R/filter_joined.R

In admiral: ADaM in R Asset Library

#' Filter Observations Taking Other Observations into Account
#'
#' @description
#'
#' The function filters observation using a condition taking other observations
#' into account. For example, it could select all observations with `AVALC ==
#' "Y"` and `AVALC == "Y"` for at least one subsequent observation. The input
#' dataset is joined with itself to enable conditions taking variables from both
#' the current observation and the other observations into account. The suffix
#' ".join" is added to the variables from the subsequent observations.
#'
#' An example usage might be checking if a patient received two required
#' medications within a certain timeframe of each other.
#'
#' In the oncology setting, for example, we use such processing to check if a
#' response value can be confirmed by a subsequent assessment. This is commonly
#' used in endpoints such as best overall response.
#'
#' @param dataset `r roxygen_param_dataset(expected_vars = c("by_vars", "order"))`
#'
#' @permitted [dataset]
#'
#' @param dataset_add Additional dataset
#'
#'   The variables specified for `by_vars`, `join_vars`, and `order` are
#'   expected.
#'
#' @permitted [dataset]
#'
#' @param by_vars By variables
#'
#'   The specified variables are used as by variables for joining the input
#'   dataset with itself.
#'
#'  `r roxygen_param_by_vars()`
#'
#' @permitted [var_list]
#'
#' @param join_vars Variables to keep from joined dataset
#'
#'   The variables needed from the other observations should be specified for
#'   this parameter. The specified variables are added to the joined dataset
#'   with suffix ".join". For example to select all observations with `AVALC ==
#'   "Y"` and `AVALC == "Y"` for at least one subsequent visit `join_vars =
#'   exprs(AVALC, AVISITN)` and `filter_join = AVALC == "Y" & AVALC.join == "Y"
#'   & AVISITN < AVISITN.join` could be specified.
#'
#'   The `*.join` variables are not included in the output dataset.
#'
#' @permitted [var_list]
#'
#' @param first_cond_lower Condition for selecting range of data (before)
#'
#'   If this argument is specified, the other observations are restricted from
#'   the first observation before the current observation where the specified
#'   condition is fulfilled up to the current observation. If the condition is
#'   not fulfilled for any of the other observations, no observations are
#'   considered, i.e., the observation is not flagged.
#'
#'   This parameter should be specified if `filter_join` contains summary
#'   functions which should not apply to all observations but only from a
#'   certain observation before the current observation up to the current
#'   observation. For examples see the "Examples" section below.
#'
#' @permitted [condition]
#'
#' @param first_cond_upper Condition for selecting range of data (after)
#'
#'   If this argument is specified, the other observations are restricted up to
#'   the first observation where the specified condition is fulfilled. If the
#'   condition is not fulfilled for any of the other observations, no
#'   observations are considered, i.e., the observation is not flagged.
#'
#'   This parameter should be specified if `filter_join` contains summary
#'   functions which should not apply to all observations but only up to the
#'   confirmation assessment. For examples see the "Examples" section below.
#'
#' @permitted [condition]
#'
#' @param order Order
#'
#'   The observations are ordered by the specified order.
#'
#'   `r roxygen_order_na_handling()`
#'
#' @permitted list of expressions created by `exprs()`, e.g.,
#'   `exprs(ADT, desc(AVAL))`
#'
#' @permitted [order_optional]
#'
#' @param tmp_obs_nr_var Temporary observation number
#'
#'   The specified variable is added to the input dataset (`dataset`) and the
#'   additional dataset (`dataset_add`). It is set to the observation number
#'   with respect to `order`. For each by group (`by_vars`) the observation
#'   number starts with `1`. If there is more than one record for specific
#'   values for `by_vars` and `order`, all records get the same observation
#'   number. By default, a warning (see `check_type`) is issued in this case.
#'   The variable can be used in the conditions (`filter_join`,
#'   `first_cond_upper`, `first_cond_lower`). It is not included in the output
#'   dataset. It can also be used to select consecutive observations or the last
#'   observation (see example below).
#'
#' @permitted [var]
#'
#' @param filter_add Filter for additional dataset (`dataset_add`)
#'
#'   Only observations from `dataset_add` fulfilling the specified condition are
#'   joined to the input dataset. If the argument is not specified, all
#'   observations are joined.
#'
#'   Variables created by the `order` argument can be used in the condition.
#'
#'   The condition can include summary functions. The additional dataset is
#'   grouped by the by variables (`by_vars`).
#'
#' @permitted [condition]
#'
#' @param filter_join Condition for selecting observations
#'
#'   The filter is applied to the joined dataset for selecting the confirmed
#'   observations. The condition can include summary functions like `all()` or
#'   `any()`. The joined dataset is grouped by the original observations. I.e.,
#'   the summary function are applied to all observations up to the confirmation
#'   observation. For example in the oncology setting when using this function
#'   for confirmed best overall response,  `filter_join = AVALC == "CR" &
#'   all(AVALC.join %in% c("CR", "NE")) & count_vals(var = AVALC.join, val =
#'   "NE") <= 1` selects observations with response "CR" and for all
#'   observations up to the confirmation observation the response is "CR" or
#'   "NE" and there is at most one "NE".
#'
#' @permitted [condition]
#'
#' @param check_type Check uniqueness?
#'
#'   If `"message"`, `"warning"`, or `"error"` is specified, the specified
#'   message is issued if the observations of the input dataset are not unique
#'   with respect to the by variables and the order.
#'
#' @permitted [msg_type]
#'
#' @inheritParams get_joined_data
#'
#' @details
#'
#'   The following steps are performed to produce the output dataset.
#'
#'   ## Step 1
#'
#'   - The variables specified by `order` are added to the additional dataset
#'   (`dataset_add`).
#'
#'   - The variables specified by `join_vars` are added to the additional dataset
#'   (`dataset_add`).
#'
#'   - The records from the additional dataset (`dataset_add`) are restricted to
#'   those matching the `filter_add` condition.
#'
#'   Then the  input dataset (`dataset`) is joined with the restricted
#'   additional dataset by the variables specified for `by_vars`. From the
#'   additional dataset only the variables specified for `join_vars` are kept.
#'   The suffix ".join" is added to those variables which are also present in
#'   the input dataset.
#'
#'   For example, for `by_vars = USUBJID`, `join_vars = exprs(AVISITN, AVALC)`
#'   and input dataset and additional dataset
#'
#'   ```{r eval=FALSE}
#'   # A tibble: 2 x 4
#'   USUBJID AVISITN AVALC  AVAL
#'   <chr>     <dbl> <chr> <dbl>
#'   1             1 Y         1
#'   1             2 N         0
#'   ```
#'
#'   the joined dataset is
#'
#'   ```{r eval=FALSE}
#'   A tibble: 4 x 6
#'   USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join
#'   <chr>     <dbl> <chr> <dbl>        <dbl> <chr>
#'   1             1 Y         1            1 Y
#'   1             1 Y         1            2 N
#'   1             2 N         0            1 Y
#'   1             2 N         0            2 N
#'   ```
#'
#'   ## Step 2
#'
#'   The joined dataset is restricted to observations with respect to
#'   `join_type` and `order`.
#'
#'   The dataset from the example in the previous step with `join_type =
#'   "after"` and `order = exprs(AVISITN)` is restricted to
#'
#'   ```{r eval=FALSE}
#'   A tibble: 4 x 6
#'   USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join
#'   <chr>     <dbl> <chr> <dbl>        <dbl> <chr>
#'   1             1 Y         1            2 N
#'   ```
#'
#'   ## Step 3
#'
#'   If `first_cond_lower` is specified, for each observation of the input
#'   dataset the joined dataset is restricted to observations from the first
#'   observation where `first_cond_lower` is fulfilled (the observation
#'   fulfilling the condition is included) up to the observation of the input
#'   dataset. If for an observation of the input dataset the condition is not
#'   fulfilled, the observation is removed.
#'
#'   If `first_cond_upper` is specified, for each observation of the input
#'   dataset the joined dataset is restricted to observations up to the first
#'   observation where `first_cond_upper` is fulfilled (the observation
#'   fulfilling the condition is included). If for an observation of the input
#'   dataset the condition is not fulfilled, the observation is removed.
#'
#'   For an example see the last example in the "Examples" section.
#'
#'   ## Step 4
#'
#'   The joined dataset is grouped by the observations from the input dataset
#'   and restricted to the observations fulfilling the condition specified by
#'   `filter_join`.
#'
#'   ## Step 5
#'
#'   The first observation of each group is selected and the `*.join` variables
#'   are dropped.
#'
#' `r roxygen_save_memory()`
#'
#' @returns A subset of the observations of the input dataset. All variables of
#'   the input dataset are included in the output dataset.
#'
#'
#' @keywords utils_fil
#' @family utils_fil
#'
#' @seealso [count_vals()], [min_cond()], [max_cond()]
#'
#' @export
#'
#' @examplesx
#' @caption Filter records considering other records (`filter_join`, `join_vars`)
#' @info In this example, the input dataset should be restricted to records with
#'   a duration longer than 30 and where a COVID AE (`ACOVFL == "Y"`) occurred
#'   before or up to seven days after the record. The condition for restricting
#'   the records is specified by the `filter_join` argument. Variables from the
#'   other records are referenced by variable names with the suffix `.join`.
#'   These variables have to be specified for the `join_vars` argument. As
#'   records before _and_ after the current record should be considered,
#'   `join_type = "all"` is specified.
#' @code
#' library(tibble)
#'
#' adae <- tribble(
#'   ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,
#'   "1",        10, "N",          1,
#'   "1",        21, "N",         50,
#'   "1",        23, "Y",         14,
#'   "1",        32, "N",         31,
#'   "1",        42, "N",         20,
#'   "2",        11, "Y",         13,
#'   "2",        23, "N",          2,
#'   "3",        13, "Y",         12,
#'   "4",        14, "N",         32,
#'   "4",        21, "N",         41
#' )
#'
#' filter_joined(
#'   adae,
#'   dataset_add = adae,
#'   by_vars = exprs(USUBJID),
#'   join_vars = exprs(ACOVFL, ADY),
#'   join_type = "all",
#'   filter_join = ADURN > 30 & ACOVFL.join == "Y" & ADY.join <= ADY + 7
#' )
#'
#' @caption Considering only records after the current one (`join_type = "after"`)
#' @info In this example, the input dataset is restricted to records with `AVALC
#'   == "Y"` and `AVALC == "Y"` at a subsequent visit. `join_type = "after"` is
#'   specified to consider only records after the current one. Please note that
#'   the `order` argument must be specified, as otherwise it is not possible to
#'   determine which records are after the current record.
#' @code
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~AVALC,
#'   "1",      1,        "Y",
#'   "1",      2,        "N",
#'   "1",      3,        "Y",
#'   "1",      4,        "N",
#'   "2",      1,        "Y",
#'   "2",      2,        "N",
#'   "3",      1,        "Y",
#'   "4",      1,        "N",
#'   "4",      2,        "N",
#' )
#'
#' filter_joined(
#'   data,
#'   dataset_add = data,
#'   by_vars = exprs(USUBJID),
#'   join_vars = exprs(AVALC, AVISITN),
#'   join_type = "after",
#'   order = exprs(AVISITN),
#'   filter_join = AVALC == "Y" & AVALC.join == "Y"
#' )
#'
#' @caption Considering a range of records only (`first_cond_lower`, `first_cond_upper`)
#' @info Consider the following data.
#' @code
#' myd <- tribble(
#'   ~subj, ~day, ~val,
#'   "1",      1, "++",
#'   "1",      2, "-",
#'   "1",      3, "0",
#'   "1",      4, "+",
#'   "1",      5, "++",
#'   "1",      6, "-",
#'   "2",      1, "-",
#'   "2",      2, "++",
#'   "2",      3, "+",
#'   "2",      4, "0",
#'   "2",      5, "-",
#'   "2",      6, "++"
#' )
#'
#' @info To select `"0"` where all results from the first `"++"` before the
#' `"0"` up to the `"0"` (excluding the `"0"`) are `"+"` or `"++"` the
#' `first_cond_lower` argument and `join_type = "before"` are specified.
#' @code
#' filter_joined(
#'   myd,
#'   dataset_add = myd,
#'   by_vars = exprs(subj),
#'   order = exprs(day),
#'   join_vars = exprs(val),
#'   join_type = "before",
#'   first_cond_lower = val.join == "++",
#'   filter_join = val == "0" & all(val.join %in% c("+", "++"))
#' )
#'
#' @info To select `"0"` where all results from the `"0"` (excluding the `"0"`)
#'   up to the first `"++"` after the `"0"` are `"+"` or `"++"` the
#'   `first_cond_upper` argument and `join_type = "after"` are specified.
#' @code
#' filter_joined(
#'   myd,
#'   dataset_add = myd,
#'   by_vars = exprs(subj),
#'   order = exprs(day),
#'   join_vars = exprs(val),
#'   join_type = "after",
#'   first_cond_upper = val.join == "++",
#'   filter_join = val == "0" & all(val.join %in% c("+", "++"))
#' )
#'
#' @caption Considering only records up to a condition (`first_cond_upper`)
#' @info In this example from deriving confirmed response in oncology, the
#'   records with
#' - `AVALC == "CR"`,
#' - `AVALC == "CR"` at a subsequent visit,
#' - only `"CR"` or `"NE"` in between, and
#' - at most one `"NE"` in between
#'
#' should be selected. The other records to be considered are restricted to
#' those up to the first occurrence of `"CR"` by specifying the
#' `first_cond_upper` argument. The `count_vals()` function is used to count the
#' `"NE"`s for the last condition.
#' @code
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~AVALC,
#'   "1",      1,        "PR",
#'   "1",      2,        "CR",
#'   "1",      3,        "NE",
#'   "1",      4,        "CR",
#'   "1",      5,        "NE",
#'   "2",      1,        "CR",
#'   "2",      2,        "PR",
#'   "2",      3,        "CR",
#'   "3",      1,        "CR",
#'   "4",      1,        "CR",
#'   "4",      2,        "NE",
#'   "4",      3,        "NE",
#'   "4",      4,        "CR",
#'   "4",      5,        "PR"
#' )
#'
#' filter_joined(
#'   data,
#'   dataset_add = data,
#'   by_vars = exprs(USUBJID),
#'   join_vars = exprs(AVALC),
#'   join_type = "after",
#'   order = exprs(AVISITN),
#'   first_cond_upper = AVALC.join == "CR",
#'   filter_join = AVALC == "CR" & all(AVALC.join %in% c("CR", "NE")) &
#'     count_vals(var = AVALC.join, val = "NE") <= 1
#' )
#'
#' @caption Considering order of values (`min_cond()`, `max_cond()`)
#' @info In this example from deriving confirmed response in oncology, records
#'   with
#' - `AVALC == "PR"`,
#' - `AVALC == "CR"` or `AVALC == "PR"` at a subsequent visit at least 20 days later,
#' - only `"CR"`, `"PR"`, or `"NE"` in between,
#' - at most one `"NE"` in between, and
#' - `"CR"` is
#'   not followed by `"PR"`
#'
#' should be selected. The last condition is realized by using `min_cond()` and
#' `max_cond()`, ensuring that the first occurrence of `"CR"` is after the last
#' occurrence of `"PR"`. The second call to `count_vals()` in the condition is
#' required to cover the case of no `"CR"`s (the `min_cond()` call returns `NA`
#' then).
#' @code
#' data <- tribble(
#'   ~USUBJID, ~ADY, ~AVALC,
#'   "1",         6, "PR",
#'   "1",        12, "CR",
#'   "1",        24, "NE",
#'   "1",        32, "CR",
#'   "1",        48, "PR",
#'   "2",         3, "PR",
#'   "2",        21, "CR",
#'   "2",        33, "PR",
#'   "3",        11, "PR",
#'   "4",         7, "PR",
#'   "4",        12, "NE",
#'   "4",        24, "NE",
#'   "4",        32, "PR",
#'   "4",        55, "PR"
#' )
#'
#' filter_joined(
#'   data,
#'   dataset_add = data,
#'   by_vars = exprs(USUBJID),
#'   join_vars = exprs(AVALC, ADY),
#'   join_type = "after",
#'   order = exprs(ADY),
#'   first_cond_upper = AVALC.join %in% c("CR", "PR") & ADY.join - ADY >= 20,
#'   filter_join = AVALC == "PR" &
#'     all(AVALC.join %in% c("CR", "PR", "NE")) &
#'     count_vals(var = AVALC.join, val = "NE") <= 1 &
#'     (
#'       min_cond(var = ADY.join, cond = AVALC.join == "CR") >
#'         max_cond(var = ADY.join, cond = AVALC.join == "PR") |
#'         count_vals(var = AVALC.join, val = "CR") == 0
#'     )
#' )
#'
#' @caption Considering the order of records (`tmp_obs_nr_var`)
#' @info In this example, the records with `CRIT1FL == "Y"` at two consecutive
#'   visits or at the last visit should be selected. A temporary order variable
#'   is created by specifying the `tmp_obs_nr_var` argument. Then it is used in
#'   `filter_join`. The temporary variable doesn't need to be specified for
#'   `join_vars`.
#' @code
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~CRIT1FL,
#'   "1",      1,        "Y",
#'   "1",      2,        "N",
#'   "1",      3,        "Y",
#'   "1",      5,        "N",
#'   "2",      1,        "Y",
#'   "2",      3,        "Y",
#'   "2",      5,        "N",
#'   "3",      1,        "Y",
#'   "4",      1,        "Y",
#'   "4",      2,        "N",
#' )
#'
#' filter_joined(
#'   data,
#'   dataset_add = data,
#'   by_vars = exprs(USUBJID),
#'   tmp_obs_nr_var = tmp_obs_nr,
#'   join_vars = exprs(CRIT1FL),
#'   join_type = "all",
#'   order = exprs(AVISITN),
#'   filter_join = CRIT1FL == "Y" & CRIT1FL.join == "Y" &
#'     (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join))
#' )
filter_joined <- function(dataset,
                          dataset_add,
                          by_vars,
                          join_vars,
                          join_type,
                          first_cond_lower = NULL,
                          first_cond_upper = NULL,
                          order = NULL,
                          tmp_obs_nr_var = NULL,
                          filter_add = NULL,
                          filter_join,
                          check_type = "warning") {
  # Check input parameters
  assert_vars(by_vars)
  assert_vars(join_vars)
  join_type <-
    assert_character_scalar(
      join_type,
      values = c("before", "after", "all"),
      case_sensitive = FALSE
    )
  first_cond_lower <- assert_filter_cond(enexpr(first_cond_lower), optional = TRUE)
  first_cond_upper <- assert_filter_cond(enexpr(first_cond_upper), optional = TRUE)
  tmp_obs_nr_var <- assert_symbol(enexpr(tmp_obs_nr_var), optional = TRUE)
  assert_expr_list(
    order,
    optional = join_type == "all" && is.null(first_cond_lower) &&
      is.null(first_cond_upper) && is.null(tmp_obs_nr_var)
  )
  filter_add <- assert_filter_cond(enexpr(filter_add), optional = TRUE)
  filter_join <- assert_filter_cond(enexpr(filter_join))
  check_type <-
    assert_character_scalar(
      check_type,
      values = c("none", "warning", "error"),
      case_sensitive = FALSE
    )
  assert_data_frame(
    dataset,
    required_vars = expr_c(by_vars, extract_vars(order))
  )

  assert_data_frame(
    dataset_add,
    required_vars = expr_c(by_vars, join_vars, extract_vars(order))
  )

  tmp_obs_nr_unique <- get_new_tmp_var(dataset, prefix = "tmp_obs_nr_unique")
  dataset <- derive_var_obs_number(
    dataset,
    by_vars = by_vars,
    new_var = !!tmp_obs_nr_unique
  )

  get_joined_data(
    dataset,
    dataset_add = dataset_add,
    by_vars = by_vars,
    join_vars = join_vars,
    join_type = join_type,
    first_cond_lower = !!first_cond_lower,
    first_cond_upper = !!first_cond_upper,
    order = order,
    tmp_obs_nr_var = !!tmp_obs_nr_var,
    filter_add = !!filter_add,
    filter_join = !!filter_join,
    check_type = check_type
  ) %>%
    # select one observation of each records of the input dataset, as the joined
    # variables are removed it doesn't matter which one, so we take just the
    # first one
    group_by(!!!by_vars, !!tmp_obs_nr_unique) %>%
    slice(1L) %>%
    ungroup() %>%
    select(colnames(dataset)) %>%
    remove_tmp_vars()
}

#' Count Number of Observations Where a Variable Equals a Value
#'
#' Count number of observations where a variable equals a value.
#'
#' @param var A vector
#'
#' @param val A value
#'
#'
#' @keywords utils_fil
#' @family utils_fil
#'
#' @export
#'
#' @examples
#'
#' library(tibble)
#' library(dplyr, warn.conflicts = FALSE)
#' library(admiral)
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~AVALC,
#'   "1",      1,        "PR",
#'   "1",      2,        "CR",
#'   "1",      3,        "NE",
#'   "1",      4,        "CR",
#'   "1",      5,        "NE",
#'   "2",      1,        "CR",
#'   "2",      2,        "PR",
#'   "2",      3,        "CR",
#'   "3",      1,        "CR",
#'   "4",      1,        "CR",
#'   "4",      2,        "NE",
#'   "4",      3,        "NE",
#'   "4",      4,        "CR",
#'   "4",      5,        "PR"
#' )
#'
#' # add variable providing the number of NEs for each subject
#' group_by(data, USUBJID) %>%
#'   mutate(nr_nes = count_vals(var = AVALC, val = "NE"))
count_vals <- function(var, val) {
  length(var[var == val])
}

#' Minimum Value on a Subset
#'
#' The function derives the minimum value of a vector/column on a subset of
#' entries/observations.
#'
#' @param var A vector
#'
#' @param cond A condition
#'
#'
#' @keywords utils_fil
#' @family utils_fil
#'
#' @export
#'
#' @examples
#'
#' library(tibble)
#' library(dplyr, warn.conflicts = FALSE)
#' library(admiral)
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~AVALC,
#'   "1",      1,        "PR",
#'   "1",      2,        "CR",
#'   "1",      3,        "NE",
#'   "1",      4,        "CR",
#'   "1",      5,        "NE",
#'   "2",      1,        "CR",
#'   "2",      2,        "PR",
#'   "2",      3,        "CR",
#' )
#'
#' # In oncology setting, when needing to check the first time a patient had
#' # a Complete Response (CR) to compare to see if any Partial Response (PR)
#' # occurred after this add variable indicating if PR occurred after CR
#' group_by(data, USUBJID) %>% mutate(
#'   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == "CR"),
#'   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == "PR"),
#'   pr_after_cr = last_pr_vis > first_cr_vis
#' )
min_cond <- function(var, cond) {
  assert_filter_cond(enexpr(cond))
  if (length(var[cond]) == 0) {
    NA
  } else {
    min(var[cond])
  }
}

#' Maximum Value on a Subset
#'
#' The function derives the maximum value of a vector/column on a subset of
#' entries/observations.
#'
#' @param var A vector
#'
#' @param cond A condition
#'
#'
#' @keywords utils_fil
#' @family utils_fil
#'
#' @export
#'
#' @examples
#'
#' library(tibble)
#' library(dplyr, warn.conflicts = FALSE)
#' library(admiral)
#' data <- tribble(
#'   ~USUBJID, ~AVISITN, ~AVALC,
#'   "1",      1,        "PR",
#'   "1",      2,        "CR",
#'   "1",      3,        "NE",
#'   "1",      4,        "CR",
#'   "1",      5,        "NE",
#'   "2",      1,        "CR",
#'   "2",      2,        "PR",
#'   "2",      3,        "CR",
#' )
#'
#' # In oncology setting, when needing to check the first time a patient had
#' # a Complete Response (CR) to compare to see if any Partial Response (PR)
#' # occurred after this add variable indicating if PR occurred after CR
#' group_by(data, USUBJID) %>% mutate(
#'   first_cr_vis = min_cond(var = AVISITN, cond = AVALC == "CR"),
#'   last_pr_vis = max_cond(var = AVISITN, cond = AVALC == "PR"),
#'   pr_after_cr = last_pr_vis > first_cr_vis
#' )
max_cond <- function(var, cond) {
  assert_filter_cond(enexpr(cond))
  if (length(var[cond]) == 0) {
    NA
  } else {
    max(var[cond])
  }
}