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_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.frames.
  • 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 operators

Simple column operations (selecting and re-naming columns)

The 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)

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.


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')) %.>%

Notice the first argument is an explicit “dot” in wrapr pipe notation.

select_columns’s action is also obvious from example.

x y
1 5
1 4
2 3

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

x_new_name y_new_name z
1 5 6
1 4 7
2 3 8

Simple row operations (selecting and re-ordering rows)

The simple row operations are:

  • select_rows
  • order_rows

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

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.

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.

Creating new columns or replacing columns with new calculated values

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) %.>%
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.

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.

Aggregating or summarizing data

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.

Combining results between two data.frames

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.

k x y
a 1 1
b 3 NA
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.

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.”

General conversion of record layouts

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

Composing operations

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.

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.

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).

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:

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.

x y z
1 4 7
2 3 8

And for SQL we have the following.

For more SQL examples, please see here.

Pipeline principles

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. The two systems can even share data manipulation code between each other (allowing very powerful R/Python inter-operation or helping port projects from one to the other).


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

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).


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.


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