Function Reference • collapse

collapse-package collapse: Advanced and Fast Data Transformation

collapse-documentation: Collapse Documentation & Overview

Fast Statistical Functions

Fast (grouped and weighted) statistical functions for vector, matrix, data frame and grouped tibble (class ‘grouped_df’, dplyr compatible).

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, matching, 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

fmatch() ckmatch() `%!in%` `%iin%` `%!iin%`: Fast Matching

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, rename/relabel, bind, join and pivot/reshape 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

fmutate() ftransform() ftransformv() settransform() settransformv() `ftransform<-`() fcompute() fcomputev(): 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

rowbind(): Row-Bind Lists / Data Frame-Like Objects

join(): Fast Table Joins

pivot(): Fast and Easy Data Reshaping

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, (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() frange(): 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_locf() na_focb() na_omit() na_insert() missing_cases() vlengths() vtypes() vgcd() fnlevels() fnrow() fncol() fdim() seq_row() seq_col() vec() 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, object checking, and help with metaprogramming.

.c() `%=%` massign() vlabels() `vlabels<-`() setLabels() vclasses() namlab() add_stub() rm_stub() 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.

set_collapse() get_collapse(): collapse Package Options