replyr

Introduction

It comes as a bit of a shock for R dplyr users when they switch from using a tbl implementation based on R in-memory data.frames to one based on a remote database or service. A lot of the power and convenience of the dplyr notation is hard to maintain with these more restricted data service providers. Things that work locally can’t always be used remotely at scale. It is emphatically not yet the case that one can practice with dplyr in one modality and hope to move to another back-end without significant debugging and work-arounds. The replyr package attempts to provide practical data manipulation affordances.

replyr supplies methods to get a grip on working with remote tbl sources (SQL databases, Spark) through dplyr. The idea is to add convenience functions to make such tasks more like working with an in-memory data.frame. Results still do depend on which dplyr service you use, but with replyr you have fairly uniform access to some useful functions.

replyr uniformly uses standard or paremtric interfaces (names of variables as strings) in favor of name capture so that you can easily program over replyr.

Primary replyr services include:

• replyr::let
• replyr::gapply
• replyr::replyr_*

replyr::let

replyr::let allows execution of arbitrary code with substituted variable names (note this is subtly different than binding values for names as with base::substitute or base::with). This allows the user to write arbitrary dplyr code in the case of “parametric variable names” (that is when variable names are not known at coding time, but will become available later at run time as values in other variables) without directly using the dplyr “underbar forms” (and the direct use of lazyeval::interp and .dots=stats::setNames to use the dplyr “underbar forms”).

Example:

library('dplyr')

# nice parametric function we write
ComputeRatioOfColumns <- function(d,NumeratorColumnName,DenominatorColumnName,ResultColumnName) {
  replyr::let(
    alias=list(NumeratorColumn=NumeratorColumnName,
               DenominatorColumn=DenominatorColumnName,
               ResultColumn=ResultColumnName),
    expr={
      # (pretend) large block of code written with concrete column names.
      # due to the let wrapper in this function it will behave as if it was
      # using the specified paremetric column names.
      d %>% mutate(ResultColumn=NumeratorColumn/DenominatorColumn)
    })
}

# example data
d <- data.frame(a=1:5,b=3:7)

# example application
d %>% ComputeRatioOfColumns('a','b','c')
#>   a b         c
#> 1 1 3 0.3333333
#> 2 2 4 0.5000000
#> 3 3 5 0.6000000
#> 4 4 6 0.6666667
#> 5 5 7 0.7142857

replyr::let makes construction of abstract functions over dplyr controlled data much easier. It is designed for the case where the “expr” block is large sequence of statements and pipelines.

Note that base::substitute is not powerful enough to remap both names and values without some helper notation (see here for an using substitute. What we mean by this is show below:

library('dplyr')

Substitute with quote notation.

d <- data.frame(Sepal_Length=c(5.8,5.7),
                Sepal_Width=c(4.0,4.4),
                Species='setosa',
                rank=c(1,2))
eval(substitute(d %>% mutate(RankColumn=RankColumn-1),
                list(RankColumn=quote(rank))))
#>   Sepal_Length Sepal_Width Species rank RankColumn
#> 1          5.8         4.0  setosa    1          0
#> 2          5.7         4.4  setosa    2          1

Substitute with as.name notation.

eval(substitute(d %>% mutate(RankColumn=RankColumn-1),
                list(RankColumn=as.name('rank'))))
#>   Sepal_Length Sepal_Width Species rank RankColumn
#> 1          5.8         4.0  setosa    1          0
#> 2          5.7         4.4  setosa    2          1

Substitute without extra notation (errors-out).

eval(substitute(d %>% mutate(RankColumn=RankColumn-1),
                list(RankColumn='rank')))
#> Error in mutate_impl(.data, dots): non-numeric argument to binary operator

Notice in both working cases the dplyr::mutate result landed in a column named RankColumn and not in the desired column rank. The replyr::let form is concise and works correctly.

Similarly base::with can not perform the needed name remapping, none of the following variations simulate a name to name substitution.

# rank <- NULL # hide binding of rank to function
env <- new.env()
assign('RankColumn',quote(rank),envir = env)
# assign('RankColumn',as.name('rank'),envir = env)
# assign('RankColumn',rank,envir = env)
# assign('RankColumn','rank',envir = env)
with(env,d %>% mutate(RankColumn=RankColumn-1))

Whereas replyr::let works and is succinct.

replyr::let(
  alias=list(RankColumn='rank'),
  d %>% mutate(RankColumn=RankColumn-1)
)
#>   Sepal_Length Sepal_Width Species rank
#> 1          5.8         4.0  setosa    0
#> 2          5.7         4.4  setosa    1

Note replyr::let only controls name bindings in the the scope of the expr={} block, and not inside functions called in the block. To be clear replyr::let is re-writing function arguments (which is how we use dplyr::mutate in the above example), but it is not re-writing data (which is why deeper in functions don’t see re-namings). This means one can not parameterize a function from the outside. For example the following function can only be used parametrically if we re-map the data frame, or if dplyr itself (or a data adapter) implemented something like the view stack proposal found here.

library('dplyr')

# example data
d <- data.frame(a=1:5,b=3:7)

# original function we do not have control of
ComputeRatioOfColumnsHardCoded <- function(d) {
  d %>% mutate(ResultColumn=NumeratorColumn/DenominatorColumn)
}

# wrapper to make function look parametric
ComputeRatioOfColumnsWrapped <- function(d,NumeratorColumnName,DenominatorColumnName,ResultColumnName) {
  d %>% replyr::replyr_mapRestrictCols(list(NumeratorColumn='a',
                                            DenominatorColumn='b')) %>%
    
    ComputeRatioOfColumnsHardCoded() %>%
    replyr::replyr_mapRestrictCols(list(a='NumeratorColumn',
                                        b='DenominatorColumn',
                                        c='ResultColumn'))
}

# example application
d %>% ComputeRatioOfColumnsWrapped('a','b','c')
#>   a b         c
#> 1 1 3 0.3333333
#> 2 2 4 0.5000000
#> 3 3 5 0.6000000
#> 4 4 6 0.6666667
#> 5 5 7 0.7142857

replyr::let is based on gtools::strmacro by Gregory R. Warnes.

replyr::gapply

replyr::gapply is a “grouped ordered apply” data operation. Many calculations can be written in terms of this primitive, including per-group rank calculation (assuming your data services supports window functions), per-group summaries, and per-group selections. It is meant to be a specialization of “The Split-Apply-Combine” strategy with all three steps wrapped into a single operator.

Example:

library('dplyr')

d <- data.frame(group=c(1,1,2,2,2),
                order=c(.1,.2,.3,.4,.5))
rank_in_group <- . %>% mutate(constcol=1) %>%
          mutate(rank=cumsum(constcol)) %>% select(-constcol)
d %>% replyr::gapply('group',rank_in_group,ocolumn='order',decreasing=TRUE)
#> # A tibble: 5 × 3
#>   group order  rank
#>   <dbl> <dbl> <dbl>
#> 1     2   0.5     1
#> 2     2   0.4     2
#> 3     2   0.3     3
#> 4     1   0.2     1
#> 5     1   0.1     2

The user supplies a function or pipeline that is meant to be applied per-group and the replyr::gapply wrapper orchestrates the calculation. In this example rank_in_group was assumed to know the column names in our data, so we directly used them instead of abstracting through replyr::let. replyr::gapply defaults to using dplyr::group_by as its splitting or partitioning control, but can also perform actual splits using ‘split’ (‘base::split’) or ‘extract’ (sequential extraction). Semantics are slightly different between cases given how dplyr treats grouping columns, the issue is illustrated in the difference between the definitions of sumgroupS and sumgroupG in this example).

replyr::replyr_*

The replyr::replyr_* functions are all convenience functions supplying common functionality (such as replyr::replyr_nrow) that works across many data services providers. These are prefixed (instead of being S3 or S4 methods) so they do not interfere with common methods. Many of these functions can expensive (which is why dplyr does not provide them as a default), or are patching around corner cases (which is why these functions appear to duplicate base:: and dplyr:: capabilities). The issues replyr::replyr_* claim to patch around have all been filed as issues on the appropriate R packages and are documented here (to confirm they are not phantoms).

Example: replyr::replyr_summary working on a database service (when base::summary does not).

d <- data.frame(x=c(1,2,2),y=c(3,5,NA),z=c(NA,'a','b'),
                stringsAsFactors = FALSE)
if (requireNamespace("RSQLite")) {
  my_db <- dplyr::src_sqlite(":memory:", create = TRUE)
  dRemote <- replyr::replyr_copy_to(my_db,d,'d')
} else {
  dRemote <- d # local stand in when we can't make remote
}

summary(dRemote)
#>     Length Class          Mode
#> src 2      src_sqlite     list
#> ops 3      op_base_remote list

replyr::replyr_summary(dRemote)
#>   column index     class nrows nna nunique min max     mean        sd lexmin lexmax
#> 1      x     1   numeric     3   0       2   1   2 1.666667 0.5773503   <NA>   <NA>
#> 2      y     2   numeric     3   1       2   3   5 4.000000 1.4142136   <NA>   <NA>
#> 3      z     3 character     3   1       2  NA  NA       NA        NA      a      b

Data types, capabilities, and row-orders all vary a lot as we switch remote data services. But the point of replyr is to provide at least some convenient version of typical functions such as: summary, nrow, unique values, and filter rows by values in a set.

replyr Data services

This is a very new package with no guarantees or claims of fitness for purpose. Some implemented operations are going to be slow and expensive (part of why they are not exposed in dplyr itself).

We will probably only ever cover:

• Native data.frames (and tbl/tibble)
• RMySQL
• RPostgreSQL
• SQLite
• sparklyr (Spark 2 preferred)

Additional functions

Additional replyr functions include: replyr::replyr_filter and replyr::replyr_inTest. These are designed to subset data based on a columns values being in a given set. These allow selection of rows by testing membership in a set (very useful for partitioning data). Example below:

library('dplyr')

values <- c(2)
dRemote %>% replyr::replyr_filter('x',values)
#> Source:   query [?? x 3]
#> Database: sqlite 3.11.1 [:memory:]
#> 
#>       x     y     z
#>   <dbl> <dbl> <chr>
#> 1     2     5     a
#> 2     2    NA     b

Commentary

I would like this to become a bit of a “stone soup” project. If you have a neat function you want to add please contribute a pull request with your attribution and assignment of ownership to Win-Vector LLC (so Win-Vector LLC can control the code, which we are currently distributing under a GPL3 license) in the code comments.

There are a few (somewhat incompatible) goals for replyr:

• Providing missing convenience functions that work well over all common dplyr service providers. Examples include replyr_summary, replyr_filter, and replyr_nrow.
• Providing a basis for “row number free” data analysis. SQL back-ends don’t commonly supply row number indexing (or even deterministic order of rows), so a lot of tasks you could do in memory by adjoining columns have to be done through formal key-based joins.
• Providing emulations of functionality missing from non-favored service providers (such as windowing functions, quantile, sample_n, cumsum; missing from SQLite and RMySQL).
• Working around corner case issues, and some variations in semantics.
• Sheer bull-headedness in emulating operations that don’t quite fit into the pure dplyr formulation.

Good code should fill one important gap and work on a variety of dplyr back ends (you can test RMySQL, and RPostgreSQL using docker as mentioned here and here; sparklyr can be tried in local mode as described here). I am especially interested in clever “you wouldn’t thing this was efficiently possible, but” solutions (which give us an expanded grammar of useful operators), and replacing current hacks with more efficient general solutions. Targets of interest include sample_n (which isn’t currently implemented for tbl_sqlite), cumsum, and quantile (currently we have an expensive implementation of quantile based on binary search: replyr::replyr_quantile).

replyr services include:

• Moving data into or out of the remote data store (including adding optional row numbers), replyr_copy_to and replyr_copy_from.
• Basic summary info: replyr_nrow, replyr_dim, and replyr_summary.
• Random row sampling (like dplyr::sample_n, but working with more service providers). Some of this functionality is provided by replyr_filter and replyr_inTest.
• Emulating The Split-Apply-Combine Strategy, which is the purpose gapply, replyr_split, and replyr_bind_rows.
• Emulating tidyr gather/spread (or pivoting and anti-pivoting), which is the purpose of replyr_gather and replyr_spread (still under development).
• Patching around differences in dplyr services providers (and documenting the reasons for the patches).
• Making use of “parameterized names” much easier (that is: writing code does not know the name of the column it is expected to work over, but instead takes the column name from a user supplied variable).

Additional desired capabilities of interest include:

• cumsum or row numbering (interestingly enough if you have row numbering you can implement cumulative sum in log-n rounds using joins to implement pointer chasing/jumping ideas, but that is unlikely to be practical, lag is enough to generate next pointers, which can be boosted to row-numberings).
• Inserting random values (or even better random unique values) in a remote column. Most service providers have a pseudo-random source you can use.

Conclusion

replyr is package for speeding up reliable data manipulation using dplyr (especially on databases and Spark). It is also a good central place to collect patches and fixes needed to work around corner cases and semantic variations between versions of data sources (such as Spark 1.6.2 versions Spark 2.0.0).

Clean up

rm(list=ls())
gc()
#>           used (Mb) gc trigger (Mb) max used (Mb)
#> Ncells  854113 45.7    1442291 77.1  1442291 77.1
#> Vcells 1690958 13.0    3225786 24.7  3225771 24.7