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Add the latest observations to the nowcast output. This is useful for plotting the nowcast against the latest observations.

Usage

enw_add_latest_obs_to_nowcast(nowcast, obs)

Arguments

nowcast

A data.frame of nowcast output from enw_nowcast_summary().

obs

An observation data.frame containing reference_date columns of the same length as the number of rows in the posterior and the most up to date observation for each date. This is used to align the posterior with the observations. The easiest source of this data is the output of latest output of enw_preprocess_data() or enw_latest_data().

Value

A data.frame of nowcast output with the latest observations added.

See also

Examples

fit <- enw_example("nowcast")
obs <- enw_example("obs")
nowcast <- summary(fit, type = "nowcast")
enw_add_latest_obs_to_nowcast(nowcast, obs)
#>     reference_date .group latest_confirm confirm report_date max_confirm
#>  1:     2021-08-03      1            156     149  2021-08-22         149
#>  2:     2021-08-04      1            183     166  2021-08-22         166
#>  3:     2021-08-05      1            147     133  2021-08-22         133
#>  4:     2021-08-06      1            155     137  2021-08-22         137
#>  5:     2021-08-07      1            159     139  2021-08-22         139
#>  6:     2021-08-08      1            119      97  2021-08-22          97
#>  7:     2021-08-09      1             65      58  2021-08-22          58
#>  8:     2021-08-10      1            204     175  2021-08-22         175
#>  9:     2021-08-11      1            275     233  2021-08-22         233
#> 10:     2021-08-12      1            273     237  2021-08-22         237
#> 11:     2021-08-13      1            270     204  2021-08-22         204
#> 12:     2021-08-14      1            262     189  2021-08-22         189
#> 13:     2021-08-15      1            192     125  2021-08-22         125
#> 14:     2021-08-16      1            140      98  2021-08-22          98
#> 15:     2021-08-17      1            323     242  2021-08-22         242
#> 16:     2021-08-18      1            409     223  2021-08-22         223
#> 17:     2021-08-19      1            370     202  2021-08-22         202
#> 18:     2021-08-20      1            361     171  2021-08-22         171
#> 19:     2021-08-21      1            339     112  2021-08-22         112
#> 20:     2021-08-22      1            258      45  2021-08-22          45
#>     location age_group cum_prop_reported delay prop_reported    mean median
#>  1:       DE       00+                 1    19   0.000000000 149.000  149.0
#>  2:       DE       00+                 1    18   0.000000000 167.453  167.0
#>  3:       DE       00+                 1    17   0.000000000 135.677  135.0
#>  4:       DE       00+                 1    16   0.000000000 140.896  141.0
#>  5:       DE       00+                 1    15   0.007194245 145.222  145.0
#>  6:       DE       00+                 1    14   0.000000000 103.248  103.0
#>  7:       DE       00+                 1    13   0.000000000  62.742   62.0
#>  8:       DE       00+                 1    12   0.000000000 185.065  185.0
#>  9:       DE       00+                 1    11   0.000000000 255.574  255.0
#> 10:       DE       00+                 1    10   0.004219409 266.480  266.0
#> 11:       DE       00+                 1     9   0.000000000 235.082  235.0
#> 12:       DE       00+                 1     8   0.015873016 229.150  229.0
#> 13:       DE       00+                 1     7   0.040000000 163.327  163.0
#> 14:       DE       00+                 1     6   0.010204082 130.404  130.0
#> 15:       DE       00+                 1     5   0.012396694 299.890  299.0
#> 16:       DE       00+                 1     4   0.017937220 303.138  301.0
#> 17:       DE       00+                 1     3   0.019801980 311.131  309.0
#> 18:       DE       00+                 1     2   0.070175439 318.522  315.0
#> 19:       DE       00+                 1     1   0.383928571 334.231  327.5
#> 20:       DE       00+                 1     0   1.000000000 322.430  306.0
#>            sd     mad  q5   q20    q35   q50    q65   q80    q95      rhat
#>  1:  0.000000  0.0000 149 149.0 149.00 149.0 149.00 149.0 149.00        NA
#>  2:  1.291301  1.4826 166 166.0 167.00 167.0 168.00 168.0 170.00 1.0002481
#>  3:  1.853674  1.4826 133 134.0 135.00 135.0 136.00 137.0 139.00 0.9986872
#>  4:  2.169768  2.9652 138 139.0 140.00 141.0 142.00 143.0 145.00 0.9999757
#>  5:  2.760133  2.9652 141 143.0 144.00 145.0 146.00 147.0 150.00 1.0007676
#>  6:  2.893591  2.9652  99 101.0 102.00 103.0 104.00 106.0 109.00 0.9991973
#>  7:  2.412313  2.9652  59  61.0  62.00  62.0  63.00  65.0  67.00 1.0036034
#>  8:  3.687054  4.4478 180 182.0 183.00 185.0 186.00 188.0 191.00 0.9989872
#>  9:  6.079925  5.9304 246 251.0 253.00 255.0 258.00 261.0 266.00 0.9991648
#> 10:  7.480345  7.4130 255 260.0 263.00 266.0 269.00 272.0 279.05 1.0073591
#> 11:  7.671123  7.4130 224 228.0 232.00 235.0 237.35 241.0 248.00 1.0012317
#> 12:  9.532280 10.3782 215 221.0 225.00 229.0 232.00 237.0 246.00 0.9999872
#> 13:  8.941478  8.8956 149 155.0 159.00 163.0 166.00 171.0 178.00 0.9994459
#> 14:  8.343284  8.8956 118 123.0 127.00 130.0 133.00 137.0 145.00 1.0012796
#> 15: 12.795923 13.3434 280 289.0 294.00 299.0 304.00 310.2 322.00 1.0017171
#> 16: 17.375755 17.0499 278 288.8 295.00 301.0 308.00 317.0 334.05 1.0055266
#> 17: 24.503638 23.7216 275 291.0 300.65 309.0 317.35 329.0 356.00 1.0105072
#> 18: 34.923992 34.0998 270 288.8 303.00 315.0 330.00 343.2 383.00 1.0047446
#> 19: 53.967621 51.1497 258 288.8 308.00 327.5 347.00 375.0 436.00 1.0005068
#> 20: 93.868922 80.8017 198 247.8 276.00 306.0 340.35 390.2 493.05 1.0003640
#>      ess_bulk  ess_tail
#>  1:        NA        NA
#>  2:  858.3035  887.5883
#>  3:  784.2638  908.5946
#>  4:  986.1909  904.5245
#>  5:  865.5054  947.2221
#>  6:  893.3750  835.9871
#>  7:  926.5402  861.1900
#>  8: 1097.4089  937.1445
#>  9: 1111.3917 1021.2936
#> 10: 1059.0036  983.0723
#> 11:  975.8115  927.8796
#> 12: 1084.7105  951.3275
#> 13: 1041.2300  974.4463
#> 14:  935.7652 1050.2310
#> 15: 1130.4067  958.2443
#> 16:  848.0051  763.8073
#> 17: 1083.0271 1028.2641
#> 18: 1063.6034  939.8372
#> 19: 1007.8998  874.2774
#> 20: 1366.8407  840.3280