---
title: "SCDB: Basic principles"
output: rmarkdown::html_vignette
bibliography: "references.bib"
vignette: >
  %\VignetteIndexEntry{SCDB: Basic principles}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r library}
library(SCDB)
```

```{r setup, echo = FALSE, results = "hide", eval = rlang::is_installed("RSQLite")}

# Setup conn to be used for examples
conn <- get_connection()

# Use a wrapper for update_snapshot which uses LoggerNull to suppress all logging
if (!"update_snapshot" %in% ls(envir = globalenv())) {
  update_snapshot <- function(...) {
    return(SCDB::update_snapshot(logger = LoggerNull$new(), ...))
  }
}

# Setup example_data table in conn
example_data <-
  dplyr::copy_to(conn,
                 dplyr::transmute(datasets::mtcars, car = rownames(mtcars), hp),
                 name = "example_data",
                 overwrite = TRUE)
```
The basic principle of the SCDB package is to enable the user to easily implement and maintain a database of
time-versioned data.

In practice, this is done by labeling each record in the data with three additional fields:

* checksum: An md5 hash of information stored the record
* from\_ts: the timestamp at which the record was introduced to the dataset
* until\_ts: the timestamp at which the record was removed from the dataset

This strategy of time versioning is often called "type 2" history [@Kimball2013].

The SCDB package provides the function `update_snapshot` to handle the insertion and deactivation of records using
this strategy.
It further includes several functions to improve the Quality of life for working with database data.

A simple example of usage is shown below.<br/>
For this example, we use a temporary, on-disk SQLite database.
Note that `get_connection()` tries to establish connection using `DBI::dbConnect()` with as few additional arguments
as possible.
Different drivers may require authentication which can be read from a configuration file.^[
In the context of the SCDB package, this is most notably `RPostgres::Postgres()`, which may read from a `.pgpass` file.
See also the [PostgreSQL documentation](https://www.postgresql.org/docs/current/libpq-pgpass.html).
]

Our example data is `datasets::mtcars` reduced to only two columns: row names converted to a column `car`, and `hp`
```{r example_data, eval = FALSE}
conn <- get_connection()
```
```{r example_data_hidden, eval = requireNamespace("RSQLite", quietly = TRUE)}
example_data <- dplyr::tbl(conn, DBI::Id(table = "example_data"))
example_data
```

Imagine on Day 1, in this case January 1st, 2020, our currently available data is the first three records of
the `example_data`.
We then store this data in a table `mtcars`:
```{r example_1, eval = requireNamespace("RSQLite", quietly = TRUE)}
data <- head(example_data, 3)

update_snapshot(
  .data = data,
  conn = conn,
  db_table = "mtcars", # the name of the DB table to store the data in
  timestamp = as.POSIXct("2020-01-01 11:00:00")
)
```

We can then access out data using the `get_table()` function, and include information on data validity period using
`include_slice_info = TRUE`:
```{r example_1_results, eval = requireNamespace("RSQLite", quietly = TRUE)}
get_table(conn, "mtcars")

get_table(conn, "mtcars", include_slice_info = TRUE)
```
Note that where e.g. `dplyr::tbl()` requires a more exact specification of the table identity
(`tbl(conn, DBI::Id(table = "mtcars"))`), `get_table()` will parse any character to a `DBI::Id()` object input
using `id()`.

The following day, the current data is now the first five rows of our example data.
We then store this data in the database using `update_snapshot()`:

```{r example_2, eval = requireNamespace("RSQLite", quietly = TRUE)}
# Let's say that the next day, our data set is now the first 5 of our example data
data <- head(example_data, 5)

update_snapshot(
  .data = data,
  conn = conn,
  db_table = "mtcars", # the name of the DB table to store the data in
  timestamp = as.POSIXct("2020-01-02 12:00:00")
)
```
We can again use the `get_table()` function to see the latest available data, including time-keeping with
`include_slice_info = TRUE`:
```{r example_2_results_a, eval = requireNamespace("RSQLite", quietly = TRUE)}
get_table(conn, "mtcars")

get_table(conn, "mtcars", include_slice_info = TRUE)
```

Since our data is time-versioned, we can recover the data from the day before
```{r example_2_results_b, eval = requireNamespace("RSQLite", quietly = TRUE)}
get_table(conn, "mtcars", slice_ts = "2020-01-01 11:00:00")
```

On day 3, we imagine that we have the same 5 records, but one of them is altered

```{r example_3, eval = requireNamespace("RSQLite", quietly = TRUE)}
data <- head(example_data, 5) %>%
  dplyr::mutate(hp = ifelse(car == "Mazda RX4", hp / 2, hp))

update_snapshot(
  .data = data,
  conn = conn,
  db_table = "mtcars", # the name of the DB table to store the data in
  timestamp = as.POSIXct("2020-01-03 10:00:00")
)
```

We can again access our data using the `get_table()` function and see that the currently
available data (with the changed hp value for Mazda RX4)
```{r example_3_results_a, eval = requireNamespace("RSQLite", quietly = TRUE)}
get_table(conn, "mtcars")
```

Finally, using `slice_ts = NULL`, the full history (and time-keeping information) is returned:
```{r example_3_results_b, eval = requireNamespace("RSQLite", quietly = TRUE)}
get_table(conn, "mtcars", slice_ts = NULL)
```

### References
