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collapse is a C/C++ based package for data transformation and statistical computing in R. It's aims are:
It is made compatible with dplyr, data.table and the plm approach to panel data.
Key Features:
Advanced statistical programming: A full set of fast statistical functions supporting grouped and weighted computations on vectors, matrices and data frames. Fast and programmable grouping, ordering, unique values / rows, factor generation and interactions. Fast and flexible functions for data manipulation and data object conversions.
Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted, parallelized and fully customized data aggregation.
Advanced transformations: Fast (grouped) replacing and sweeping out of statistics, and (grouped, weighted) scaling / standardizing, between (averaging) and (quasi-)within (centering / demeaning) transformations, higher-dimensional centering (i.e. multiple fixed effects transformations), linear prediction and partialling-out.
Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated, quasi-, log-) differences and growth rates on (unordered) time series and panel data. Multivariate auto-, partial- and cross-correlation functions for panel data. Panel data to (ts-)array conversions.
List processing: (Recursive) list search / identification, extraction / subsetting, data-apply, and generalized row-binding / unlisting in 2D.
Advanced data exploration: Fast (grouped, weighted, panel-decomposed) summary statistics for complex multilevel / panel data.
collapse is mainly coded in C++ and built with Rcpp, but also uses C functions from data.table, lfe and stats. Effort has been expended to minimize the execution speed of R code employed.
# From the R console call
install.packages("collapse")collapse installs with a built-in structured documentation, implemented via a set of separate help pages. Calling help('collapse-documentation') from the R console brings up the the top-level documentation page, which provides an overview of the entire functionality of the package and links to all other documentation pages.
In addition, collapse provides 3 vignettes:
Introduction to collapse: Introduces all main features of the package in a structured way.
collapse and dplyr: Demonstrates the integration of collapse with dplyr / tidyverse workflows and associated performance improvements.
collapse and plm: Demonstrates the integration of collapse with the plm package and provides examples of fast and easy programming with panel data.
This provides a simple set of examples introducing some important features of collapse. It should be easy to follow for readers familiar with R.
library(collapse)
data("iris") # iris dataset in base R
v <- iris$Sepal.Length # Vector
d <- num_vars(iris) # Saving numeric variables (could also be a matrix, statistical functions are S3 generic)
g <- iris$Species # Grouping variable (could also be a list of variables)
## Advanced Statistical Programming -----------------------------------------------------------------------------
# Simple (column-wise) statistics...
fmedian(v) # Vector
fsd(qM(d)) # Matrix (qM is a faster as.matrix)
fmode(d) # data.frame
fmean(qM(d), drop = FALSE) # Still a matrix
fmax(d, drop = FALSE) # Still a data.frame
# Fast grouped and/or weighted statistics
wt <- abs(rnorm(fnrow(iris)))
fmedian(d, w = wt) # Simple weighted statistics
fnth(d, 0.75, g) # Grouped statistics (grouped third quartile)
fmedian(d, g, wt) # Groupwise-weighted statistics
fsd(v, g, wt) # Similarly for vectors
fmode(qM(d), g, wt, ties = "max") # Or matrices (grouped and weighted maximum mode) ...
# A fast set of data manipulation functions allows complex piped programming at high speeds
library(magrittr) # Pipe operators
iris %>% fgroup_by(Species) %>% fNdistinct # Grouped distinct value counts
iris %>% fgroup_by(Species) %>% fmedian(wt) # Weighted group medians
iris %>% add_vars(wt) %>% # Adding weight vector to dataset
fsubset(Sepal.Length < fmean(Sepal.Length), Species, Sepal.Width:wt) %>% # Fast selecting and subsetting
fgroup_by(Species) %>% # Grouping (efficiently creates a grouped tibble)
fvar(wt) %>% # Frequency-weighted group-variance, default (keep.w = TRUE)
roworder(sum.wt) # also saves group weights in a column called 'sum.wt'
# Can also use dplyr (but dplyr manipulation verbs are a lot slower)
library(dplyr)
iris %>% add_vars(wt) %>%
filter(Sepal.Length < fmean(Sepal.Length)) %>%
select(Species, Sepal.Width:wt) %>%
group_by(Species) %>%
fvar(wt) %>% arrange(sum.wt)
## Advanced Aggregation -----------------------------------------------------------------------------------------
collap(iris, Sepal.Length + Sepal.Width ~ Species, fmean) # Simple aggregation using the mean..
collap(iris, ~ Species, list(fmean, fmedian, fmode)) # Multiple functions applied to each column
add_vars(iris) <- wt # Adding weights, return in long format..
collap(iris, ~ Species, list(fmean, fmedian, fmode), w = ~ wt, return = "long")
# Generate some additional logical data
settransform(iris, AWMSL = Sepal.Length > fmedian(Sepal.Length, w = wt),
AWMSW = Sepal.Width > fmedian(Sepal.Width, w = wt))
# Multi-type data aggregation: catFUN applies to all categorical columns (here AMWSW)
collap(iris, ~ Species + AWMSL, list(fmean, fmedian, fmode),
catFUN = fmode, w = ~ wt, return = "long")
# Custom aggregation gives the greatest possible flexibility: directly mapping functions to columns
collap(iris, ~ Species + AWMSL,
custom = list(fmean = 2:3, fsd = 3:4, fmode = "AWMSL"), w = ~ wt,
wFUN = list(fsum, fmin, fmax), # Here also aggregating the weight vector with 3 different functions
keep.col.order = FALSE) # Column order not maintained -> grouping and weight variables first
# Can also use grouped tibble: weighted median for numeric, weighted mode for categorical columns
iris %>% fgroup_by(Species, AWMSL) %>% collapg(fmedian, fmode, w = wt)
## Advanced Transformations -------------------------------------------------------------------------------------
# All Fast Statistical Functions have a TRA argument, supporting 10 different replacing and sweeping operations
fmode(d, TRA = "replace") # Replacing values with the mode
fsd(v, TRA = "/") # dividing by the overall standard deviation (scaling)
fsum(d, TRA = "%") # Computing percentages
fsd(d, g, TRA = "/") # Grouped scaling
fmin(d, g, TRA = "-") # Setting the minimum value in each species to 0
ffirst(d, g, TRA = "%%") # Taking modulus of first value in each species
fmedian(d, g, wt, "-") # Groupwise centering by the weighted median
fnth(d, 0.95, g, wt, "%") # Expressing data in percentages of the weighted species-wise 95th percentile
fmode(d, g, wt, "replace", # Replacing data by the species-wise weighted minimum-mode
ties = "min")
# TRA() can also be called directly to replace or sweep with a matching set of computed statistics
TRA(v, sd(v), "/") # Same as fsd(v, TRA = "/")
TRA(d, fmedian(d, g, wt), "-", g) # Same as fmedian(d, g, wt, "-")
TRA(d, BY(d, g, quantile, 0.95), "%", g) # Same as fnth(d, 0.95, g, TRA = "%") (apart from quantile algorithm)
# For common uses, there are some faster and more advanced functions
fbetween(d, g) # Grouped averaging [same as fmean(d, g, TRA = "replace") but faster]
fwithin(d, g) # Grouped centering [same as fmean(d, g, TRA = "-") but faster]
fwithin(d, g, wt) # Grouped and weighted centering [same as fmean(d, g, wt, "-")]
fwithin(d, g, wt, theta = 0.76) # Quasi-centering i.e. d - theta*fbetween(d, g, wt)
fwithin(d, g, wt, mean = "overall.mean") # Preserving the overall weighted mean of the data
fscale(d) # Scaling and centering (default mean = 0, sd = 1)
fscale(d, mean = 5, sd = 3) # Custom scaling and centering
fscale(d, mean = FALSE, sd = 3) # Mean preserving scaling
fscale(d, g, wt) # Grouped and weighted scaling and centering
fscale(d, g, wt, mean = "overall.mean", # Setting group means to overall weighted mean,
sd = "within.sd") # and group sd's to fsd(fwithin(d, g, wt), w = wt)
get_vars(iris, 1:2) # Use get_vars for fast selecting data.frame columns, gv is shortcut
fHDbetween(gv(iris, 1:2), gv(iris, 3:5)) # Linear prediction with factors and continuous covariates
fHDwithin(gv(iris, 1:2), gv(iris, 3:5)) # Linear partialling out factors and continuous covariates
# This again opens up new possibilities for data manipulation...
iris %>%
ftransform(ASWMSL = Sepal.Length > fmedian(Sepal.Length, Species, wt, "replace")) %>%
fgroup_by(ASWMSL) %>% collapg(w = wt, keep.col.order = FALSE)
iris %>% fgroup_by(Species) %>% num_vars %>% fwithin(wt) # Weighted demeaning
## Time Series and Panel Series ---------------------------------------------------------------------------------
flag(AirPassengers, -1:3) # A sequence of lags and leads
EuStockMarkets %>% # A sequence of first and second seasonal differences
fdiff(0:1 * frequency(.), 1:2)
fdiff(EuStockMarkets, rho = 0.95) # Quasi-difference [x - rho*flag(x)]
fdiff(EuStockMarkets, log = TRUE) # Log-difference [log(x/flag(x))]
EuStockMarkets %>% fgrowth(c(1, frequency(.))) # Ordinary and seasonal growth rate
EuStockMarkets %>% fgrowth(logdiff = TRUE) # Log-difference growth rate [log(x/flag(x))*100]
# Creating panel data
pdata <- EuStockMarkets %>% list(`A` = ., `B` = .) %>%
unlist2d(idcols = "Id", row.names = "Time")
L(pdata, -1:3, ~Id, ~Time) # Sequence of fully identified panel-lags (L is operator for flag)
pdata %>% fgroup_by(Id) %>% flag(-1:3, Time) # Same thing..
# collapse supports pseries and pdata.frame's, provided by the plm package
pdata <- plm::pdata.frame(pdata, index = c("Id", "Time"))
L(pdata, -1:3) # Same as above, ...
psacf(pdata) # Multivariate panel-ACF
psmat(pdata) %>% plot # 3D-array of time series from panel data + plotting
HDW(pdata) # This projects out id and time fixed effects.. (HDW is operator for fHDwithin)
W(pdata, effect = "Id") # Only Id effects.. (W is operator for fwithin)
## List Processing ----------------------------------------------------------------------------------------------
# Some nested list of heterogenous data objects..
l <- list(a = qM(mtcars[1:8]), # Matrix
b = list(c = mtcars[4:11], # data.frame
d = list(e = mtcars[2:10],
f = fsd(mtcars)))) # Vector
ldepth(l) # List has 4 levels of nesting (considering that mtcars is a data.frame)
is.unlistable(l) # Can be unlisted
has_elem(l, "f") # Contains an element by the name of "f"
has_elem(l, is.matrix) # Contains a matrix
get_elem(l, "f") # Recursive extraction of elements..
get_elem(l, c("c","f"))
get_elem(l, c("c","f"), keep.tree = TRUE)
unlist2d(l, row.names = TRUE) # Intelligent recursive row-binding to data.frame
rapply2d(l, fmean) %>% unlist2d # Taking the mean of all elements and repeating
# Application: extracting and tidying results from (potentially nested) lists of model objects
list(mod1 = lm(mpg ~ carb, mtcars),
mod2 = lm(mpg ~ carb + hp, mtcars)) %>%
lapply(summary) %>%
get_elem("coef", regex = TRUE) %>% # Regular expression search and extraction
unlist2d(idcols = "Model", row.names = "Predictor")
## Summary Statistics -------------------------------------------------------------------------------------------
irisNA <- na_insert(iris, prop = 0.15) # Randmonly set 15% missing
fNobs(irisNA) # Observation count
pwNobs(irisNA) # Pairwise observation count
fNobs(irisNA, g) # Grouped observation count
fNdistinct(irisNA) # Same with distinct values... (default na.rm = TRUE skips NA's)
fNdistinct(irisNA, g)
descr(iris) # Detailed statistical description of data
varying(iris, ~ Species) # Show which variables vary within Species
varying(pdata) # Which are time-varying ?
qsu(iris, w = ~ wt) # Fast (one-pass) summary (with weights)
qsu(iris, ~ Species, w = ~ wt, higher = TRUE) # Grouped summary + higher moments
qsu(pdata, higher = TRUE) # Panel-data summary (between and within entities)
pwcor(num_vars(irisNA), N = TRUE, P = TRUE) # Pairwise correlations with p-value and observations
pwcor(W(pdata, keep.ids = FALSE), P = TRUE) # Within-correlations
Evaluated and more extensive sets of examples are provided on the package page (also accessible from R by calling example('collapse-package')), and further in the vignettes and documentation.
Some simple benchmarks against dplyr, data.table and plm are provided in this blog post and in the vignettes. In general:
For simple aggregations of large data (~ 10 mio. obs) the performance is comparable to data.table (e.g. see here and here)^[Huge aggregations with simple functions like mean or sum and meaningful parallel processing power are faster on data.table, whereas collapse is typically faster on 2-core machines / laptops.].
For more complex categorical or weighed aggregations and for transformations like grouped replacing and sweeping out statistics (data.table::':=' or dplyr::mutate operations), collapse is ~10x faster than data.table. Notable are very fast algorithms for (grouped) statistical mode and distinct value counts, variance, various weighted statistics, scaling, centering, panel-lags, differences and growth rates.
Due to its highly optimized R code, collapse is very efficient for programming. On smaller data a collapse implementation will execute within microseconds, whereas packages like dplyr or data.table will typically evaluate in the millisecond domain (up to ~100x slower).
This performance extends to grouped and weighted computations on vectors and matrices (collapse provides separate vector, matrix and data.frame methods written in C++, the performance in matrix computations is comparable to Rfast and matrixStats).
fselect/get_vars, fgroup_by, fsubset, ftransform/TRA, roworder, colorder, frename, funique, na_omit, etc.). See also this vignette.qDT efficiently converts various R objects to data.table, and several functions (mrtl, mctl, unlist2d, ...) have an option to return a data.table.install.packages('collapse')