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# | |_) || (_) || || | | || |_ | |_) || || (_| || | | || <
# | .__/ \___/ |_||_| |_| \__||_.__/ |_| \__,_||_| |_||_|\_\
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# |_|
#
# This file is part of the 'rich-iannone/pointblank' package.
#
# (c) Richard Iannone <riannone@me.com>
#
# For full copyright and license information, please look at
# https://rich-iannone.github.io/pointblank/LICENSE.html
#
# nocov start
#' 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()].
#'
#' @param 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.
#' @param 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()`).
#' @param dbname The database name.
#' @param host,port The database host and optional port number.
#' @param 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.
#' @param 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.
#'
#' @return A `tbl_dbi` object.
#'
#' @section 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.
#'
#' ```r
#' small_table_duckdb <-
#' db_tbl(
#' table = small_table,
#' dbtype = "duckdb",
#' dbname = ":memory:"
#' )
#'
#' small_table_duckdb
#' ```
#'
#' \preformatted{## # 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"`:
#'
#' ```r
#' small_table_sqlite <-
#' db_tbl(
#' table = small_table,
#' dbtype = "sqlite",
#' dbname = ":memory:"
#' )
#'
#' small_table_sqlite
#' ```
#'
#' \preformatted{## # 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.
#'
#' ```r
#' 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
#' ```
#'
#'
#' \preformatted{## # 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()`.
#'
#' ```r
#' 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
#' ```
#'
#' \preformatted{## # 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:
#'
#' ```r
#' 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.
#'
#' ```r
#' 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 `~`).
#'
#' ```r
#' tbl_get(tbl = "small_table_duck", store = store)
#' ```
#'
#' \preformatted{## # 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)
#' ```
#'
#' \preformatted{## # 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.
#'
#' ```r
#' agent <-
#' create_agent(
#' tbl = ~ tbl_source(
#' tbl = "small_table_duck",
#' store = tbls
#' )
#' )
#' ```
#'
#' ```r
#' informant <-
#' create_informant(
#' tbl = ~ tbl_source(
#' tbl = "small_high_duck",
#' store = tbls
#' )
#' )
#' ```
#'
#' @family Planning and Prep
#' @section Function ID:
#' 1-6
#'
#' @export
db_tbl <- function(
table,
dbtype,
dbname = NULL,
host = NULL,
port = NULL,
user = NULL,
password = NULL,
bq_project = NULL,
bq_dataset = NULL,
bq_billing = bq_project
) {
force(table)
# For BigQuery, the `bq_*` arguments are to be provided but there is
# some redundancy with `dbname`; this copies the `bq_project` name
# to `dbname` for the later connection statement
if (
is.null(dbname) &&
!is.null(bq_project) &&
!is.null(bq_dataset) &&
!is.null(bq_billing)
) {
dbname <- bq_project
}
if (!requireNamespace("DBI", quietly = TRUE)) {
stop(
"Accessing a database table requires the DBI package:\n",
"* It can be installed with `install.packages(\"DBI\")`.",
call. = FALSE
)
}
if (is.character(dbtype)) {
dbtype <- tolower(dbtype)
# nolint start
driver_function <-
switch(
dbtype,
postgresql = ,
postgres = ,
pgsql = RPostgres_driver(),
mysql = RMySQL_driver(),
bq = ,
bigquery = bigrquery_driver(),
duckdb = DuckDB_driver(),
sqlite = RSQLite_driver(),
unknown_driver()
)
# nolint end
} else {
driver_function <- dbtype
}
#
# Create the DB connection object
#
if (any(c("bq", "bigquery") %in% tolower(dbtype))) {
#
# Connection object for BigQuery
#
# Stop function if any of the required values for
# BigQuery are missing
if (
is.null(bq_project) ||
is.null(bq_dataset) ||
is.null(bq_billing)
) {
stop(
"When getting a BigQuery table, all `bq_*` arguments are required.",
call. = FALSE
)
}
connection <-
DBI::dbConnect(
drv = driver_function,
project = bq_project,
dataset = bq_dataset,
billing = bq_billing
)
} else {
connection <-
DBI::dbConnect(
drv = driver_function,
user = ifelse(
inherits(user, "AsIs"),
user, Sys.getenv(user)
),
password = ifelse(
inherits(password, "AsIs"),
password, Sys.getenv(password)
),
host = host,
dbname = dbname
)
}
# Insert data if is supplied, in the right format, and
# if the DB connection is in-memory
if (
dbname == ":memory:" &&
is.data.frame(table) &&
tolower(dbtype) %in% c("duckdb", "sqlite")
) {
# Obtain the name of the data table
if ("pb_tbl_name" %in% names(attributes(table))) {
table_name <- table_stmt <- attr(table, "pb_tbl_name", exact = TRUE)
} else {
table_name <- table_stmt <- deparse(match.call()$table)[1]
}
# Copy the tabular data into the `connection` object
dplyr::copy_to(
dest = connection,
df = table,
name = table_name,
temporary = FALSE
)
}
if (is.character(table)) {
if (any(c("bq", "bigquery") %in% tolower(dbtype))) {
table_name <- paste0(bq_dataset, ".", table)
table_stmt <- table
} else if (length(table) == 1) {
table_stmt <- table
table_name <- table
} else if (length(table) == 2) {
table_stmt <- dbplyr::in_schema(schema = table[1], table = table[2])
table_name <- table[2]
} else {
stop(
"The length of `table` should be either 1 or 2.",
call. = FALSE
)
}
}
access_time <- Sys.time()
x <- dplyr::tbl(src = connection, table_stmt)
con_desc <- dbplyr::db_connection_describe(con = connection)
attr(x, "pb_tbl_name") <- table_name
attr(x, "pb_con_desc") <- con_desc
attr(x, "pb_access_time") <- access_time
if (any(c("bq", "bigquery") %in% tolower(dbtype))) {
attr(x, "pb_bq_project") <- bq_project
attr(x, "pb_bq_dataset") <- bq_dataset
}
x
}
# nolint start
RPostgres_driver <- function() {
if (!requireNamespace("RPostgres", quietly = TRUE)) {
stop(
"Accessing a PostgreSQL table requires the RPostgres package:\n",
"* It can be installed with `install.packages(\"RPostgres\")`.",
call. = FALSE
)
}
RPostgres::Postgres()
}
RMySQL_driver <- function() {
if (!requireNamespace("RMySQL", quietly = TRUE)) {
stop(
"Accessing a MariaDB or MySQL table requires the RMySQL package:\n",
"* It can be installed with `install.packages(\"RMySQL\")`.",
call. = FALSE
)
}
RMySQL::MySQL()
}
bigrquery_driver <- function() {
if (!requireNamespace("bigrquery", quietly = TRUE)) {
stop(
"Accessing a BigQuery table requires the bigrquery package:\n",
"* It can be installed with `install.packages(\"bigrquery\")`.",
call. = FALSE
)
}
bigrquery::bigquery()
}
DuckDB_driver <- function() {
if (!requireNamespace("duckdb", quietly = TRUE)) {
stop(
"Accessing a DuckDB table requires the duckdb package:\n",
"* It can be installed with `install.packages(\"duckdb\")`.",
call. = FALSE
)
}
duckdb::duckdb()
}
RSQLite_driver <- function() {
if (!requireNamespace("RSQLite", quietly = TRUE)) {
stop(
"Accessing a SQLite table requires the RSQLite package:\n",
"* It can be installed with `install.packages(\"RSQLite\")`.",
call. = FALSE
)
}
RSQLite::SQLite()
}
# nolint end
unknown_driver <- function() {
stop(
"The supplied value for `db` doesn't correspond to a supported ",
"database type:\n",
"* Acceptable values are: \"postgres\", \"mysql\", ",
"\"sqlite\", and \"duckdb\".",
call. = FALSE
)
}
# nocov end
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