---
title: "Create a dm object from a database"
date: "`r Sys.Date()`"
author: James Wondrasek, Kirill Müller
output:
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    md_extensions: -autolink_bare_uris
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{How to: Create a dm object from a database}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  chunk_output_type: console
---


``````{r setup, include = FALSE}
source("setup/setup.R")
``````

{dm} was designed to make connecting to and working with a relational database management system (RDBMS) as straightforward as possible.
To this end, a dm object can be created from any database that has a {[DBI](https://dbi.r-dbi.org/)} backend available ([see list](https://github.com/r-dbi/backends)).

When a dm object is created via a DBI connection to an RDBMS, it can import all the tables in the database, the active schema, or a limited set.
For some RDBMS, such as Postgres, SQL Server and MariaDB, primary and foreign keys are also imported and do not have to be manually added afterwards.

To demonstrate, we will connect to a `r href("relational dataset repository", "https://relational.fit.cvut.cz/")` with a database server that is publicly accessible without registration.
It hosts a `r href("financial dataset", "https://relational.fit.cvut.cz/dataset/Financial")` that contains loan data along with relevant account information and transactions.
We chose this dataset because the relationships between `loan`, `account`, and `transactions` tables are a good representation of databases that record real-world business transactions.

Below, we open a connection to the publicly accessible database server using their documented connection parameters.
Connection details vary from database to database. Before connecting to your own RDBMS, you may want to read `vignette("DBI", package = "DBI")` for further information.


``````{r eval = FALSE}
library(RMariaDB)

my_db <- dbConnect(
  MariaDB(),
  username = "guest",
  password = "relational",
  dbname = "Financial_ijs",
  host = "relational.fit.cvut.cz"
)
``````

``````{r echo = FALSE}
library(RMariaDB)
my_db <- dm:::financial_db_con()
``````

Creating a dm object takes a single call to `dm_from_con()` with the DBI connection object as its argument.

``````{r}
library(dm)

my_dm <- dm_from_con(my_db)
my_dm
``````

The components of the `my_dm` object are lazy tables powered by {[dbplyr](https://dbplyr.tidyverse.org/)}.
{dbplyr} translates the {[dplyr](https://dplyr.tidyverse.org/)} grammar of data manipulation into queries the database server understands.
Lazy tables defer downloading of table data until results are required for printing or local processing.

## Building a dm from a subset of tables

A dm can also be constructed from individual tables or views.
This is useful for when you want to work with a subset of a database's tables, perhaps from different schemas.

Below, we use the `$` notation to extract two tables from the financial database.
Then we create our dm by passing the tables in as arguments.
Note that the tables arguments have to all be from the same source, in this case `my_db`.

``````{r }
dbListTables(my_db)

library(dbplyr)
loans <- tbl(my_db, "loans")
accounts <- tbl(my_db, "accounts")

my_manual_dm <- dm(loans, accounts)
my_manual_dm
``````

## Defining Primary and Foreign Keys

Primary keys and foreign keys are how relational database tables are linked with each other.
A primary key is a column or column tuple that has a unique value for each row within a table.
A foreign key is a column or column tuple containing the primary key for a row in another table.
Foreign keys act as cross references between tables.
They specify the relationships that gives us the *relational* database.
For more information on keys and a crash course on databases, see `vignette("howto-dm-theory")`.

In many cases, `dm_from_con()` already returns a dm with all keys set.
If not, dm allows us to define primary and foreign keys ourselves.
For this, we use `learn_keys = FALSE` to obtain a `dm` object with only the tables.

``````{r load}
library(dm)

fin_dm <- dm_from_con(my_db, learn_keys = FALSE)
fin_dm
``````

The `r href("model diagram", "https://relational.fit.cvut.cz/assets/img/datasets-generated/financial.svg")` provided by our test database loosely illustrates the intended relationships between tables.
In the diagram, we can see that the `loans` table should be linked to the `accounts` table.
Below, we create those links in 3 steps:

1. Add a primary key `id` to the `accounts` table
1. Add a primary key `id` to the `loans` table
1. Add a foreign key `account_id` to the `loans` table referencing the `accounts` table

Then we assign colors to the tables and draw the structure of the dm.

Note that when the foreign key is created, the primary key in the referenced table does not need to be *specified*, but the primary key must already be *defined*.
And, as mentioned above, primary and foreign key constraints on the database are currently only imported for Postgres, SQL Server databases and MariaDB, and only when `dm_from_con()` is used.
This process of key definition needs to be done manually for other databases.

``````{r }
my_dm_keys <-
  my_manual_dm %>%
  dm_add_pk(accounts, id) %>%
  dm_add_pk(loans, id) %>%
  dm_add_fk(loans, account_id, accounts) %>%
  dm_set_colors(green = loans, orange = accounts)

my_dm_keys %>%
  dm_draw()
``````

Once you have instantiated a dm object, you can continue to add tables to it.
For tables from the original source for the dm, use `dm()`

``````{r }
trans <- tbl(my_db, "trans")

my_dm_keys %>%
  dm(trans)
``````

## Serializing a dm object

A dm object is always linked to a database connection.
This connection is lost when the dm object is saved to disk, e.g., when saving the workspace in R or in Posit Workbench, or when using knitr chunks:

``````{r error = TRUE}
unserialize(serialize(my_dm_keys, NULL))
``````

The connection is tightly coupled with the tables in the dm object and cannot be replaced.
A practical solution is to define, for each dm object your project uses, a function that recreates it using a new database connection:

``````{r }
my_db_fun <- function() {
  dbConnect(
    MariaDB(),
    username = "guest",
    password = "relational",
    dbname = "Financial_ijs",
    host = "relational.fit.cvut.cz"
  )
}

my_dm_fun <- function(my_db = my_db_fun()) {
  loans <- tbl(my_db, "loans")
  accounts <- tbl(my_db, "accounts")
  dm(loans, accounts) %>%
    dm_add_pk(accounts, id) %>%
    dm_add_pk(loans, id) %>%
    dm_add_fk(loans, account_id, accounts) %>%
    dm_set_colors(green = loans, orange = accounts)
}
``````

``````{r echo = FALSE}
my_db_fun <- function() {
  dm:::financial_db_con()
}
``````

``````{r echo = FALSE}
my_dm_fun() %>%
  dm_draw()
``````

To avoid reconnecting and/or recreating every time you need a dm object, you can use `memoise::memoise()` to memoize the connection and/or dm functions.

## Transient nature of operations

Like other R objects, a dm is immutable and all operations performed on it are transient unless stored in a new variable.

``````{r }
my_dm_keys

my_dm_trans <-
  my_dm_keys %>%
  dm(trans)

my_dm_trans
``````

And, like {dbplyr}, results are never written to a database unless explicitly requested.

``````{r }
my_dm_keys %>%
  dm_flatten_to_tbl(loans)

my_dm_keys %>%
  dm_flatten_to_tbl(loans) %>%
  sql_render()
``````

## Performing operations on tables by "zooming"

As the dm is a collection of tables, if we wish to perform operations on an individual table, we set it as the context for those operations using `dm_zoom_to()`.
See `vignette("tech-dm-zoom")` for more detail on zooming.

dm operations are transient unless persistence is explicitly requested.
To make our chain of manipulations on the selected table permanent, we assign the result of `dm_insert_zoomed()` to a new object, `my_dm_total`.
This is a new dm object, derived from `my_dm_keys`, with a new lazy table `total_loans` linked to the `accounts` table.

``````{r }
my_dm_total <-
  my_dm_keys %>%
  dm_zoom_to(loans) %>%
  group_by(account_id) %>%
  summarize(total_amount = sum(amount, na.rm = TRUE)) %>%
  ungroup() %>%
  dm_insert_zoomed("total_loans")
``````

Context is set to the table "loans" using `dm_zoom_to(loans)`.
You can learn more about zooming in the tutorial `vignette("tech-dm-zoom")`.
We then use {[dplyr](https://dplyr.tidyverse.org/)} functions on the zoomed table to generate a new summary table.

`summarize()` returns a temporary table with one row for each group created by the preceding `group_by()` function.
The columns in the temporary table are constrained to the columns passed as arguments to the `group_by()` function and the column(s) created by the `summarize()` function.

`dm_insert_zoomed("total_loans")` adds the temporary table created by `summarize()` to the data model under a new name, `total_loans`.
Because the grouping variable `account_id` is a primary key, the new derived table is automatically linked to the `accounts` table.

```{r}
my_dm_total %>%
  dm_set_colors(violet = total_loans) %>%
  dm_draw()
```

The resulting table `total_loans` can be accessed like any other table in the dm object.

``````{r }
my_dm_total$total_loans
``````

It is a *lazy table* powered by the {[dbplyr](https://dbplyr.tidyverse.org/)} package: the results are not materialized; instead, an SQL query is built and executed each time the data is requested.

``````{r}
my_dm_total$total_loans %>%
  sql_render()
``````

Use `compute()` on a zoomed table to materialize it to a temporary table and avoid recomputing.
See `vignette("howto-dm-copy")` for more details.



## Downloading data

When it becomes necessary to move data locally for analysis or reporting, the {dm} method `collect()` is used.
Operations on dm objects for databases are limited to report only the first ten results.
`collect()` forces the evaluation of all SQL queries and the generation of the complete set of results.
The resulting tables are transferred from the RDBMS and stored as local tibbles.

``````{r }
my_dm_local <-
  my_dm_total %>%
  collect()

my_dm_local$total_loans
``````

Use this method with caution.
If you are not sure of the size of the dataset you will be downloading, you can call `dm_nrow()` on your `dm` for the row count of your data model's tables.

```{r}
my_dm_total %>%
  dm_nrow()
```


## Persisting results {#persist}

It is just as simple to move a local relational model into an RDBMS as is using `collect()` to download it.
The method used is `copy_dm_to()` and it takes as arguments a database connection and a dm object.
In the example below, a local SQLite database is used to demonstrate it, but {dm} is designed to work with any RDBMS supported by {DBI}.

``````{r }
destination_db <- DBI::dbConnect(RSQLite::SQLite())

deployed_dm <- copy_dm_to(destination_db, my_dm_local)

deployed_dm
my_dm_local
``````

In the output, you can observe that the `src` for `deployed_dm` is the SQLite database, while for `my_dm_local` the source is the local R environment.

Persisting tables are covered in more detail in `vignette("howto-dm-copy")`.


When done, do not forget to disconnect:

``````{r disconnect}
DBI::dbDisconnect(destination_db)
DBI::dbDisconnect(my_db)
``````

## Conclusion {#conclusion}

In this tutorial, we have demonstrated how simple it is to load a database into a `dm` object and begin working with it.
Currently, loading a dm from most RDBMS requires you to manually set key relations, but {dm} provides methods to make this straightforward.
It is planned that future versions of dm will support automatic key creation for more RDBMS.

The next step is to read `vignette("howto-dm-copy")`, where copying your tables to and from an RDBMS is covered.
`vignette("howto-dm-rows")` discusses manipulation of individual rows in a database.


## Further reading

`vignette("howto-dm-df")` -- Is your data in local data frames? This article covers creating a data model from your local data frames, including building the relationships in your data model, verifying your model, and leveraging the power of dplyr to operate on your data model.

`vignette("howto-dm-theory")` -- Do you know all about data frames but very little about relational data models? This quick introduction will walk you through the key similarities and differences, and show you how to move from individual data frames to a relational data model.
