collapse-package: Advanced and Fast Data Transformation
In collapse: Advanced and Fast Data Transformation
collapse-package R Documentation
Advanced and Fast Data Transformation
Description
collapse is a C/C++ based package for data transformation and statistical computing in R. Its aims are:
To facilitate complex data transformation, exploration and computing tasks in R.
To help make R code fast, flexible, parsimonious and programmer friendly.
It is made compatible with the tidyverse, data.table, sf, units, xts/zoo, and the plm approach to panel data.
Getting Started
Read the short vignette on documentation resources, and check out the built in documentation.
Details
collapse provides an integrated suite of statistical and data manipulation functions that greatly extend and enhance the capabilities of base R. In a nutshell, collapse provides:
Fast C/C++ based (grouped, weighted) computations embedded in highly optimized R code.
More complex statistical, time series / panel data and recursive (list-processing) operations.
A flexible and generic approach supporting and preserving many R objects.
Optimized programming in standard and non-standard evaluation.
The statistical functions in collapse are S3 generic with core methods for vectors, matrices and data frames, and internally support grouped and weighted computations carried out in C/C++.
Additional methods and C-level features enable broad based compatibility with dplyr (grouped tibble), data.table, sf and plm panel data classes. Functions and core methods seek to preserve object attributes (including column attributes such as variable labels), ensuring flexibility and effective workflows with a very broad range of R objects (including most time-series classes). See also the vignette on collapse's handling of R objects.
Missing values are efficiently skipped at C/C++ level. The package default is na.rm = TRUE. This can be changed using set_collapse(na.rm = FALSE). Missing weights are generally supported.
collapse installs with a built-in hierarchical documentation facilitating the use of the package.
The package is coded both in C and C++ and built with Rcpp, but also uses C/C++ functions from data.table, kit, fixest, weights, stats and RcppArmadillo / RcppEigen.
Author(s)
Maintainer: Sebastian Krantz sebastian.krantz@graduateinstitute.ch
Other contributors from packages collapse utilizes:
Matt Dowle, Arun Srinivasan and contributors worldwide (data.table)
Dirk Eddelbuettel and contributors worldwide (Rcpp, RcppArmadillo, RcppEigen)
Morgan Jacob (kit)
Laurent Berge (fixest)
Josh Pasek (weights)
R Core Team and contributors worldwide (stats)
I thank many people from diverse fields for helpful answers on Stackoverflow, Joris Meys for encouraging me and helping to set up the GitHub repository for collapse, and many other people for feature requests and helpful suggestions.
Developing / Bug Reporting
Please report issues at https://github.com/SebKrantz/collapse/issues.
Please send pull-requests to the 'development' branch of the repository.
Examples
## Note: this set of examples is is certainly non-exhaustive and does not
## showcase many recent features, but remains a very good starting point
## Let's start with some statistical programming
v <- iris$Sepal.Length
d <- num_vars(iris) # Saving numeric variables
f <- iris$Species # Factor
# Simple statistics
fmean(v) # vector
fmean(qM(d)) # matrix (qM is a faster as.matrix)
fmean(d) # data.frame
# Preserving data structure
fmean(qM(d), drop = FALSE) # Still a matrix
fmean(d, drop = FALSE) # Still a data.frame
# Weighted statistics, supported by most functions...
w <- abs(rnorm(fnrow(iris)))
fmean(d, w = w)
# Grouped statistics...
fmean(d, f)
# Groupwise-weighted statistics...
fmean(d, f, w)
# Simple Transformations...
head(fmode(d, TRA = "replace")) # Replacing values with the mode
head(fmedian(d, TRA = "-")) # Subtracting the median
head(fsum(d, TRA = "%")) # Computing percentages
head(fsd(d, TRA = "/")) # Dividing by the standard-deviation (scaling), etc...
# Weighted Transformations...
head(fnth(d, 0.75, w = w, TRA = "replace")) # Replacing by the weighted 3rd quartile
# Grouped Transformations...
head(fvar(d, f, TRA = "replace")) # Replacing values with the group variance
head(fsd(d, f, TRA = "/")) # Grouped scaling
head(fmin(d, f, TRA = "-")) # Setting the minimum value in each species to 0
head(fsum(d, f, TRA = "/")) # Dividing by the sum (proportions)
head(fmedian(d, f, TRA = "-")) # Groupwise de-median
head(ffirst(d, f, TRA = "%%")) # Taking modulus of first group-value, etc. ...
# Grouped and weighted transformations...
head(fsd(d, f, w, "/"), 3) # weighted scaling
head(fmedian(d, f, w, "-"), 3) # subtracting the weighted group-median
head(fmode(d, f, w, "replace"), 3) # replace with weighted statistical mode
## Some more advanced transformations...
head(fbetween(d)) # Averaging (faster t.: fmean(d, TRA = "replace"))
head(fwithin(d)) # Centering (faster than: fmean(d, TRA = "-"))
head(fwithin(d, f, w)) # Grouped and weighted (same as fmean(d, f, w, "-"))
head(fwithin(d, f, w, mean = 5)) # Setting a custom mean
head(fwithin(d, f, w, theta = 0.76)) # Quasi-centering i.e. d - theta*fbetween(d, f, w)
head(fwithin(d, f, w, mean = "overall.mean")) # Preserving the overall mean of the data
head(fscale(d)) # Scaling and centering
head(fscale(d, mean = 5, sd = 3)) # Custom scaling and centering
head(fscale(d, mean = FALSE, sd = 3)) # Mean preserving scaling
head(fscale(d, f, w)) # Grouped and weighted scaling and centering
head(fscale(d, f, w, mean = 5, sd = 3)) # Custom grouped and weighted scaling and centering
head(fscale(d, f, w, mean = FALSE, # Preserving group means
sd = "within.sd")) # and setting group-sd to fsd(fwithin(d, f, w), w = w)
head(fscale(d, f, w, mean = "overall.mean", # Full harmonization of group means and variances,
sd = "within.sd")) # while preserving the level and scale of the data.
head(get_vars(iris, 1:2)) # Use get_vars for fast selecting, gv is shortcut
head(fhdbetween(gv(iris, 1:2), gv(iris, 3:5))) # Linear prediction with factors and covariates
head(fhdwithin(gv(iris, 1:2), gv(iris, 3:5))) # Linear partialling out factors and covariates
ss(iris, 1:10, 1:2) # Similarly fsubset/ss for fast subsetting rows
# Simple Time-Computations..
head(flag(AirPassengers, -1:3)) # One lead and three lags
head(fdiff(EuStockMarkets, # Suitably lagged first and second differences
c(1, frequency(EuStockMarkets)), diff = 1:2))
head(fdiff(EuStockMarkets, rho = 0.87)) # Quasi-differences (x_t - rho*x_t-1)
head(fdiff(EuStockMarkets, log = TRUE)) # Log-differences
head(fgrowth(EuStockMarkets)) # Exact growth rates (percentage change)
head(fgrowth(EuStockMarkets, logdiff = TRUE)) # Log-difference growth rates (percentage change)
# Note that it is not necessary to use factors for grouping.
fmean(gv(mtcars, -c(2,8:9)), mtcars$cyl) # Can also use vector (internally converted using qF())
fmean(gv(mtcars, -c(2,8:9)),
gv(mtcars, c(2,8:9))) # or a list of vector (internally grouped using GRP())
g <- GRP(mtcars, ~ cyl + vs + am) # It is also possible to create grouping objects
print(g) # These are instructive to learn about the grouping,
plot(g) # and are directly handed down to C++ code
fmean(gv(mtcars, -c(2,8:9)), g) # This can speed up multiple computations over same groups
fsd(gv(mtcars, -c(2,8:9)), g)
# Factors can efficiently be created using qF()
f1 <- qF(mtcars$cyl) # Unlike GRP objects, factors are checked for NA's
f2 <- qF(mtcars$cyl, na.exclude = FALSE) # This can however be avoided through this option
class(f2) # Note the added class
library(microbenchmark)
microbenchmark(fmean(mtcars, f1), fmean(mtcars, f2)) # A minor difference, larger on larger data
with(mtcars, finteraction(cyl, vs, am)) # Efficient interactions of vectors and/or factors
finteraction(gv(mtcars, c(2,8:9))) # .. or lists of vectors/factors
# Simple row- or column-wise computations on matrices or data frames with dapply()
dapply(mtcars, quantile) # column quantiles
dapply(mtcars, quantile, MARGIN = 1) # Row-quantiles
# dapply preserves the data structure of any matrices / data frames passed
# Some fast matrix row/column functions are also provided by the matrixStats package
# Similarly, BY performs grouped comptations
BY(mtcars, f2, quantile)
BY(mtcars, f2, quantile, expand.wide = TRUE)
# For efficient (grouped) replacing and sweeping out computed statistics, use TRA()
sds <- fsd(mtcars)
head(TRA(mtcars, sds, "/")) # Simple scaling (if sd's not needed, use fsd(mtcars, TRA = "/"))
microbenchmark(TRA(mtcars, sds, "/"), sweep(mtcars, 2, sds, "/")) # A remarkable performance gain..
sds <- fsd(mtcars, f2)
head(TRA(mtcars, sds, "/", f2)) # Groupd scaling (if sd's not needed: fsd(mtcars, f2, TRA = "/"))
# All functions above perserve the structure of matrices / data frames
# If conversions are required, use these efficient functions:
mtcarsM <- qM(mtcars) # Matrix from data.frame
head(qDF(mtcarsM)) # data.frame from matrix columns
head(mrtl(mtcarsM, TRUE, "data.frame")) # data.frame from matrix rows, etc..
head(qDT(mtcarsM, "cars")) # Saving row.names when converting matrix to data.table
head(qDT(mtcars, "cars")) # Same use a data.frame
## Now let's get some real data and see how we can use this power for data manipulation
head(wlddev) # World Bank World Development Data: 216 countries, 61 years, 5 series (columns 9-13)
# Starting with some discriptive tools...
namlab(wlddev, class = TRUE) # Show variable names, labels and classes
fnobs(wlddev) # Observation count
pwnobs(wlddev) # Pairwise observation count
head(fnobs(wlddev, wlddev$country)) # Grouped observation count
fndistinct(wlddev) # Distinct values
descr(wlddev) # Describe data
varying(wlddev, ~ country) # Show which variables vary within countries
qsu(wlddev, pid = ~ country, # Panel-summarize columns 9 though 12 of this data
cols = 9:12, vlabels = TRUE) # (between and within countries)
qsu(wlddev, ~ region, ~ country, # Do all of that by region and also compute higher moments
cols = 9:12, higher = TRUE) # -> returns a 4D array
qsu(wlddev, ~ region, ~ country, cols = 9:12,
higher = TRUE, array = FALSE) |> # Return as a list of matrices..
unlist2d(c("Variable","Trans"), row.names = "Region") |> head()# and turn into a tidy data.frame
pwcor(num_vars(wlddev), P = TRUE) # Pairwise correlations with p-value
pwcor(fmean(num_vars(wlddev), wlddev$country), P = TRUE) # Correlating country means
pwcor(fwithin(num_vars(wlddev), wlddev$country), P = TRUE) # Within-country correlations
psacf(wlddev, ~country, ~year, cols = 9:12) # Panel-data Autocorrelation function
pspacf(wlddev, ~country, ~year, cols = 9:12) # Partial panel-autocorrelations
psmat(wlddev, ~iso3c, ~year, cols = 9:12) |> plot() # Convert panel to 3D array and plot
## collapse offers a few very efficent functions for data manipulation:
# Fast selecting and replacing columns
series <- get_vars(wlddev, 9:12) # Same as wlddev[9:12] but 2x faster
series <- fselect(wlddev, PCGDP:ODA) # Same thing: > 100x faster than dplyr::select
get_vars(wlddev, 9:12) <- series # Replace, 8x faster wlddev[9:12] <- series + replaces names
fselect(wlddev, PCGDP:ODA) <- series # Same thing
# Fast subsetting
head(fsubset(wlddev, country == "Ireland", -country, -iso3c))
head(fsubset(wlddev, country == "Ireland" & year > 1990, year, PCGDP:ODA))
ss(wlddev, 1:10, 1:10) # This is an order of magnitude faster than wlddev[1:10, 1:10]
# Fast transforming
head(ftransform(wlddev, ODA_GDP = ODA / PCGDP, ODA_LIFEEX = sqrt(ODA) / LIFEEX))
settransform(wlddev, ODA_GDP = ODA / PCGDP, ODA_LIFEEX = sqrt(ODA) / LIFEEX) # by reference
head(ftransform(wlddev, PCGDP = NULL, ODA = NULL, GINI_sum = fsum(GINI)))
head(ftransformv(wlddev, 9:12, log)) # Can also transform with lists of columns
head(ftransformv(wlddev, 9:12, fscale, apply = FALSE)) # apply = FALSE invokes fscale.data.frame
settransformv(wlddev, 9:12, fscale, apply = FALSE) # Changing the data by reference
ftransform(wlddev) <- fscale(gv(wlddev, 9:12)) # Same thing (using replacement method)
library(magrittr) # Same thing, using magrittr
wlddev %<>% ftransformv(9:12, fscale, apply = FALSE)
wlddev %>% ftransform(gv(., 9:12) |> # With compound pipes: Scaling and lagging
fscale() |> flag(0:2, iso3c, year)) |> head()
# Fast reordering
head(roworder(wlddev, -country, year))
head(colorder(wlddev, country, year))
# Fast renaming
head(frename(wlddev, country = Ctry, year = Yr))
setrename(wlddev, country = Ctry, year = Yr) # By reference
head(frename(wlddev, tolower, cols = 9:12))
# Fast grouping
fgroup_by(wlddev, Ctry, decade) |> fgroup_vars() |> head()
rm(wlddev) # .. but only works with collapse functions
## Now lets start putting things together
wlddev |> fsubset(year > 1990, region, income, PCGDP:ODA) |>
fgroup_by(region, income) |> fmean() # Fast aggregation using the mean
# Same thing using dplyr manipulation verbs
library(dplyr)
wlddev |> filter(year > 1990) |> select(region, income, PCGDP:ODA) |>
group_by(region,income) |> fmean() # This is already a lot faster than summarize_all(mean)
wlddev |> fsubset(year > 1990, region, income, PCGDP:POP) |>
fgroup_by(region, income) |> fmean(POP) # Weighted group means
wlddev |> fsubset(year > 1990, region, income, PCGDP:POP) |>
fgroup_by(region, income) |> fsd(POP) # Weighted group standard deviations
wlddev |> na_omit(cols = "POP") |> fgroup_by(region, income) |>
fselect(PCGDP:POP) |> fnth(0.75, POP) # Weighted group third quartile
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |>
fwithin() |> head() # Within transformation
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |>
fmedian(TRA = "-") |> head() # Grouped centering using the median
# Replacing data points by the weighted first quartile:
wlddev |> na_omit(cols = "POP") |> fgroup_by(country) |>
fselect(country, year, PCGDP:POP) %>%
ftransform(fselect(., -country, -year) |>
fnth(0.25, POP, "fill")) |> head()
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |> fscale() |> head() # Standardizing
wlddev |> fgroup_by(country) |> fselect(PCGDP:POP) |>
fscale(POP) |> head() # Weighted..
wlddev |> fselect(country, year, PCGDP:ODA) |> # Adding 1 lead and 2 lags of each variable
fgroup_by(country) |> flag(-1:2, year) |> head()
wlddev |> fselect(country, year, PCGDP:ODA) |> # Adding 1 lead and 10-year growth rates
fgroup_by(country) |> fgrowth(c(0:1,10), 1, year) |> head()
# etc...
# Aggregation with multiple functions
wlddev |> fsubset(year > 1990, region, income, PCGDP:ODA) |>
fgroup_by(region, income) %>% {
add_vars(fgroup_vars(., "unique"),
fmedian(., keep.group_vars = FALSE) |> add_stub("median_"),
fmean(., keep.group_vars = FALSE) |> add_stub("mean_"),
fsd(., keep.group_vars = FALSE) |> add_stub("sd_"))
} |> head()
# Transformation with multiple functions
wlddev |> fselect(country, year, PCGDP:ODA) |>
fgroup_by(country) %>% {
add_vars(fdiff(., c(1,10), 1, year) |> flag(0:2, year), # Sequence of lagged differences
ftransform(., fselect(., PCGDP:ODA) |> fwithin() |> add_stub("W.")) |>
flag(0:2, year, keep.ids = FALSE)) # Sequence of lagged demeaned vars
} |> head()
# With ftransform, can also easily do one or more grouped mutations on the fly..
settransform(wlddev, median_ODA = fmedian(ODA, list(region, income), TRA = "fill"))
settransform(wlddev, sd_ODA = fsd(ODA, list(region, income), TRA = "fill"),
mean_GDP = fmean(PCGDP, country, TRA = "fill"))
wlddev %<>% ftransform(fmedian(list(median_ODA = ODA, median_GDP = PCGDP),
list(region, income), TRA = "fill"))
# On a groped data frame it is also possible to grouped transform certain columns
# but perform aggregate operatins on others:
wlddev |> fgroup_by(region, income) %>%
ftransform(gmedian_GDP = fmedian(PCGDP, GRP(.), TRA = "replace"),
omedian_GDP = fmedian(PCGDP, TRA = "replace"), # "replace" preserves NA's
omedian_GDP_fill = fmedian(PCGDP)) |> tail()
rm(wlddev)
## For multi-type data aggregation, the function collap() offers ease and flexibility
# Aggregate this data by country and decade: Numeric columns with mean, categorical with mode
head(collap(wlddev, ~ country + decade, fmean, fmode))
# taking weighted mean and weighted mode:
head(collap(wlddev, ~ country + decade, fmean, fmode, w = ~ POP, wFUN = fsum))
# Multi-function aggregation of certain columns
head(collap(wlddev, ~ country + decade,
list(fmean, fmedian, fsd),
list(ffirst, flast), cols = c(3,9:12)))
# Customized Aggregation: Assign columns to functions
head(collap(wlddev, ~ country + decade,
custom = list(fmean = 9:10, fsd = 9:12, flast = 3, ffirst = 6:8)))
# For grouped data frames use collapg
wlddev |> fsubset(year > 1990, country, region, income, PCGDP:ODA) |>
fgroup_by(country) |> collapg(fmean, ffirst) |>
ftransform(AMGDP = PCGDP > fmedian(PCGDP, list(region, income), TRA = "fill"),
AMODA = ODA > fmedian(ODA, income, TRA = "replace_fill")) |> head()
## Additional flexibility for data transformation tasks is offerend by tidy transformation operators
# Within-transformation (centering on overall mean)
head(W(wlddev, ~ country, cols = 9:12, mean = "overall.mean"))
# Partialling out country and year fixed effects
head(HDW(wlddev, PCGDP + LIFEEX ~ qF(country) + qF(year)))
# Same, adding ODA as continuous regressor
head(HDW(wlddev, PCGDP + LIFEEX ~ qF(country) + qF(year) + ODA))
# Standardizing (scaling and centering) by country
head(STD(wlddev, ~ country, cols = 9:12))
# Computing 1 lead and 3 lags of the 4 series
head(L(wlddev, -1:3, ~ country, ~year, cols = 9:12))
# Computing the 1- and 10-year first differences
head(D(wlddev, c(1,10), 1, ~ country, ~year, cols = 9:12))
head(D(wlddev, c(1,10), 1:2, ~ country, ~year, cols = 9:12)) # ..first and second differences
# Computing the 1- and 10-year growth rates
head(G(wlddev, c(1,10), 1, ~ country, ~year, cols = 9:12))
# Adding growth rate variables to dataset
add_vars(wlddev) <- G(wlddev, c(1, 10), 1, ~ country, ~year, cols = 9:12, keep.ids = FALSE)
get_vars(wlddev, "G1.", regex = TRUE) <- NULL # Deleting again
# These operators can conveniently be used in regression formulas:
# Using a Mundlak (1978) procedure to estimate the effect of OECD on LIFEEX, controlling for PCGDP
lm(LIFEEX ~ log(PCGDP) + OECD + B(log(PCGDP), country),
wlddev |> fselect(country, OECD, PCGDP, LIFEEX) |> na_omit())
# Adding 10-year lagged life-expectancy to allow for some convergence effects (dynamic panel model)
lm(LIFEEX ~ L(LIFEEX, 10, country) + log(PCGDP) + OECD + B(log(PCGDP), country),
wlddev |> fselect(country, OECD, PCGDP, LIFEEX) |> na_omit())
# Tranformation functions and operators also support indexed data classes:
wldi <- findex_by(wlddev, country, year)
head(W(wldi$PCGDP)) # Country-demeaning
head(W(wldi, cols = 9:12))
head(W(wldi$PCGDP, effect = 2)) # Time-demeaning
head(W(wldi, effect = 2, cols = 9:12))
head(HDW(wldi$PCGDP)) # Country- and time-demeaning
head(HDW(wldi, cols = 9:12))
head(STD(wldi$PCGDP)) # Standardizing by country
head(STD(wldi, cols = 9:12))
head(L(wldi$PCGDP, -1:3)) # Panel-lags
head(L(wldi, -1:3, 9:12))
head(G(wldi$PCGDP)) # Panel-Growth rates
head(G(wldi, 1, 1, 9:12))
lm(Dlog(PCGDP) ~ L(Dlog(LIFEEX), 0:3), wldi) # Panel data regression
rm(wldi)
# Remove all objects used in this example section
rm(v, d, w, f, f1, f2, g, mtcarsM, sds, series, wlddev)
collapse documentation built on Nov. 3, 2024, 9:08 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| collapse-package | R Documentation |
Advanced and Fast Data Transformation
Description
collapse is a C/C++ based package for data transformation and statistical computing in R. Its aims are:
To facilitate complex data transformation, exploration and computing tasks in R.
To help make R code fast, flexible, parsimonious and programmer friendly.
It is made compatible with the tidyverse, data.table, sf, units, xts/zoo, and the plm approach to panel data.
Getting Started
Read the short vignette on documentation resources, and check out the built in documentation.
Details
collapse provides an integrated suite of statistical and data manipulation functions that greatly extend and enhance the capabilities of base R. In a nutshell, collapse provides:
Fast C/C++ based (grouped, weighted) computations embedded in highly optimized R code.
More complex statistical, time series / panel data and recursive (list-processing) operations.
A flexible and generic approach supporting and preserving many R objects.
Optimized programming in standard and non-standard evaluation.
The statistical functions in collapse are S3 generic with core methods for vectors, matrices and data frames, and internally support grouped and weighted computations carried out in C/C++.
Additional methods and C-level features enable broad based compatibility with dplyr (grouped tibble), data.table, sf and plm panel data classes. Functions and core methods seek to preserve object attributes (including column attributes such as variable labels), ensuring flexibility and effective workflows with a very broad range of R objects (including most time-series classes). See also the vignette on collapse's handling of R objects.
Missing values are efficiently skipped at C/C++ level. The package default is na.rm = TRUE. This can be changed using set_collapse(na.rm = FALSE). Missing weights are generally supported.
collapse installs with a built-in hierarchical documentation facilitating the use of the package.
The package is coded both in C and C++ and built with Rcpp, but also uses C/C++ functions from data.table, kit, fixest, weights, stats and RcppArmadillo / RcppEigen.
Author(s)
Maintainer: Sebastian Krantz sebastian.krantz@graduateinstitute.ch
Other contributors from packages collapse utilizes:
Matt Dowle, Arun Srinivasan and contributors worldwide (data.table)
Dirk Eddelbuettel and contributors worldwide (Rcpp, RcppArmadillo, RcppEigen)
Morgan Jacob (kit)
Laurent Berge (fixest)
Josh Pasek (weights)
R Core Team and contributors worldwide (stats)
I thank many people from diverse fields for helpful answers on Stackoverflow, Joris Meys for encouraging me and helping to set up the GitHub repository for collapse, and many other people for feature requests and helpful suggestions.
Developing / Bug Reporting
Please report issues at https://github.com/SebKrantz/collapse/issues.
Please send pull-requests to the 'development' branch of the repository.
Examples
## Note: this set of examples is is certainly non-exhaustive and does not
## showcase many recent features, but remains a very good starting point
## Let's start with some statistical programming
v <- iris$Sepal.Length
d <- num_vars(iris) # Saving numeric variables
f <- iris$Species # Factor
# Simple statistics
fmean(v) # vector
fmean(qM(d)) # matrix (qM is a faster as.matrix)
fmean(d) # data.frame
# Preserving data structure
fmean(qM(d), drop = FALSE) # Still a matrix
fmean(d, drop = FALSE) # Still a data.frame
# Weighted statistics, supported by most functions...
w <- abs(rnorm(fnrow(iris)))
fmean(d, w = w)
# Grouped statistics...
fmean(d, f)
# Groupwise-weighted statistics...
fmean(d, f, w)
# Simple Transformations...
head(fmode(d, TRA = "replace")) # Replacing values with the mode
head(fmedian(d, TRA = "-")) # Subtracting the median
head(fsum(d, TRA = "%")) # Computing percentages
head(fsd(d, TRA = "/")) # Dividing by the standard-deviation (scaling), etc...
# Weighted Transformations...
head(fnth(d, 0.75, w = w, TRA = "replace")) # Replacing by the weighted 3rd quartile
# Grouped Transformations...
head(fvar(d, f, TRA = "replace")) # Replacing values with the group variance
head(fsd(d, f, TRA = "/")) # Grouped scaling
head(fmin(d, f, TRA = "-")) # Setting the minimum value in each species to 0
head(fsum(d, f, TRA = "/")) # Dividing by the sum (proportions)
head(fmedian(d, f, TRA = "-")) # Groupwise de-median
head(ffirst(d, f, TRA = "%%")) # Taking modulus of first group-value, etc. ...
# Grouped and weighted transformations...
head(fsd(d, f, w, "/"), 3) # weighted scaling
head(fmedian(d, f, w, "-"), 3) # subtracting the weighted group-median
head(fmode(d, f, w, "replace"), 3) # replace with weighted statistical mode
## Some more advanced transformations...
head(fbetween(d)) # Averaging (faster t.: fmean(d, TRA = "replace"))
head(fwithin(d)) # Centering (faster than: fmean(d, TRA = "-"))
head(fwithin(d, f, w)) # Grouped and weighted (same as fmean(d, f, w, "-"))
head(fwithin(d, f, w, mean = 5)) # Setting a custom mean
head(fwithin(d, f, w, theta = 0.76)) # Quasi-centering i.e. d - theta*fbetween(d, f, w)
head(fwithin(d, f, w, mean = "overall.mean")) # Preserving the overall mean of the data
head(fscale(d)) # Scaling and centering
head(fscale(d, mean = 5, sd = 3)) # Custom scaling and centering
head(fscale(d, mean = FALSE, sd = 3)) # Mean preserving scaling
head(fscale(d, f, w)) # Grouped and weighted scaling and centering
head(fscale(d, f, w, mean = 5, sd = 3)) # Custom grouped and weighted scaling and centering
head(fscale(d, f, w, mean = FALSE, # Preserving group means
sd = "within.sd")) # and setting group-sd to fsd(fwithin(d, f, w), w = w)
head(fscale(d, f, w, mean = "overall.mean", # Full harmonization of group means and variances,
sd = "within.sd")) # while preserving the level and scale of the data.
head(get_vars(iris, 1:2)) # Use get_vars for fast selecting, gv is shortcut
head(fhdbetween(gv(iris, 1:2), gv(iris, 3:5))) # Linear prediction with factors and covariates
head(fhdwithin(gv(iris, 1:2), gv(iris, 3:5))) # Linear partialling out factors and covariates
ss(iris, 1:10, 1:2) # Similarly fsubset/ss for fast subsetting rows
# Simple Time-Computations..
head(flag(AirPassengers, -1:3)) # One lead and three lags
head(fdiff(EuStockMarkets, # Suitably lagged first and second differences
c(1, frequency(EuStockMarkets)), diff = 1:2))
head(fdiff(EuStockMarkets, rho = 0.87)) # Quasi-differences (x_t - rho*x_t-1)
head(fdiff(EuStockMarkets, log = TRUE)) # Log-differences
head(fgrowth(EuStockMarkets)) # Exact growth rates (percentage change)
head(fgrowth(EuStockMarkets, logdiff = TRUE)) # Log-difference growth rates (percentage change)
# Note that it is not necessary to use factors for grouping.
fmean(gv(mtcars, -c(2,8:9)), mtcars$cyl) # Can also use vector (internally converted using qF())
fmean(gv(mtcars, -c(2,8:9)),
gv(mtcars, c(2,8:9))) # or a list of vector (internally grouped using GRP())
g <- GRP(mtcars, ~ cyl + vs + am) # It is also possible to create grouping objects
print(g) # These are instructive to learn about the grouping,
plot(g) # and are directly handed down to C++ code
fmean(gv(mtcars, -c(2,8:9)), g) # This can speed up multiple computations over same groups
fsd(gv(mtcars, -c(2,8:9)), g)
# Factors can efficiently be created using qF()
f1 <- qF(mtcars$cyl) # Unlike GRP objects, factors are checked for NA's
f2 <- qF(mtcars$cyl, na.exclude = FALSE) # This can however be avoided through this option
class(f2) # Note the added class
library(microbenchmark)
microbenchmark(fmean(mtcars, f1), fmean(mtcars, f2)) # A minor difference, larger on larger data
with(mtcars, finteraction(cyl, vs, am)) # Efficient interactions of vectors and/or factors
finteraction(gv(mtcars, c(2,8:9))) # .. or lists of vectors/factors
# Simple row- or column-wise computations on matrices or data frames with dapply()
dapply(mtcars, quantile) # column quantiles
dapply(mtcars, quantile, MARGIN = 1) # Row-quantiles
# dapply preserves the data structure of any matrices / data frames passed
# Some fast matrix row/column functions are also provided by the matrixStats package
# Similarly, BY performs grouped comptations
BY(mtcars, f2, quantile)
BY(mtcars, f2, quantile, expand.wide = TRUE)
# For efficient (grouped) replacing and sweeping out computed statistics, use TRA()
sds <- fsd(mtcars)
head(TRA(mtcars, sds, "/")) # Simple scaling (if sd's not needed, use fsd(mtcars, TRA = "/"))
microbenchmark(TRA(mtcars, sds, "/"), sweep(mtcars, 2, sds, "/")) # A remarkable performance gain..
sds <- fsd(mtcars, f2)
head(TRA(mtcars, sds, "/", f2)) # Groupd scaling (if sd's not needed: fsd(mtcars, f2, TRA = "/"))
# All functions above perserve the structure of matrices / data frames
# If conversions are required, use these efficient functions:
mtcarsM <- qM(mtcars) # Matrix from data.frame
head(qDF(mtcarsM)) # data.frame from matrix columns
head(mrtl(mtcarsM, TRUE, "data.frame")) # data.frame from matrix rows, etc..
head(qDT(mtcarsM, "cars")) # Saving row.names when converting matrix to data.table
head(qDT(mtcars, "cars")) # Same use a data.frame
## Now let's get some real data and see how we can use this power for data manipulation
head(wlddev) # World Bank World Development Data: 216 countries, 61 years, 5 series (columns 9-13)
# Starting with some discriptive tools...
namlab(wlddev, class = TRUE) # Show variable names, labels and classes
fnobs(wlddev) # Observation count
pwnobs(wlddev) # Pairwise observation count
head(fnobs(wlddev, wlddev$country)) # Grouped observation count
fndistinct(wlddev) # Distinct values
descr(wlddev) # Describe data
varying(wlddev, ~ country) # Show which variables vary within countries
qsu(wlddev, pid = ~ country, # Panel-summarize columns 9 though 12 of this data
cols = 9:12, vlabels = TRUE) # (between and within countries)
qsu(wlddev, ~ region, ~ country, # Do all of that by region and also compute higher moments
cols = 9:12, higher = TRUE) # -> returns a 4D array
qsu(wlddev, ~ region, ~ country, cols = 9:12,
higher = TRUE, array = FALSE) |> # Return as a list of matrices..
unlist2d(c("Variable","Trans"), row.names = "Region") |> head()# and turn into a tidy data.frame
pwcor(num_vars(wlddev), P = TRUE) # Pairwise correlations with p-value
pwcor(fmean(num_vars(wlddev), wlddev$country), P = TRUE) # Correlating country means
pwcor(fwithin(num_vars(wlddev), wlddev$country), P = TRUE) # Within-country correlations
psacf(wlddev, ~country, ~year, cols = 9:12) # Panel-data Autocorrelation function
pspacf(wlddev, ~country, ~year, cols = 9:12) # Partial panel-autocorrelations
psmat(wlddev, ~iso3c, ~year, cols = 9:12) |> plot() # Convert panel to 3D array and plot
## collapse offers a few very efficent functions for data manipulation:
# Fast selecting and replacing columns
series <- get_vars(wlddev, 9:12) # Same as wlddev[9:12] but 2x faster
series <- fselect(wlddev, PCGDP:ODA) # Same thing: > 100x faster than dplyr::select
get_vars(wlddev, 9:12) <- series # Replace, 8x faster wlddev[9:12] <- series + replaces names
fselect(wlddev, PCGDP:ODA) <- series # Same thing
# Fast subsetting
head(fsubset(wlddev, country == "Ireland", -country, -iso3c))
head(fsubset(wlddev, country == "Ireland" & year > 1990, year, PCGDP:ODA))
ss(wlddev, 1:10, 1:10) # This is an order of magnitude faster than wlddev[1:10, 1:10]
# Fast transforming
head(ftransform(wlddev, ODA_GDP = ODA / PCGDP, ODA_LIFEEX = sqrt(ODA) / LIFEEX))
settransform(wlddev, ODA_GDP = ODA / PCGDP, ODA_LIFEEX = sqrt(ODA) / LIFEEX) # by reference
head(ftransform(wlddev, PCGDP = NULL, ODA = NULL, GINI_sum = fsum(GINI)))
head(ftransformv(wlddev, 9:12, log)) # Can also transform with lists of columns
head(ftransformv(wlddev, 9:12, fscale, apply = FALSE)) # apply = FALSE invokes fscale.data.frame
settransformv(wlddev, 9:12, fscale, apply = FALSE) # Changing the data by reference
ftransform(wlddev) <- fscale(gv(wlddev, 9:12)) # Same thing (using replacement method)
library(magrittr) # Same thing, using magrittr
wlddev %<>% ftransformv(9:12, fscale, apply = FALSE)
wlddev %>% ftransform(gv(., 9:12) |> # With compound pipes: Scaling and lagging
fscale() |> flag(0:2, iso3c, year)) |> head()
# Fast reordering
head(roworder(wlddev, -country, year))
head(colorder(wlddev, country, year))
# Fast renaming
head(frename(wlddev, country = Ctry, year = Yr))
setrename(wlddev, country = Ctry, year = Yr) # By reference
head(frename(wlddev, tolower, cols = 9:12))
# Fast grouping
fgroup_by(wlddev, Ctry, decade) |> fgroup_vars() |> head()
rm(wlddev) # .. but only works with collapse functions
## Now lets start putting things together
wlddev |> fsubset(year > 1990, region, income, PCGDP:ODA) |>
fgroup_by(region, income) |> fmean() # Fast aggregation using the mean
# Same thing using dplyr manipulation verbs
library(dplyr)
wlddev |> filter(year > 1990) |> select(region, income, PCGDP:ODA) |>
group_by(region,income) |> fmean() # This is already a lot faster than summarize_all(mean)
wlddev |> fsubset(year > 1990, region, income, PCGDP:POP) |>
fgroup_by(region, income) |> fmean(POP) # Weighted group means
wlddev |> fsubset(year > 1990, region, income, PCGDP:POP) |>
fgroup_by(region, income) |> fsd(POP) # Weighted group standard deviations
wlddev |> na_omit(cols = "POP") |> fgroup_by(region, income) |>
fselect(PCGDP:POP) |> fnth(0.75, POP) # Weighted group third quartile
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |>
fwithin() |> head() # Within transformation
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |>
fmedian(TRA = "-") |> head() # Grouped centering using the median
# Replacing data points by the weighted first quartile:
wlddev |> na_omit(cols = "POP") |> fgroup_by(country) |>
fselect(country, year, PCGDP:POP) %>%
ftransform(fselect(., -country, -year) |>
fnth(0.25, POP, "fill")) |> head()
wlddev |> fgroup_by(country) |> fselect(PCGDP:ODA) |> fscale() |> head() # Standardizing
wlddev |> fgroup_by(country) |> fselect(PCGDP:POP) |>
fscale(POP) |> head() # Weighted..
wlddev |> fselect(country, year, PCGDP:ODA) |> # Adding 1 lead and 2 lags of each variable
fgroup_by(country) |> flag(-1:2, year) |> head()
wlddev |> fselect(country, year, PCGDP:ODA) |> # Adding 1 lead and 10-year growth rates
fgroup_by(country) |> fgrowth(c(0:1,10), 1, year) |> head()
# etc...
# Aggregation with multiple functions
wlddev |> fsubset(year > 1990, region, income, PCGDP:ODA) |>
fgroup_by(region, income) %>% {
add_vars(fgroup_vars(., "unique"),
fmedian(., keep.group_vars = FALSE) |> add_stub("median_"),
fmean(., keep.group_vars = FALSE) |> add_stub("mean_"),
fsd(., keep.group_vars = FALSE) |> add_stub("sd_"))
} |> head()
# Transformation with multiple functions
wlddev |> fselect(country, year, PCGDP:ODA) |>
fgroup_by(country) %>% {
add_vars(fdiff(., c(1,10), 1, year) |> flag(0:2, year), # Sequence of lagged differences
ftransform(., fselect(., PCGDP:ODA) |> fwithin() |> add_stub("W.")) |>
flag(0:2, year, keep.ids = FALSE)) # Sequence of lagged demeaned vars
} |> head()
# With ftransform, can also easily do one or more grouped mutations on the fly..
settransform(wlddev, median_ODA = fmedian(ODA, list(region, income), TRA = "fill"))
settransform(wlddev, sd_ODA = fsd(ODA, list(region, income), TRA = "fill"),
mean_GDP = fmean(PCGDP, country, TRA = "fill"))
wlddev %<>% ftransform(fmedian(list(median_ODA = ODA, median_GDP = PCGDP),
list(region, income), TRA = "fill"))
# On a groped data frame it is also possible to grouped transform certain columns
# but perform aggregate operatins on others:
wlddev |> fgroup_by(region, income) %>%
ftransform(gmedian_GDP = fmedian(PCGDP, GRP(.), TRA = "replace"),
omedian_GDP = fmedian(PCGDP, TRA = "replace"), # "replace" preserves NA's
omedian_GDP_fill = fmedian(PCGDP)) |> tail()
rm(wlddev)
## For multi-type data aggregation, the function collap() offers ease and flexibility
# Aggregate this data by country and decade: Numeric columns with mean, categorical with mode
head(collap(wlddev, ~ country + decade, fmean, fmode))
# taking weighted mean and weighted mode:
head(collap(wlddev, ~ country + decade, fmean, fmode, w = ~ POP, wFUN = fsum))
# Multi-function aggregation of certain columns
head(collap(wlddev, ~ country + decade,
list(fmean, fmedian, fsd),
list(ffirst, flast), cols = c(3,9:12)))
# Customized Aggregation: Assign columns to functions
head(collap(wlddev, ~ country + decade,
custom = list(fmean = 9:10, fsd = 9:12, flast = 3, ffirst = 6:8)))
# For grouped data frames use collapg
wlddev |> fsubset(year > 1990, country, region, income, PCGDP:ODA) |>
fgroup_by(country) |> collapg(fmean, ffirst) |>
ftransform(AMGDP = PCGDP > fmedian(PCGDP, list(region, income), TRA = "fill"),
AMODA = ODA > fmedian(ODA, income, TRA = "replace_fill")) |> head()
## Additional flexibility for data transformation tasks is offerend by tidy transformation operators
# Within-transformation (centering on overall mean)
head(W(wlddev, ~ country, cols = 9:12, mean = "overall.mean"))
# Partialling out country and year fixed effects
head(HDW(wlddev, PCGDP + LIFEEX ~ qF(country) + qF(year)))
# Same, adding ODA as continuous regressor
head(HDW(wlddev, PCGDP + LIFEEX ~ qF(country) + qF(year) + ODA))
# Standardizing (scaling and centering) by country
head(STD(wlddev, ~ country, cols = 9:12))
# Computing 1 lead and 3 lags of the 4 series
head(L(wlddev, -1:3, ~ country, ~year, cols = 9:12))
# Computing the 1- and 10-year first differences
head(D(wlddev, c(1,10), 1, ~ country, ~year, cols = 9:12))
head(D(wlddev, c(1,10), 1:2, ~ country, ~year, cols = 9:12)) # ..first and second differences
# Computing the 1- and 10-year growth rates
head(G(wlddev, c(1,10), 1, ~ country, ~year, cols = 9:12))
# Adding growth rate variables to dataset
add_vars(wlddev) <- G(wlddev, c(1, 10), 1, ~ country, ~year, cols = 9:12, keep.ids = FALSE)
get_vars(wlddev, "G1.", regex = TRUE) <- NULL # Deleting again
# These operators can conveniently be used in regression formulas:
# Using a Mundlak (1978) procedure to estimate the effect of OECD on LIFEEX, controlling for PCGDP
lm(LIFEEX ~ log(PCGDP) + OECD + B(log(PCGDP), country),
wlddev |> fselect(country, OECD, PCGDP, LIFEEX) |> na_omit())
# Adding 10-year lagged life-expectancy to allow for some convergence effects (dynamic panel model)
lm(LIFEEX ~ L(LIFEEX, 10, country) + log(PCGDP) + OECD + B(log(PCGDP), country),
wlddev |> fselect(country, OECD, PCGDP, LIFEEX) |> na_omit())
# Tranformation functions and operators also support indexed data classes:
wldi <- findex_by(wlddev, country, year)
head(W(wldi$PCGDP)) # Country-demeaning
head(W(wldi, cols = 9:12))
head(W(wldi$PCGDP, effect = 2)) # Time-demeaning
head(W(wldi, effect = 2, cols = 9:12))
head(HDW(wldi$PCGDP)) # Country- and time-demeaning
head(HDW(wldi, cols = 9:12))
head(STD(wldi$PCGDP)) # Standardizing by country
head(STD(wldi, cols = 9:12))
head(L(wldi$PCGDP, -1:3)) # Panel-lags
head(L(wldi, -1:3, 9:12))
head(G(wldi$PCGDP)) # Panel-Growth rates
head(G(wldi, 1, 1, 9:12))
lm(Dlog(PCGDP) ~ L(Dlog(LIFEEX), 0:3), wldi) # Panel data regression
rm(wldi)
# Remove all objects used in this example section
rm(v, d, w, f, f1, f2, g, mtcarsM, sds, series, wlddev)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.