Derive a flag which depends on other observations of the dataset. For example, flagging events which need to be confirmed by a second event.
derive_var_joined_exist_flag(
dataset,
dataset_add,
by_vars,
order = NULL,
new_var,
tmp_obs_nr_var = NULL,
join_vars,
join_type,
first_cond_lower = NULL,
first_cond_upper = NULL,
filter_add = NULL,
filter_join,
true_value = "Y",
false_value = NA_character_,
check_type = "warning"
)The input dataset with the variable specified by new_var added.
Input dataset
The variables specified by the by_vars and join_vars arguments are expected to be in the dataset.
a dataset, i.e., a data.frame or tibble
none
Additional dataset
The variables specified for by_vars, join_vars, and order are
expected.
a dataset, i.e., a data.frame or tibble
none
Grouping variables
The specified variables are used for joining the input
dataset (dataset) with the additional dataset (dataset_add).
list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
none
Order
The observations are ordered by the specified order if join_type = "after", join_type = "before", first_cond_lower, first_cond_upper,
or tmp_obs_nr_var are specified.
For handling of NAs in sorting variables see Sort Order.
list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
NULL
New variable
The specified variable is added to the input dataset.
an unquoted symbol, e.g., AVAL
none
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 flag consecutive observations or the last
observation (see last example below).
an unquoted symbol, e.g., AVAL
NULL
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 flag 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.
list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
none
Observations to keep after joining
The argument determines which of the joined observations are kept with
respect to the original observation. For example, if join_type = "after"
is specified all observations after the original observations are kept.
For example for confirmed response or BOR in the oncology setting or
confirmed deterioration in questionnaires the confirmatory assessment must
be after the assessment. Thus join_type = "after" could be used.
Whereas, sometimes you might allow for confirmatory observations to occur
prior to the observation. For example, to identify AEs occurring on or
after seven days before a COVID AE. Thus join_type = "all" could be used.
"before", "after", "all"
none
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 an example see the last example below.
an unquoted condition, e.g., AVISIT == "BASELINE"
NULL
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 an example see the third example below.
an unquoted condition, e.g., AVISIT == "BASELINE"
NULL
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 order or new_vars arguments can be used in the
condition.
The condition can include summary functions like all() or any(). The
additional dataset is grouped by the by variables (by_vars).
an unquoted condition, e.g., AVISIT == "BASELINE"
NULL
Condition for selecting observations
The filter is applied to the joined dataset for flagging 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, 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".
an unquoted condition, e.g., AVISIT == "BASELINE"
none
Value of new_var for flagged observations
a character scalar, i.e., a character vector of length one
"Y"
Value of new_var for observations not flagged
a character scalar, i.e., a character vector of length one
NA_character_
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.
"none", "message", "warning", "error"
"warning"
filter_join, join_vars)
In this example, records with a duration longer than 30 and where a
COVID AE (ACOVFL == "Y") occurred before or up to seven days after the
record should be flagged. The condition for flagging 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.
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
)
derive_var_joined_exist_flag(
adae,
dataset_add = adae,
new_var = ALCOVFL,
by_vars = exprs(USUBJID),
join_vars = exprs(ACOVFL, ADY),
join_type = "all",
filter_join = ADURN > 30 & ACOVFL.join == "Y" & ADY.join <= ADY + 7
)
#> # A tibble: 10 × 5
#> USUBJID ADY ACOVFL ADURN ALCOVFL
#> <chr> <dbl> <chr> <dbl> <chr>
#> 1 1 10 N 1 <NA>
#> 2 1 21 N 50 Y
#> 3 1 23 Y 14 <NA>
#> 4 1 32 N 31 Y
#> 5 1 42 N 20 <NA>
#> 6 2 11 Y 13 <NA>
#> 7 2 23 N 2 <NA>
#> 8 3 13 Y 12 <NA>
#> 9 4 14 N 32 <NA>
#> 10 4 21 N 41 <NA>
join_type = "after", true_value, false_value)
In this example, records with AVALC == "Y" and AVALC == "Y" at a
subsequent visit should be flagged. 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.
Please note that a numeric flag is created here by specifying the
true_value and the false_value argument.
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",
)derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
new_var = CONFFLN,
join_vars = exprs(AVALC, AVISITN),
join_type = "after",
order = exprs(AVISITN),
filter_join = AVALC == "Y" & AVALC.join == "Y",
true_value = 1,
false_value = 0
)
#> # A tibble: 9 × 4
#> USUBJID AVISITN AVALC CONFFLN
#> <chr> <dbl> <chr> <dbl>
#> 1 1 1 Y 1
#> 2 1 2 N 0
#> 3 1 3 Y 0
#> 4 1 4 N 0
#> 5 2 1 Y 0
#> 6 2 2 N 0
#> 7 3 1 Y 0
#> 8 4 1 N 0
#> 9 4 2 N 0
first_cond_lower, first_cond_upper)
Consider the following data.
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, "++"
)To flag "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.
derive_var_joined_exist_flag(
myd,
dataset_add = myd,
by_vars = exprs(subj),
order = exprs(day),
new_var = flag,
join_vars = exprs(val),
join_type = "before",
first_cond_lower = val.join == "++",
filter_join = val == "0" & all(val.join %in% c("+", "++"))
)
#> # A tibble: 12 × 4
#> subj day val flag
#> <chr> <dbl> <chr> <chr>
#> 1 1 1 ++ <NA>
#> 2 1 2 - <NA>
#> 3 1 3 0 <NA>
#> 4 1 4 + <NA>
#> 5 1 5 ++ <NA>
#> 6 1 6 - <NA>
#> 7 2 1 - <NA>
#> 8 2 2 ++ <NA>
#> 9 2 3 + <NA>
#> 10 2 4 0 Y
#> 11 2 5 - <NA>
#> 12 2 6 ++ <NA> To flag "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.
derive_var_joined_exist_flag(
myd,
dataset_add = myd,
by_vars = exprs(subj),
order = exprs(day),
new_var = flag,
join_vars = exprs(val),
join_type = "after",
first_cond_upper = val.join == "++",
filter_join = val == "0" & all(val.join %in% c("+", "++"))
)
#> # A tibble: 12 × 4
#> subj day val flag
#> <chr> <dbl> <chr> <chr>
#> 1 1 1 ++ <NA>
#> 2 1 2 - <NA>
#> 3 1 3 0 Y
#> 4 1 4 + <NA>
#> 5 1 5 ++ <NA>
#> 6 1 6 - <NA>
#> 7 2 1 - <NA>
#> 8 2 2 ++ <NA>
#> 9 2 3 + <NA>
#> 10 2 4 0 <NA>
#> 11 2 5 - <NA>
#> 12 2 6 ++ <NA> first_cond_upper)
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 flagged. 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.
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"
)derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
join_vars = exprs(AVALC),
join_type = "after",
order = exprs(AVISITN),
new_var = CONFFL,
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
)
#> # A tibble: 14 × 4
#> USUBJID AVISITN AVALC CONFFL
#> <chr> <dbl> <chr> <chr>
#> 1 1 1 PR <NA>
#> 2 1 2 CR Y
#> 3 1 3 NE <NA>
#> 4 1 4 CR <NA>
#> 5 1 5 NE <NA>
#> 6 2 1 CR <NA>
#> 7 2 2 PR <NA>
#> 8 2 3 CR <NA>
#> 9 3 1 CR <NA>
#> 10 4 1 CR <NA>
#> 11 4 2 NE <NA>
#> 12 4 3 NE <NA>
#> 13 4 4 CR <NA>
#> 14 4 5 PR <NA>
min_cond(), max_cond())
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 flagged. 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).
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"
)derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
join_vars = exprs(AVALC, ADY),
join_type = "after",
order = exprs(ADY),
new_var = CONFFL,
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
)
)
#> # A tibble: 14 × 4
#> USUBJID ADY AVALC CONFFL
#> <chr> <dbl> <chr> <chr>
#> 1 1 6 PR <NA>
#> 2 1 12 CR <NA>
#> 3 1 24 NE <NA>
#> 4 1 32 CR <NA>
#> 5 1 48 PR <NA>
#> 6 2 3 PR <NA>
#> 7 2 21 CR <NA>
#> 8 2 33 PR <NA>
#> 9 3 11 PR <NA>
#> 10 4 7 PR <NA>
#> 11 4 12 NE <NA>
#> 12 4 24 NE <NA>
#> 13 4 32 PR Y
#> 14 4 55 PR <NA>
tmp_obs_nr_var)
In this example, the records with CRIT1FL == "Y" at two consecutive
visits or at the last visit should be flagged. 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.
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",
)derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
new_var = CONFFL,
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))
)
#> # A tibble: 10 × 4
#> USUBJID AVISITN CRIT1FL CONFFL
#> <chr> <dbl> <chr> <chr>
#> 1 1 1 Y <NA>
#> 2 1 2 N <NA>
#> 3 1 3 Y <NA>
#> 4 1 5 N <NA>
#> 5 2 1 Y Y
#> 6 2 3 Y <NA>
#> 7 2 5 N <NA>
#> 8 3 1 Y Y
#> 9 4 1 Y <NA>
#> 10 4 2 N <NA>
tmp_obs_nr_var)
ex <- tribble(
~USUBJID, ~EXSTDTM, ~EXDOSE,
"1", "2024-01-01T08:00", 2,
"1", "2024-01-02T08:00", 4,
"2", "2024-01-01T08:30", 1,
"2", "2024-01-02T08:30", 4,
"2", "2024-01-03T08:30", 3,
"2", "2024-01-04T08:30", 2,
"2", "2024-01-05T08:30", 2
)derive_var_joined_exist_flag(
ex,
dataset_add = ex,
by_vars = exprs(USUBJID),
order = exprs(EXSTDTM),
new_var = DOSREDFL,
tmp_obs_nr_var = tmp_dose_nr,
join_vars = exprs(EXDOSE),
join_type = "before",
filter_join = (
tmp_dose_nr == tmp_dose_nr.join + 1 # Look only at adjacent doses
& EXDOSE > 0 & EXDOSE.join > 0 # Both doses are valid
& EXDOSE < EXDOSE.join # Dose is lower than previous
)
)
#> # A tibble: 7 × 4
#> USUBJID EXSTDTM EXDOSE DOSREDFL
#> <chr> <chr> <dbl> <chr>
#> 1 1 2024-01-01T08:00 2 <NA>
#> 2 1 2024-01-02T08:00 4 <NA>
#> 3 2 2024-01-01T08:30 1 <NA>
#> 4 2 2024-01-02T08:30 4 <NA>
#> 5 2 2024-01-03T08:30 3 Y
#> 6 2 2024-01-04T08:30 2 Y
#> 7 2 2024-01-05T08:30 2 <NA>
In this example a definitive deterioration flag should be derived as
any deterioration (CHGCAT1 = "Worsened") by parameter that is not
followed by a non-deterioration. Please note that join_type = "after"
can't by used here, as otherwise the last record wouldn't be flagged.
adqs <- tribble(
~USUBJID, ~PARAMCD, ~ADY, ~CHGCAT1,
"1", "QS1", 10, "Improved",
"1", "QS1", 21, "Improved",
"1", "QS1", 23, "Improved",
"1", "QS2", 32, "Worsened",
"1", "QS2", 42, "Improved",
"2", "QS1", 11, "Worsened",
"2", "QS1", 24, "Worsened"
)derive_var_joined_exist_flag(
adqs,
dataset_add = adqs,
new_var = DDETERFL,
by_vars = exprs(USUBJID, PARAMCD),
join_vars = exprs(CHGCAT1, ADY),
join_type = "all",
filter_join = all(CHGCAT1.join == "Worsened" | ADY > ADY.join)
)
#> # A tibble: 7 × 5
#> USUBJID PARAMCD ADY CHGCAT1 DDETERFL
#> <chr> <chr> <dbl> <chr> <chr>
#> 1 1 QS1 10 Improved <NA>
#> 2 1 QS1 21 Improved <NA>
#> 3 1 QS1 23 Improved <NA>
#> 4 1 QS2 32 Worsened <NA>
#> 5 1 QS2 42 Improved <NA>
#> 6 2 QS1 11 Worsened Y
#> 7 2 QS1 24 Worsened Y
check_type)
If the order argument is used, it is checked if the records are
unique with respect to by_vars and order. Consider for example the
derivation of CONFFL which flags records with AVALC == "Y" which are
confirmed at a subsequent visit.
data <- tribble(
~USUBJID, ~AVISITN, ~ADY, ~AVALC,
"1", 1, 1, "Y",
"1", 2, 8, "N",
"1", 3, 15, "Y",
"1", 4, 22, "N",
"2", 1, 1, "Y",
"2", 2, 8, "Y",
"2", 2, 10, "Y"
)derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
new_var = CONFFL,
join_vars = exprs(AVALC, AVISITN),
join_type = "after",
order = exprs(AVISITN),
filter_join = AVALC == "Y" & AVALC.join == "Y"
)
#> # A tibble: 7 × 5
#> USUBJID AVISITN ADY AVALC CONFFL
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 1 1 1 Y Y
#> 2 1 2 8 N <NA>
#> 3 1 3 15 Y <NA>
#> 4 1 4 22 N <NA>
#> 5 2 1 1 Y Y
#> 6 2 2 8 Y <NA>
#> 7 2 2 10 Y <NA>
#> Warning: Dataset `dataset` contains duplicate records with respect to `USUBJID` and
#> `AVISITN`
#> i Run `admiral::get_duplicates_dataset()` to access the duplicate records
#> Warning: Dataset `dataset_add` contains duplicate records with respect to `USUBJID` and
#> `AVISITN`
#> i Run `admiral::get_duplicates_dataset()` to access the duplicate records
The records for USUBJID == "2" are not unique with respect to
USUBJID and AVISITN. Thus a warning is issued. The duplicates can be
accessed by calling get_duplicates_dataset():
get_duplicates_dataset()
#> Duplicate records with respect to `USUBJID` and `AVISITN`.
#> # A tibble: 2 × 4
#> USUBJID AVISITN ADY AVALC
#> * <chr> <dbl> <dbl> <chr>
#> 1 2 2 8 Y
#> 2 2 2 10 Y In this example, confirmation is required at a subsequent visit.
Please note that the first record for subject "2" at visit 2 is not
flagged. Thus the warning can be suppressed by specifying check_type = "none".
derive_var_joined_exist_flag(
data,
dataset_add = data,
by_vars = exprs(USUBJID),
new_var = CONFFL,
join_vars = exprs(AVALC, AVISITN),
join_type = "after",
order = exprs(AVISITN),
filter_join = AVALC == "Y" & AVALC.join == "Y",
check_type = "none"
)
#> # A tibble: 7 × 5
#> USUBJID AVISITN ADY AVALC CONFFL
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 1 1 1 Y Y
#> 2 1 2 8 N <NA>
#> 3 1 3 15 Y <NA>
#> 4 1 4 22 N <NA>
#> 5 2 1 1 Y Y
#> 6 2 2 8 Y <NA>
#> 7 2 2 10 Y <NA> An example usage might be flagging if a patient received two required medications within a certain timeframe of each other.
In the oncology setting, for example, the function could be used to flag if a response value can be confirmed by an other assessment. This is commonly used in endpoints such as best overall response.
The following steps are performed to produce the output dataset.
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.
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 also exist in the input dataset.
For example, for by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC)
and input dataset and additional dataset
# 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
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
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
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
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 examples see the "Examples" section.
The joined dataset is grouped by the observations from the input dataset
and restricted to the observations fulfilling the condition specified by
filter_join.
The first observation of each group is selected.
The variable specified by new_var is added to the input dataset. It is
set to true_value for all observations which were selected in the
previous step. For the other observations it is set to false_value.
Note: This function creates temporary datasets which may be much bigger
than the input datasets. If this causes memory issues, please try setting
the admiral option save_memory to TRUE (see set_admiral_options()).
This reduces the memory consumption but increases the run-time.
filter_joined(), derive_vars_joined()
General Derivation Functions for all ADaMs that returns variable appended to dataset:
derive_var_extreme_flag(),
derive_var_merged_ef_msrc(),
derive_var_merged_exist_flag(),
derive_var_merged_summary(),
derive_var_obs_number(),
derive_var_relative_flag(),
derive_vars_cat(),
derive_vars_computed(),
derive_vars_joined(),
derive_vars_joined_summary(),
derive_vars_merged(),
derive_vars_merged_lookup(),
derive_vars_transposed()