collapse for tidyverse Users
Sebastian Krantz
2024-04-13
Source:vignettes/collapse_for_tidyverse_users.Rmd
collapse_for_tidyverse_users.Rmdcollapse is a C/C++ based package for data transformation and statistical computing in R that aims to enable greater performance and statistical complexity in data manipulation tasks and offers a stable, class-agnostic, and lightweight API. It is part of the core fastverse, a suite of lightweight packages with similar objectives.
The tidyverse set of packages provides a rich, expressive, and consistent syntax for data manipulation in R centering on the tibble object and tidy data principles (each observation is a row, each variable is a column).
collapse fully supports the tibble object and provides many tidyverse-like functions for data manipulation. It can thus be used to write tidyverse-like data manipulation code that, thanks to low-level vectorization of many statistical operations and optimized R code, typically runs much faster than native tidyverse code (in addition to being much more lightweight in dependencies).
Its aim is not to create a faster tidyverse, i.e., it does not implements all aspects of the rich tidyverse grammar or changes to it1, and also takes inspiration from other leading data manipulation libraries to serve broad aims of performance, parsimony, complexity, and robustness in data manipulation for R.
Namespace and Global Options
collapse data manipulation functions familiar to
tidyverse users include fselect,
fgroup_by, fsummarise, fmutate,
across, frename, and fcount.
Other functions like fsubset, ftransform, and
get_vars are inspired by base R, while again other
functions like join, pivot,
roworder, colorder, rowbind, etc.
are inspired by other data manipulation libraries such das
data.table and polars.
By virtue of the f- prefixes, the collapse namespace has no conflicts with the tidyverse, and these functions can easily be substituted in a tidyverse workflow.
R users willing to replace the tidyverse have the additional
option to mask functions and eliminate the prefixes with
set_collapse. For example
library(collapse)
set_collapse(mask = "manip") # version >= 2.0.0 makes available functions select, group_by,
summarise, mutate, rename,
count, subset, and transform in
the collapse namespace and detaches and re-attaches the
package, such that the following code is executed by
collapse:
mtcars |>
subset(mpg > 11) |>
group_by(cyl, vs, am) |>
summarise(across(c(mpg, carb, hp), mean),
qsec_wt = weighted.mean(qsec, wt))
# cyl vs am mpg carb hp qsec_wt
# 1 4 0 1 26.00000 2.000000 91.00000 16.70000
# 2 4 1 0 22.90000 1.666667 84.66667 21.04028
# 3 4 1 1 28.37143 1.428571 80.57143 18.75509
# 4 6 0 1 20.56667 4.666667 131.66667 16.33306
# 5 6 1 0 19.12500 2.500000 115.25000 19.21275
# 6 8 0 0 15.98000 2.900000 191.00000 17.01239
# 7 8 0 1 15.40000 6.000000 299.50000 14.55297Note that the correct documentation still needs to be called
with prefixes, i.e., ?fsubset. See
?set_collapse for further options to the package, which
also includes optimization options such as nthreads,
na.rm, sort, and stable.algo.
Note also that if you use collapse’s namespace
masking, you can use fastverse::fastverse_conflicts() to
check for namespace conflicts with other packages.
Using the Fast Statistical Functions
A key feature of collapse is that it not only provides functions for data manipulation, but also a full set of statistical functions and algorithms to speed up statistical calculations and perform more complex statistical operations (e.g. involving weights or time series data).
Notably among these, the Fast Statistical Functions is a consistent set of S3-generic statistical functions providing fully vectorized statistical operations in R.
Specifically, operations such as calculating the mean via the S3
generic fmean() function are vectorized across columns and
groups and may also involve weights or transformations of the original
data:
fmean(mtcars$mpg) # Vector
# [1] 20.09062
fmean(EuStockMarkets) # Matrix
# DAX SMI CAC FTSE
# 2530.657 3376.224 2227.828 3565.643
fmean(mtcars) # Data Frame
# mpg cyl disp hp drat wt qsec vs am
# 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750 0.437500 0.406250
# gear carb
# 3.687500 2.812500
fmean(mtcars$mpg, w = mtcars$wt) # Weighted mean
# [1] 18.54993
fmean(mtcars$mpg, g = mtcars$cyl) # Grouped mean
# 4 6 8
# 26.66364 19.74286 15.10000
fmean(mtcars$mpg, g = mtcars$cyl, w = mtcars$wt) # Weighted group mean
# 4 6 8
# 25.93504 19.64578 14.80643
fmean(mtcars[5:10], g = mtcars$cyl, w = mtcars$wt) # Of data frame
# drat wt qsec vs am gear
# 4 4.031264 2.414750 19.38044 0.9148868 0.6498031 4.047250
# 6 3.569170 3.152060 18.12198 0.6212191 0.3787809 3.821036
# 8 3.205658 4.133116 16.88529 0.0000000 0.1203808 3.240762
fmean(mtcars$mpg, g = mtcars$cyl, w = mtcars$wt, TRA = "fill") # Replace data by weighted group mean
# [1] 19.64578 19.64578 25.93504 19.64578 14.80643 19.64578 14.80643 25.93504 25.93504 19.64578
# [11] 19.64578 14.80643 14.80643 14.80643 14.80643 14.80643 14.80643 25.93504 25.93504 25.93504
# [21] 25.93504 14.80643 14.80643 14.80643 14.80643 25.93504 25.93504 25.93504 14.80643 19.64578
# [31] 14.80643 25.93504
# etc...The data manipulation functions of collapse are integrated with these Fast Statistical Functions to enable vectorized statistical operations. For example, the following code
mtcars |>
subset(mpg > 11) |>
group_by(cyl, vs, am) |>
summarise(across(c(mpg, carb, hp), fmean),
qsec_wt = fmean(qsec, wt))
# cyl vs am mpg carb hp qsec_wt
# 1 4 0 1 26.00000 2.000000 91.00000 16.70000
# 2 4 1 0 22.90000 1.666667 84.66667 21.04028
# 3 4 1 1 28.37143 1.428571 80.57143 18.75509
# 4 6 0 1 20.56667 4.666667 131.66667 16.33306
# 5 6 1 0 19.12500 2.500000 115.25000 19.21275
# 6 8 0 0 15.98000 2.900000 191.00000 17.01239
# 7 8 0 1 15.40000 6.000000 299.50000 14.55297gives exactly the same result as above, but the execution is much
faster (especially on larger data), because with Fast Statistical
Functions, the data does not need to be split by groups, and there
is no need to call lapply() inside the
across() statement: fmean.data.frame() is
simply applied to a subset of the data containing columns
mpg, carb and hp.
The Fast Statistical Functions also have a method for
grouped data, so if we did not want to calculate the weighted mean of
qsec, the code would simplify as follows:
mtcars |>
subset(mpg > 11) |>
group_by(cyl, vs, am) |>
select(mpg, carb, hp) |>
fmean()
# cyl vs am mpg carb hp
# 1 4 0 1 26.00000 2.000000 91.00000
# 2 4 1 0 22.90000 1.666667 84.66667
# 3 4 1 1 28.37143 1.428571 80.57143
# 4 6 0 1 20.56667 4.666667 131.66667
# 5 6 1 0 19.12500 2.500000 115.25000
# 6 8 0 0 15.98000 2.900000 191.00000
# 7 8 0 1 15.40000 6.000000 299.50000Note that all functions in collapse, including the Fast
Statistical Functions, have the default na.rm = TRUE,
i.e., missing values are skipped in calculations. This can be changed
using set_collapse(na.rm = FALSE) to give behavior more
consistent with base R.
Another thing to be aware of when using Fast Statistical Functions inside data manipulation functions is that they toggle vectorized execution wherever they are used. E.g.
mtcars |> group_by(cyl) |> summarise(mpg = fmean(mpg) + min(qsec)) # Vectorized
# cyl mpg
# 1 4 41.16364
# 2 6 34.24286
# 3 8 29.60000calculates a grouped mean of mpg but adds the overall
minimum of qsec to the result, whereas
mtcars |> group_by(cyl) |> summarise(mpg = fmean(mpg) + fmin(qsec)) # Vectorized
# cyl mpg
# 1 4 43.36364
# 2 6 35.24286
# 3 8 29.60000
mtcars |> group_by(cyl) |> summarise(mpg = mean(mpg) + min(qsec)) # Not vectorized
# cyl mpg
# 1 4 43.36364
# 2 6 35.24286
# 3 8 29.60000both give the mean + the minimum within each group, but calculated in
different ways: the former is equivalent to
fmean(mpg, g = cyl) / fmin(qsec, g = cyl), whereas the
latter is equal to
sapply(gsplit(mpg, cyl), function(x) mean(x) + min(x)).
See ?fsummarise and ?fmutate for more
detailed examples. This eager vectorization approach is
intentional as it allows users to vectorize complex expressions and fall
back to base R if this is not desired.
To take full advantage of collapse, it is highly recommended
to use the Fast Statistical Functions as much as possible. You
can also set set_collapse(mask = "all") to replace
statistical functions in base R like sum and
mean with the collapse versions (toggling vectorized
execution in all cases), but this may affect other parts of your code2.
Writing Efficient Code
It is also performance-critical to correctly sequence operations and
limit excess computations. tidyverse code is often inefficient
simply because the tidyverse allows you to do everything. For
example,
mtcars |> group_by(cyl) |> filter(mpg > 13) |> arrange(mpg)
is permissible but inefficient code as it filters and reorders grouped
data, requiring modifications to both the data frame and the attached
grouping object. collapse does not allow calls to
fsubset() on grouped data, and messages about it in
roworder(), encouraging you to write more efficient
code.
The above example can also be optimized because we are subsetting the whole frame and then doing computations on a subset of columns. It would be more efficient to select all required columns during the subset operation:
mtcars |>
subset(mpg > 11, cyl, vs, am, mpg, carb, hp, qsec, wt) |>
group_by(cyl, vs, am) |>
summarise(across(c(mpg, carb, hp), fmean),
qsec_wt = fmean(qsec, wt))
# cyl vs am mpg carb hp qsec_wt
# 1 4 0 1 26.00000 2.000000 91.00000 16.70000
# 2 4 1 0 22.90000 1.666667 84.66667 21.04028
# 3 4 1 1 28.37143 1.428571 80.57143 18.75509
# 4 6 0 1 20.56667 4.666667 131.66667 16.33306
# 5 6 1 0 19.12500 2.500000 115.25000 19.21275
# 6 8 0 0 15.98000 2.900000 191.00000 17.01239
# 7 8 0 1 15.40000 6.000000 299.50000 14.55297Without the weighted mean of qsec, this would simplify
to
mtcars |>
subset(mpg > 11, cyl, vs, am, mpg, carb, hp) |>
group_by(cyl, vs, am) |>
fmean()
# cyl vs am mpg carb hp
# 1 4 0 1 26.00000 2.000000 91.00000
# 2 4 1 0 22.90000 1.666667 84.66667
# 3 4 1 1 28.37143 1.428571 80.57143
# 4 6 0 1 20.56667 4.666667 131.66667
# 5 6 1 0 19.12500 2.500000 115.25000
# 6 8 0 0 15.98000 2.900000 191.00000
# 7 8 0 1 15.40000 6.000000 299.50000Finally, we could set the following options to toggle unsorted grouping, no missing value skipping, and multithreading across the three columns for more efficient execution.
mtcars |>
subset(mpg > 11, cyl, vs, am, mpg, carb, hp) |>
group_by(cyl, vs, am, sort = FALSE) |>
fmean(nthreads = 3, na.rm = FALSE)
# cyl vs am mpg carb hp
# 1 6 0 1 20.56667 4.666667 131.66667
# 2 4 1 1 28.37143 1.428571 80.57143
# 3 6 1 0 19.12500 2.500000 115.25000
# 4 8 0 0 15.98000 2.900000 191.00000
# 5 4 1 0 22.90000 1.666667 84.66667
# 6 4 0 1 26.00000 2.000000 91.00000
# 7 8 0 1 15.40000 6.000000 299.50000Setting these options globally using
set_collapse(sort = FALSE, nthreads = 3, na.rm = FALSE)
avoids the need to set them repeatedly.
Conclusion
collapse enhances R both statistically and computationally and is a good option for tidyverse users searching for more efficient and lightweight solutions to data manipulation and statistical computing problems in R. For more information, I recommend starting with the short vignette on Documentation Resources.
R users willing to write efficient/lightweight code and completely replace the tidyverse in their workflow are also encouraged to closely examine the fastverse suite of packages. collapse alone may not always suffice, but 99% of tidyverse code can be replaced with an efficient and lightweight fastverse solution.