Merge a summary variable from a dataset to the input dataset.
derive_var_merged_summary(
dataset,
dataset_add,
by_vars,
new_vars = NULL,
filter_add = NULL,
missing_values = NULL
)Input dataset
The variables specified by the by_vars argument are expected to be in the dataset.
a dataset, i.e., a data.frame or tibble
none
Additional dataset
The variables specified by the by_vars and the variables used on the left
hand sides of the new_vars arguments are expected.
a dataset, i.e., a data.frame or tibble
none
Grouping variables
The expressions on the left hand sides of new_vars are evaluated by the
specified variables. Then the resulting values are merged to the input
dataset (dataset) by the specified variables.
list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
none
New variables to add
The specified variables are added to the input dataset.
A named list of expressions is expected:
LHS refer to a variable.
RHS refers to the values to set to the variable. This can be a string, a
symbol, a numeric value, an expression or NA. If summary functions are
used, the values are summarized by the variables specified for by_vars.
For example:
new_vars = exprs(
DOSESUM = sum(AVAL),
DOSEMEAN = mean(AVAL)
)list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
NULL
Filter for additional dataset (dataset_add)
Only observations fulfilling the specified condition are taken into account for summarizing. If the argument is not specified, all observations are considered.
an unquoted condition, e.g., AVISIT == "BASELINE"
NULL
Values for non-matching observations
For observations of the input dataset (dataset) which do not have a
matching observation in the additional dataset (dataset_add) the values
of the specified variables are set to the specified value. Only variables
specified for new_vars can be specified for missing_values.
list of named expressions created by a formula using exprs(), e.g., exprs(AVALC = VSSTRESC, AVAL = yn_to_numeric(AVALC))
NULL
The output dataset contains all observations and variables of the
input dataset and additionally the variables specified for new_vars.
The records from the additional dataset (dataset_add) are restricted
to those matching the filter_add condition.
The new variables (new_vars) are created for each by group (by_vars)
in the additional dataset (dataset_add) by calling summarize(). I.e.,
all observations of a by group are summarized to a single observation.
The new variables are merged to the input dataset. For observations
without a matching observation in the additional dataset the new variables
are set to NA. Observations in the additional dataset which have no
matching observation in the input dataset are ignored.
derive_summary_records(), get_summary_records()
General Derivation Functions for all ADaMs that returns variable appended to dataset:
derive_var_extreme_flag(),
derive_var_joined_exist_flag(),
derive_var_merged_ef_msrc(),
derive_var_merged_exist_flag(),
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()
library(tibble)
# Add a variable for the mean of AVAL within each visit
adbds <- tribble(
~USUBJID, ~AVISIT, ~ASEQ, ~AVAL,
"1", "WEEK 1", 1, 10,
"1", "WEEK 1", 2, NA,
"1", "WEEK 2", 3, NA,
"1", "WEEK 3", 4, 42,
"1", "WEEK 4", 5, 12,
"1", "WEEK 4", 6, 12,
"1", "WEEK 4", 7, 15,
"2", "WEEK 1", 1, 21,
"2", "WEEK 4", 2, 22
)
derive_var_merged_summary(
adbds,
dataset_add = adbds,
by_vars = exprs(USUBJID, AVISIT),
new_vars = exprs(
MEANVIS = mean(AVAL, na.rm = TRUE),
MAXVIS = max(AVAL, na.rm = TRUE)
)
)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `MAXVIS = max(AVAL, na.rm = TRUE)`.
#> ℹ In group 2: `USUBJID = "1"` `AVISIT = "WEEK 2"`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments to max; returning -Inf
#> # A tibble: 9 × 6
#> USUBJID AVISIT ASEQ AVAL MEANVIS MAXVIS
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 WEEK 1 1 10 10 10
#> 2 1 WEEK 1 2 NA 10 10
#> 3 1 WEEK 2 3 NA NaN -Inf
#> 4 1 WEEK 3 4 42 42 42
#> 5 1 WEEK 4 5 12 13 15
#> 6 1 WEEK 4 6 12 13 15
#> 7 1 WEEK 4 7 15 13 15
#> 8 2 WEEK 1 1 21 21 21
#> 9 2 WEEK 4 2 22 22 22
# Add a variable listing the lesion ids at baseline
adsl <- tribble(
~USUBJID,
"1",
"2",
"3"
)
adtr <- tribble(
~USUBJID, ~AVISIT, ~LESIONID,
"1", "BASELINE", "INV-T1",
"1", "BASELINE", "INV-T2",
"1", "BASELINE", "INV-T3",
"1", "BASELINE", "INV-T4",
"1", "WEEK 1", "INV-T1",
"1", "WEEK 1", "INV-T2",
"1", "WEEK 1", "INV-T4",
"2", "BASELINE", "INV-T1",
"2", "BASELINE", "INV-T2",
"2", "BASELINE", "INV-T3",
"2", "WEEK 1", "INV-T1",
"2", "WEEK 1", "INV-N1"
)
derive_var_merged_summary(
adsl,
dataset_add = adtr,
by_vars = exprs(USUBJID),
filter_add = AVISIT == "BASELINE",
new_vars = exprs(LESIONSBL = paste(LESIONID, collapse = ", "))
)
#> # A tibble: 3 × 2
#> USUBJID LESIONSBL
#> <chr> <chr>
#> 1 1 INV-T1, INV-T2, INV-T3, INV-T4
#> 2 2 INV-T1, INV-T2, INV-T3
#> 3 3 NA