Change/update the attributes of a tbl_now object
change.Rd![[Stable]](figures/lifecycle-stable.svg)
Functions to change the attributes of a tbl_now object.
Usage
change_now(x, now = NULL)
update_now(x)
change_event_date(x, event_date)
change_report_date(x, report_date)
change_case_count(x, case_count)
change_is_censored(x, is_censored)
change_strata(x, ..., warn_now = TRUE, warn_non_uniqueness = TRUE)
change_covariates(x, ..., warn_now = TRUE, warn_non_uniqueness = TRUE)Arguments
- x
A
tbl_nowobject- now
(optional) Date or
NULL(default). The date that is considered thenowof the nowcast. If nonowis given then the function automatically uses the lastevent_date.- event_date
tidy-select name of the column containing the event date. Optional when
delayis provided together withreport_date; the event date will be computed asreport_date - delay.- report_date
tidy-select name of the column containing the report date. Optional when
delayis provided together withevent_date; the report date will be computed asevent_date + delay.- case_count
(optional) tidy-select or
NULLName of the column with the case counts ifdata_typeis "count-incidence" or "count-cumulative".- is_censored
(optional) tidy-select or
NULL(default). The name of a column containing eitherTRUEorFALSEindicating whether thereport_dateis correctly specified or corresponds to abatchand thus is censored. In other words, if thereport_dateis accurately measured setis_censored = FALSEbut if thereport_datecorresponds to an error and is only an upper bound of the real report date setis_censored = TRUE.- ...
tidy-select with the columns for the attribute. In the case of
covariatesandstrataargument...can refer to multiple columns.- warn_now
Boolean. Whether to warn if now is before last report or too far in the future.
- warn_non_uniqueness
(optional) Logical. Whether to throw a warning if data has multiple observations for same event and report date (conditional on covariates and strata)
Details
Variable selection is done via tidy-select
and can be used with the auxiliary dplyr verbs such as dplyr::starts_with(), dplyr::all_of(),
and dplyr::where(). See dplyr::select() for additional information.
The update_now() function updates the now to the latest date in
a tibble. For example:
#Get the data
data(denguedat)
ndata <- tbl_now(denguedat,
event_date = onset_week,
report_date = report_week,
verbose = FALSE)
#The now is in 2010 because the data reaches all the way there
get_now(ndata)
#> [1] "2010-12-20"
#We can filter the data
ndata_1992 <- ndata |>
dplyr::filter(onset_week <= as.Date("1992/01/01") &
report_week <= as.Date("1992/01/01"))
#But the now will still be in 2010
get_now(ndata_1992)
#> [1] "2010-12-20"
#The update now brings it to the closest date before the cut
ndata_1992 <- ndata_1992 |> update_now()
#Which is now correct and set to the latest date before the cut
get_now(ndata_1992)
#> [1] "1991-12-30"Examples
data(denguedat)
ndata <- tbl_now(denguedat,
event_date = onset_week,
report_date = report_week,
verbose = FALSE
)
# Change the event_date column to a different date column
ndata$new_onset_week <- ndata$onset_week - lubridate::days(1)
ndata <- ndata |> change_event_date(new_onset_week)
ndata
#> # A tibble: 52,987 × 7
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [report_date] [...] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0.143 0.143 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0.143 0.143 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0.143 0.143 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1.14 1.14 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1.14 1.14 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2.14 2.14 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2.14 2.14 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2.14 2.14 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3.14 3.14 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1.14 1.14 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2010-12-20 | Event date: "new_onset_week" | Report date: "report_week"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
# Change the report_date column to a different column
ndata$new_report_week <- ndata$report_week - lubridate::days(1)
ndata <- ndata |> change_report_date(new_report_week)
ndata
#> # A tibble: 52,987 × 8
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [...] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2010-12-20 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 1 more variable: new_report_week <date>
# Change strata to different strata
ndata$age_group <- sample(c("< 18", "20-60", "60+"), nrow(ndata), replace = TRUE)
ndata <- ndata |> change_strata(gender, age_group)
ndata
#> # A tibble: 52,987 × 9
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2010-12-20 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Strata: "gender" and "age_group"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 2 more variables: new_report_week <date>, age_group <chr>
# Change covariates
ndata$temperature <- rnorm(nrow(ndata), 25, 4)
ndata$humidity <- rbeta(nrow(ndata), 0.6, 0.4)
ndata <- ndata |> change_covariates(temperature, humidity)
ndata
#> # A tibble: 52,987 × 11
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2010-12-20 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 4 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>
# Change now
ndata <- change_now(ndata, now = as.Date("2025-01-01"))
ndata
#> # A tibble: 52,987 × 11
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 4 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>
# Change case count column
count_data <- ndata |>
to_count(to = "count-incidence")
count_data |>
dplyr::mutate(n2 = 1.15 * n) |>
change_case_count(n2)
#> # A tibble: 52,987 × 11
#> # Data type: "count-incidence"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> new_onset_week new_report_week .event_num .report_num gender age_group
#> <date> <date> <dbl> <dbl> <chr> <chr>
#> [event_date] [report_date] [...] [...] [strata] [strata]
#> 1 1989-12-31 1990-01-07 0 1 Female 20-60
#> 2 1989-12-31 1990-01-14 0 2 Female 20-60
#> 3 1989-12-31 1990-01-14 0 2 Female 20-60
#> 4 1989-12-31 1990-01-14 0 2 Female 20-60
#> 5 1989-12-31 1989-12-31 0 0 Female 20-60
#> 6 1989-12-31 1989-12-31 0 0 Female 20-60
#> 7 1989-12-31 1990-01-07 0 1 Female 20-60
#> 8 1989-12-31 1990-01-21 0 3 Female 20-60
#> 9 1989-12-31 1990-01-07 0 1 Female 20-60
#> 10 1989-12-31 1990-01-07 0 1 Female 20-60
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 5 more variables: temperature <dbl>, humidity <dbl>, n <int>, .delay <dbl>,
#> # n2 <dbl>
# Change is_censored
ndata$is_censored <- FALSE
ndata <- ndata |>
change_is_censored(is_censored)
ndata
#> # A tibble: 52,987 × 12
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Right-censored indicator: "is_censored"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 5 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>, is_censored <lgl>
# Change covariates
ndata$temperature <- rnorm(nrow(ndata), 25, 4)
ndata$humidity <- rbeta(nrow(ndata), 0.6, 0.4)
ndata <- ndata |> change_covariates(temperature, humidity)
ndata
#> # A tibble: 52,987 × 12
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Right-censored indicator: "is_censored"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 5 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>, is_censored <lgl>
# Add temporal effects, remove and replace them
ndata <- ndata |>
add_temporal_effects(disease_data,
t_effects = temporal_effects(week_of_year = TRUE, month_of_year = TRUE)
)
# Use the compute to calculate them
ndata |> compute_temporal_effects()
#> # A tibble: 52,987 × 14
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Right-censored indicator: "is_censored"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # T. effects: [event_date] month_of_year, week_of_year
#> # T. effect cols: ".event_month_of_year" and ".event_week_of_year"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 7 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>, is_censored <lgl>,
#> # .event_month_of_year <int>, .event_week_of_year <int>
# Use replace to change them
ndata |> replace_temporal_effects(t_effects = temporal_effects(seasons = 52))
#> # A tibble: 52,987 × 12
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Right-censored indicator: "is_censored"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # T. effects (lazy): [event_date] season(52)
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 5 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>, is_censored <lgl>
# Use remove to delete them
ndata |> remove_temporal_effects()
#> # A tibble: 52,987 × 12
#> # Data type: "linelist"
#> # Frequency: Event: `weeks` | Report: `weeks`
#> onset_week report_week gender .event_num .report_num .delay new_onset_week
#> <date> <date> <chr> <dbl> <dbl> <dbl> <date>
#> [...] [...] [strata] [...] [...] [...] [event_date]
#> 1 1990-01-01 1990-01-01 Male 0 0 0 1989-12-31
#> 2 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 3 1990-01-01 1990-01-01 Female 0 0 0 1989-12-31
#> 4 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> 5 1990-01-01 1990-01-08 Male 0 1 1 1989-12-31
#> 6 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 7 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 8 1990-01-01 1990-01-15 Female 0 2 2 1989-12-31
#> 9 1990-01-01 1990-01-22 Female 0 3 3 1989-12-31
#> 10 1990-01-01 1990-01-08 Female 0 1 1 1989-12-31
#> # ────────────────────────────────────────────────────────────────────────────────
#> # Now: 2025-01-01 | Event date: "new_onset_week" | Report date: "new_report_week"
#> # Right-censored indicator: "is_censored"
#> # Strata: "gender" and "age_group"
#> # Covariates: "temperature" and "humidity"
#> # ────────────────────────────────────────────────────────────────────────────────
#> # ℹ 52,977 more rows
#> # ℹ 5 more variables: new_report_week <date>, age_group <chr>,
#> # temperature <dbl>, humidity <dbl>, is_censored <lgl>