Function reference • collapse

Fast Statistical Functions Fast (grouped and weighted) statistical functions for vector, matrix, data frame and grouped tibble (class ‘grouped_df’, dplyr compatible).
`collapse-package`	Advanced and Fast Data Transformation
`fast-statistical-functions`	Fast (Grouped, Weighted) Statistical Functions for Matrix-Like Objects
`fmean()`	Fast (Grouped, Weighted) Mean for Matrix-Like Objects
`fnth()` `fmedian()`	Fast (Grouped, Weighted) N'th Element/Quantile for Matrix-Like Objects
`fmode()`	Fast (Grouped, Weighted) Statistical Mode for Matrix-Like Objects
`fsum()`	Fast (Grouped, Weighted) Sum for Matrix-Like Objects
`fprod()`	Fast (Grouped, Weighted) Product for Matrix-Like Objects
`fvar()` `fsd()`	Fast (Grouped, Weighted) Variance and Standard Deviation for Matrix-Like Objects
`fmax()` `fmin()`	Fast (Grouped) Maxima and Minima for Matrix-Like Objects
`ffirst()` `flast()`	Fast (Grouped) First and Last Value for Matrix-Like Objects
`fnobs()`	Fast (Grouped) Observation Count for Matrix-Like Objects
`fndistinct()`	Fast (Grouped) Distinct Value Count for Matrix-Like Objects
Fast Grouping and Ordering Fast (ordered) groupings from vectors, data.frames, lists. Fast ordering, unique values / rows and counts, factor generation and interactions, run-length type grouping and grouping of time-sequences.
`fast-grouping-ordering`	Fast Grouping and Ordering
`GRP()` `is_GRP()` `length(<GRP>)` `GRPN()` `GRPid()` `GRPnames()` `as_factor_GRP()` `gsplit()` `greorder()` `fgroup_by()` `fgroup_vars()` `fungroup()` `print(<GRP>)` `plot(<GRP>)`	Fast Grouping / collapse Grouping Objects
`radixorder()` `radixorderv()`	Fast Radix-Based Ordering
`group()`	Fast Hash-Based Grouping
`funique()` `fnunique()fduplicated()any_duplicated()`	Fast Unique Elements / Rows
`fcount()` `fcountv()`	Efficiently Count Observations by Group
`qF()` `qG()` `is_qG()` `as_factor_qG()` `finteraction()`	Fast Factor Generation, Interactions and Vector Grouping
`fdroplevels()`	Fast Removal of Unused Factor Levels
`groupid()`	Generate Run-Length Type Group-Id
`seqid()`	Generate Group-Id from Integer Sequences
`timeid()`	Generate Integer-Id From Time/Date Sequences
Fast Data Manipulation Fast and flexible select, replace, add, subset, transform, sort / reorder and rename / relabel data / data frame columns.
`fast-data-manipulation`	Fast Data Manipulation
`fselect()` `fselect<-`() `get_vars()` `get_vars<-`() `add_vars()` `add_vars<-`() `num_vars()` `num_vars<-`() `cat_vars()` `cat_vars<-`() `char_vars()` `char_vars<-`() `fact_vars()` `fact_vars<-`() `logi_vars()` `logi_vars<-`() `date_vars()` `date_vars<-`()	Fast Select, Replace or Add Data Frame Columns
`fsubset()` `ss()`	Fast Subsetting Matrix-Like Objects
`fsummarise()` `fsummarize()`	Fast Summarise
`ftransform()` `ftransformv()` `settransform()` `settransformv()` `ftransform<-`() `fcompute()` `fcomputev()` `fmutate()`	Fast Transform and Compute Columns on a Data Frame
`across()`	Apply Functions Across Multiple Columns
`roworder()` `roworderv()`	Fast Reordering of Data Frame Rows
`colorder()` `colorderv()`	Fast Reordering of Data Frame Columns
`frename()` `setrename()` `relabel()` `setrelabel()`	Fast Renaming and Relabelling Objects
Quick Data Conversion Quick conversions between data.frame’s, data.table’s, tibbles, matrices, arrays, lists, vectors and factors - fast and flexible, without method dispatch or extensive checks.
`qDF()` `qDT()` `qTBL()` `qM()` `mctl()` `mrtl()` `as_numeric_factor()` `as_character_factor()`	Quick Data Conversion
`qF()` `qG()` `is_qG()` `as_factor_qG()` `finteraction()`	Fast Factor Generation, Interactions and Vector Grouping
Advanced Data Aggregation Fast and easy multi-data-type, multi-function, weighted and parallelized data aggregation.
`collap()` `collapv()` `collapg()`	Advanced Data Aggregation
Data Transformations Fast row / column arithmetic, efficient row / column apply to data objects and Split-Apply-Combine computing. Fast (grouped, weighted) replacing and sweeping out of statistics, scaling / standardizing, (quasi-)centering, higher-dimensional centering, linear prediction / partialling-out.
`data-transformations`	Data Transformations
`%rr%` `%r+%` `%r-%` `%r%` `%r/%` `%cr%` `%c+%` `%c-%` `%c%` `%c/%`	Fast Row/Column Arithmetic for Matrix-Like Objects
`dapply()`	Data Apply
`BY()`	Split-Apply-Combine Computing
`TRA()` `setTRA()`	Transform Data by (Grouped) Replacing or Sweeping out Statistics
`fscale()` `STD()`	Fast (Grouped, Weighted) Scaling and Centering of Matrix-like Objects
`fbetween()` `fwithin()` `B()` `W()`	Fast Between (Averaging) and (Quasi-)Within (Centering) Transformations
`fhdbetween()` `fhdwithin()` `HDB()` `HDW()`	Higher-Dimensional Centering and Linear Prediction
Linear Models Fast (weighted) linear model fitting. A fast F-test for high-dimensional linear models.
`flm()`	Fast (Weighted) Linear Model Fitting
`fFtest()`	Fast (Weighted) F-test for Linear Models (with Factors)
Time Series and Panel Series Fast and flexible indexed time series and panel data classes. Fast (sequences of) lags / leads, and (lagged / leaded, iterated, quasi-, log-) differences and (compounded) growth rates on (irregular) time series and panel data. Auto-, partial- and cross-correlation functions for panel data. Panel data to (ts-)array conversion.
`time-series-panel-series`	Time Series and Panel Series
`findex_by()` `findex()` `unindex()` `reindex()` `is_irregular()` `to_plm()` `print(<index_df>)`	Fast Indexed Time Series and Panels
`timeid()`	Generate Integer-Id From Time/Date Sequences
`flag()` `L()`	Fast Lags and Leads for Time Series and Panel Data
`fdiff()` `D()` `Dlog()`	Fast (Quasi-, Log-) Differences for Time Series and Panel Data
`fgrowth()` `G()`	Fast Growth Rates for Time Series and Panel Data
`fcumsum()`	Fast (Grouped, Ordered) Cumulative Sum for Matrix-Like Objects
`psacf()` `pspacf()` `psccf()`	Auto- and Cross- Covariance and Correlation Function Estimation for Panel Series
`psmat()` `plot(<psmat>)`	Matrix / Array from Panel Series
List Processing Recursive list search, splitting, extraction/subsetting, apply, and generalized row-binding / unlisting to data frame.
`list-processing`	List Processing
`is_unlistable()`	Unlistable Lists
`ldepth()`	Determine the Depth / Level of Nesting of a List
`atomic_elem()` `atomic_elem<-`() `list_elem()` `list_elem<-`() `reg_elem()` `irreg_elem()` `get_elem()` `has_elem()`	Find and Extract / Subset List Elements
`rsplit()`	Fast (Recursive) Splitting
`t_list()`	Efficient List Transpose
`rapply2d()`	Recursively Apply a Function to a List of Data Objects
`unlist2d()`	Recursive Row-Binding / Unlisting in 2D - to Data Frame
Summary Statistics Fast (grouped, weighted, panel-decomposed) summary statistics and descriptive tools.
`summary-statistics`	Summary Statistics
`qsu()` `as.data.frame(<qsu>)` `print(<qsu>)`	Fast (Grouped, Weighted) Summary Statistics for Cross-Sectional and Panel Data
`qtab()` `qtable()`	Fast (Weighted) Cross Tabulation
`descr()` `as.data.frame(<descr>)` `print(<descr>)`	Detailed Statistical Description of Data Frame
`pwcor()` `pwcov()` `pwnobs()` `print(<pwcor>)` `print(<pwcov>)`	(Pairwise, Weighted) Correlations, Covariances and Observation Counts
`varying()`	Fast Check of Variation in Data
Other Statistical Fast euclidean distance computations, (weighted) sample quantiles, and range of vector.
`fdist()`	Fast and Flexible Distance Computations
`fquantile()` `.quantile()` `frange()` `.range()`	Fast (Weighted) Sample Quantiles and Range
Recode and Replace Values Efficiently recode and replace values in matrix-like objects.
`recode_num()` `recode_char()` `replace_NA()` `replace_Inf()` `replace_outliers()`	Recode and Replace Values in Matrix-Like Objects
`pad()`	Pad Matrix-Like Objects with a Value
(Memory) Efficient Programming Functions to make R programming more efficient.
`anyv()` `allv()` `allNA()` `whichv()` `whichNA()` `%==%` `%!=%` `alloc()` `copyv()` `setv()` `setop()` `%+=%` `%-=%` `%*=%` `%/=%` `na_rm()` `na_omit()` `na_insert()` `missing_cases()` `vlengths()` `vtypes()` `vgcd()` `fnlevels()` `fnrow()` `fncol()` `fdim()` `seq_row()` `seq_col()` `cinv()`	Small Functions to Make R Programming More Efficient
Small (Helper) Functions Convenience functions to perform multiple-assignment, nonstandard concatenation, and deal with variable names, labels, other attributes, matching and object checking, and help with metaprogramming.
`.c()` `%=%` `massign()` `vlabels()` `vlabels<-`() `setLabels()` `vclasses()` `namlab()` `add_stub()` `rm_stub()` `%!in%` `ckmatch()` `all_identical()` `all_obj_equal()` `all_funs()` `setRownames()` `setColnames()` `setDimnames()` `unattrib()` `setAttrib()` `setattrib()` `copyAttrib()` `copyMostAttrib()` `is_categorical()` `is_date()`	Small (Helper) Functions
Data Groningen Growth and Development Centre 10-Sector Database and World Bank World Development dataset.
`GGDC10S`	Groningen Growth and Development Centre 10-Sector Database
`wlddev`	World Development Dataset
Package Options Global options affecting package operation.
`collapse-options` `set_collapse()get_collapse()`

Documentation & Overview

Fast Statistical Functions

Fast Grouping and Ordering

Fast Data Manipulation

Quick Data Conversion

Advanced Data Aggregation

Data Transformations

Linear Models

Time Series and Panel Series

List Processing

Summary Statistics

Other Statistical

Recode and Replace Values

(Memory) Efficient Programming

Small (Helper) Functions

Data

Package Options