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:
data.frame
s.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(rqdatatable) drop_columns(d, c('y', 'z'))
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:
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:
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.
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_29495934854436339343_0000000000
## ) tsql_29495934854436339343_0000000001
## WHERE `row_number` = 1
## ) tsql_29495934854436339343_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:
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.table
disk.frame
dbplyr
dplyr
dtplyr
maditr
nc
poorman
rqdatatable
SparkR
sparklyr
sqldf
table.express
tidyfast
tidyfst
tidyquery
tidyr
tidytable
(formerly gdt
/tidydt
)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.