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.frames, 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_columnsselect_columnsrename_columnsselect_rowsorder_rowsextendprojectnatural_joinconvert_records(supplied by thecdatapackage).
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.frames. - General conversion of record layouts (supplied by the
cdatapackage).
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.
The simple column operations are as follows.
drop_columnsselect_columnsrename_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_rowsorder_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.
To 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 ~1973QUEL~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.tabledisk.framedbplyrdplyrdtplyrmaditrncpoormanrqdatatableSparkRsparklyrsqldftable.expresstidyfasttidyfsttidyquerytidyrtidytable(formerlygdt/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.