, y = ..y..), fun = dgamma, colour="brown", n = length(data$myVar1), args = list(shape=shapeML, rate=rateML))
p1 + stat + stat2 + stat3 + stat4## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot beta probability density
pSpecial <- ggplot(data=data, aes(x=myVar1/(max(myVar1 + 0.1)), y=..density..)) +
geom_histogram(color="grey60",fill="cornsilk",size=0.2) +
xlim(c(0,1)) +
geom_density(size=0.75,linetype="dotted")
betaPars <- fitdistr(x=data$myVar1/max(data$myVar1 + 0.1),start=list(shape1=1,shape2=2),"beta")## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs produced
## Warning in densfun(x, parm[1], parm[2], ...): NaNs producedshape1ML <- betaPars$estimate["shape1"]
shape2ML <- betaPars$estimate["shape2"]
statSpecial <- stat_function(aes(x = xval, y = ..y..), fun = dbeta, colour="orchid", n = length(data$myVar1), args = list(shape1=shape1ML,shape2=shape2ML))
pSpecial + statSpecial## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_bar()`).
creating a simulated dataset based on the gamma probability density of my data. I used rgamma in this case
sim_data <- rgamma(n=length(data$myVar1), shape = shapeML, rate = rateML)
sim_d <- 1:length(data$myVar1)
sim_dataset <- data.frame(sim_data, sim_d)
str(sim_dataset)## 'data.frame': 233 obs. of 2 variables:
## $ sim_data: num 7.36 3.99 7.41 4.65 6 ...
## $ sim_d : int 1 2 3 4 5 6 7 8 9 10 ...summary(sim_dataset)## sim_data sim_d
## Min. : 2.876 Min. : 1
## 1st Qu.: 5.630 1st Qu.: 59
## Median : 6.889 Median :117
## Mean : 7.050 Mean :117
## 3rd Qu.: 8.149 3rd Qu.:175
## Max. :13.268 Max. :233plotting the data based on the gamma probability density
p2 <- ggplot(data=sim_dataset, aes(x=sim_data, y=..density..)) +
geom_histogram(color="grey60",fill="cornsilk",linewidth=0.2)
p2 + stat4## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Doesn’t look like gamma distribution fits my data in this case. However, there is a clear bimodal distribution of the data across two peaks. I should definitely try other stuff.