Introduction to the R Language - Functions
The scoping rules for R are the main feature that make it di erent from the original S language The scoping rules determine how a value is associated with a free variable in a function R uses lexical scoping or static scoping A common alternative is dynamic scoping Related to the scoping rules is how R uses the search list to bind a value to
Writing R Functions - CMU Statistics
Programming in R is organized around functions You all know what a mathemat-ical function is, like logx or (z) or sin : it is a rule which takes some inputs and delivers a definite output A function in R, like a mathematical function, takes zero or more inputs, also called arguments, and returns an output The output is arrived
Apply functions with purrr : : CHEAT SHEET
purrr::map_lgl( x, f, Apply f element-wise to x, return a logical vector n_iris > transmute(n = map_lgl(data, is matrix)) purrr::map_int( x, f, Apply f
Package ‘futureapply’ - The Comprehensive R Archive Network
future apply future apply: Apply Function to Elements in Parallel using Futures Description The future apply packages provides parallel implementations of common "apply" functions pro-vided by base R The parallel processing is performed via the future ecosystem, which provides
list of some useful R functions - Columbia University
integrate() - adaptive quadrature over a nite or in nite interval 4 Plotting plot() - generic R object plotting par() - set or query graphical parameters
A ``Level-Zero Tutorial for Getting Started with R
a “level-zero" tutorial for getting started with r 2 This tutorial will use screenshots from the Mac version, but other than appearance, everything should be similar in Windows Opening R for the First Time When you open R, the main window that opens is the R console, shown in Figure 2 A second window, for a script editor, may also open
∫bf (x) ∫Af (x - ConsultGLP
Now, let’s see how we can use R language to plot a density function Define a vector x over the domain We can then apply the distribution’s density function to x and then plot the result The code sniper plots the standard normal distribution: > x plot(x,dnorm(x)) >
Package ‘xts’ - R
Apply a function to the data of an existing xts plot object and plot the result FUN should have arguments x or R for the data of the existing xts plot object to be passed to All other additional arguments for FUN are passed through Usage addPanel(FUN, main = "", on = NA, type = "l", col = NULL, lty = 1, lwd = 1, pch = 1, ) Arguments
Introduction to the eventstudies package in R
Introduction to the eventstudies package in R Ajay Shah Sargam Jain June 1, 2020 1 The standard event study in nance In this section, we look at using the ‘eventstudies’ package for the purpose of doing the standard event study using daily returns data in nancial economics This is a workhorse application of event studies
III17 The Lambert W Function - Princeton University
thereof Images of Wk(reiθ)for various k, r, and θare shown in figure 2 In contrast to more commonly encountered multi-branched functions, such as the inverse sine or cosine, the branches of Ware not linearly related However, by rephrasing things slightly, in terms of the unwinding number K(z):= z−ln(ez) 2πi and the related single
[PDF] néon configuration électronique
[PDF] ion carbone formule
[PDF] le territoire français des milieux aménagés des milieux ? ménager
[PDF] ion sulfite
[PDF] exercice enthalpie libre et potentiel chimique
[PDF] palme funeraire
[PDF] ion sodium nombre de charges positives du noyau
[PDF] exercice potentiel chimique
[PDF] td potentiel chimique
[PDF] composition de l'atome d'oxygène
[PDF] exercices corrigés sur les équilibres chimiques pdf
[PDF] humidité du sol calcul
[PDF] potentiel matriciel du sol définition
[PDF] teneur en eau sol
Package 'future.apply"
May 21, 2023
Version1.11.0
TitleApply Function to Elements in Parallel using FuturesDependsR (>= 3.2.0), future (>= 1.28.0)
Importsglobals (>= 0.16.1), parallel, utils
Suggestsdatasets, stats, tools, listenv (>= 0.8.0), R.rsp, markdownVignetteBuilderR.rsp
Description
Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sap- ply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. paral- lel on the local machine or distributed on a compute cluster. These future_*apply() func- tions come with the same pros and cons as the corresponding base-R *apply() func- tions but with the additional feature of being able to be processed via the future framework.LicenseGPL (>= 2)
LazyLoadTRUE
URLhttps://future.apply.futureverse.org,
RoxygenNote7.2.3
NeedsCompilationno
AuthorHenrik Bengtsson [aut, cre, cph],
R Core Team [cph, ctb]
MaintainerHenrik Bengtsson
RepositoryCRAN
Date/Publication2023-05-21 04:10:02 UTC
Rtopics documented:
future.apply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 future_apply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 12future.apply
future_by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 future_eapply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 future_Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Index17future.applyfuture.apply: Apply Function to Elements in Parallel using FuturesDescription Thefuture.applypackages provides parallel implementations of common "apply" functions pro- vided by baseR. The parallel processing is performed via thefutureecosystem, which provides a large number of parallel backends, e.g. on the local machine, a remote cluster, and a high- performance compute cluster.Details
Currently implemented functions are:
•future_apply(): a parallel version ofapply() •future_by(): a parallel version ofby() •future_eapply(): a parallel version ofeapply() •future_lapply(): a parallel version oflapply() •future_mapply(): a parallel version ofmapply() •future_sapply(): a parallel version ofsapply() •future_tapply(): a parallel version oftapply() •future_vapply(): a parallel version ofv apply() •future_Map(): a parallel version ofMap() •future_replicate(): a parallel version ofreplicate() •future_.mapply(): a parallel version of.mapply() Reproducibility is part of the core design, which means that perfect, parallel random number gener- ation (RNG) is supported regardless of the amount of chunking, type of load balancing, and future backend being used. Since thesefuture_*()functions have the same arguments as the corresponding baseRfunction,start using them is often as simple as renaming the function in the code. For example, after attaching
the package: library(future.apply) code such as: x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE)) y <- lapply(x, quantile, probs = 1:3/4) future.apply3 can be updated to: y <- future_lapply(x, quantile, probs = 1:3/4) The default settings in thefutureframework is to process codesequentially. To run the above in parallel on the local machine (on any operating system), use: plan(multisession) first. That"s it! To go back to sequential processing, useplan(sequential). If you have access to multiple ma- chines on your local network, use: plan(cluster, workers = c("n1", "n2", "n2", "n3")) This will set up four workers, one onn1andn3, and two onn2. If you have SSH access to some remote machines, use: plan(cluster, workers = c("m1.myserver.org", "m2.myserver.org)) See thefuturepackage andfuture::plan()for more examples. Thefuture.batchtoolspackage provides support for high-performance compute (HPC) cluster schedulers such as SGE, Slurm, and TORQUE / PBS. For example, •plan(batchtools_slurm): Process via a Slurm scheduler job queue. •plan(batchtools_torque): Process via a TORQUE / PBS scheduler job queue. This builds on top of the queuing framework that thebatchtoolspackage provides. For more details on backend configuration, please see thefuture.batchtoolsandbatchtoolspackages. These are just a few examples of parallel/distributed backend for the future ecosystem. For more alternatives, see the "Reverse dependencies" section on the future CRAN package pageAuthor(s)
HenrikBengtsson, exceptfortheimplementationsoffuture_apply(),future_Map(),future_replicate(), future_sapply(), andfuture_tapply(), which are adopted from the source code of the corre- sponding baseRfunctions, which are licensed under GPL (>= 2) with "The R Core Team" as the copyright holder. Because of these dependencies, the license of this package is GPL (>= 2).4future_applyfuture_applyApply Functions Over Array Margins via FuturesDescription
future_apply()implementsbase::apply()using future with perfect replication of results, re-gardless of future backend used. It returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix. Usage future_apply( X,MARGIN,
FUN, simplify = TRUE, future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_apply-%d"Arguments
Xan array, including a matrix.
MARGINA vector giving the subscripts which the function will be applied over. For example, for a matrix1indicates rows,2indicates columns,c(1, 2)indicates rows and columns. WhereXhas named dimnames, it can be a character vector selecting dimension names.FUNA function taking at least one argument.
simplifya logical indicating whether results should be simplified if possible. future.envirAnen vironmentpassed as ar gumentenvirtofuture::future()as-is. future.stdoutIfTRUE(default), then the standard output of the underlying futures is captured, and re-outputted as soon as possible. IfFALSE, any output is silenced (by sinking it to the null device as it is outputted). IfNA(not recommended), output isnot intercepted. future.conditions A character string of conditions classes to be captured and relayed. The default is the same as theconditionargument offuture::Future(). To not intercept conditions, useconditions = character(0L). Errors are always relayed. future_apply5 future.globalsA logical, a character vector, or a named list for controlling how globals are handled. For details, see below section. future.packages (optional) a character vector specifying packages to be attached in the R envi- ronment evaluating the future. future.seedA logical or an integer (of length one or seven), or a list oflength(X)with pre-generated random seeds. For details, see below section. future.scheduling Average number of futures ("chunks") per worker. If0.0, then a single future is used to process all elements ofX. If1.0orTRUE, then one future per worker is used. If2.0, then each worker will process two futures (if there are enough elements inX). IfInforFALSE, then one future per element ofXis used. Only used iffuture.chunk.sizeisNULL. future.chunk.size The average number of elements per future ("chunk"). IfInf, then all elements are processed in a single future. IfNULL, then argumentfuture.schedulingis used. future.labelIfacharacterstring, theneachfutureisassignedalabelsprintf(future.label, chunk_idx). IfTRUE,thenthesameasfuture.label = "future_lapply-%d".If FALSE, no labels are assigned.
...(optional) Additional arguments passed toFUN(), exceptfuture.*arguments, which are passed on tofuture_lapply()used internally. ValueReturns a vector or array or list of values obtained by applying a function to margins of an array or
matrix. Seebase::apply()for details.Author(s)
The implementations offuture_apply()is adopted from the source code of the corresponding baseRfunction, which is licensed under GPL (>= 2) with "The R Core Team" as the copyright holder.Examples
## apply()X <- matrix(c(1:4, 1, 6:8), nrow = 2L)
Y0 <- apply(X, MARGIN = 1L, FUN = table)
Y1 <- future_apply(X, MARGIN = 1L, FUN = table)
print(Y1) stopifnot(all.equal(Y1, Y0, check.attributes = FALSE)) ## FIXME Y0 <- apply(X, MARGIN = 1L, FUN = stats::quantile) Y1 <- future_apply(X, MARGIN = 1L, FUN = stats::quantile) print(Y1)6future_by
stopifnot(all.equal(Y1, Y0)) ## Parallel Random Number Generation ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identicalX <- matrix(c(1:4, 1, 6:8), nrow = 2L)
plan(multisession) set.seed(0xBEEF) Y1 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y1) plan(sequential) set.seed(0xBEEF) Y2 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y2)stopifnot(all.equal(Y1, Y2))future_byApply a Function to a Data Frame Split by Factors via FuturesDescription
Apply a Function to a Data Frame Split by Factors via Futures Usage future_by( data,INDICES,
FUN, simplify = TRUE, future.envir = parent.frame()Arguments
dataAnRobject, normally a data frame, possibly a matrix. INDICESA factor or a list of factors, each of lengthnrow(data). future_by7 FUNa function to be applied to (usually data-frame) subsets ofdata. simplifylogical: seebase::tapply . future.envirAnen vironmentpassed as ar gumentenvirtofuture::future()as-is. ...Additional arguments pass tofuture_lapply()and then toFUN().Details
Internally,datais grouped byINDICESinto a list ofdatasubset elements which is then processed byfuture_lapply(). When the groups differ significantly in size, the processing time may differ significantly between the groups. To correct for processing-time imbalances, adjust the amount of chunking via argumentsfuture.schedulingandfuture.chunk.size. ValueAn object of class "by", giving the results for each subset. This is always a list if simplify is false,
otherwise a list or array (see base::tapply ). See alsobase::by()for details.