dataset_queries argument in derive_vars_query()R/create_query_data.R
create_query_data.RdCreates a queries dataset as input dataset to the dataset_queries argument
in the derive_vars_query() function as defined in the Queries Dataset Documentation.
create_query_data(queries, version = NULL, get_terms_fun = NULL)List of queries
A list of query() objects is expected.
none
Dictionary version
The dictionary version used for coding the terms should be specified. If any of the queries is a basket (SMQ, SDG, ....) or a customized query including a basket, the parameter needs to be specified.
A character string (the expected format is company-specific)
NULL
Function which returns the terms
For each query specified for the queries parameter referring to a basket
(i.e., those where the definition field is set to a basket_select()
object or a list which contains at least one basket_select() object) the
specified function is called to retrieve the terms defining the query.
This function is not provided by admiral as it is company specific, i.e.,
it has to be implemented at company level.
The function must return a dataset with all the terms defining the basket. The output dataset must contain the following variables.
SRCVAR: the variable to be used for defining a term of the basket,
e.g., AEDECOD
TERMCHAR: the name of the term if the variable SRCVAR is
referring to is character
TERMNUM the numeric id of the term if the variable SRCVAR is
referring to is numeric
GRPNAME: the name of the basket. The values must be the same for
all observations.
The function must provide the following parameters
basket_select: A basket_select() object.
version: The dictionary version. The value specified for the
version in the create_query_data() call is passed to this
parameter.
keep_id: If set to TRUE, the output dataset must contain the
GRPID variable. The variable must be set to the numeric id of the basket.
temp_env: A temporary environment is passed to this parameter. It can
be used to store data which is used for all baskets in the
create_query_data() call. For example if SMQs need to be read from a
database all SMQs can be read and stored in the environment when the first
SMQ is handled. For the other SMQs the terms can be retrieved from the
environment instead of accessing the database again.
NULL
A dataset to be used as input dataset to the dataset_queries
argument in derive_vars_query()
For each query() object listed in the queries argument, the terms belonging
to the query (SRCVAR, TERMCHAR, TERMNUM) are determined with respect
to the definition field of the query: if the definition field of the
query() object is
a basket_select() object, the terms are read from the basket
database by calling the function specified for the get_terms_fun parameter.
a data frame, the terms stored in the data frame are used.
a list of data frames and basket_select() objects, all terms from
the data frames and all terms read from the basket database referenced by the
basket_select() objects are collated.
The following variables (as described in Queries Dataset Documentation) are created:
PREFIX: Prefix of the variables to be created by
derive_vars_query() as specified by the prefix element.
GRPNAME: Name of the query as specified by the name element.
GRPID: Id of the query as specified by the id element. If the id
element is not specified for a query, the variable is set to NA. If the
id element is not specified for any query, the variable is not created.
SCOPE: scope of the query as specified by the scope element of
the basket_select() object. For queries not defined by a basket_select()
object, the variable is set to NA. If none of the queries is defined by a
basket_select() object, the variable is not created.
SCOPEN: numeric scope of the query. It is set to 1 if the
scope is broad. Otherwise it is set to 2. If the add_scope_num element
equals FALSE, the variable is set to NA. If the add_scope_num element
equals FALSE for all baskets or none of the queries is an basket , the variable
is not created.
SRCVAR: Name of the variable used to identify the terms.
TERMCHAR: Value of the term variable if it is a character variable.
TERMNUM: Value of the term variable if it is a numeric variable.
VERSION: Set to the value of the version argument. If it is not
specified, the variable is not created.
derive_vars_query(), query(), basket_select(), Queries Dataset Documentation
Creating auxiliary datasets:
consolidate_metadata(),
create_period_dataset(),
create_single_dose_dataset()
library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(pharmaversesdtm)
library(admiral)
# creating a query dataset for a customized query
cqterms <- tribble(
~TERMCHAR, ~TERMNUM,
"APPLICATION SITE ERYTHEMA", 10003041L,
"APPLICATION SITE PRURITUS", 10003053L
) %>%
mutate(SRCVAR = "AEDECOD")
cq <- query(
prefix = "CQ01",
name = "Application Site Issues",
definition = cqterms
)
create_query_data(queries = list(cq))
#> # A tibble: 2 × 5
#> TERMCHAR TERMNUM SRCVAR PREFIX GRPNAME
#> <chr> <int> <chr> <chr> <chr>
#> 1 APPLICATION SITE ERYTHEMA 10003041 AEDECOD CQ01 Application Site Issues
#> 2 APPLICATION SITE PRURITUS 10003053 AEDECOD CQ01 Application Site Issues
# create a query dataset for SMQs
pregsmq <- query(
prefix = "SMQ02",
id = auto,
definition = basket_select(
name = "Pregnancy and neonatal topics (SMQ)",
scope = "NARROW",
type = "smq"
)
)
bilismq <- query(
prefix = "SMQ04",
definition = basket_select(
id = 20000121L,
scope = "BROAD",
type = "smq"
)
)
# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
queries = list(pregsmq, bilismq),
get_terms_fun = pharmaversesdtm:::get_terms,
version = "20.1"
)
#> # A tibble: 43 × 7
#> TERMCHAR SRCVAR GRPNAME GRPID SCOPE PREFIX VERSION
#> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 Achromotrichia congenital AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 2 Craniosynostosis AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 3 Hypophosphatasia AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 4 Congenital pyelocaliectasis AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 5 Uterine contractions during pregn… AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 6 Ductus arteriosus premature closu… AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 7 Pseudotruncus arteriosus AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 8 Lipomeningocele AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 9 Macrocephaly AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> 10 Carnitine palmitoyltransferase de… AEDEC… Pregna… 2.00e7 NARR… SMQ02 20.1
#> # ℹ 33 more rows
# create a query dataset for SDGs
sdg <- query(
prefix = "SDG01",
id = auto,
definition = basket_select(
name = "5-aminosalicylates for ulcerative colitis",
scope = NA_character_,
type = "sdg"
)
)
# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
queries = list(sdg),
get_terms_fun = pharmaversesdtm:::get_terms,
version = "2019-09"
)
#> # A tibble: 16 × 7
#> TERMCHAR SRCVAR GRPNAME GRPID SCOPE PREFIX VERSION
#> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 AMINOSALICYLIC ACID CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 2 AMINOSALICYLATE CALCIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 3 AMINOSALICYLATE CALCIUM ALUMINIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 4 AMINOSALICYLATE SODIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 5 SODIUM AMINOSALICYLATE DIHYDRATE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 6 AMINOSALICYLATE SODIUM;AMINOSALICY… CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 7 SULFASALAZINE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 8 CALCIUM BENZAMIDOSALICYLATE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 9 OLSALAZINE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 10 OLSALAZINE SODIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 11 MESALAZINE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 12 BALSALAZIDE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 13 BALSALAZIDE SODIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 14 BALSALAZIDE DISODIUM DIHYDRATE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 15 DERSALAZINE CMDEC… 5-amin… 220 NA SDG01 2019-09
#> 16 DERSALAZINE SODIUM CMDEC… 5-amin… 220 NA SDG01 2019-09
# creating a query dataset for a customized query including SMQs
# The get_terms function from pharmaversesdtm is used for this example.
# In a real application a company-specific function must be used.
create_query_data(
queries = list(
query(
prefix = "CQ03",
name = "Special issues of interest",
definition = list(
basket_select(
name = "Pregnancy and neonatal topics (SMQ)",
scope = "NARROW",
type = "smq"
),
cqterms
)
)
),
get_terms_fun = pharmaversesdtm:::get_terms,
version = "20.1"
)
#> # A tibble: 23 × 6
#> TERMCHAR SRCVAR GRPNAME TERMNUM PREFIX VERSION
#> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 Achromotrichia congenital AEDEC… Specia… NA CQ03 20.1
#> 2 Craniosynostosis AEDEC… Specia… NA CQ03 20.1
#> 3 Hypophosphatasia AEDEC… Specia… NA CQ03 20.1
#> 4 Congenital pyelocaliectasis AEDEC… Specia… NA CQ03 20.1
#> 5 Uterine contractions during pregnancy AEDEC… Specia… NA CQ03 20.1
#> 6 Ductus arteriosus premature closure AEDEC… Specia… NA CQ03 20.1
#> 7 Pseudotruncus arteriosus AEDEC… Specia… NA CQ03 20.1
#> 8 Lipomeningocele AEDEC… Specia… NA CQ03 20.1
#> 9 Macrocephaly AEDEC… Specia… NA CQ03 20.1
#> 10 Carnitine palmitoyltransferase deficie… AEDEC… Specia… NA CQ03 20.1
#> # ℹ 13 more rows