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plyr: the split-apply-combine paradigm for R.

Description

The plyr package is a set of clean and consistent tools that implement thesplit-apply-combine pattern in R. This is an extremely common pattern indata analysis: you solve a complex problem by breaking it down into smallpieces, doing something to each piece and then combining the results backtogether again.

Details

The plyr functions are named according to what sort of data structure theysplit up and what sort of data structure they return:

a

array

l

list

d

data.frame

m

multiple inputs

r

repeat multiple times

_

nothing

Soddply takes a data frame as input and returns a data frameas output, andl_ply takes a list as input and returns nothingas output.

Row names

By design, no plyr function will preserve row names - in general it is toohard to know what should be done with them for many of the operationssupported by plyr. If you want to preserve row names, usename_rows to convert them into an explicit column in yourdata frame, perform the plyr operations, and then usename_rowsagain to convert the column back into row names.

Helpers

Plyr also provides a set of helper functions for common data analysisproblems:

• arrange: re-order the rows of a data frame byspecifying the columns to order by

• mutate: add new columns or modifying existing columns,liketransform, but new columns can refer to other columnsthat you just created.

• summarise: likemutate but create anew data frame, not preserving any columns in the old data frame.

• join: an adapation ofmerge which ismore similar to SQL, and has a much faster implementation if you onlywant to find the first match.

• match_df: a version ofjoin that insteadof returning the two tables combined together, only returns the rowsin the first table that match the second.

• colwise: make any function work colwise on a dataframe

• rename: easily rename columns in a data frame

• round_any: round a number to any degree of precision

• count: quickly count unique combinations and returnreturn as a data frame.

Quote variables to create a list of unevaluated expressions for laterevaluation.

Description

This function is similar to~ in that it is used tocapture the name of variables, not their current value. This is usedthroughout plyr to specify the names of variables (or more complicatedexpressions).

Usage

.(..., .env = parent.frame())

Arguments

Details

Similar tricks can be performed withsubstitute, but whenfunctions can be called in multiple ways it becomes increasingly trickyto ensure that the values are extracted from the correct frame. Substitutetricks also make it difficult to program against the functions that usethem, while thequoted class providesas.quoted.character to convert strings to the appropriatedata structure.

Value

list of symbol and language primitives

Examples

.(a, b, c).(first = a, second = b, third = c).(a ^ 2, b - d, log(c))as.quoted(~ a + b + c)as.quoted(a ~ b + c)as.quoted(c("a", "b", "c"))# Some examples using ddply - look at the column namesddply(mtcars, "cyl", each(nrow, ncol))ddply(mtcars, ~ cyl, each(nrow, ncol))ddply(mtcars, .(cyl), each(nrow, ncol))ddply(mtcars, .(log(cyl)), each(nrow, ncol))ddply(mtcars, .(logcyl = log(cyl)), each(nrow, ncol))ddply(mtcars, .(vs + am), each(nrow, ncol))ddply(mtcars, .(vsam = vs + am), each(nrow, ncol))

Subset splits.

Description

Subset splits, ensuring that labels keep matching

Usage

## S3 method for class 'split'x[i, ...]

Arguments

Split array, apply function, and discard results.

Description

For each slice of an array, apply function and discard results

Usage

a_ply(  .data,  .margins,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .print = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

Nothing

Input

This function splits matrices, arrays and data frames bydimensions

Output

All output is discarded. This is useful for functions that you arecalling purely for their side effects like displaying plots orsaving output.

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other array input:aaply(),adply(),alply()

Other no output:d_ply(),l_ply(),m_ply()

Split array, apply function, and return results in an array.

Description

For each slice of an array, apply function, keeping results as an array.

Usage

aaply(  .data,  .margins,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

This function is very similar toapply, except that it willalways return an array, and when the function returns >1 d data structures,those dimensions are added on to the highest dimensions, rather than thelowest dimensions. This makesaaply idempotent, so thataaply(input, X, identity) is equivalent toaperm(input, X).

Value

if results are atomic with same type and dimensionality, avector, matrix or array; otherwise, a list-array (a list withdimensions)

Warning

Contrary toalply andadply, passing a dataframe as first argument toaaply may lead to unexpected resultssuch as huge memory allocations.

Input

This function splits matrices, arrays and data frames bydimensions

Output

If there are no results, then this function will return a vector oflength 0 (vector()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other array input:a_ply(),adply(),alply()

Other array output:daply(),laply(),maply()

Examples

dim(ozone)aaply(ozone, 1, mean)aaply(ozone, 1, mean, .drop = FALSE)aaply(ozone, 3, mean)aaply(ozone, c(1,2), mean)dim(aaply(ozone, c(1,2), mean))dim(aaply(ozone, c(1,2), mean, .drop = FALSE))aaply(ozone, 1, each(min, max))aaply(ozone, 3, each(min, max))standardise <- function(x) (x - min(x)) / (max(x) - min(x))aaply(ozone, 3, standardise)aaply(ozone, 1:2, standardise)aaply(ozone, 1:2, diff)

Split array, apply function, and return results in a data frame.

Description

For each slice of an array, apply function then combine results into a dataframe.

Usage

adply(  .data,  .margins,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL,  .id = NA)

Arguments

Value

A data frame, as described in the output section.

Input

This function splits matrices, arrays and data frames bydimensions

Output

The most unambiguous behaviour is achieved when.fun returns adata frame - in that case pieces will be combined withrbind.fill. If.fun returns an atomic vector offixed length, it will berbinded together and converted to a dataframe. Any other values will result in an error.

If there are no results, then this function will return a dataframe with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other array input:a_ply(),aaply(),alply()

Other data frame output:ddply(),ldply(),mdply()

Split array, apply function, and return results in a list.

Description

For each slice of an array, apply function then combine results into alist.

Usage

alply(  .data,  .margins,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL,  .dims = FALSE)

Arguments

Details

The list will have "dims" and "dimnames" corresponding to themargins given. For instancealply(x, c(3,2), ...) wherex has dimsc(4,3,2) will give a result with dimsc(2,3).

alply is somewhat similar toapply for caseswhere the results are not atomic.

Value

list of results

Input

This function splits matrices, arrays and data frames bydimensions

Output

If there are no results, then this function will returna list of length 0 (list()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other array input:a_ply(),aaply(),adply()

Other list output:dlply(),llply(),mlply()

Examples

alply(ozone, 3, quantile)alply(ozone, 3, function(x) table(round(x)))

Dimensions.

Description

Consistent dimensions for vectors, matrices and arrays.

Usage

amv_dim(x)

Arguments

Dimension names.

Description

Consistent dimnames for vectors, matrices and arrays.

Usage

amv_dimnames(x)

Arguments

Details

Unlikedimnames no part of the output will ever benull. If a component of dimnames is omitted,amv_dimnameswill return an integer sequence of the appropriate length.

Order a data frame by its colums.

Description

This function completes the subsetting, transforming and ordering triadwith a function that works in a similar way tosubset andtransform but for reordering a data frame by its columns.This saves a lot of typing!

Usage

arrange(df, ...)

Arguments

See Also

order for sorting function in the base package

Examples

# sort mtcars data by cylinder and displacementmtcars[with(mtcars, order(cyl, disp)), ]# Same result using arrange: no need to use with(), as the context is implicit# NOTE: plyr functions do NOT preserve row.namesarrange(mtcars, cyl, disp)# Let's keep the row.names in this examplemyCars = cbind(vehicle=row.names(mtcars), mtcars)arrange(myCars, cyl, disp)# Sort with displacement in descending orderarrange(myCars, cyl, desc(disp))

Make a function return a data frame.

Description

Create a new function that returns the existing function wrapped in adata.frame with a single column,value.

Usage

## S3 method for class ''function''as.data.frame(x, row.names, optional, ...)

Arguments

Details

This is useful when calling*dply functions with a function thatreturns a vector, and you want the output in rows, rather than columns.Thevalue column is always created, even for empty inputs.

Convert split list to regular list.

Description

Strip off label related attributed to make a strip list as regular list

Usage

## S3 method for class 'split'as.list(x, ...)

Arguments

Convert input to quoted variables.

Description

Convert characters, formulas and calls to quoted .variables

Usage

as.quoted(x, env = parent.frame())

Arguments

Details

This method is called by default on all plyr functions that take a.variables argument, so that equivalent forms can be used anywhere.

Currently conversions exist for character vectors, formulas andcall objects.

Value

a list of quoted variables

See Also

.

Examples

as.quoted(c("a", "b", "log(d)"))as.quoted(a ~ b + log(d))

Yearly batting records for all major league baseball players

Description

This data frame contains batting statistics for a subset of playerscollected fromhttp://www.baseball-databank.org/. There are a totalof 21,699 records, covering 1,228 players from 1871 to 2007. Only playerswith more 15 seasons of play are included.

Usage

baseball

Format

A 21699 x 22 data frame

Variables

Variables:

• id, unique player id

• year, year of data

• stint

• team, team played for

• lg, league

• g, number of games

• ab, number of times at bat

• r, number of runs

• h, hits, times reached base because of a batted, fair ball withouterror by the defense

• X2b, hits on which the batter reached second base safely

• X3b, hits on which the batter reached third base safely

• hr, number of home runs

• rbi, runs batted in

• sb, stolen bases

• cs, caught stealing

• bb, base on balls (walk)

• so, strike outs

• ibb, intentional base on balls

• hbp, hits by pitch

• sh, sacrifice hits

• sf, sacrifice flies

• gidp, ground into double play

References

http://www.baseball-databank.org/

Examples

baberuth <- subset(baseball, id == "ruthba01")baberuth$cyear <- baberuth$year - min(baberuth$year) + 1calculate_cyear <- function(df) {  mutate(df,    cyear = year - min(year),    cpercent = cyear / (max(year) - min(year))  )}baseball <- ddply(baseball, .(id), calculate_cyear)baseball <- subset(baseball, ab >= 25)model <- function(df) {  lm(rbi / ab ~ cyear, data=df)}model(baberuth)models <- dlply(baseball, .(id), model)

Column-wise function.

Description

Turn a function that operates on a vector into a function that operatescolumn-wise on a data.frame.

Usage

colwise(.fun, .cols = true, ...)catcolwise(.fun, ...)numcolwise(.fun, ...)

Arguments

Details

catcolwise andnumcolwise provide version that only operateon discrete and numeric variables respectively.

Examples

# Count number of missing valuesnmissing <- function(x) sum(is.na(x))# Apply to every column in a data framecolwise(nmissing)(baseball)# This syntax looks a little different.  It is shorthand for the# the following:f <- colwise(nmissing)f(baseball)# This is particularly useful in conjunction with d*plyddply(baseball, .(year), colwise(nmissing))# To operate only on specified columns, supply them as the second# argument.  Many different forms are accepted.ddply(baseball, .(year), colwise(nmissing, .(sb, cs, so)))ddply(baseball, .(year), colwise(nmissing, c("sb", "cs", "so")))ddply(baseball, .(year), colwise(nmissing, ~ sb + cs + so))# Alternatively, you can specify a boolean function that determines# whether or not a column should be includedddply(baseball, .(year), colwise(nmissing, is.character))ddply(baseball, .(year), colwise(nmissing, is.numeric))ddply(baseball, .(year), colwise(nmissing, is.discrete))# These last two cases are particularly common, so some shortcuts are# provided:ddply(baseball, .(year), numcolwise(nmissing))ddply(baseball, .(year), catcolwise(nmissing))# You can supply additional arguments to either colwise, or the function# it generates:numcolwise(mean)(baseball, na.rm = TRUE)numcolwise(mean, na.rm = TRUE)(baseball)

Compact list.

Description

Remove all NULL entries from a list

Usage

compact(l)

Arguments

Count the number of occurences.

Description

Equivalent toas.data.frame(table(x)), but does not includecombinations with zero counts.

Usage

count(df, vars = NULL, wt_var = NULL)

Arguments

Details

Speed-wise count is competitive withtable for singlevariables, but it really comes into its own when summarising multipledimensions because it only counts combinations that actually occur in thedata.

Compared totable +as.data.frame,countalso preserves the type of the identifier variables, instead of convertingthem to characters/factors.

Value

a data frame with label and freq columns

See Also

table for related functionality in the base package

Examples

# Count of each value of "id" in the first 100 casescount(baseball[1:100,], vars = "id")# Count of ids, weighted by their "g" loadingcount(baseball[1:100,], vars = "id", wt_var = "g")count(baseball, "id", "ab")count(baseball, "lg")# How many stints do players do?count(baseball, "stint")# Count of times each player appeared in each of the years they playedcount(baseball[1:100,], c("id", "year"))# Count of countscount(count(baseball[1:100,], c("id", "year")), "id", "freq")count(count(baseball, c("id", "year")), "freq")

Create progress bar.

Description

Create progress bar object from text string.

Usage

create_progress_bar(name = "none", ...)

Arguments

Details

Progress bars give feedback on how apply step is proceeding. Thisis mainly useful for long running functions, as for short functions, thetime taken up by splitting and combining may be on the same order (orlonger) as the apply step. Additionally, for short functions, the timeneeded to update the progress bar can significantly slow down the process.For the trivial examples below, using the tk progress bar slows things downby a factor of a thousand.

Note the that progress bar is approximate, and if the time taken byindividual function applications is highly non-uniform it may not be veryinformative of the time left.

There are currently four types of progress bar: "none", "text", "tk", and"win". See the individual documentation for more details. In plyrfunctions, these can either be specified by name, or you can create theprogress bar object yourself if you want more control over its apperance.See the examples.

See Also

progress_none,progress_text,progress_tk,progress_win

Examples

# No progress barl_ply(1:100, identity, .progress = "none")## Not run: # Use the Tcl/Tk interfacel_ply(1:100, identity, .progress = "tk")## End(Not run)# Text-based progress (|======|)l_ply(1:100, identity, .progress = "text")# Choose a progress character, run a length of time you can seel_ply(1:10000, identity, .progress = progress_text(char = "."))

Split data frame, apply function, and discard results.

Description

For each subset of a data frame, apply function and discard results.To apply a function for each row, usea_ply with.margins set to1.

Usage

d_ply(  .data,  .variables,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .print = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

Nothing

Input

This function splits data frames by variables.

Output

All output is discarded. This is useful for functions that you arecalling purely for their side effects like displaying plots orsaving output.

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other data frame input:daply(),ddply(),dlply()

Other no output:a_ply(),l_ply(),m_ply()

Split data frame, apply function, and return results in an array.

Description

For each subset of data frame, apply function then combine results intoan array.daply with a function that operates column-wise issimilar toaggregate.To apply a function for each row, useaaply with.margins set to1.

Usage

daply(  .data,  .variables,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .drop_i = TRUE,  .drop_o = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

if results are atomic with same type and dimensionality, avector, matrix or array; otherwise, a list-array (a list withdimensions)

Input

This function splits data frames by variables.

Output

If there are no results, then this function will return a vector oflength 0 (vector()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other array output:aaply(),laply(),maply()

Other data frame input:d_ply(),ddply(),dlply()

Examples

daply(baseball, .(year), nrow)# Several different ways of summarising by variables that should not be# included in the summarydaply(baseball[, c(2, 6:9)], .(year), colwise(mean))daply(baseball[, 6:9], .(baseball$year), colwise(mean))daply(baseball, .(year), function(df) colwise(mean)(df[, 6:9]))

Split data frame, apply function, and return results in a data frame.

Description

For each subset of a data frame, apply function then combine results into adata frame.To apply a function for each row, useadply with.margins set to1.

Usage

ddply(  .data,  .variables,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

A data frame, as described in the output section.

Input

This function splits data frames by variables.

Output

The most unambiguous behaviour is achieved when.fun returns adata frame - in that case pieces will be combined withrbind.fill. If.fun returns an atomic vector offixed length, it will berbinded together and converted to a dataframe. Any other values will result in an error.

If there are no results, then this function will return a dataframe with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

tapply for similar functionality in the base package

Other data frame input:d_ply(),daply(),dlply()

Other data frame output:adply(),ldply(),mdply()

Examples

# Summarize a dataset by two variablesdfx <- data.frame(  group = c(rep('A', 8), rep('B', 15), rep('C', 6)),  sex = sample(c("M", "F"), size = 29, replace = TRUE),  age = runif(n = 29, min = 18, max = 54))# Note the use of the '.' function to allow# group and sex to be used without quotingddply(dfx, .(group, sex), summarize, mean = round(mean(age), 2), sd = round(sd(age), 2))# An example using a formula for .variablesddply(baseball[1:100,], ~ year, nrow)# Applying two functions; nrow and ncolddply(baseball, .(lg), c("nrow", "ncol"))# Calculate mean runs batted in for each yearrbi <- ddply(baseball, .(year), summarise,  mean_rbi = mean(rbi, na.rm = TRUE))# Plot a line chart of the resultplot(mean_rbi ~ year, type = "l", data = rbi)# make new variable career_year based on the# start year for each player (id)base2 <- ddply(baseball, .(id), mutate, career_year = year - min(year) + 1)

Set defaults.

Description

Convient method for combining a list of values with their defaults.

Usage

defaults(x, y)

Arguments

Descending order.

Description

Transform a vector into a format that will be sorted in descending order.

Usage

desc(x)

Arguments

Examples

desc(1:10)desc(factor(letters))first_day <- seq(as.Date("1910/1/1"), as.Date("1920/1/1"), "years")desc(first_day)

Number of dimensions.

Description

Number of dimensions of an array or vector

Usage

dims(x)

Arguments

Split data frame, apply function, and return results in a list.

Description

For each subset of a data frame, apply function then combine results into alist.dlply is similar toby except that the resultsare returned in a different format.To apply a function for each row, usealply with.margins set to1.

Usage

dlply(  .data,  .variables,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

list of results

Input

This function splits data frames by variables.

Output

If there are no results, then this function will returna list of length 0 (list()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other data frame input:d_ply(),daply(),ddply()

Other list output:alply(),llply(),mlply()

Examples

linmod <- function(df) {  lm(rbi ~ year, data = mutate(df, year = year - min(year)))}models <- dlply(baseball, .(id), linmod)models[[1]]coef <- ldply(models, coef)with(coef, plot(`(Intercept)`, year))qual <- laply(models, function(mod) summary(mod)$r.squared)hist(qual)

Aggregate multiple functions into a single function.

Description

Combine multiple functions into a single function returning a named vectorof outputs.Note: you cannot supply additional parameters for the summary functions

Usage

each(...)

Arguments

See Also

summarise for applying summary functions to data

Examples

# Call min() and max() on the vector 1:10each(min, max)(1:10)# This syntax looks a little different.  It is shorthand for the# the following:f<- each(min, max)f(1:10)# Three equivalent ways to call min() and max() on the vector 1:10each("min", "max")(1:10)each(c("min", "max"))(1:10)each(c(min, max))(1:10)# Call length(), min() and max() on a random normal vectoreach(length, mean, var)(rnorm(100))

Check if a data frame is empty.

Description

Empty if it's null or it has 0 rows or columns

Usage

empty(df)

Arguments

Evaluate a quoted list of variables.

Description

Evaluates quoted variables in specified environment

Usage

eval.quoted(exprs, envir = NULL, enclos = NULL, try = FALSE)

Arguments

Value

a list

Fail with specified value.

Description

Modify a function so that it returns a default value when there is anerror.

Usage

failwith(default = NULL, f, quiet = FALSE)

Arguments

Value

a function

See Also

try_default

Examples

f <- function(x) if (x == 1) stop("Error!") else 1## Not run: f(1)f(2)## End(Not run)safef <- failwith(NULL, f)safef(1)safef(2)

Capture current evaluation context.

Description

This function captures the current context, making it easierto use**ply with functions that do special evaluation andneed access to the environment where ddply was called from.

Usage

here(f)

Arguments

Author(s)

Peter Meilstrup,https://github.com/crowding

Examples

df <- data.frame(a = rep(c("a","b"), each = 10), b = 1:20)f1 <- function(label) {   ddply(df, "a", mutate, label = paste(label, b))}## Not run: f1("name:")# Doesn't work because mutate can't find label in the current scopef2 <- function(label) {   ddply(df, "a", here(mutate), label = paste(label, b))}f2("name:")# Works :)

Compute a unique numeric id for each unique row in a data frame.

Description

Properties:

• order(id) is equivalent todo.call(order, df)

• rows containing the same data have the same value

• ifdrop = FALSE then room for all possibilites

Usage

id(.variables, drop = FALSE)

Arguments

Value

a numeric vector with attribute n, giving total number ofpossibilities

See Also

id_var

Numeric id for a vector.

Description

Numeric id for a vector.

Usage

id_var(x, drop = FALSE)

Construct an immutable data frame.

Description

An immutable data frame works like an ordinary data frame, except that whenyou subset it, it returns a reference to the original data frame, not aa copy. This makes subsetting substantially faster and has a big impactwhen you are working with large datasets with many groups.

Usage

idata.frame(df)

Arguments

Details

This method is still a little experimental, so please let me know if yourun into any problems.

Value

an immutable data frame

Examples

system.time(dlply(baseball, "id", nrow))system.time(dlply(idata.frame(baseball), "id", nrow))

An indexed array.

Description

Create a indexed array, a space efficient way of indexing into a largearray.

Usage

indexed_array(env, index)

Arguments

An indexed data frame.

Description

Create a indexed list, a space efficient way of indexing into a large data frame

Usage

indexed_df(data, index, vars)

Arguments

Determine if a vector is discrete.

Description

A discrete vector is a factor or a character vector

Usage

is.discrete(x)

Arguments

Examples

is.discrete(1:10)is.discrete(c("a", "b", "c"))is.discrete(factor(c("a", "b", "c")))

Is a formula?Checks if argument is a formula

Description

Is a formula?Checks if argument is a formula

Usage

is.formula(x)

Split iterator that returns values, not indices.

Description

Split iterator that returns values, not indices.

Usage

isplit2(x, f, drop = FALSE, ...)

Warning

Deprecated, do not use in new code.

See Also

plyr-deprecated

Join two data frames together.

Description

Join, like merge, is designed for the types of problemswhere you would use a sql join.

Usage

join(x, y, by = NULL, type = "left", match = "all")

Arguments

Details

The four join types return:

• inner: only rows with matching keys in both x and y

• left: all rows in x, adding matching columns from y

• right: all rows in y, adding matching columns from x

• full: all rows in x with matching columns in y, then therows of y that don't match x.

Note that from plyr 1.5,join will (by default) return all matches,not just the first match, as it did previously.

Unlike merge, preserves the order of x no matter what join type is used.If needed, rows from y will be added to the bottom. Join is often fasterthan merge, although it is somewhat less featureful - it currently offersno way to rename output or merge on different variables in the x and ydata frames.

Examples

first <- ddply(baseball, "id", summarise, first = min(year))system.time(b2 <- merge(baseball, first, by = "id", all.x = TRUE))system.time(b3 <- join(baseball, first, by = "id"))b2 <- arrange(b2, id, year, stint)b3 <- arrange(b3, id, year, stint)stopifnot(all.equal(b2, b3))

Join keys.Given two data frames, create a unique key for each row.

Description

Join keys.Given two data frames, create a unique key for each row.

Usage

join.keys(x, y, by)

Arguments

Recursively join a list of data frames.

Description

Recursively join a list of data frames.

Usage

join_all(dfs, by = NULL, type = "left", match = "all")

Arguments

Examples

dfs <- list(  a = data.frame(x = 1:10, a = runif(10)),  b = data.frame(x = 1:10, b = runif(10)),  c = data.frame(x = 1:10, c = runif(10)))join_all(dfs)join_all(dfs, "x")

Split list, apply function, and discard results.

Description

For each element of a list, apply function and discard results

Usage

l_ply(  .data,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .print = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Value

Nothing

Input

This function splits lists by elements.

Output

All output is discarded. This is useful for functions that you arecalling purely for their side effects like displaying plots orsaving output.

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other list input:laply(),ldply(),llply()

Other no output:a_ply(),d_ply(),m_ply()

Examples

l_ply(llply(mtcars, round), table, .print = TRUE)l_ply(baseball, function(x) print(summary(x)))

Split list, apply function, and return results in an array.

Description

For each element of a list, apply function then combine results into anarray.

Usage

laply(  .data,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

laply is similar in spirit tosapply exceptthat it will always return an array, and the output is transposed withrespectsapply - each element of the list corresponds to a row,not a column.

Value

if results are atomic with same type and dimensionality, avector, matrix or array; otherwise, a list-array (a list withdimensions)

Input

This function splits lists by elements.

Output

If there are no results, then this function will return a vector oflength 0 (vector()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other list input:l_ply(),ldply(),llply()

Other array output:aaply(),daply(),maply()

Examples

laply(baseball, is.factor)# cfldply(baseball, is.factor)colwise(is.factor)(baseball)laply(seq_len(10), identity)laply(seq_len(10), rep, times = 4)laply(seq_len(10), matrix, nrow = 2, ncol = 2)

Split list, apply function, and return results in a data frame.

Description

For each element of a list, apply function then combine results into a dataframe.

Usage

ldply(  .data,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL,  .id = NA)

Arguments

Value

A data frame, as described in the output section.

Input

This function splits lists by elements.

Output

The most unambiguous behaviour is achieved when.fun returns adata frame - in that case pieces will be combined withrbind.fill. If.fun returns an atomic vector offixed length, it will berbinded together and converted to a dataframe. Any other values will result in an error.

If there are no results, then this function will return a dataframe with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other list input:l_ply(),laply(),llply()

Other data frame output:adply(),ddply(),mdply()

Experimental iterator based version of llply.

Description

Because iterators do not have known length,liply starts byallocating an output list of length 50, and then doubles that lengthwhenever it runs out of space. This gives O(n ln n) performance ratherthan the O(n ^ 2) performance from the naive strategy of growing the listeach time.

Usage

liply(.iterator, .fun = NULL, ...)

Arguments

Warning

Deprecated, do not use in new code.

See Also

plyr-deprecated

List to array.

Description

Reduce/simplify a list of homogenous objects to an array

Usage

list_to_array(res, labels = NULL, .drop = FALSE)

Arguments

See Also

Other list simplification functions:list_to_dataframe(),list_to_vector()

List to data frame.

Description

Reduce/simplify a list of homogenous objects to a data frame. AllNULL entries are removed. Remaining entries must be all atomicor all data frames.

Usage

list_to_dataframe(res, labels = NULL, id_name = NULL, id_as_factor = FALSE)

Arguments

See Also

Other list simplification functions:list_to_array(),list_to_vector()

List to vector.

Description

Reduce/simplify a list of homogenous objects to a vector

Usage

list_to_vector(res)

Arguments

See Also

Other list simplification functions:list_to_array(),list_to_dataframe()

Split list, apply function, and return results in a list.

Description

For each element of a list, apply function, keeping results as a list.

Usage

llply(  .data,  .fun = NULL,  ...,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

llply is equivalent tolapply except that it willpreserve labels and can display a progress bar.

Value

list of results

Input

This function splits lists by elements.

Output

If there are no results, then this function will returna list of length 0 (list()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other list input:l_ply(),laply(),ldply()

Other list output:alply(),dlply(),mlply()

Examples

llply(llply(mtcars, round), table)llply(baseball, summary)# Examples from ?lapplyx <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))llply(x, mean)llply(x, quantile, probs = 1:3/4)

Loop apply

Description

An optimised version of lapply for the special case of operating onseq_len(n)

Usage

loop_apply(n, f, env = parent.frame())

Arguments

Call function with arguments in array or data frame, discarding results.

Description

Call a multi-argument function with values taken from columns of andata frame or array, and discard results into a list.

Usage

m_ply(  .data,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .print = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

Them*ply functions are theplyr version ofmapply,specialised according to the type of output they produce. These functionsare just a convenient wrapper arounda*ply withmargins = 1and.fun wrapped insplat.

Value

Nothing

Input

Call a multi-argument function with values taken fromcolumns of an data frame or array

Output

All output is discarded. This is useful for functions that you arecalling purely for their side effects like displaying plots orsaving output.

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other multiple arguments input:maply(),mdply(),mlply()

Other no output:a_ply(),d_ply(),l_ply()

Call function with arguments in array or data frame, returning an array.

Description

Call a multi-argument function with values taken from columns of andata frame or array, and combine results into an array

Usage

maply(  .data,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .drop = TRUE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

Them*ply functions are theplyr version ofmapply,specialised according to the type of output they produce. These functionsare just a convenient wrapper arounda*ply withmargins = 1and.fun wrapped insplat.

Value

if results are atomic with same type and dimensionality, avector, matrix or array; otherwise, a list-array (a list withdimensions)

Input

Call a multi-argument function with values taken fromcolumns of an data frame or array

Output

If there are no results, then this function will return a vector oflength 0 (vector()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other multiple arguments input:m_ply(),mdply(),mlply()

Other array output:aaply(),daply(),laply()

Examples

maply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)maply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 5)maply(cbind(1:5, 1:5), rnorm, n = 5)

Replace specified values with new values, in a vector or factor.

Description

Item inx that match itemsfrom will be replaced byitems into, matched by position. For example, items inx thatmatch the first element infrom will be replaced by the firstelement ofto.

Usage

mapvalues(x, from, to, warn_missing = TRUE)

Arguments

Details

Ifx is a factor, the matching levels of the factor will bereplaced with the new values.

The relatedrevalue function works only on character vectorsand factors, but this function works on vectors of any type and factors.

See Also

revalue to do the same thing but with a singlenamed vector instead of two separate vectors.

Examples

x <- c("a", "b", "c")mapvalues(x, c("a", "c"), c("A", "C"))# Works on factorsy <- factor(c("a", "b", "c", "a"))mapvalues(y, c("a", "c"), c("A", "C"))# Works on numeric vectorsz <- c(1, 4, 5, 9)mapvalues(z, from = c(1, 5, 9), to = c(10, 50, 90))

Extract matching rows of a data frame.

Description

Match works in the same way as join, but instead of return the combineddataset, it only returns the matching rows from the first dataset. This isparticularly useful when you've summarised the data in some wayand want to subset the original data by a characteristic of the subset.

Usage

match_df(x, y, on = NULL)

Arguments

Details

match_df shares the same semantics asjoin, notmatch:

• the match criterion is==, notidentical).

• it doesn't work for columns that are not atomic vectors

• if there are no matches, the row will be omitted'

Value

a data frame

See Also

join to combine the columns from both x and yandmatch for the base function selecting matching items

Examples

# count the occurrences of each id in the baseball dataframe, then get the subset with a freq >25longterm <- subset(count(baseball, "id"), freq > 25)# longterm#             id freq# 30   ansonca01   27# 48   baineha01   27# ...# Select only rows from these longterm players from the baseball dataframe# (match would default to match on shared column names, but here was explicitly set "id")bb_longterm <- match_df(baseball, longterm, on="id")bb_longterm[1:5,]

Call function with arguments in array or data frame, returning a data frame.

Description

Call a multi-argument function with values taken from columns of andata frame or array, and combine results into a data frame

Usage

mdply(  .data,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

Them*ply functions are theplyr version ofmapply,specialised according to the type of output they produce. These functionsare just a convenient wrapper arounda*ply withmargins = 1and.fun wrapped insplat.

Value

A data frame, as described in the output section.

Input

Call a multi-argument function with values taken fromcolumns of an data frame or array

Output

The most unambiguous behaviour is achieved when.fun returns adata frame - in that case pieces will be combined withrbind.fill. If.fun returns an atomic vector offixed length, it will berbinded together and converted to a dataframe. Any other values will result in an error.

If there are no results, then this function will return a dataframe with zero rows and columns (data.frame()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other multiple arguments input:m_ply(),maply(),mlply()

Other data frame output:adply(),ddply(),ldply()

Examples

mdply(data.frame(mean = 1:5, sd = 1:5), rnorm, n = 2)mdply(expand.grid(mean = 1:5, sd = 1:5), rnorm, n = 2)mdply(cbind(mean = 1:5, sd = 1:5), rnorm, n = 5)mdply(cbind(mean = 1:5, sd = 1:5), as.data.frame(rnorm), n = 5)

Call function with arguments in array or data frame, returning a list.

Description

Call a multi-argument function with values taken from columns of andata frame or array, and combine results into a list.

Usage

mlply(  .data,  .fun = NULL,  ...,  .expand = TRUE,  .progress = "none",  .inform = FALSE,  .parallel = FALSE,  .paropts = NULL)

Arguments

Details

Them*ply functions are theplyr version ofmapply,specialised according to the type of output they produce. These functionsare just a convenient wrapper arounda*ply withmargins = 1and.fun wrapped insplat.

Value

list of results

Input

Call a multi-argument function with values taken fromcolumns of an data frame or array

Output

If there are no results, then this function will returna list of length 0 (list()).

References

Hadley Wickham (2011). The Split-Apply-Combine Strategyfor Data Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

See Also

Other multiple arguments input:m_ply(),maply(),mdply()

Other list output:alply(),dlply(),llply()

Examples

mlply(cbind(1:4, 4:1), rep)mlply(cbind(1:4, times = 4:1), rep)mlply(cbind(1:4, 4:1), seq)mlply(cbind(1:4, length = 4:1), seq)mlply(cbind(1:4, by = 4:1), seq, to = 20)

Mutate a data frame by adding new or replacing existing columns.

Description

This function is very similar totransform but it executesthe transformations iteratively so that later transformations can use thecolumns created by earlier transformations. Like transform, unnamedcomponents are silently dropped.

Usage

mutate(.data, ...)

Arguments

Details

Mutate seems to be considerably faster than transform for large dataframes.

See Also

subset,summarise,arrange. For another somewhat different approach tosolving the same problem, seewithin.

Examples

# Examples from transformmutate(airquality, Ozone = -Ozone)mutate(airquality, new = -Ozone, Temp = (Temp - 32) / 1.8)# Things transform can't domutate(airquality, Temp = (Temp - 32) / 1.8, OzT = Ozone / Temp)# mutate is rather faster than transformsystem.time(transform(baseball, avg_ab = ab / g))system.time(mutate(baseball, avg_ab = ab / g))

Toggle row names between explicit and implicit.

Description

Plyr functions ignore row names, so this function provides a way to preservethem by converting them to an explicit column in the data frame. After theplyr operation, you can then applyname_rows again to convert backfrom the explicit column to the implicitrownames.

Usage

name_rows(df)

Arguments

Examples

name_rows(mtcars)name_rows(name_rows(mtcars))df <- data.frame(a = sample(10))arrange(df, a)arrange(name_rows(df), a)name_rows(arrange(name_rows(df), a))

Compute names of quoted variables.

Description

Figure out names of quoted variables, using specified names if they exist,otherwise converting the values to character strings. This may createvariable names that can only be accessed using``.

Usage

## S3 method for class 'quoted'names(x)

Number of unique values.

Description

Calculate number of unique values of a variable as efficiently as possible.

Usage

nunique(x)

Arguments

Monthly ozone measurements over Central America.

Description

This data set is a subset of the data from the 2006 ASA Data expochallenge,https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2006.The data are monthly ozone averages on a very coarse 24 by 24 grid covering CentralAmerica, from Jan 1995 to Dec 2000. The data is stored in a 3d area withthe first two dimensions representing latitude and longitude, and the thirdrepresenting time.

Usage

ozone

Format

A 24 x 24 x 72 numeric array

References

https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2006

Examples

value <- ozone[1, 1, ]time <- 1:72month.abbr <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")month <- factor(rep(month.abbr, length = 72), levels = month.abbr)year <- rep(1:6, each = 12)deseasf <- function(value) lm(value ~ month - 1)models <- alply(ozone, 1:2, deseasf)coefs <- laply(models, coef)dimnames(coefs)[[3]] <- month.abbrnames(dimnames(coefs))[3] <- "month"deseas <- laply(models, resid)dimnames(deseas)[[3]] <- 1:72names(dimnames(deseas))[3] <- "time"dim(coefs)dim(deseas)

Deprecated Functions in Package plyr

Description

These functions are provided for compatibility with older versions ofplyr only, and may be defunct as soon as the next release.

Details

• liply

• isplit2

Print quoted variables.

Description

Display thestructure of quoted variables

Usage

## S3 method for class 'quoted'print(x, ...)

Print split.

Description

Don't print labels, so it appears like a regular list

Usage

## S3 method for class 'split'print(x, ...)

Arguments

Null progress bar

Description

A progress bar that does nothing

Usage

progress_none()

Details

This the default progress bar used by plyr functions. It's very simple tounderstand - it does nothing!

See Also

Other progress bars:progress_text(),progress_time(),progress_tk(),progress_win()

Examples

l_ply(1:100, identity, .progress = "none")

Text progress bar.

Description

A textual progress bar

Usage

progress_text(style = 3, ...)

Arguments

Details

This progress bar displays a textual progress bar that works on allplatforms. It is a thin wrapper around the built-insetTxtProgressBar and can be customised in the same way.

See Also

Other progress bars:progress_none(),progress_time(),progress_tk(),progress_win()

Examples

l_ply(1:100, identity, .progress = "text")l_ply(1:100, identity, .progress = progress_text(char = "-"))

Text progress bar with time.

Description

A textual progress bar that estimates time remaining. It displays theestimated time remaining and, when finished, total duration.

Usage

progress_time()

See Also

Other progress bars:progress_none(),progress_text(),progress_tk(),progress_win()

Examples

l_ply(1:100, function(x) Sys.sleep(.01), .progress = "time")

Graphical progress bar, powered by Tk.

Description

A graphical progress bar displayed in a Tk window

Usage

progress_tk(title = "plyr progress", label = "Working...", ...)

Arguments

Details

This graphical progress will appear in a separate window.

See Also

tkProgressBar for the function that powers this progress bar

Other progress bars:progress_none(),progress_text(),progress_time(),progress_win()

Examples

## Not run: l_ply(1:100, identity, .progress = "tk")l_ply(1:100, identity, .progress = progress_tk(width=400))l_ply(1:100, identity, .progress = progress_tk(label=""))## End(Not run)

Graphical progress bar, powered by Windows.

Description

A graphical progress bar displayed in a separate window

Usage

progress_win(title = "plyr progress", ...)

Arguments

Details

This graphical progress only works on Windows.

See Also

winProgressBar for the function that powers this progress bar

Other progress bars:progress_none(),progress_text(),progress_time(),progress_tk()

Examples

## Not run: l_ply(1:100, identity, .progress = "win")l_ply(1:100, identity, .progress = progress_win(title="Working..."))## End(Not run)

Quick data frame.

Description

Experimental version ofas.data.frame that converts alist to a data frame, but doesn't do any checks to make sure it's avalid format. Much faster.

Usage

quickdf(list)

Arguments

Replicate expression and discard results.

Description

Evalulate expression n times then discard results

Usage

r_ply(.n, .expr, .progress = "none", .print = FALSE)

Arguments

Details

This function runs an expression multiple times, discarding the results.This function is equivalent toreplicate, but never returnsanything

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy forData Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

Examples

r_ply(10, plot(runif(50)))r_ply(25, hist(runif(1000)))

Replicate expression and return results in a array.

Description

Evalulate expression n times then combine results into an array

Usage

raply(.n, .expr, .progress = "none", .drop = TRUE)

Arguments

Details

This function runs an expression multiple times, and combines theresult into a data frame. If there are no results, then this functionreturns a vector of length 0 (vector(0)).This function is equivalent toreplicate, but will alwaysreturn results as a vector, matrix or array.

Value

if results are atomic with same type and dimensionality, a vector, matrix or array; otherwise, a list-array (a list with dimensions)

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy forData Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

Examples

raply(100, mean(runif(100)))raply(100, each(mean, var)(runif(100)))raply(10, runif(4))raply(10, matrix(runif(4), nrow=2))# See the central limit theorem in actionhist(raply(1000, mean(rexp(10))))hist(raply(1000, mean(rexp(100))))hist(raply(1000, mean(rexp(1000))))

Combine data.frames by row, filling in missing columns.

Description

rbinds a list of data frames filling missing columns with NA.

Usage

rbind.fill(...)

Arguments

Details

This is an enhancement torbind that adds in columnsthat are not present in all inputs, accepts a list of data frames, andoperates substantially faster.

Column names and types in the output will appear in the order in whichthey were encountered.

Unordered factor columns will have their levels unified andcharacter data bound with factors will be converted tocharacter. POSIXct data will be converted to be in the same timezone. Array and matrix columns must have identical dimensions afterthe row count. Aside from these there are no general checks thateach column is of consistent data type.

Value

a single data frame

See Also

Other binding functions:rbind.fill.matrix()

Examples

rbind.fill(mtcars[c("mpg", "wt")], mtcars[c("wt", "cyl")])

Bind matrices by row, and fill missing columns with NA.

Description

The matrices are bound together using their column names or the columnindices (in that order of precedence.) Numeric columns may be converted tocharacter beforehand, e.g. using format. If a matrix doesn't havecolnames, the column number is used. Note that this means that acolumn with name"1" is merged with the first column of a matrixwithout name and so on. The returned matrix will always have column names.

Usage

rbind.fill.matrix(...)

Arguments

Details

Vectors are converted to 1-column matrices.

Matrices of factors are not supported. (They are anyways quiteinconvenient.) You may convert them first to either numeric or charactermatrices. If a matrices of different types are merged, then normalcovnersion precendence will apply.

Row names are ignored.

Value

a matrix with column names

Author(s)

C. Beleites

See Also

rbind,cbind,rbind.fill

Other binding functions:rbind.fill()

Examples

A <- matrix (1:4, 2)B <- matrix (6:11, 2)ABrbind.fill.matrix (A, B)colnames (A) <- c (3, 1)Arbind.fill.matrix (A, B)rbind.fill.matrix (A, 99)

Replicate expression and return results in a data frame.

Description

Evaluate expression n times then combine results into a data frame

Usage

rdply(.n, .expr, .progress = "none", .id = NA)

Arguments

Details

This function runs an expression multiple times, and combines the result intoa data frame. If there are no results, then this function returns a dataframe with zero rows and columns (data.frame()). This function isequivalent toreplicate, but will always return results as adata frame.

Value

a data frame

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy for DataAnalysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

Examples

rdply(20, mean(runif(100)))rdply(20, each(mean, var)(runif(100)))rdply(20, data.frame(x = runif(2)))

Reduce dimensions.

Description

Remove extraneous dimensions

Usage

reduce_dim(x)

Arguments

Modify names by name, not position.

Description

Modify names by name, not position.

Usage

rename(x, replace, warn_missing = TRUE, warn_duplicated = TRUE)

Arguments

Examples

x <- c("a" = 1, "b" = 2, d = 3, 4)# Rename column d to "c", updating the variable "x" with the resultx <- rename(x, replace = c("d" = "c"))x# Rename column "disp" to "displacement"rename(mtcars, c("disp" = "displacement"))

Replace specified values with new values, in a factor or character vector.

Description

Ifx is a factor, the named levels of the factor will bereplaced with the new values.

Usage

revalue(x, replace = NULL, warn_missing = TRUE)

Arguments

Details

This function works only on character vectors and factors, but therelatedmapvalues function works on vectors of any type and factors,and instead of a named vector specifying the original and replacement values,it takes two separate vectors

See Also

mapvalues to replace values with vectors of any type

Examples

x <- c("a", "b", "c")revalue(x, c(a = "A", c = "C"))revalue(x, c("a" = "A", "c" = "C"))y <- factor(c("a", "b", "c", "a"))revalue(y, c(a = "A", c = "C"))

Replicate expression and return results in a list.

Description

Evalulate expression n times then combine results into a list

Usage

rlply(.n, .expr, .progress = "none")

Arguments

Details

This function runs an expression multiple times, and combines theresult into a list. If there are no results, then this function will returna list of length 0 (list()). This function is equivalent toreplicate, but will always return results as a list.

Value

list of results

References

Hadley Wickham (2011). The Split-Apply-Combine Strategy forData Analysis. Journal of Statistical Software, 40(1), 1-29.https://www.jstatsoft.org/v40/i01/.

Examples

mods <- rlply(100, lm(y ~ x, data=data.frame(x=rnorm(100), y=rnorm(100))))hist(laply(mods, function(x) summary(x)$r.squared))

Round to multiple of any number.

Description

Round to multiple of any number.

Usage

round_any(x, accuracy, f = round)

Arguments

Examples

round_any(135, 10)round_any(135, 100)round_any(135, 25)round_any(135, 10, floor)round_any(135, 100, floor)round_any(135, 25, floor)round_any(135, 10, ceiling)round_any(135, 100, ceiling)round_any(135, 25, ceiling)round_any(Sys.time() + 1:10, 5)round_any(Sys.time() + 1:10, 5, floor)round_any(Sys.time(), 3600)

‘Splat’ arguments to a function.

Description

Wraps a function in do.call, so instead of taking multiple arguments, ittakes a single named list which will be interpreted as its arguments.

Usage

splat(flat)

Arguments

Details

This is useful when you want to pass a function a row of data frame orarray, and don't want to manually pull it apart in your function.

Value

a function

Examples

hp_per_cyl <- function(hp, cyl, ...) hp / cylsplat(hp_per_cyl)(mtcars[1,])splat(hp_per_cyl)(mtcars)f <- function(mpg, wt, ...) data.frame(mw = mpg / wt)ddply(mtcars, .(cyl), splat(f))

Split indices.

Description

An optimised version of split for the special case of splitting rowindices into groups, as used bysplitter_d.

Usage

split_indices(group, n = 0L)

Arguments

Examples

split_indices(sample(10, 100, rep = TRUE))split_indices(sample(10, 100, rep = TRUE), 10)

Generate labels for split data frame.

Description

Create data frame giving labels for split data frame.

Usage

split_labels(splits, drop, id = plyr::id(splits, drop = TRUE))

Arguments

Split an array by .margins.

Description

Split a 2d or higher data structure into lower-d pieces based

Usage

splitter_a(data, .margins = 1L, .expand = TRUE, .id = NA)

Arguments

Details

This is the workhorse of thea*ply functions. Given a >1 ddata structure (matrix, array, data.frame), it splits it into piecesbased on the subscripts that you supply. Each piece is a lower dimensionalslice.

The margins are specified in the same way asapply, butsplitter_a just splits up the data, whileapply alsoapplies a function and combines the pieces back together. This functionalso includes enough information to recreate the split from attributes onthe list of pieces.

Value

a list of lower-d slices, with attributes that record split details

See Also

Other splitter functions:splitter_d()

Examples

plyr:::splitter_a(mtcars, 1)plyr:::splitter_a(mtcars, 2)plyr:::splitter_a(ozone, 2)plyr:::splitter_a(ozone, 3)plyr:::splitter_a(ozone, 1:2)

Split a data frame by variables.

Description

Split a data frame into pieces based on variable contained in that data frame

Usage

splitter_d(data, .variables = NULL, drop = TRUE)

Arguments

Details

This is the workhorse of thed*ply functions. Based on the variablesyou supply, it breaks up a single data frame into a list of data frames,each containing a single combination from the levels of the specifiedvariables.

This is basically a thin wrapper aroundsplit whichevaluates the variables in the context of the data, and includes enoughinformation to reconstruct the labelling of the data frame afterother operations.

Value

a list of data.frames, with attributes that record split details

See Also

. for quoting variables,split

Other splitter functions:splitter_a()

Examples

plyr:::splitter_d(mtcars, .(cyl))plyr:::splitter_d(mtcars, .(vs, am))plyr:::splitter_d(mtcars, .(am, vs))mtcars$cyl2 <- factor(mtcars$cyl, levels = c(2, 4, 6, 8, 10))plyr:::splitter_d(mtcars, .(cyl2), drop = TRUE)plyr:::splitter_d(mtcars, .(cyl2), drop = FALSE)mtcars$cyl3 <- ifelse(mtcars$vs == 1, NA, mtcars$cyl)plyr:::splitter_d(mtcars, .(cyl3))plyr:::splitter_d(mtcars, .(cyl3, vs))plyr:::splitter_d(mtcars, .(cyl3, vs), drop = FALSE)

Remove splitting variables from a data frame.

Description

This is useful when you want to perform some operation to every columnin the data frame, except the variables that you have used to split it.These variables will be automatically added back on to the result whencombining all results together.

Usage

strip_splits(df)

Arguments

Examples

dlply(mtcars, c("vs", "am"))dlply(mtcars, c("vs", "am"), strip_splits)

Summarise a data frame.

Description

Summarise works in an analogous way tomutate, exceptinstead of adding columns to an existing data frame, it creates a newdata frame. This is particularly useful in conjunction withddply as it makes it easy to perform group-wise summaries.

Usage

summarise(.data, ...)

Arguments

Note

Be careful when using existing variable names; the correspondingcolumns will be immediately updated with the new data and this can affectsubsequent operations referring to those variables.

Examples

# Let's extract the number of teams and total period of time# covered by the baseball dataframesummarise(baseball, duration = max(year) - min(year), nteams = length(unique(team)))# Combine with ddply to do that for each separate idddply(baseball, "id", summarise, duration = max(year) - min(year), nteams = length(unique(team)))

Take a subset along an arbitrary dimension

Description

Take a subset along an arbitrary dimension

Usage

take(x, along, indices, drop = FALSE)

Arguments

Examples

x <- array(seq_len(3 * 4 * 5), c(3, 4, 5))take(x, 3, 1)take(x, 2, 1)take(x, 1, 1)take(x, 3, 1, drop = TRUE)take(x, 2, 1, drop = TRUE)take(x, 1, 1, drop = TRUE)

Function that always returns true.

Description

Function that always returns true.

Usage

true(...)

Arguments

Value

TRUE

See Also

colwise which uses it

Try, with default in case of error.

Description

try_default wraps try so that it returns a default value in the case of error.tryNULL provides a useful special case when dealing with lists.

Usage

try_default(expr, default, quiet = FALSE)tryNULL(expr)

Arguments

See Also

tryapply

Apply with built in try.Uses compact, lapply and tryNULL

Description

Apply with built in try.Uses compact, lapply and tryNULL

Usage

tryapply(list, fun, ...)

Arguments

Un-rowname.

Description

Strip rownames from an object

Usage

unrowname(x)

Arguments

Vector aggregate.

Description

This function is somewhat similar totapply, but is designed foruse in conjunction withid. It is simpler in that it onlyaccepts a single grouping vector (useid if you have more)and usesvapply internally, using the.default valueas the template.

Usage

vaggregate(.value, .group, .fun, ..., .default = NULL, .n = nlevels(.group))

Arguments

Details

vaggregate should be faster thantapply in most situationsbecause it avoids making a copy of the data.

Examples

# Some examples of use borrowed from ?tapplyn <- 17; fac <- factor(rep(1:3, length.out = n), levels = 1:5)table(fac)vaggregate(1:n, fac, sum)vaggregate(1:n, fac, sum, .default = NA_integer_)vaggregate(1:n, fac, range)vaggregate(1:n, fac, range, .default = c(NA, NA) + 0)vaggregate(1:n, fac, quantile)# Unlike tapply, vaggregate does not support multi-d output:tapply(warpbreaks$breaks, warpbreaks[,-1], sum)vaggregate(warpbreaks$breaks, id(warpbreaks[,-1]), sum)# But it is about 10x fasterx <- rnorm(1e6)y1 <- sample.int(10, 1e6, replace = TRUE)system.time(tapply(x, y1, mean))system.time(vaggregate(x, y1, mean))