db_tbl: Get a table from a database
In pointblank: Data Validation and Organization of Metadata for Local and Remote Tables
db_tbl R Documentation
Get a table from a database
Description
If your target table is in a database, the db_tbl() function is a handy way
of accessing it. This function simplifies the process of getting a tbl_dbi
object, which usually involves a combination of building a connection to a
database and using the dplyr::tbl() function with the connection and the
table name (or a reference to a table in a schema). You can use db_tbl() as
the basis for obtaining a database table for the tbl parameter in
create_agent() or create_informant(). Another great option is supplying a
table-prep formula involving db_tbl() to tbl_store() so that you have
access to database tables though single names via a table store.
The username and password are supplied through environment variable names. If
desired, values for the username and password can be supplied directly by
enclosing such values in I().
Usage
db_tbl(
table,
dbtype,
dbname = NULL,
host = NULL,
port = NULL,
user = NULL,
password = NULL,
bq_project = NULL,
bq_dataset = NULL,
bq_billing = bq_project
)
Arguments
table
The name of the table, or, a reference to a table in a schema
(two-element vector with the names of schema and table). Alternatively,
this can be supplied as a data table to copy into an in-memory database
connection. This only works if: (1) the db is chosen as either "sqlite"
or "duckdb", (2) the dbname was is set to ":memory:", and (3) the
object supplied to table is a data frame or a tibble object.
dbtype
Either an appropriate driver function (e.g.,
RPostgres::Postgres()) or a shortname for the database type. Valid names
are: "postgresql", "postgres", or "pgsql" (PostgreSQL, using the
RPostgres::Postgres() driver function); "mysql" (MySQL, using
RMySQL::MySQL()); bigquery or bq (BigQuery, using
bigrquery::bigquery()); "duckdb" (DuckDB, using duckdb::duckdb());
and "sqlite" (SQLite, using RSQLite::SQLite()).
dbname
The database name.
host, port
The database host and optional port number.
user, password
The environment variables used to access the username
and password for the database. Enclose in I() when using literal username
or password values.
bq_project, bq_dataset, bq_billing
If accessing a table from a
BigQuery data source, there's the requirement to provide the table's
associated project (bq_project) and dataset (bq_dataset) names. By
default, the project to be billed will be the same as the one provided for
bq_project but the bq_billing argument can be changed to reflect a
different BigQuery project.
Value
A tbl_dbi object.
Examples
Obtaining in-memory database tables
You can use an in-memory database table and by supplying it with an in-memory
table. This works with the DuckDB database and the key thing is to use
dbname = ":memory" in the db_tbl() call.
small_table_duckdb <-
db_tbl(
table = small_table,
dbtype = "duckdb",
dbname = ":memory:"
)
small_table_duckdb
## # Source: table<small_table> [?? x 8]
## # Database: duckdb_connection
## date_time date a b c d e f
## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl> <chr>
## 1 2016-01-04 11:00:00 2016-01-04 2 1-bc… 3 3423. TRUE high
## 2 2016-01-04 00:32:00 2016-01-04 3 5-eg… 8 10000. TRUE low
## 3 2016-01-05 13:32:00 2016-01-05 6 8-kd… 3 2343. TRUE high
## 4 2016-01-06 17:23:00 2016-01-06 2 5-jd… NA 3892. FALSE mid
## 5 2016-01-09 12:36:00 2016-01-09 8 3-ld… 7 284. TRUE low
## 6 2016-01-11 06:15:00 2016-01-11 4 2-dh… 4 3291. TRUE mid
## 7 2016-01-15 18:46:00 2016-01-15 7 1-kn… 3 843. TRUE high
## 8 2016-01-17 11:27:00 2016-01-17 4 5-bo… 2 1036. FALSE low
## 9 2016-01-20 04:30:00 2016-01-20 3 5-bc… 9 838. FALSE high
## 10 2016-01-20 04:30:00 2016-01-20 3 5-bc… 9 838. FALSE high
## # … with more rows
The in-memory option also works using the SQLite database. The only change
required is setting the dbtype to "sqlite":
small_table_sqlite <-
db_tbl(
table = small_table,
dbtype = "sqlite",
dbname = ":memory:"
)
small_table_sqlite
## # Source: table<small_table> [?? x 8]
## # Database: sqlite 3.37.0 [:memory:]
## date_time date a b c d e f
## <dbl> <dbl> <int> <chr> <dbl> <dbl> <int> <chr>
## 1 1451905200 16804 2 1-bcd-345 3 3423. 1 high
## 2 1451867520 16804 3 5-egh-163 8 10000. 1 low
## 3 1452000720 16805 6 8-kdg-938 3 2343. 1 high
## 4 1452100980 16806 2 5-jdo-903 NA 3892. 0 mid
## 5 1452342960 16809 8 3-ldm-038 7 284. 1 low
## 6 1452492900 16811 4 2-dhe-923 4 3291. 1 mid
## 7 1452883560 16815 7 1-knw-093 3 843. 1 high
## 8 1453030020 16817 4 5-boe-639 2 1036. 0 low
## 9 1453264200 16820 3 5-bce-642 9 838. 0 high
## 10 1453264200 16820 3 5-bce-642 9 838. 0 high
## # … with more rows
It's also possible to obtain a table from a remote file and shove it into an
in-memory database. For this, we can use the all-powerful file_tbl() +
db_tbl() combo.
all_revenue_large_duckdb <-
db_tbl(
table = file_tbl(
file = from_github(
file = "sj_all_revenue_large.rds",
repo = "rich-iannone/intendo",
subdir = "data-large"
)
),
dbtype = "duckdb",
dbname = ":memory:"
)
all_revenue_large_duckdb
## # Source: table<sj_all_revenue_large.rds> [?? x 11]
## # Database: duckdb_connection
## player_id session_id session_start time
## <chr> <chr> <dttm> <dttm>
## 1 IRZKSAOYUJME796 IRZKSAOYUJM… 2015-01-01 00:18:41 2015-01-01 00:18:53
## 2 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:13:07
## 3 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:23:37
## 4 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:24:37
## 5 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:31:01
## 6 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:31:43
## 7 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:36:01
## 8 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:31:27
## 9 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:36:57
## 10 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:37:45
## # … with more rows, and 7 more variables: item_type <chr>,
## # item_name <chr>, item_revenue <dbl>, session_duration <dbl>,
## # start_day <date>, acquisition <chr>, country <chr>
And that's really it.
Obtaining remote database tables
For remote databases, we have to specify quite a few things but it's a
one-step process nonetheless. Here's an example that accesses the rna table
(in the RNA Central public database) using db_tbl(). Here, for the user
and password entries we are using the literal username and password values
(publicly available when visiting the RNA Central website) by enclosing the
values in I().
rna_db_tbl <-
db_tbl(
table = "rna",
dbtype = "postgres",
dbname = "pfmegrnargs",
host = "hh-pgsql-public.ebi.ac.uk",
port = 5432,
user = I("reader"),
password = I("NWDMCE5xdipIjRrp")
)
rna_db_tbl
## # Source: table<rna> [?? x 9]
## # Database: postgres
## # [reader@hh-pgsql-public.ebi.ac.uk:5432/pfmegrnargs]
## id upi timestamp userstamp crc64 len seq_short
## <int64> <chr> <dttm> <chr> <chr> <int> <chr>
## 1 25222431 URS00… 2019-12-02 13:26:46 rnacen E65C… 521 AGAGTTTG…
## 2 25222432 URS00… 2019-12-02 13:26:46 rnacen 6B91… 520 AGAGTTCG…
## 3 25222433 URS00… 2019-12-02 13:26:46 rnacen 03B8… 257 TACGTAGG…
## 4 25222434 URS00… 2019-12-02 13:26:46 rnacen E925… 533 AGGGTTTG…
## 5 25222435 URS00… 2019-12-02 13:26:46 rnacen C2D0… 504 GACGAACG…
## 6 25222436 URS00… 2019-12-02 13:26:46 rnacen 9EF6… 253 TACAGAGG…
## 7 25222437 URS00… 2019-12-02 13:26:46 rnacen 685A… 175 GAGGCAGC…
## 8 25222438 URS00… 2019-12-02 13:26:46 rnacen 4228… 556 AAAACATC…
## 9 25222439 URS00… 2019-12-02 13:26:46 rnacen B7CC… 515 AGGGTTCG…
## 10 25222440 URS00… 2019-12-02 13:26:46 rnacen 038B… 406 ATTGAACG…
## # … with more rows, and 2 more variables: seq_long <chr>, md5 <chr>
You'd normally want to use the names of environment variables (envvars) to
more securely access the appropriate username and password values when
connecting to a DB. Here are all the necessary inputs:
example_db_tbl <-
db_tbl(
table = "<table_name>",
dbtype = "<database_type_shortname>",
dbname = "<database_name>",
host = "<connection_url>",
port = "<connection_port>",
user = "<DB_USER_NAME>",
password = "<DB_PASSWORD>"
)
Environment variables can be created by editing the user .Renviron file and
the usethis::edit_r_environ() function makes this pretty easy to do.
DB table access and prep via the table store
Using table-prep formulas in a centralized table store can make it easier to
work with DB tables in pointblank. Here's how to generate a table store
with two named entries for table preparations involving the tbl_store() and
db_tbl() functions.
store <-
tbl_store(
small_table_duck ~ db_tbl(
table = pointblank::small_table,
dbtype = "duckdb",
dbname = ":memory:"
),
small_high_duck ~ {{ small_table_duck }} %>%
dplyr::filter(f == "high")
)
Now it's easy to obtain either of these tables via tbl_get(). We can
reference the table in the store by its name (given to the left of the ~).
tbl_get(tbl = "small_table_duck", store = store)
## # Source: table<pointblank::small_table> [?? x 8]
## # Database: duckdb_connection
## date_time date a b c d e
## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl>
## 1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-… 3 3423. TRUE
## 2 2016-01-04 00:32:00 2016-01-04 3 5-egh-… 8 10000. TRUE
## 3 2016-01-05 13:32:00 2016-01-05 6 8-kdg-… 3 2343. TRUE
## 4 2016-01-06 17:23:00 2016-01-06 2 5-jdo-… NA 3892. FALSE
## 5 2016-01-09 12:36:00 2016-01-09 8 3-ldm-… 7 284. TRUE
## 6 2016-01-11 06:15:00 2016-01-11 4 2-dhe-… 4 3291. TRUE
## 7 2016-01-15 18:46:00 2016-01-15 7 1-knw-… 3 843. TRUE
## 8 2016-01-17 11:27:00 2016-01-17 4 5-boe-… 2 1036. FALSE
## 9 2016-01-20 04:30:00 2016-01-20 3 5-bce-… 9 838. FALSE
## 10 2016-01-20 04:30:00 2016-01-20 3 5-bce-… 9 838. FALSE
## # … with more rows, and 1 more variable: f <chr>
The second table in the table store is a mutated
version of the first. It's just as easily obtainable via tbl_get():
tbl_get(tbl = "small_high_duck", store = store)
## # Source: lazy query [?? x 8]
## # Database: duckdb_connection
## date_time date a b c d e
## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl>
## 1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-345 3 3423. TRUE
## 2 2016-01-05 13:32:00 2016-01-05 6 8-kdg-938 3 2343. TRUE
## 3 2016-01-15 18:46:00 2016-01-15 7 1-knw-093 3 843. TRUE
## 4 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE
## 5 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE
## 6 2016-01-30 11:23:00 2016-01-30 1 3-dka-303 NA 2230. TRUE
## # … with more rows, and 1 more variable: f <chr>
The table-prep formulas in the store object could also be used in functions
with a tbl argument (like create_agent() and create_informant()). This
is accomplished most easily with the tbl_source() function.
agent <-
create_agent(
tbl = ~ tbl_source(
tbl = "small_table_duck",
store = tbls
)
)
informant <-
create_informant(
tbl = ~ tbl_source(
tbl = "small_high_duck",
store = tbls
)
)
Function ID
1-6
See Also
Other Planning and Prep:
action_levels(),
create_agent(),
create_informant(),
draft_validation(),
file_tbl(),
scan_data(),
tbl_get(),
tbl_source(),
tbl_store(),
validate_rmd()
pointblank documentation built on April 25, 2023, 5:06 p.m.
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| db_tbl | R Documentation |
Get a table from a database
Description
If your target table is in a database, the db_tbl() function is a handy way
of accessing it. This function simplifies the process of getting a tbl_dbi
object, which usually involves a combination of building a connection to a
database and using the dplyr::tbl() function with the connection and the
table name (or a reference to a table in a schema). You can use db_tbl() as
the basis for obtaining a database table for the tbl parameter in
create_agent() or create_informant(). Another great option is supplying a
table-prep formula involving db_tbl() to tbl_store() so that you have
access to database tables though single names via a table store.
The username and password are supplied through environment variable names. If
desired, values for the username and password can be supplied directly by
enclosing such values in I().
Usage
db_tbl(
table,
dbtype,
dbname = NULL,
host = NULL,
port = NULL,
user = NULL,
password = NULL,
bq_project = NULL,
bq_dataset = NULL,
bq_billing = bq_project
)
Arguments
table |
The name of the table, or, a reference to a table in a schema
(two-element vector with the names of schema and table). Alternatively,
this can be supplied as a data table to copy into an in-memory database
connection. This only works if: (1) the |
dbtype |
Either an appropriate driver function (e.g.,
|
dbname |
The database name. |
host, port |
The database host and optional port number. |
user, password |
The environment variables used to access the username
and password for the database. Enclose in |
bq_project, bq_dataset, bq_billing |
If accessing a table from a
BigQuery data source, there's the requirement to provide the table's
associated project ( |
Value
A tbl_dbi object.
Examples
Obtaining in-memory database tables
You can use an in-memory database table and by supplying it with an in-memory
table. This works with the DuckDB database and the key thing is to use
dbname = ":memory" in the db_tbl() call.
small_table_duckdb <-
db_tbl(
table = small_table,
dbtype = "duckdb",
dbname = ":memory:"
)
small_table_duckdb
## # Source: table<small_table> [?? x 8] ## # Database: duckdb_connection ## date_time date a b c d e f ## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl> <chr> ## 1 2016-01-04 11:00:00 2016-01-04 2 1-bc… 3 3423. TRUE high ## 2 2016-01-04 00:32:00 2016-01-04 3 5-eg… 8 10000. TRUE low ## 3 2016-01-05 13:32:00 2016-01-05 6 8-kd… 3 2343. TRUE high ## 4 2016-01-06 17:23:00 2016-01-06 2 5-jd… NA 3892. FALSE mid ## 5 2016-01-09 12:36:00 2016-01-09 8 3-ld… 7 284. TRUE low ## 6 2016-01-11 06:15:00 2016-01-11 4 2-dh… 4 3291. TRUE mid ## 7 2016-01-15 18:46:00 2016-01-15 7 1-kn… 3 843. TRUE high ## 8 2016-01-17 11:27:00 2016-01-17 4 5-bo… 2 1036. FALSE low ## 9 2016-01-20 04:30:00 2016-01-20 3 5-bc… 9 838. FALSE high ## 10 2016-01-20 04:30:00 2016-01-20 3 5-bc… 9 838. FALSE high ## # … with more rows
The in-memory option also works using the SQLite database. The only change
required is setting the dbtype to "sqlite":
small_table_sqlite <-
db_tbl(
table = small_table,
dbtype = "sqlite",
dbname = ":memory:"
)
small_table_sqlite
## # Source: table<small_table> [?? x 8] ## # Database: sqlite 3.37.0 [:memory:] ## date_time date a b c d e f ## <dbl> <dbl> <int> <chr> <dbl> <dbl> <int> <chr> ## 1 1451905200 16804 2 1-bcd-345 3 3423. 1 high ## 2 1451867520 16804 3 5-egh-163 8 10000. 1 low ## 3 1452000720 16805 6 8-kdg-938 3 2343. 1 high ## 4 1452100980 16806 2 5-jdo-903 NA 3892. 0 mid ## 5 1452342960 16809 8 3-ldm-038 7 284. 1 low ## 6 1452492900 16811 4 2-dhe-923 4 3291. 1 mid ## 7 1452883560 16815 7 1-knw-093 3 843. 1 high ## 8 1453030020 16817 4 5-boe-639 2 1036. 0 low ## 9 1453264200 16820 3 5-bce-642 9 838. 0 high ## 10 1453264200 16820 3 5-bce-642 9 838. 0 high ## # … with more rows
It's also possible to obtain a table from a remote file and shove it into an
in-memory database. For this, we can use the all-powerful file_tbl() +
db_tbl() combo.
all_revenue_large_duckdb <-
db_tbl(
table = file_tbl(
file = from_github(
file = "sj_all_revenue_large.rds",
repo = "rich-iannone/intendo",
subdir = "data-large"
)
),
dbtype = "duckdb",
dbname = ":memory:"
)
all_revenue_large_duckdb
## # Source: table<sj_all_revenue_large.rds> [?? x 11] ## # Database: duckdb_connection ## player_id session_id session_start time ## <chr> <chr> <dttm> <dttm> ## 1 IRZKSAOYUJME796 IRZKSAOYUJM… 2015-01-01 00:18:41 2015-01-01 00:18:53 ## 2 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:13:07 ## 3 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:23:37 ## 4 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:24:37 ## 5 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:31:01 ## 6 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:31:43 ## 7 CJVYRASDZTXO674 CJVYRASDZTX… 2015-01-01 01:13:01 2015-01-01 01:36:01 ## 8 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:31:27 ## 9 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:36:57 ## 10 ECPANOIXLZHF896 ECPANOIXLZH… 2015-01-01 01:31:03 2015-01-01 01:37:45 ## # … with more rows, and 7 more variables: item_type <chr>, ## # item_name <chr>, item_revenue <dbl>, session_duration <dbl>, ## # start_day <date>, acquisition <chr>, country <chr>
And that's really it.
Obtaining remote database tables
For remote databases, we have to specify quite a few things but it's a
one-step process nonetheless. Here's an example that accesses the rna table
(in the RNA Central public database) using db_tbl(). Here, for the user
and password entries we are using the literal username and password values
(publicly available when visiting the RNA Central website) by enclosing the
values in I().
rna_db_tbl <-
db_tbl(
table = "rna",
dbtype = "postgres",
dbname = "pfmegrnargs",
host = "hh-pgsql-public.ebi.ac.uk",
port = 5432,
user = I("reader"),
password = I("NWDMCE5xdipIjRrp")
)
rna_db_tbl
## # Source: table<rna> [?? x 9] ## # Database: postgres ## # [reader@hh-pgsql-public.ebi.ac.uk:5432/pfmegrnargs] ## id upi timestamp userstamp crc64 len seq_short ## <int64> <chr> <dttm> <chr> <chr> <int> <chr> ## 1 25222431 URS00… 2019-12-02 13:26:46 rnacen E65C… 521 AGAGTTTG… ## 2 25222432 URS00… 2019-12-02 13:26:46 rnacen 6B91… 520 AGAGTTCG… ## 3 25222433 URS00… 2019-12-02 13:26:46 rnacen 03B8… 257 TACGTAGG… ## 4 25222434 URS00… 2019-12-02 13:26:46 rnacen E925… 533 AGGGTTTG… ## 5 25222435 URS00… 2019-12-02 13:26:46 rnacen C2D0… 504 GACGAACG… ## 6 25222436 URS00… 2019-12-02 13:26:46 rnacen 9EF6… 253 TACAGAGG… ## 7 25222437 URS00… 2019-12-02 13:26:46 rnacen 685A… 175 GAGGCAGC… ## 8 25222438 URS00… 2019-12-02 13:26:46 rnacen 4228… 556 AAAACATC… ## 9 25222439 URS00… 2019-12-02 13:26:46 rnacen B7CC… 515 AGGGTTCG… ## 10 25222440 URS00… 2019-12-02 13:26:46 rnacen 038B… 406 ATTGAACG… ## # … with more rows, and 2 more variables: seq_long <chr>, md5 <chr>
You'd normally want to use the names of environment variables (envvars) to more securely access the appropriate username and password values when connecting to a DB. Here are all the necessary inputs:
example_db_tbl <-
db_tbl(
table = "<table_name>",
dbtype = "<database_type_shortname>",
dbname = "<database_name>",
host = "<connection_url>",
port = "<connection_port>",
user = "<DB_USER_NAME>",
password = "<DB_PASSWORD>"
)
Environment variables can be created by editing the user .Renviron file and
the usethis::edit_r_environ() function makes this pretty easy to do.
DB table access and prep via the table store
Using table-prep formulas in a centralized table store can make it easier to
work with DB tables in pointblank. Here's how to generate a table store
with two named entries for table preparations involving the tbl_store() and
db_tbl() functions.
store <-
tbl_store(
small_table_duck ~ db_tbl(
table = pointblank::small_table,
dbtype = "duckdb",
dbname = ":memory:"
),
small_high_duck ~ {{ small_table_duck }} %>%
dplyr::filter(f == "high")
)
Now it's easy to obtain either of these tables via tbl_get(). We can
reference the table in the store by its name (given to the left of the ~).
tbl_get(tbl = "small_table_duck", store = store)
## # Source: table<pointblank::small_table> [?? x 8] ## # Database: duckdb_connection ## date_time date a b c d e ## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl> ## 1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-… 3 3423. TRUE ## 2 2016-01-04 00:32:00 2016-01-04 3 5-egh-… 8 10000. TRUE ## 3 2016-01-05 13:32:00 2016-01-05 6 8-kdg-… 3 2343. TRUE ## 4 2016-01-06 17:23:00 2016-01-06 2 5-jdo-… NA 3892. FALSE ## 5 2016-01-09 12:36:00 2016-01-09 8 3-ldm-… 7 284. TRUE ## 6 2016-01-11 06:15:00 2016-01-11 4 2-dhe-… 4 3291. TRUE ## 7 2016-01-15 18:46:00 2016-01-15 7 1-knw-… 3 843. TRUE ## 8 2016-01-17 11:27:00 2016-01-17 4 5-boe-… 2 1036. FALSE ## 9 2016-01-20 04:30:00 2016-01-20 3 5-bce-… 9 838. FALSE ## 10 2016-01-20 04:30:00 2016-01-20 3 5-bce-… 9 838. FALSE ## # … with more rows, and 1 more variable: f <chr>
The second table in the table store is a mutated
version of the first. It's just as easily obtainable via tbl_get():
tbl_get(tbl = "small_high_duck", store = store)
## # Source: lazy query [?? x 8] ## # Database: duckdb_connection ## date_time date a b c d e ## <dttm> <date> <int> <chr> <dbl> <dbl> <lgl> ## 1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-345 3 3423. TRUE ## 2 2016-01-05 13:32:00 2016-01-05 6 8-kdg-938 3 2343. TRUE ## 3 2016-01-15 18:46:00 2016-01-15 7 1-knw-093 3 843. TRUE ## 4 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE ## 5 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 838. FALSE ## 6 2016-01-30 11:23:00 2016-01-30 1 3-dka-303 NA 2230. TRUE ## # … with more rows, and 1 more variable: f <chr>
The table-prep formulas in the store object could also be used in functions
with a tbl argument (like create_agent() and create_informant()). This
is accomplished most easily with the tbl_source() function.
agent <-
create_agent(
tbl = ~ tbl_source(
tbl = "small_table_duck",
store = tbls
)
)
informant <-
create_informant(
tbl = ~ tbl_source(
tbl = "small_high_duck",
store = tbls
)
)
Function ID
1-6
See Also
Other Planning and Prep:
action_levels(),
create_agent(),
create_informant(),
draft_validation(),
file_tbl(),
scan_data(),
tbl_get(),
tbl_source(),
tbl_store(),
validate_rmd()
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