replyr is post-maintenance, we are no longer bug-fixing or updating this package. It has been pointless to track shifting dplyr/dbplyr/rlang APIs and data structures post dplyr 0.5. Most of what it does is now done better in one of our newer non-monolithic packages:

• Programming and meta-programming tools: wrapr.
• Big data data manipulation: rquery and cdata.
• Adapting to standard evaluation interfaces: seplyr.

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This document describes replyr, an R package available from Github and CRAN.

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 to make code perform similarly on local or remote (big) data.

Note: replyr is meant only for “tame data frames” that is data frames with non-duplicate column names that are also valid simple (without quotes) R variables names and columns that are R simple vector types (numbers, strings, and such).

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. The rule of thumb is: try dplyr first, and if that does not work check if replyr has researched a work-around.

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

Primary replyr services include:

• Join Controller
• Join Planner
• replyr::replyr_split
• replyr::replyr_bind_rows
• replyr::gapply
• replyr::replyr_summary
• replyr::replyr_apply_f_mapped
• wrapr::let

wrapr::let

wrapr::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, .dots=stats::setNames, or rlang/tidyeval).

Example:

library('dplyr')

# nice parametric function we write
ComputeRatioOfColumns <- function(d,
                                  NumeratorColumnName,
                                  DenominatorColumnName,
                                  ResultColumnName) {
  wrapr::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

wrapr::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.

replyr::replyr_apply_f_mapped

wrapr::let was only the secondary proposal in the original 2016 “Parametric variable names” article. What we really wanted was a stack of view so the data pretended to have names that matched the code (i.e., re-mapping the data, not the code).

With a bit of thought we can achieve this if we associate the data re-mapping with a function environment instead of with the data. So a re-mapping is active as long as a given controlling function is in control. In our case that function is replyr::replyr_apply_f_mapped() and works as follows:

Suppose the operation we wish to use is a rank-reducing function that has been supplied as function from somewhere else that we do not have control of (such as a package). The function could be simple such as the following, but we are going to assume we want to use it without alteration (including the without the small alteration of introducing wrapr::let()).

# an external function with hard-coded column names
DecreaseRankColumnByOne <- function(d) {
  d$RankColumn <- d$RankColumn - 1
  d
}

To apply this function to d (which doesn’t have the expected column names!) we use replyr::replyr_apply_f_mapped() to create a new parametrized adapter as follows:

# our data
d <- data.frame(Sepal_Length = c(5.8,5.7),
                Sepal_Width = c(4.0,4.4),
                Species = 'setosa',
                rank = c(1,2))

# a wrapper to introduce parameters
DecreaseRankColumnByOneNamed <- function(d, ColName) {
  replyr::replyr_apply_f_mapped(d, 
                                f = DecreaseRankColumnByOne, 
                                nmap = c(RankColumn = ColName),
                                restrictMapIn = FALSE, 
                                restrictMapOut = FALSE)
}

# use
dF <- DecreaseRankColumnByOneNamed(d, 'rank')
print(dF)
 #    Sepal_Length Sepal_Width Species rank
 #  1          5.8         4.0  setosa    0
 #  2          5.7         4.4  setosa    1

replyr::replyr_apply_f_mapped() renames the columns to the names expected by DecreaseRankColumnByOne (the mapping specified in nmap), applies DecreaseRankColumnByOne, and then inverts the mapping before returning the value.

replyr::replyr_split

replyr::replyr_split and replyr::replyr_bind_rows work over many remote data types including Spark. This allows code like the following:

suppressPackageStartupMessages(library("dplyr"))
library("replyr")
sc <- sparklyr::spark_connect(version='2.0.2', 
                              master = "local")
                              
diris <- copy_to(sc, iris, 'diris')

f2 <- . %>% 
  arrange(Sepal_Length, Sepal_Width, Petal_Length, Petal_Width) %>%
  head(2)

diris %>% 
  replyr_split('Species') %>%
  lapply(f2) %>%
  replyr_bind_rows()

## Source:   query [6 x 5]
## Database: spark connection master=local[4] app=sparklyr local=TRUE
## 
## # A tibble: 6 x 5
##      Species Sepal_Length Sepal_Width Petal_Length Petal_Width
##        <chr>        <dbl>       <dbl>        <dbl>       <dbl>
## 1 versicolor          5.0         2.0          3.5         1.0
## 2 versicolor          4.9         2.4          3.3         1.0
## 3     setosa          4.3         3.0          1.1         0.1
## 4     setosa          4.4         2.9          1.4         0.2
## 5  virginica          4.9         2.5          4.5         1.7
## 6  virginica          5.6         2.8          4.9         2.0

sparklyr::spark_disconnect(sc)

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)
 #    group order rank
 #  1     1   0.2    1
 #  2     1   0.1    2
 #  3     2   0.5    1
 #  4     2   0.4    2
 #  5     2   0.3    3

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 wrapr::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=rep(c(1,2,2), 5),
                y=c(3,5,NA),
                z=c(NA,'a','b'),
                stringsAsFactors = FALSE)
my_db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
RSQLite::initExtension(my_db) # filed as dplyr issue https://github.com/tidyverse/dplyr/issues/3150
dRemote <- replyr::replyr_copy_to(my_db,d,'d')

summary(dRemote)
 #      Length Class                Mode
 #  src 2      src_SQLiteConnection list
 #  ops 2      op_base_remote       list
glimpse(dRemote)
 #  Rows: ??
 #  Columns: 3
 #  Database: sqlite 3.33.0 [:memory:]
 #  $ x <dbl> 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2
 #  $ y <dbl> 3, 5, NA, 3, 5, NA, 3, 5, NA, 3, 5, NA, 3, 5, NA
 #  $ z <chr> NA, "a", "b", NA, "a", "b", NA, "a", "b", NA, "a", "b", NA, "a", "b"

replyr::replyr_summary(dRemote)
 #    column index     class nrows nna nunique min max     mean       sd lexmin lexmax
 #  1      x     1   numeric    15   0      NA   1   2 1.666667 0.487950   <NA>   <NA>
 #  2      y     2   numeric    15   5      NA   3   5 4.000000 1.054093   <NA>   <NA>
 #  3      z     3 character    15   5      NA  NA  NA       NA       NA      a      b
cdata::qlook(my_db, 'd')
 #  table `d` SQLiteConnection 
 #   nrow: 15 
 #   NOTE: "obs" below is count of sample, not number of rows of data.
 #  'data.frame':   10 obs. of  3 variables:
 #   $ x: num  1 2 2 1 2 2 1 2 2 1
 #   $ y: num  3 5 NA 3 5 NA 3 5 NA 3
 #   $ z: chr  NA "a" "b" NA ...

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)
• sparklyr (Spark 2.0.0 or greater)
• RPostgreSQL
• SQLite
• RMySQL (limited support in some cases)

Additional functions

Additional replyr functions include:

• replyr::replyr_filter
• 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)
 #  Warning: `as.tbl()` is deprecated as of dplyr 1.0.0.
 #  Please use `tibble::as_tibble()` instead.
 #  This warning is displayed once every 8 hours.
 #  Call `lifecycle::last_warnings()` to see where this warning was generated.
 #  # Source:   table<replyr_filter_70965071119671872805_0000000001> [?? x 3]
 #  # Database: sqlite 3.33.0 [:memory:]
 #         x     y z    
 #     <dbl> <dbl> <chr>
 #   1     2     5 a    
 #   2     2    NA b    
 #   3     2     5 a    
 #   4     2    NA b    
 #   5     2     5 a    
 #   6     2    NA b    
 #   7     2     5 a    
 #   8     2    NA b    
 #   9     2     5 a    
 #  10     2    NA b

Commentary

There are a few 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).
• 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.

Clean up

rm(list=ls())
gc()
 #            used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
 #  Ncells 1026656 54.9    2076977  111         NA  1383707 73.9
 #  Vcells 1925172 14.7    8388608   64      49152  2686998 20.6

Note

Note: replyr is targeted at data with “tame column names” (column names that are valid both in databases, and as R unquoted variable names) and basic types (column values that are simple R types such as character, numeric, logical, and so on).

Also replyr tries to be a “source agnostic” package, meaning it minimizes the number of places it checks for data source and uses specialized code, this can mean some operations are slow. For example replyr does not (yet) use sparklyr::sdf_pivot().