`rquery`

`rquery`

is a piped query
generator based on Codd’s relational
algebra (updated to
reflect lessons learned from working with
`R`

,
`SQL`

, and
`dplyr`

at big data scale in
production).

`rquery`

is a data wrangling
system designed to express complex data manipulation as a series of
simple data transforms. This is in the spirit of `R`

’s
`base::transform()`

, or `dplyr`

’s `dplyr::mutate()`

and uses a pipe in
the style popularized in `R`

with `magrittr`

. The operators themselves
follow the selections in Codd’s relational algebra, with the addition of
the traditional `SQL`

“window functions.” More on the background and
context of `rquery`

can be found
here.

The `R`

/`rquery`

version of this introduction is
here,
and the `Python`

/`data_algebra`

version of this introduction is
here.

In transform formulations data manipulation is written as
transformations that produce new `data.frame`

s, instead of as
alterations of a primary data structure (as is the case with
`data.table`

). Transform system *can* use more space and time than
in-place methods. However, in our opinion, transform systems have a
number of pedagogical advantages.

In `rquery`

’s case the primary set of data operators is as follows:

`drop_columns`

`select_columns`

`rename_columns`

`select_rows`

`order_rows`

`extend`

`project`

`natural_join`

`convert_records`

(supplied by the`cdata`

package).

These operations break into a small number of themes:

- Simple column operations (selecting and re-naming columns).
- Simple row operations (selecting and re-ordering rows).
- Creating new columns or replacing columns with new calculated values.
- Aggregating or summarizing data.
- Combining results between two
`data.frame`

s. - General conversion of record layouts (supplied by the
`cdata`

package).

The point is: Codd worked out that a great number of data
transformations can be decomposed into a small number of the above
steps. `rquery`

supplies a high performance implementation of these
methods that scales from in-memory scale up through big data scale (to
just about anything that supplies a sufficiently powerful `SQL`

interface, such as PostgreSQL, Apache Spark, or Google BigQuery).

We will work through simple examples/demonstrations of the `rquery`

data
manipulation operators.

`rquery`

operatorsThe simple column operations are as follows.

`drop_columns`

`select_columns`

`rename_columns`

These operations are easy to demonstrate.

We set up some simple data.

```
d <- data.frame(
x = c(1, 1, 2),
y = c(5, 4, 3),
z = c(6, 7, 8)
)
knitr::kable(d)
```

| x | y | z | |----:|----:|----:| | 1 | 5 | 6 | | 1 | 4 | 7 | | 2 | 3 | 8 |

For example: `drop_columns`

works as follows. `drop_columns`

creates a
new `data.frame`

without certain columns.

```
library(rquery)
```

```
## Loading required package: wrapr
```

```
library(rqdatatable)
drop_columns(d, c('y', 'z'))
```

```
## x
## 1 1
## 2 1
## 3 2
```

In all cases the first argument of a `rquery`

operator is either the
data to be processed, or an earlier `rquery`

pipeline to be extended. We
will take about composing `rquery`

operations after we work through
examples of all of the basic operations.

We can write the above in piped notation (using the `wrapr`

pipe
in this case):

```
d %.>%
drop_columns(., c('y', 'z')) %.>%
knitr::kable(.)
```

| x | |----:| | 1 | | 1 | | 2 |

Notice the first argument is an explicit “dot” in `wrapr`

pipe
notation.

`select_columns`

’s action is also obvious from example.

```
d %.>%
select_columns(., c('x', 'y')) %.>%
knitr::kable(.)
```

| x | y | |----:|----:| | 1 | 5 | | 1 | 4 | | 2 | 3 |

`rename_columns`

is given as name-assignments of the form
`'new_name' = 'old_name'`

:

```
d %.>%
rename_columns(.,
c('x_new_name' = 'x',
'y_new_name' = 'y')
) %.>%
knitr::kable(.)
```

| x_new_name | y_new_name | z | |-----------:|-----------:|----:| | 1 | 5 | 6 | | 1 | 4 | 7 | | 2 | 3 | 8 |

The simple row operations are:

`select_rows`

`order_rows`

`select_rows`

keeps the set of rows that meet a given predicate
expression.

```
d %.>%
select_rows(., x == 1) %.>%
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 1 | 5 | 6 | | 1 | 4 | 7 |

Notes on how to use a variable to specify column names in `select_rows`

can be found
here.

`order_rows`

re-orders rows by a selection of column names (and allows
reverse ordering by naming which columns to reverse in the optional
`reverse`

argument). Multiple columns can be selected in the order, each
column breaking ties in the earlier comparisons.

```
d %.>%
order_rows(.,
c('x', 'y'),
reverse = 'x') %.>%
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 2 | 3 | 8 | | 1 | 4 | 7 | | 1 | 5 | 6 |

General `rquery`

operations do not depend on row-order and are not
guaranteed to preserve row-order, so if you do want to order rows you
should make it the last step of your pipeline.

The important create or replace column operation is:

`extend`

`extend`

accepts arbitrary expressions to create new columns (or replace
existing ones). For example:

```
d %.>%
extend(., zzz := y / x) %.>%
knitr::kable(.)
```

| x | y | z | zzz | |----:|----:|----:|----:| | 1 | 5 | 6 | 5.0 | | 1 | 4 | 7 | 4.0 | | 2 | 3 | 8 | 1.5 |

We can use `=`

or `:=`

for column assignment. In these examples we will
use `:=`

to keep column assignment clearly distinguishable from argument
binding.

`extend`

allows for very powerful per-group operations akin to what
`SQL`

calls “window
functions”. When the
optional `partitionby`

argument is set to a vector of column names then
aggregate calculations can be performed per-group. For example.

```
shift <- data.table::shift
d %.>%
extend(.,
max_y := max(y),
shift_z := shift(z),
row_number := row_number(),
cumsum_z := cumsum(z),
partitionby = 'x',
orderby = c('y', 'z')) %.>%
knitr::kable(.)
```

| x | y | z | max_y | shift_z | row_number | cumsum_z | |----:|----:|----:|------:|--------:|-----------:|---------:| | 1 | 4 | 7 | 5 | NA | 1 | 7 | | 1 | 5 | 6 | 5 | 7 | 2 | 13 | | 2 | 3 | 8 | 3 | NA | 1 | 8 |

Notice the aggregates were performed per-partition (a set of rows with
matching partition key values, specified by `partitionby`

) and in the
order determined by the `orderby`

argument (without the `orderby`

argument order is not guaranteed, so always set `orderby`

for windowed
operations that depend on row order!).

More on the window functions can be found
here.
Notes on how to use a variable to specify column names in `extend`

can
be found
here.

The main aggregation method for `rquery`

is:

`project`

`project`

performs per-group calculations, and returns only the grouping
columns (specified by `groupby`

) and derived aggregates. For example:

```
d %.>%
project(.,
max_y := max(y),
count := n(),
groupby = 'x') %.>%
knitr::kable(.)
```

| x | max_y | count | |----:|------:|------:| | 1 | 5 | 2 | | 2 | 3 | 1 |

Notice we only get one row for each unique combination of the grouping
variables. We can also aggregate into a single row by not specifying any
`groupby`

columns.

```
d %.>%
project(.,
max_y := max(y),
count := n()) %.>%
knitr::kable(.)
```

| max_y | count | |------:|------:| | 5 | 3 |

Notes on how to use a variable to specify column names in `project`

can
be found
here.

`data.frame`

sTo combine multiple tables in `rquery`

one uses what we call the
`natural_join`

operator. In the `rquery`

`natural_join`

, rows are
matched by column keys and any two columns with the same name are
*coalesced* (meaning the first table with a non-missing values supplies
the answer). This is easiest to demonstrate with an example.

Let’s set up new example tables.

```
d_left <- data.frame(
k = c('a', 'a', 'b'),
x = c(1, NA, 3),
y = c(1, NA, NA),
stringsAsFactors = FALSE
)
knitr::kable(d_left)
```

| k | x | y | |:----|----:|----:| | a | 1 | 1 | | a | NA | NA | | b | 3 | NA |

```
d_right <- data.frame(
k = c('a', 'b', 'q'),
y = c(10, 20, 30),
stringsAsFactors = FALSE
)
knitr::kable(d_right)
```

| k | y | |:----|----:| | a | 10 | | b | 20 | | q | 30 |

To perform a join we specify which set of columns our our row-matching
conditions (using the `by`

argument) and what type of join we want
(using the `jointype`

argument). For example we can use
`jointype = 'LEFT'`

to augment our `d_left`

table with additional values
from `d_right`

.

```
natural_join(d_left, d_right,
by = 'k',
jointype = 'LEFT') %.>%
knitr::kable(.)
```

| k | x | y | |:----|----:|----:| | a | 1 | 1 | | a | NA | 10 | | b | 3 | 20 |

In a left-join (as above) if the right-table has unique keys then we get a table with the same structure as the left-table- but with more information per row. This is a very useful type of join in data science projects. Notice columns with matching names are coalesced into each other, which we interpret as “take the value from the left table, unless it is missing.”

Record transformation is “simple once you get it”. However, we suggest reading up on that as a separate topic here.

We could, of course, perform complicated data manipulation by sequencing
`rquery`

operations. For example to select one row with minimal `y`

per-`x`

group we could work in steps as follows.

```
. <- d
. <- extend(.,
row_number := row_number(),
partitionby = 'x',
orderby = c('y', 'z'))
. <- select_rows(.,
row_number == 1)
. <- drop_columns(.,
"row_number")
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 1 | 4 | 7 | | 2 | 3 | 8 |

The above discipline has the advantage that it is easy to debug, as we can run line by line and inspect intermediate values. We can even use the Bizarro pipe to make this look like a pipeline of operations.

```
d ->.;
extend(.,
row_number := row_number(),
partitionby = 'x',
orderby = c('y', 'z')) ->.;
select_rows(.,
row_number == 1) ->.;
drop_columns(.,
"row_number") ->.;
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 1 | 4 | 7 | | 2 | 3 | 8 |

Or we can use the `wrapr`

pipe
on the data, which we call “immediate mode” (for more on modes please
see
here).

```
d %.>%
extend(.,
row_number := row_number(),
partitionby = 'x',
orderby = c('y', 'z')) %.>%
select_rows(.,
row_number == 1) %.>%
drop_columns(.,
"row_number") %.>%
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 1 | 4 | 7 | | 2 | 3 | 8 |

`rquery`

operators can also act on `rquery`

pipelines instead of acting
on data. We can write our operations as follows:

```
ops <- local_td(d) %.>%
extend(.,
row_number := row_number(),
partitionby = 'x',
orderby = c('y', 'z')) %.>%
select_rows(.,
row_number == 1) %.>%
drop_columns(.,
"row_number")
cat(format(ops))
```

```
## mk_td("d", c(
## "x",
## "y",
## "z")) %.>%
## extend(.,
## row_number := row_number(),
## partitionby = c('x'),
## orderby = c('y', 'z'),
## reverse = c()) %.>%
## select_rows(.,
## row_number == 1) %.>%
## drop_columns(.,
## c('row_number'))
```

And we can re-use this pipeline, both on local data and to generate
`SQL`

to be run in remote databases. Applying this operator pipeline to
our `data.frame`

`d`

is performed as follows.

```
d %.>%
ops %.>%
knitr::kable(.)
```

| x | y | z | |----:|----:|----:| | 1 | 4 | 7 | | 2 | 3 | 8 |

And for `SQL`

we have the following.

```
raw_connection <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
RSQLite::initExtension(raw_connection)
db <- rquery_db_info(
connection = raw_connection,
is_dbi = TRUE,
connection_options = rq_connection_tests(raw_connection))
cat(to_sql(ops, db))
```

```
## SELECT
## `x`,
## `y`,
## `z`
## FROM (
## SELECT * FROM (
## SELECT
## `x`,
## `y`,
## `z`,
## row_number ( ) OVER ( PARTITION BY `x` ORDER BY `y`, `z` ) AS `row_number`
## FROM (
## SELECT
## `x`,
## `y`,
## `z`
## FROM
## `d`
## ) tsql_87263209472242564970_0000000000
## ) tsql_87263209472242564970_0000000001
## WHERE `row_number` = 1
## ) tsql_87263209472242564970_0000000002
```

```
# clean up
DBI::dbDisconnect(raw_connection)
```

For more `SQL`

examples, please see
here.

What we are trying to illustrate above: there is a continuum of notations possible between:

- Working over values with explicit intermediate variables.
- Working over values with a pipeline.
- Working over operators with a pipeline.

Being able to see these as all related gives some flexibility in decomposing problems into solutions. We have some more advanced notes on the differences in working modalities here and here.

`rquery`

supplies a very teachable grammar of data manipulation based on
Codd’s relational algebra and experience with pipelined data transforms
(such as `base::transform()`

, `dplyr`

, and `data.table`

).

For in-memory situations `rquery`

uses `data.table`

as the
implementation provider (through the small adapter package
`rqdatatable`

) and is routinely faster than any other `R`

data
manipulation system *except* `data.table`

itself.

For bigger than memory situations `rquery`

can translate to any
sufficiently powerful `SQL`

dialect, allowing `rquery`

pipelines to be
executed on PostgreSQL, Apache Spark, or Google BigQuery.

In addition the
`data_algebra`

Python
package supplies a nearly identical system for working with data in
Python. # Background

There are many prior relational algebra inspired specialized query languages. Just a few include:

`Alpha`

~1971.`ISBL`

/ Information system based language ~1973`QUEL`

~1974.`IBM System R`

~1974.`SQL`

~1974.`Tutorial D`

~1994.`data.table`

~2006.`LINQ`

~2007.`pandas`

~2008.`dplyr`

~2014.`Apache Calcite`

~2014.

`rquery`

is realized as a thin translation to an underlying `SQL`

provider. We are trying to put the Codd relational operators front and
center (using the original naming, and back-porting `SQL`

progress such
as window functions to the appropriate relational operator).

Some related work includes:

`data.table`

`disk.frame`

`dbplyr`

`dplyr`

`dtplyr`

`maditr`

`nc`

`poorman`

`rqdatatable`

`SparkR`

`sparklyr`

`sqldf`

`table.express`

`tidyfast`

`tidyfst`

`tidyquery`

`tidyr`

`tidytable`

(formerly`gdt`

/`tidydt`

)`data_algebra`

To install `rquery`

please try `install.packages("rquery")`

.

`rquery`

is intended to work with “tame column names”, that is column
names that are legitimate symbols in `R`

and `SQL`

.

The previous `rquery`

introduction is available
here.

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