---
title: "SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor"
author:
- name: Qian Liu
  affiliation: Roswell Park Comprehensive Cancer Center, Buffalo, NY
- name: Martin Morgan
  affiliation: Roswell Park Comprehensive Cancer Center, Buffalo, NY
date: "last compiled: `r Sys.Date()`"
output:
    BiocStyle::html_document:
        toc: true
        toc_float: true
package: SQLDataFrame
vignette: >
  %\VignetteIndexEntry{SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```
last edit: 10/7/2019

# Introduction

SQL database are very commonly used in the storage of very large
genomic data resources. Many useful tools, such as [DBI][], [dbplyr][]
have provided convenient interfaces for R users to check and
manipulate the data. These tools represent the SQL tables in tidy
formats and support lazy and quick aggregation operations (e.g,
`*_join`, `union`, etc.) for tables from same resources. Cross
database aggregation is also supported when opted (using `copy=TRUE`)
but become very expensive due to the internal copying process of a
whole table into the other connection. Use of advanced functions often
involves specialized SQL knowledge which brings challenges for common
_R_ users. The interoperability of existing bioinformatics tools are
suboptimal, e.g., the [SummarizedExperiment][] container for
representation of sequencing or genotyping experiments that many
modern bioinformatics pipelines are based.

The SQLDataFrame package was developed using familiar DataFrame-like
paradigm and lazily represents the very large dataset from different
SQL databases, such as SQLite and MySQL. The DataFrame-like interface
provides familiarity for common _R_ users in easy data manipulations
such as square bracket subsetting, rbinding, etc. For modern _R_
users, it also recognizes the `tidy` data analysis and [dplyr][]
grammar by supporting `%>%`, `select`, `filter`, `mutate`, etc. More
importantly, database type-specific strategies were implemented in
SQLDataFrame to efficiently handle the cross-database operations
without incurring any internally expensive processes (especially for
database with write permission). Some previously difficult data
operations are made quick and easy in _R_, such as cross-database ID
matching and conversion, variant annotation extraction, etc. The
scalability and interoperability of SQLDataFrame are expected to
significantly promote the handling of very large genomic data
resources and facilitating the overall bioinformatics analysis.

Currently SQLDataFrame supports the DBI backend of SQLite, MySQL and
Google BigQuery, which are most commonly used SQL-based databases. In
the future or upon feature request, we would implement this package so
that users could choose to use different database backend for
`SQLDataFrame` representation.

Here is a list of commonly used backends (bolded are already supported!):

- **RSQLite**: embeds a SQLite database. (now used as default)
- **RMySQL**: connects to MySQL and MariaDB
- **bigrquery** connects to Google’s BigQuery.
- _RPostgreSQL_: connects to Postgres and Redshift.
- _odbc_ connects to many commercial databases via the open database
  connectivity protocol.

[DBI]: https://CRAN.R-project.org/package=DBI 
[dbplyr]:  https://CRAN.R-project.org/package=dbplyr
[dplyr]: https://CRAN.R-project.org/package=dplyr
[SummarizedExperiment]: https://bioconductor.org/packages/SummarizedExperiment/ 

# Installation

1. Download the package. 

```{r getPackage, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SQLDataFrame")
```
The development version is also available to download from Github. 
```{r getDevel, eval=FALSE}
BiocManager::install("Liubuntu/SQLDataFrame")
```
2. Load the package(s) into R session.
```{r Load, message=FALSE, eval = TRUE}
library(SQLDataFrame)
library(DBI)
```

# `SQLDataFrame` class

## `SQLDataFrame` constructor
There are two ways to construct a `SQLDataFrame` object: 

1) Provide an argument of `conn`, `dbtable` and `dbkey`. `conn` is a
valid DBIConnection from SQLite or MySQL; `dbtable` specifies the
database table name that is going to be represented as `SQLDataFrame`
object. If only one table is available in the specified database name,
this argument could be left blank. The `dbkey` argument is used to
specify the column name in the table which could uniquely identify all
the data observations (rows). 

2) Provide `dbtable`, `dbkey` as specified above, and credentials to
build valid DBIConnections. for SQLite, the credential argument
includes `dbname`. For MySQL, the credential arguments are `host`,
`user`, `password`. Additional to the credentials, users must provide
the `type` argument to specify the SQL database type. Supported types
are "SQLite" and "MySQL". If not specified, "SQLite" is used by
default. Supported database tables could be on-disk or remote on the
web or cloud.

```{r constructor_conn}
dbfile <- system.file("extdata/test.db", package = "SQLDataFrame")
conn <- DBI::dbConnect(DBI::dbDriver("SQLite"), dbname = dbfile)
obj <- SQLDataFrame(conn = conn, dbtable = "state",
                    dbkey = "state")
```

construction from database credentials:
```{r constructor_credentials}
obj1 <- SQLDataFrame(dbname = dbfile, type = "SQLite",
                     dbtable = "state", dbkey = "state")
all.equal(obj, obj1)
```
Note that after reading the database table into `SQLDataFrame`, the
key columns will be kept as fixed columns showing on the left hand
side, with `|` separating key column(s) with the other columns. The
`ncol`, `colnames`, and corresponding column subsetting will only
apply to the non-key-columns.

```{r}
obj
dim(obj)
colnames(obj)
```

## Slot & accessors

To make the `SQLDataFrame` object as light and compact as possible,
there are only 5 slots contained in the object: `tblData`, `dbkey`,
`dbnrows`, `dbconcatKey`, `indexes`. Metadata information could be
returned through these 5 slots using slot accessors or other utility
functions.  

```{r}
slotNames(obj)
dbtable(obj)
dbkey(obj)
connSQLDataFrame(obj)
```

Besides, many useful common methods are defined on `SQLDataFrame`
object to make it a more DataFrame-like data structure. e.g., we can
use `dimnames()` to return the row/colnames of the data. It returns an
unnamed list, with the first element being rownames which is always
`NULL`, and 2nd element being colnames (could also use `colnames()`
method). `dim()` method is defined to return the dimension of the
database table, which enables the `nrow()/ncol()` to extract a
specific dimension. `length()` method is also defined which works same
as `ncol()`.  
Note that the `rownames(SQLDataFrame)` would always be `NULL` as
rownames are not supported in `SQLDataFrame`. However, `ROWNAMES(obj)`
was implemented for the `[` subsetting with characters. 
 
```{r methods}
dim(obj)
dimnames(obj)
length(obj)
ROWNAMES(obj)
```

**NOTE** that the `dbtable()` accessor only works for a `SQLDataFrame`
object that the lazy tbl carried in `tblData` slot corresponds to a
single database. If the `SQLDataFrame` was generated from `rbind`,
`union` or `*_join`, call `saveSQLDataFrame()` to save the lazy tbl to
disk so that `dbtable()` will be activated.

```{r}
dbtable(obj)
aa <- rbind(obj[1:5, ], obj[6:10, ])
aa
dbtable(aa)  ## message
bb <- saveSQLDataFrame(aa, dbname = tempfile(fileext=".db"),
                       dbtable = "aa")
connSQLDataFrame(bb)
dbtable(bb)
```

# makeSQLDataFrame

We could also construct a `SQLDataFrame` object directly from a file
name. The `makeSQLDataFrame` function takes input of character value
of file name for common text files (.csv, .txt, etc.), write into
database tables, and open as `SQLDataFrame` object. Users could
provide values for the `dbname` and `dbtable` argument. If NULL,
default value for `dbname` would be a temporary database file, and
`dbtable` would be the `basename(filename)` without extension.

**NOTE** that the input file must have one or multiple columns that
could uniquely identify each observation (row) to be used the
`dbkey()` for `SQLDataFrame`. Also the file must be rectangular, i.e.,
rownames are not accepted. But users could save rownames as a separate
column. 

```{r}
mtc <- tibble::rownames_to_column(mtcars)[,1:6]
filename <- file.path(tempdir(), "mtc.csv")
write.csv(mtc, file= filename, row.names = FALSE)
aa <- makeSQLDataFrame(filename, dbkey = "rowname", sep = ",",
                       overwrite = TRUE)
aa
connSQLDataFrame(aa)
dbtable(aa)
```

# saveSQLDataFrame

With all the methods (`[` subsetting, `rbind`, `*_join`, etc.,)
provided in the next section, the `SQLDataFrame` always work like a
lazy representation until users explicitly call the `saveSQLDataFrame`
function for realization. `saveSQLDataFrame` write the lazy tbl
carried in `tblData` slot into an on-disk database table, and re-open
the `SQLDataFrame` object from the new path.

It's also recommended that users call `saveSQLDataFrame` frequently to
avoid too many lazy layers which slows down the data processing. 

```{r}
connSQLDataFrame(obj)
dbtable(obj)
obj1 <- saveSQLDataFrame(obj, dbname = tempfile(fileext = ".db"),
                        dbtable = "obj_copy")
connSQLDataFrame(obj1)
dbtable(obj1)
```

# SQLDataFrame methods

## `[[` subsetting
`[[,SQLDataFrame` Behaves similarly to `[[,DataFrame` and returns a
realized vector of values from a single column. `$,SQLDataFrame` is
also defined to conveniently extract column values.

```{r}
head(obj[[1]])
head(obj[["region"]])
head(obj$size)
```

We can also get the key column values using character extraction. 
```{r}
head(obj[["state"]])
```

## `[` subsetting

`SQLDataFrame` instances can be subsetted in a similar way of
`DataFrame` following the usual _R_ conventions, with numeric,
character or logical vectors; logical vectors are recycled to the
appropriate length. 

**NOTE**, use `drop=FALSE` explicitly for single column subsetting if
you want to return a `SQLDataFrame` object, otherwise, the default
`drop=TRUE` would always return a realized value for that column.

```{r, subsetting}
obj[1:3, 1:2]
obj[c(TRUE, FALSE), c(TRUE, FALSE), drop=FALSE]
obj[1:3, "population", drop=FALSE]
obj[, "population"]  ## realized column value
```

Subsetting with character vector works for the `SQLDataFrame`
objects. With composite keys, users need to concatenate the key values
by `:` for row subsetting (See the vignette for internal
implementation for more details).

```{r}
rnms <- ROWNAMES(obj)
obj[c("Alabama", "Colorado"), ]
```

```{r}
obj1 <- SQLDataFrame(conn = conn, dbtable = "state",
                     dbkey = c("region", "population"))
rnms <- ROWNAMES(obj1)
obj1[c("South:3615.0", "West:365.0"), ]
```

List style subsetting is also allowed to extract certain columns from
the `SQLDataFrame` object which returns `SQLDataFrame` by default.  

```{r}
obj[1]
obj["region"]
```

## filter & mutate

We have also enabled the S3 methods of `filter` and `mutate` from
`dplyr` package, so that users could have the convenience in filtering
data observations and adding new columns.

```{r}
obj1 %>% filter(division == "South Atlantic" & size == "medium")
```

```{r}
obj1 %>% mutate(p1 = population/10, s1 = size)
```


## union & rbind

To be consistent with `DataFrame`, `union` and `rbind` methods were
implemented for `SQLDataFrame`, where `union` returns the
`SQLDataFrame` sorted by the `dbkey(obj)`, and `rbind` keeps the
original orders of input objects.

```{r}
dbfile1 <- system.file("extdata/test.db", package = "SQLDataFrame")
con1 <- DBI::dbConnect(dbDriver("SQLite"), dbname = dbfile1)
dbfile2 <- system.file("extdata/test1.db", package = "SQLDataFrame")
con2 <- DBI::dbConnect(dbDriver("SQLite"), dbname = dbfile2)
ss1 <- SQLDataFrame(conn = con1, dbtable = "state",
                    dbkey = c("state"))
ss2 <- SQLDataFrame(conn = con2, dbtable = "state1",
                    dbkey = c("state"))
ss11 <- ss1[sample(5), ]
ss21 <- ss2[sample(10, 5), ]
```

- union
```{r, eval=FALSE}
obj1 <- union(ss11, ss21) 
obj1  ## reordered by the "dbkey()"
```

- rbind
```{r}
obj2 <- rbind(ss11, ss21) 
obj2  ## keeping the original order by updating the row index
```

## *_join methods

The `*_join` family methods was implemented for `SQLDataFrame`
objects, including the `left_join`, `inner_join`, `semi_join` and
`anti_join`, which provides the capability of merging database files
from different sources.

```{r}
ss12 <- ss1[1:10, 1:2]
ss22 <- ss2[6:15, 3:4]
left_join(ss12, ss22)
inner_join(ss12, ss22)
semi_join(ss12, ss22)
anti_join(ss12, ss22)
```


# Support of MySQL database data

SQLDataFrame now supports the MySQL database tables through [RMySQL][],
for local MySQL servers, or remote ones on the web or cloud. The
SQLDataFrame construction, `*_join` functions, `union`, `rbind`, and
saving are all supported. Aggregation operations are supported for
same or cross MySQL databases. Details please see the function
documentations.

Here I'll show a simple use case for MySQL tables from ensembl. 

[RMySQL]: https://CRAN.R-project.org/package=RMySQL

```{r, mysql}
library(RMySQL)
ensbConn <- dbConnect(dbDriver("MySQL"),
                        host="genome-mysql.soe.ucsc.edu",
                        user = "genome",
                        dbname = "xenTro9")
enssdf <- SQLDataFrame(conn = ensbConn,
                       dbtable = "xenoRefGene",
                       dbkey = c("name", "txStart"))
enssdf1 <- enssdf[1:20, 1:2]
enssdf2 <- enssdf[11:30,3:4]
res <- left_join(enssdf1, enssdf2)
```

# Support of Google BigQuery 

SQLDataFrame has just added support for Google BigQuery
tables. Construction and queries using `[` and `filter` are supported!

"Authentication and authorization" will be needed when using
[bigrquery][]. Check [here](https://github.com/r-dbi/bigrquery) for
more details. 

Also note that, the support of BigQuery tables has implemented
specialized strategy for efficient data representation. The `dbkey()`
is assigned by default as `SurrogateKey`, and `dbkey` argument will be
ignored during construction.

[bigrquery]: https://CRAN.R-project.org/package=bigrquery

```{r bigquery, eval=FALSE}
library(bigrquery)
bigrquery::bq_auth()  ## use this to authorize bigrquery in the
                      ## browser.
bqConn <- DBI::dbConnect(dbDriver("bigquery"),
                      project = "bigquery-public-data",
                      dataset = "human_variant_annotation",
                      billing = "") ## if not previous provided
                                    ## authorization, must specify a
                                    ## project name that was already
                                    ## linked with Google Cloud with
                                    ## billing info.
sdf <- SQLDataFrame(conn = bqConn, dbtable = "ncbi_clinvar_hg38_20180701")
sdf[1:5, 1:5]
sdf %>% select(GENEINFO)
sdf %>% filter(GENEINFO == "PYGL:5836")
sdf %>% filter(reference_name == "21")
```

# SessionInfo()

```{r}
sessionInfo()
```
