incidence2

Usage

The following sections give an overview of the package utilising two different data sets. The first of these datasets comes from the {outbreaks} package and is a synthetic linelist generated from a simulated Ebola Virus Disease (EVD) outbreak. The second data set is available within {incidence2} and represents a pre-aggregated time-series of Covid cases, tests, hospitalisations, and deaths for UK regions that was obtained using the {covidregionaldata} package (extracted on 2021-06-03).

library(outbreaks)  # for the underlying data
library(ggplot2)    # For custom plotting later
library(incidence2) 
#> Loading required package: grates

ebola <- ebola_sim_clean$linelist
str(ebola)
#> 'data.frame':    5829 obs. of  11 variables:
#>  $ case_id                : chr  "d1fafd" "53371b" "f5c3d8" "6c286a" ...
#>  $ generation             : int  0 1 1 2 2 0 3 3 2 3 ...
#>  $ date_of_infection      : Date, format: NA "2014-04-09" ...
#>  $ date_of_onset          : Date, format: "2014-04-07" "2014-04-15" ...
#>  $ date_of_hospitalisation: Date, format: "2014-04-17" "2014-04-20" ...
#>  $ date_of_outcome        : Date, format: "2014-04-19" NA ...
#>  $ outcome                : Factor w/ 2 levels "Death","Recover": NA NA 2 1 2 NA 2 1 2 1 ...
#>  $ gender                 : Factor w/ 2 levels "f","m": 1 2 1 1 1 1 1 1 2 2 ...
#>  $ hospital               : Factor w/ 5 levels "Connaught Hospital",..: 2 1 3 NA 3 NA 1 4 3 5 ...
#>  $ lon                    : num  -13.2 -13.2 -13.2 -13.2 -13.2 ...
#>  $ lat                    : num  8.47 8.46 8.48 8.46 8.45 ...
covid <- covidregionaldataUK
str(covid)
#> 'data.frame':    6370 obs. of  13 variables:
#>  $ date           : Date, format: "2020-01-30" "2020-01-30" ...
#>  $ region         : chr  "East Midlands" "East of England" "England" "London" ...
#>  $ region_code    : chr  "E12000004" "E12000006" "E92000001" "E12000007" ...
#>  $ cases_new      : num  NA NA 2 NA NA NA NA NA NA NA ...
#>  $ cases_total    : num  NA NA 2 NA NA NA NA NA NA NA ...
#>  $ deaths_new     : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ deaths_total   : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ recovered_new  : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ recovered_total: num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ hosp_new       : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ hosp_total     : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ tested_new     : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ tested_total   : num  NA NA NA NA NA NA NA NA NA NA ...

Computing incidence from a linelist

To compute daily incidence we pass to incidence() a linelist of observation data. This input should be in the form of a data frame and we must also pass the name of a variable in the data that we can use to index the input. Note that whilst we we refer to this index as the date_index there is no restriction on it’s type, save it needing represent the relative time of an observation (i.e. it has an ordering).

daily <- incidence(ebola, date_index = "date_of_onset")
daily
#> # incidence:  367 x 3
#> # count vars: date_of_onset
#>    date_index count_variable count
#>  * <date>     <chr>          <int>
#>  1 2014-04-07 date_of_onset      1
#>  2 2014-04-15 date_of_onset      1
#>  3 2014-04-21 date_of_onset      2
#>  4 2014-04-25 date_of_onset      1
#>  5 2014-04-26 date_of_onset      1
#>  6 2014-04-27 date_of_onset      1
#>  7 2014-05-01 date_of_onset      2
#>  8 2014-05-03 date_of_onset      1
#>  9 2014-05-04 date_of_onset      1
#> 10 2014-05-05 date_of_onset      1
#> # ℹ 357 more rows
plot(daily)

The daily data is quite noisy, so we may want to pre group dates prior to calculating the incidence. One way to do this is to utilise functions from the {grates} package. Here we use the as_isoweek() function to convert the ‘date of onset’ to an isoweek (a week starting on a Monday) before calculating the incidence incidence:

# isoweek incidence
weekly_ebola <- transform(ebola, date_of_onset = as_isoweek(date_of_onset))
inci <- incidence(weekly_ebola, date_index = "date_of_onset")
inci
#> # incidence:  56 x 3
#> # count vars: date_of_onset
#>    date_index count_variable count
#>  * <isowk>    <chr>          <int>
#>  1 2014-W15   date_of_onset      1
#>  2 2014-W16   date_of_onset      1
#>  3 2014-W17   date_of_onset      5
#>  4 2014-W18   date_of_onset      4
#>  5 2014-W19   date_of_onset     12
#>  6 2014-W20   date_of_onset     17
#>  7 2014-W21   date_of_onset     15
#>  8 2014-W22   date_of_onset     19
#>  9 2014-W23   date_of_onset     23
#> 10 2014-W24   date_of_onset     21
#> # ℹ 46 more rows
plot(inci, border_colour = "white")

By grouping dates prior to calling incidence() it makes it clear to future readers of your code (including yourself) which transformations are being applied to your input data. This grouping, however, is such a common and useful operation that we have chosen to integrate much of {grates} functionality directly in to incidence2. This integration is done via an interval parameter in the incidence() call. This can take values:

day or daily (mapping to <Date> objects);
week(s), isoweek(s) or weekly (mapping to <grates_isoweek>);
epiweek(s) (mapping to <grates_epiweek>);
month(s), yearmonth(s) or monthly (<grates_yearmonth>);
quarter(s), yearquarter(s) or quarterly (<grates_yearquarter>);
year(s) or yearly (<grates_year>).

As an example, the following is equivalent to the inci output above:

# isoweek incidence using the interval parameter
inci2 <- incidence(ebola, date_index = "date_of_onset", interval = "isoweek")
inci2
#> # incidence:  56 x 3
#> # count vars: date_of_onset
#>    date_index count_variable count
#>  * <isowk>    <chr>          <int>
#>  1 2014-W15   date_of_onset      1
#>  2 2014-W16   date_of_onset      1
#>  3 2014-W17   date_of_onset      5
#>  4 2014-W18   date_of_onset      4
#>  5 2014-W19   date_of_onset     12
#>  6 2014-W20   date_of_onset     17
#>  7 2014-W21   date_of_onset     15
#>  8 2014-W22   date_of_onset     19
#>  9 2014-W23   date_of_onset     23
#> 10 2014-W24   date_of_onset     21
#> # ℹ 46 more rows

# check equivalent
identical(inci, inci2)
#> [1] TRUE

If we wish to aggregate by specified groups we can use the groups argument. For instance, computing incidence by gender:

inci_by_gender <- incidence(
    ebola,
    date_index = "date_of_onset",
    groups = "gender",
    interval = "isoweek"
)
inci_by_gender
#> # incidence:  109 x 4
#> # count vars: date_of_onset
#> # groups:     gender
#>    date_index gender count_variable count
#>  * <isowk>    <fct>  <chr>          <int>
#>  1 2014-W15   f      date_of_onset      1
#>  2 2014-W16   m      date_of_onset      1
#>  3 2014-W17   f      date_of_onset      4
#>  4 2014-W17   m      date_of_onset      1
#>  5 2014-W18   f      date_of_onset      4
#>  6 2014-W19   f      date_of_onset      9
#>  7 2014-W19   m      date_of_onset      3
#>  8 2014-W20   f      date_of_onset      7
#>  9 2014-W20   m      date_of_onset     10
#> 10 2014-W21   f      date_of_onset      8
#> # ℹ 99 more rows

For grouped data, the plot method will create a faceted plot across groups unless a fill variable is specified:

plot(inci_by_gender, border_colour = "white", angle = 45)

plot(inci_by_gender, border_colour = "white", angle = 45, fill = "gender")

incidence() also supports multiple date inputs:

grouped_inci <- incidence(
    ebola,
    date_index = c(
        onset = "date_of_onset",
        infection = "date_of_infection"
    ), 
    interval = "isoweek",
    groups = "gender"
)
grouped_inci
#> # incidence:  218 x 4
#> # count vars: infection, onset
#> # groups:     gender
#>    date_index gender count_variable count
#>  * <isowk>    <fct>  <chr>          <int>
#>  1 2014-W12   f      infection          1
#>  2 2014-W15   f      onset              1
#>  3 2014-W15   m      infection          1
#>  4 2014-W16   f      infection          1
#>  5 2014-W16   m      onset              1
#>  6 2014-W17   f      infection          4
#>  7 2014-W17   f      onset              4
#>  8 2014-W17   m      onset              1
#>  9 2014-W18   f      infection          7
#> 10 2014-W18   f      onset              4
#> # ℹ 208 more rows

When multiple date indices are given, they are used for rows of the resultant plot, unless the resultant variable is used to fill:

plot(grouped_inci, angle = 45, border_colour = "white")

plot(grouped_inci, angle = 45, border_colour = "white", fill = "count_variable")

Computing incidence from pre-aggregated data

The Covid data set is in a wide format with multiple count values given for each day. To convert this to long form incidence we specify similar variables to before but also include the count variables we are interested in:

monthly_covid <- 
    covid |> 
    subset(!region %in% c("England", "Scotland", "Northern Ireland", "Wales")) |> 
    incidence(
        date_index = "date",
        groups = "region",
        counts = c("cases_new"),
        interval = "yearmonth"
    )
monthly_covid
#> # incidence:  162 x 4
#> # count vars: cases_new
#> # groups:     region
#>    date_index region                   count_variable count
#>  * <yrmon>    <chr>                    <fct>          <dbl>
#>  1 2020-Jan   East Midlands            cases_new         NA
#>  2 2020-Jan   East of England          cases_new         NA
#>  3 2020-Jan   London                   cases_new         NA
#>  4 2020-Jan   North East               cases_new         NA
#>  5 2020-Jan   North West               cases_new         NA
#>  6 2020-Jan   South East               cases_new         NA
#>  7 2020-Jan   South West               cases_new         NA
#>  8 2020-Jan   West Midlands            cases_new         NA
#>  9 2020-Jan   Yorkshire and The Humber cases_new          1
#> 10 2020-Feb   East Midlands            cases_new         NA
#> # ℹ 152 more rows
plot(monthly_covid, nrow = 3, angle = 45, border_colour = "white")
#> Warning: Removed 26 rows containing missing values (`position_stack()`).

Plotting in style of European Programme for Intervention Epidemiology Training (EPIET)

For small datasets it is convention of EPIET to display individual cases as rectangles. We can do this by setting show_cases = TRUE in the call to plot() which will display each case as an individual square with a white border.

dat <- ebola[160:180, ]
i_epiet <- incidence(dat, date_index = "date_of_onset", date_names_to = "date")
plot(i_epiet, color = "white", show_cases = TRUE, angle = 45, n_breaks = 10)

i_epiet2 <- incidence(
    dat, date_index = "date_of_onset",
    groups = "gender", date_names_to = "date"
)
plot(
    i_epiet2, show_cases = TRUE,
    color = "white", angle = 45, n_breaks = 10, fill = "gender"
)

Modifying incidence objects

`regroup()`

Sometimes you may find you’ve created a grouped incidence but now want to change the internal grouping. Assuming you are after a subset of the grouping already generated, you can use regroup() to get the desired aggregation:

# generate an incidence object with 3 groups
x <- incidence(
    ebola,
    date_index = "date_of_onset",
    interval = "isoweek",
    groups = c("gender", "hospital", "outcome")
)

# regroup to just one group
xx <- regroup(x, c("gender", "outcome"))
xx
#> # incidence:  320 x 5
#> # count vars: date_of_onset
#> # groups:     gender, outcome
#>    date_index gender outcome count_variable count
#>  * <isowk>    <fct>  <fct>   <chr>          <int>
#>  1 2014-W15   f      <NA>    date_of_onset      1
#>  2 2014-W16   m      <NA>    date_of_onset      1
#>  3 2014-W17   f      <NA>    date_of_onset      1
#>  4 2014-W17   f      Death   date_of_onset      1
#>  5 2014-W17   f      Recover date_of_onset      2
#>  6 2014-W17   m      Recover date_of_onset      1
#>  7 2014-W18   f      Death   date_of_onset      1
#>  8 2014-W18   f      Recover date_of_onset      3
#>  9 2014-W19   f      <NA>    date_of_onset      4
#> 10 2014-W19   f      Death   date_of_onset      2
#> # ℹ 310 more rows

# drop all groups
regroup(x)
#> # incidence:  56 x 3
#> # count vars: date_of_onset
#>    date_index count_variable count
#>  * <isowk>    <chr>          <int>
#>  1 2014-W15   date_of_onset      1
#>  2 2014-W16   date_of_onset      1
#>  3 2014-W17   date_of_onset      5
#>  4 2014-W18   date_of_onset      4
#>  5 2014-W19   date_of_onset     12
#>  6 2014-W20   date_of_onset     17
#>  7 2014-W21   date_of_onset     15
#>  8 2014-W22   date_of_onset     19
#>  9 2014-W23   date_of_onset     23
#> 10 2014-W24   date_of_onset     21
#> # ℹ 46 more rows

`cumulate()`

We also provide a helper function, cumulate() to easily generate cumulative incidences:

y <- regroup(x, "hospital")
y <- cumulate(y)
y
#> # incidence:  315 x 4
#> # count vars: date_of_onset
#> # groups:     hospital
#>    date_index hospital count_variable cumulative_count
#>  * <isowk>    <fct>    <chr>                     <int>
#>  1 2014-W17   <NA>     date_of_onset                 2
#>  2 2014-W18   <NA>     date_of_onset                 3
#>  3 2014-W19   <NA>     date_of_onset                 4
#>  4 2014-W20   <NA>     date_of_onset                 8
#>  5 2014-W21   <NA>     date_of_onset                12
#>  6 2014-W22   <NA>     date_of_onset                16
#>  7 2014-W23   <NA>     date_of_onset                26
#>  8 2014-W24   <NA>     date_of_onset                34
#>  9 2014-W25   <NA>     date_of_onset                42
#> 10 2014-W26   <NA>     date_of_onset                51
#> # ℹ 305 more rows
plot(y, angle = 45, nrow = 3)

subsetting and other manipulations

`keep_first()`, `keep_last()` and `keep_peaks()`

Once your data is grouped by date, you may want to select the first or last few entries based on a particular date grouping using keep_first() and keep_last():

inci <- incidence(
    ebola,
    date_index = "date_of_onset",
    interval = "isoweek",
    groups = c("hospital", "gender")
)

keep_first(inci, 3)
#> # incidence:  36 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    date_index hospital           gender count_variable count
#>  * <isowk>    <fct>              <fct>  <chr>          <int>
#>  1 2014-W15   <NA>               f      date_of_onset      0
#>  2 2014-W16   <NA>               f      date_of_onset      0
#>  3 2014-W17   <NA>               f      date_of_onset      2
#>  4 2014-W15   <NA>               m      date_of_onset      0
#>  5 2014-W16   <NA>               m      date_of_onset      0
#>  6 2014-W17   <NA>               m      date_of_onset      0
#>  7 2014-W15   Connaught Hospital f      date_of_onset      0
#>  8 2014-W16   Connaught Hospital f      date_of_onset      0
#>  9 2014-W17   Connaught Hospital f      date_of_onset      0
#> 10 2014-W15   Connaught Hospital m      date_of_onset      0
#> # ℹ 26 more rows
keep_last(inci, 3)
#> # incidence:  36 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    date_index hospital           gender count_variable count
#>  * <isowk>    <fct>              <fct>  <chr>          <int>
#>  1 2015-W16   <NA>               f      date_of_onset      9
#>  2 2015-W17   <NA>               f      date_of_onset      4
#>  3 2015-W18   <NA>               f      date_of_onset      1
#>  4 2015-W16   <NA>               m      date_of_onset      5
#>  5 2015-W17   <NA>               m      date_of_onset      8
#>  6 2015-W18   <NA>               m      date_of_onset      1
#>  7 2015-W16   Connaught Hospital f      date_of_onset      9
#>  8 2015-W17   Connaught Hospital f      date_of_onset      5
#>  9 2015-W18   Connaught Hospital f      date_of_onset      7
#> 10 2015-W16   Connaught Hospital m      date_of_onset      5
#> # ℹ 26 more rows

Similarly you may want to quickly view the incidence peaks:

keep_peaks(inci)
#> # incidence:  14 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    date_index hospital                               gender count_variable count
#>  * <isowk>    <fct>                                  <fct>  <chr>          <int>
#>  1 2014-W38   <NA>                                   f      date_of_onset     46
#>  2 2014-W40   <NA>                                   m      date_of_onset     41
#>  3 2014-W39   Connaught Hospital                     f      date_of_onset     57
#>  4 2014-W38   Connaught Hospital                     m      date_of_onset     48
#>  5 2014-W38   Military Hospital                      f      date_of_onset     26
#>  6 2014-W38   Military Hospital                      m      date_of_onset     28
#>  7 2014-W42   other                                  f      date_of_onset     32
#>  8 2014-W38   other                                  m      date_of_onset     26
#>  9 2014-W38   Princess Christian Maternity Hospital… f      date_of_onset     13
#> 10 2014-W40   Princess Christian Maternity Hospital… f      date_of_onset     13
#> 11 2014-W38   Princess Christian Maternity Hospital… m      date_of_onset     12
#> 12 2014-W42   Rokupa Hospital                        f      date_of_onset     15
#> 13 2014-W40   Rokupa Hospital                        m      date_of_onset     17
#> 14 2014-W44   Rokupa Hospital                        m      date_of_onset     17

`complete_dates()`

Sometimes your incidence data does not span consecutive units of time, or different groupings may cover different periods. To this end we provide a complete_dates() function which ensures a complete and identical range of dates are given counts (by default filling with a 0 value).

dat <- data.frame(
    dates = as.Date(c("2020-01-01", "2020-01-04")),
    gender = c("male", "female")
)
i <- incidence(dat, date_index = "dates", groups = "gender")
i
#> # incidence:  2 x 4
#> # count vars: dates
#> # groups:     gender
#>   date_index gender count_variable count
#> * <date>     <chr>  <chr>          <int>
#> 1 2020-01-01 male   dates              1
#> 2 2020-01-04 female dates              1
complete_dates(i)
#> # incidence:  8 x 4
#> # count vars: dates
#> # groups:     gender
#>   date_index gender count_variable count
#>   <date>     <chr>  <chr>          <int>
#> 1 2020-01-01 female dates              0
#> 2 2020-01-01 male   dates              1
#> 3 2020-01-02 female dates              0
#> 4 2020-01-02 male   dates              0
#> 5 2020-01-03 female dates              0
#> 6 2020-01-03 male   dates              0
#> 7 2020-01-04 female dates              1
#> 8 2020-01-04 male   dates              0

preservation of class

<incidence2> objects have been carefully constructed to preserve their structure under a range of different operations that can be applied to data frames. By this we mean that if an operation is applied to an <incidence2> object then as long as the invariants of the object are preserved (i.e. groups, interval and uniqueness of rows) then the object retain it’s incidence class. If the invariants are not preserved then a <data.frame> will be returned instead.


# filtering preserves class
subset(inci, gender == "f" & hospital == "Rokupa Hospital")
#> # incidence:  48 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    date_index hospital        gender count_variable count
#>  * <isowk>    <fct>           <fct>  <chr>          <int>
#>  1 2014-W18   Rokupa Hospital f      date_of_onset      1
#>  2 2014-W20   Rokupa Hospital f      date_of_onset      1
#>  3 2014-W22   Rokupa Hospital f      date_of_onset      1
#>  4 2014-W23   Rokupa Hospital f      date_of_onset      1
#>  5 2014-W25   Rokupa Hospital f      date_of_onset      1
#>  6 2014-W27   Rokupa Hospital f      date_of_onset      1
#>  7 2014-W28   Rokupa Hospital f      date_of_onset      4
#>  8 2014-W29   Rokupa Hospital f      date_of_onset      2
#>  9 2014-W30   Rokupa Hospital f      date_of_onset      1
#> 10 2014-W31   Rokupa Hospital f      date_of_onset      1
#> # ℹ 38 more rows
inci[c(1L, 3L, 5L), ]
#> # incidence:  3 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>   date_index hospital          gender count_variable count
#> * <isowk>    <fct>             <fct>  <chr>          <int>
#> 1 2014-W15   Military Hospital f      date_of_onset      1
#> 2 2014-W17   <NA>              f      date_of_onset      2
#> 3 2014-W17   other             m      date_of_onset      1

# Adding columns preserve class
inci$future <- inci$date_index + 999L
inci
#> # incidence:  601 x 6
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    date_index hospital                        gender count_variable count future
#>  * <isowk>    <fct>                           <fct>  <chr>          <int> <isow>
#>  1 2014-W15   Military Hospital               f      date_of_onset      1 2033-…
#>  2 2014-W16   Connaught Hospital              m      date_of_onset      1 2033-…
#>  3 2014-W17   <NA>                            f      date_of_onset      2 2033-…
#>  4 2014-W17   other                           f      date_of_onset      2 2033-…
#>  5 2014-W17   other                           m      date_of_onset      1 2033-…
#>  6 2014-W18   <NA>                            f      date_of_onset      1 2033-…
#>  7 2014-W18   Connaught Hospital              f      date_of_onset      1 2033-…
#>  8 2014-W18   Princess Christian Maternity H… f      date_of_onset      1 2033-…
#>  9 2014-W18   Rokupa Hospital                 f      date_of_onset      1 2033-…
#> 10 2014-W19   <NA>                            f      date_of_onset      1 2033-…
#> # ℹ 591 more rows

# rename preserve class
names(inci)[names(inci) == "date_index"] <- "isoweek"
inci
#> # incidence:  601 x 6
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    isoweek  hospital                          gender count_variable count future
#>  * <isowk>  <fct>                             <fct>  <chr>          <int> <isow>
#>  1 2014-W15 Military Hospital                 f      date_of_onset      1 2033-…
#>  2 2014-W16 Connaught Hospital                m      date_of_onset      1 2033-…
#>  3 2014-W17 <NA>                              f      date_of_onset      2 2033-…
#>  4 2014-W17 other                             f      date_of_onset      2 2033-…
#>  5 2014-W17 other                             m      date_of_onset      1 2033-…
#>  6 2014-W18 <NA>                              f      date_of_onset      1 2033-…
#>  7 2014-W18 Connaught Hospital                f      date_of_onset      1 2033-…
#>  8 2014-W18 Princess Christian Maternity Hos… f      date_of_onset      1 2033-…
#>  9 2014-W18 Rokupa Hospital                   f      date_of_onset      1 2033-…
#> 10 2014-W19 <NA>                              f      date_of_onset      1 2033-…
#> # ℹ 591 more rows

# select returns a data frame unless all date, count and group variables are
# preserved in the output
str(inci[,-1L])
#> 'data.frame':    601 obs. of  5 variables:
#>  $ hospital      : Factor w/ 5 levels "Connaught Hospital",..: 2 1 NA 3 3 NA 1 4 5 NA ...
#>  $ gender        : Factor w/ 2 levels "f","m": 1 2 1 1 2 1 1 1 1 1 ...
#>  $ count_variable: chr  "date_of_onset" "date_of_onset" "date_of_onset" "date_of_onset" ...
#>  $ count         : int  1 1 2 2 1 1 1 1 1 1 ...
#>  $ future        : 'grates_isoweek' int  2033-W22 2033-W23 2033-W24 2033-W24 2033-W24 2033-W25 2033-W25 2033-W25 2033-W25 2033-W26 ...
inci[, -6L]
#> # incidence:  601 x 5
#> # count vars: date_of_onset
#> # groups:     hospital, gender
#>    isoweek  hospital                                 gender count_variable count
#>  * <isowk>  <fct>                                    <fct>  <chr>          <int>
#>  1 2014-W15 Military Hospital                        f      date_of_onset      1
#>  2 2014-W16 Connaught Hospital                       m      date_of_onset      1
#>  3 2014-W17 <NA>                                     f      date_of_onset      2
#>  4 2014-W17 other                                    f      date_of_onset      2
#>  5 2014-W17 other                                    m      date_of_onset      1
#>  6 2014-W18 <NA>                                     f      date_of_onset      1
#>  7 2014-W18 Connaught Hospital                       f      date_of_onset      1
#>  8 2014-W18 Princess Christian Maternity Hospital (… f      date_of_onset      1
#>  9 2014-W18 Rokupa Hospital                          f      date_of_onset      1
#> 10 2014-W19 <NA>                                     f      date_of_onset      1
#> # ℹ 591 more rows

Accessing variable information

We provide multiple accessors to easily access information about an <incidence2> object’s structure:

# the name of the date_index variable of x
get_date_index_name(inci)
#> [1] "isoweek"

# alias for `get_date_index_name()`
get_dates_name(inci)
#> [1] "isoweek"

# the name of the count variable of x
get_count_variable_name(inci)
#> [1] "count_variable"

# the name of the count value of x
get_count_value_name(inci)
#> [1] "count"

# the name(s) of the group variable(s) of x
get_group_names(inci)
#> [1] "hospital" "gender"

# list containing date_index variable of x
str(get_date_index(inci))
#>  'grates_isoweek' int [1:601] 2014-W15 2014-W16 2014-W17 2014-W17 2014-W17 2014-W18 2014-W18 2014-W18 2014-W18 2014-W19 ...

# alias for get_date_index
str(get_dates(inci))
#>  'grates_isoweek' int [1:601] 2014-W15 2014-W16 2014-W17 2014-W17 2014-W17 2014-W18 2014-W18 2014-W18 2014-W18 2014-W19 ...

# list containing the count variable of x
str(get_count_variable(inci))
#>  chr [1:601] "date_of_onset" "date_of_onset" "date_of_onset" ...

# list containing count value of x
str(get_count_value(inci))
#>  int [1:601] 1 1 2 2 1 1 1 1 1 1 ...

# list of the group variable(s) of x
str(get_groups(inci)) 
#> List of 2
#>  $ hospital: Factor w/ 5 levels "Connaught Hospital",..: 2 1 NA 3 3 NA 1 4 5 NA ...
#>  $ gender  : Factor w/ 2 levels "f","m": 1 2 1 1 2 1 1 1 1 1 ...