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+# Determine First Isolates
+
+Determine first isolates of all microorganisms of every patient per
+episode and (if needed) per specimen type. These functions support all
+four methods as summarised by Hindler *et al.* in 2007
+([doi:10.1086/511864](https://doi.org/10.1086/511864) ). To determine
+patient episodes not necessarily based on microorganisms, use
+[`is_new_episode()`](https://amr-for-r.org/reference/get_episode.md)
+that also supports grouping with the `dplyr` package.
+
+## Usage
+
+``` r
+first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,
+  col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
+  col_icu = NULL, col_keyantimicrobials = NULL, episode_days = 365,
+  testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL,
+  type = "points", method = c("phenotype-based", "episode-based",
+  "patient-based", "isolate-based"), ignore_I = TRUE, points_threshold = 2,
+  info = interactive(), include_unknown = FALSE,
+  include_untested_sir = TRUE, ...)
+
+filter_first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,
+  col_mo = NULL, episode_days = 365, method = c("phenotype-based",
+  "episode-based", "patient-based", "isolate-based"), ...)
+```
+
+## Source
+
+Methodology of these functions is strictly based on:
+
+- **M39 Analysis and Presentation of Cumulative Antimicrobial
+  Susceptibility Test Data, 5th Edition**, 2022, *Clinical and
+  Laboratory Standards Institute (CLSI)*.
+  <https://clsi.org/standards/products/microbiology/documents/m39/>.
+
+- Hindler JF and Stelling J (2007). **Analysis and Presentation of
+  Cumulative Antibiograms: A New Consensus Guideline from the Clinical
+  and Laboratory Standards Institute.** Clinical Infectious Diseases,
+  44(6), 867-873. [doi:10.1086/511864](https://doi.org/10.1086/511864)
+
+## Arguments
+
+- x:
+
+  A [data.frame](https://rdrr.io/r/base/data.frame.html) containing
+  isolates. Can be left blank for automatic determination, see
+  *Examples*.
+
+- col_date:
+
+  Column name of the result date (or date that is was received on the
+  lab) - the default is the first column with a date class.
+
+- col_patient_id:
+
+  Column name of the unique IDs of the patients - the default is the
+  first column that starts with 'patient' or 'patid' (case insensitive).
+
+- col_mo:
+
+  Column name of the names or codes of the microorganisms (see
+  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md)) - the default
+  is the first column of class
+  [`mo`](https://amr-for-r.org/reference/as.mo.md). Values will be
+  coerced using [`as.mo()`](https://amr-for-r.org/reference/as.mo.md).
+
+- col_testcode:
+
+  Column name of the test codes. Use `col_testcode = NULL` to **not**
+  exclude certain test codes (such as test codes for screening). In that
+  case `testcodes_exclude` will be ignored.
+
+- col_specimen:
+
+  Column name of the specimen type or group.
+
+- col_icu:
+
+  Column name of the logicals (`TRUE`/`FALSE`) whether a ward or
+  department is an Intensive Care Unit (ICU). This can also be a
+  [logical](https://rdrr.io/r/base/logical.html) vector with the same
+  length as rows in `x`.
+
+- col_keyantimicrobials:
+
+  (only useful when `method = "phenotype-based"`) column name of the key
+  antimicrobials to determine first isolates, see
+  [`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md).
+  The default is the first column that starts with 'key' followed by
+  'ab' or 'antibiotics' or 'antimicrobials' (case insensitive). Use
+  `col_keyantimicrobials = FALSE` to prevent this. Can also be the
+  output of
+  [`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md).
+
+- episode_days:
+
+  Episode in days after which a genus/species combination will be
+  determined as 'first isolate' again. The default of 365 days is based
+  on the guideline by CLSI, see *Source*.
+
+- testcodes_exclude:
+
+  A [character](https://rdrr.io/r/base/character.html) vector with test
+  codes that should be excluded (case-insensitive).
+
+- icu_exclude:
+
+  A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
+  ICU isolates should be excluded (rows with value `TRUE` in the column
+  set with `col_icu`).
+
+- specimen_group:
+
+  Value in the column set with `col_specimen` to filter on.
+
+- type:
+
+  Type to determine weighed isolates; can be `"keyantimicrobials"` or
+  `"points"`, see *Details*.
+
+- method:
+
+  The method to apply, either `"phenotype-based"`, `"episode-based"`,
+  `"patient-based"` or `"isolate-based"` (can be abbreviated), see
+  *Details*. The default is `"phenotype-based"` if antimicrobial test
+  results are present in the data, and `"episode-based"` otherwise.
+
+- ignore_I:
+
+  [logical](https://rdrr.io/r/base/logical.html) to indicate whether
+  antibiotic interpretations with `"I"` will be ignored when
+  `type = "keyantimicrobials"`, see *Details*.
+
+- points_threshold:
+
+  Minimum number of points to require before differences in the
+  antibiogram will lead to inclusion of an isolate when
+  `type = "points"`, see *Details*.
+
+- info:
+
+  A [logical](https://rdrr.io/r/base/logical.html) to indicate info
+  should be printed - the default is `TRUE` only in interactive mode.
+
+- include_unknown:
+
+  A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
+  'unknown' microorganisms should be included too, i.e. microbial code
+  `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means
+  that all records with organism code `"con"` (*contamination*) will be
+  excluded at default. Isolates with a microbial ID of `NA` will always
+  be excluded as first isolate.
+
+- include_untested_sir:
+
+  A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
+  also rows without antibiotic results are still eligible for becoming a
+  first isolate. Use `include_untested_sir = FALSE` to always return
+  `FALSE` for such rows. This checks the data set for columns of class
+  `sir` and consequently requires transforming columns with antibiotic
+  results using [`as.sir()`](https://amr-for-r.org/reference/as.sir.md)
+  first.
+
+- ...:
+
+  Arguments passed on to `first_isolate()` when using
+  `filter_first_isolate()`, otherwise arguments passed on to
+  [`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md)
+  (such as `universal`, `gram_negative`, `gram_positive`).
+
+## Value
+
+A [logical](https://rdrr.io/r/base/logical.html) vector
+
+## Details
+
+The methodology implemented in these functions is strictly based on the
+recommendations outlined in [CLSI Guideline
+M39](https://clsi.org/standards/products/microbiology/documents/m39) and
+the research overview by Hindler *et al.* (2007,
+[doi:10.1086/511864](https://doi.org/10.1086/511864) ).
+
+To conduct epidemiological analyses on antimicrobial resistance data,
+only so-called first isolates should be included to prevent
+overestimation and underestimation of antimicrobial resistance.
+Different methods can be used to do so, see below.
+
+These functions are context-aware. This means that the `x` argument can
+be left blank if used inside a
+[data.frame](https://rdrr.io/r/base/data.frame.html) call, see
+*Examples*.
+
+The `first_isolate()` function is a wrapper around the
+[`is_new_episode()`](https://amr-for-r.org/reference/get_episode.md)
+function, but more efficient for data sets containing microorganism
+codes or names.
+
+All isolates with a microbial ID of `NA` will be excluded as first
+isolate.
+
+### Different methods
+
+According to previously-mentioned sources, there are different methods
+(algorithms) to select first isolates with increasing reliability:
+isolate-based, patient-based, episode-based and phenotype-based. All
+methods select on a combination of the taxonomic genus and species (not
+subspecies).
+
+All mentioned methods are covered in the `first_isolate()` function:
+
+|                                                 |                                                        |
+|-------------------------------------------------|--------------------------------------------------------|
+| **Method**                                      | **Function to apply**                                  |
+| **Isolate-based**                               | `first_isolate(x, method = "isolate-based")`           |
+| *(= all isolates)*                              |                                                        |
+|                                                 |                                                        |
+|                                                 |                                                        |
+| **Patient-based**                               | `first_isolate(x, method = "patient-based")`           |
+| *(= first isolate per patient)*                 |                                                        |
+|                                                 |                                                        |
+|                                                 |                                                        |
+| **Episode-based**                               | `first_isolate(x, method = "episode-based")`, or:      |
+| *(= first isolate per episode)*                 |                                                        |
+| \- 7-Day interval from initial isolate          | \- `first_isolate(x, method = "e", episode_days = 7)`  |
+| \- 30-Day interval from initial isolate         | \- `first_isolate(x, method = "e", episode_days = 30)` |
+|                                                 |                                                        |
+|                                                 |                                                        |
+| **Phenotype-based**                             | `first_isolate(x, method = "phenotype-based")`, or:    |
+| *(= first isolate per phenotype)*               |                                                        |
+| \- Major difference in any antimicrobial result | \- `first_isolate(x, type = "points")`                 |
+| \- Any difference in key antimicrobial results  | \- `first_isolate(x, type = "keyantimicrobials")`      |
+
+**Isolate-based**
+
+*Minimum variables required: Microorganism identifier*
+
+This method does not require any selection, as all isolates should be
+included. It does, however, respect all arguments set in the
+`first_isolate()` function. For example, the default setting for
+`include_unknown` (`FALSE`) will omit selection of rows without a
+microbial ID.
+
+**Patient-based**
+
+*Minimum variables required: Microorganism identifier, Patient
+identifier*
+
+This method includes every genus-species combination per patient once.
+This method makes sure that no duplicate isolates are selected from the
+same patient. This method is preferred to e.g. identify the first MRSA
+finding of each patient to determine the incidence. Conversely, in a
+large longitudinal data set, this could mean that isolates are
+*excluded* that were found years after the initial isolate.
+
+**Episode-based**
+
+*Minimum variables required: Microorganism identifier, Patient
+identifier, Date*
+
+To include every genus-species combination per patient episode once, set
+the `episode_days` to a sensible number of days. Depending on the type
+of analysis, this could be e.g., 14, 30, 60 or 365. Short episodes are
+common for analysing specific hospital or ward data or ICU cases, long
+episodes are common for analysing regional and national data.
+
+This is the most common method to correct for duplicate isolates.
+Patients are categorised into episodes based on their ID and dates
+(e.g., the date of specimen receipt or laboratory result). While this is
+a common method, it does not take into account antimicrobial test
+results. This means that e.g. a methicillin-resistant *Staphylococcus
+aureus* (MRSA) isolate cannot be differentiated from a wildtype
+*Staphylococcus aureus* isolate.
+
+**Phenotype-based**
+
+*Minimum variables required: Microorganism identifier, Patient
+identifier, Date, Antimicrobial test results*
+
+This is a more reliable method, since it also *weighs* the antibiogram
+(antimicrobial test results) yielding so-called 'first weighted
+isolates'. There are two different methods to weigh the antibiogram:
+
+1.  Using `type = "points"` and argument `points_threshold` (default)
+
+    This method weighs *all* antimicrobial drugs available in the data
+    set. Any difference from I to S or R (or vice versa) counts as `0.5`
+    points, a difference from S to R (or vice versa) counts as `1`
+    point. When the sum of points exceeds `points_threshold`, which
+    defaults to `2`, an isolate will be selected as a first weighted
+    isolate.
+
+    All antimicrobials are internally selected using the
+    [`all_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md)
+    function. The output of this function does not need to be passed to
+    the `first_isolate()` function.
+
+2.  Using `type = "keyantimicrobials"` and argument `ignore_I`
+
+    This method only weighs specific antimicrobial drugs, called *key
+    antimicrobials*. Any difference from S to R (or vice versa) in these
+    key antimicrobials will select an isolate as a first weighted
+    isolate. With `ignore_I = FALSE`, also differences from I to S or R
+    (or vice versa) will lead to this.
+
+    Key antimicrobials are internally selected using the
+    [`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md)
+    function, but can also be added manually as a variable to the data
+    and set in the `col_keyantimicrobials` argument. Another option is
+    to pass the output of the
+    [`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md)
+    function directly to the `col_keyantimicrobials` argument.
+
+The default method is phenotype-based (using `type = "points"`) and
+episode-based (using `episode_days = 365`). This makes sure that every
+genus-species combination is selected per patient once per year, while
+taking into account all antimicrobial test results. If no antimicrobial
+test results are available in the data set, only the episode-based
+method is applied at default.
+
+## See also
+
+[`key_antimicrobials()`](https://amr-for-r.org/reference/key_antimicrobials.md)
+
+## Examples
+
+``` r
+# `example_isolates` is a data set available in the AMR package.
+# See ?example_isolates.
+
+example_isolates[first_isolate(info = TRUE), ]
+#> ℹ Determining first isolates using an episode length of 365 days
+#> ℹ Using column 'date' as input for `col_date`.
+#> ℹ Using column 'patient' as input for `col_patient_id`.
+#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
+#>   of 2
+#> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo')
+#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
+#>    microbial ID was available)
+#> # A tibble: 1,387 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA 
+#>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R  
+#>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA 
+#> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA 
+#> # ℹ 1,377 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+# \donttest{
+# get all first Gram-negatives
+example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]
+#> ℹ Using column 'mo' as input for `mo_is_gram_negative()`
+#> # A tibble: 441 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R  
+#>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA 
+#>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R  
+#>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R  
+#>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R  
+#>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG   R     NA    NA    R  
+#>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI   R     NA    NA    NA 
+#> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI   R     NA    NA    NA 
+#> # ℹ 431 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+
+if (require("dplyr")) {
+  # filter on first isolates using dplyr:
+  example_isolates %>%
+    filter(first_isolate(info = TRUE))
+}
+#> ℹ Determining first isolates using an episode length of 365 days
+#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
+#>   of 2
+#> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo')
+#> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a
+#>    microbial ID was available)
+#> # A tibble: 1,387 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA 
+#>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R  
+#>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA 
+#> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA 
+#> # ℹ 1,377 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+if (require("dplyr")) {
+  # short-hand version:
+  example_isolates %>%
+    filter_first_isolate(info = FALSE)
+}
+#> # A tibble: 1,387 × 46
+#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
+#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
+#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
+#>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
+#>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
+#>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA 
+#>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA 
+#>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R  
+#>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA 
+#> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA 
+#> # ℹ 1,377 more rows
+#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
+#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
+#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
+#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
+#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
+#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
+if (require("dplyr")) {
+  # flag the first isolates per group:
+  example_isolates %>%
+    group_by(ward) %>%
+    mutate(first = first_isolate(info = TRUE)) %>%
+    select(ward, date, patient, mo, first)
+}
+#> ℹ Determining first isolates using an episode length of 365 days
+#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
+#>   of 2
+#> 
+#> Group: ward = "Clinical"
+#> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo')
+#> => Found 865 'phenotype-based' first isolates (70.1% of total where a
+#>    microbial ID was available)
+#> 
+#> Group: ward = "ICU"
+#> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo')
+#> => Found 452 'phenotype-based' first isolates (70.0% of total where a
+#>    microbial ID was available)
+#> 
+#> Group: ward = "Outpatient"
+#> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo')
+#> => Found 99 'phenotype-based' first isolates (82.5% of total where a
+#>    microbial ID was available)
+#> # A tibble: 2,000 × 5
+#> # Groups:   ward [3]
+#>    ward     date       patient mo           first
+#>    <chr>    <date>     <chr>   <mo>         <lgl>
+#>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE 
+#>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE
+#>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE 
+#>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE
+#>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE
+#>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE
+#>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE 
+#>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE
+#>  9 ICU      2002-01-16 067927  B_STPHY_EPDR TRUE 
+#> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE 
+#> # ℹ 1,990 more rows
+# }
+```