Package 'reshape'

Title: Flexibly Reshape Data
Description: Flexibly restructure and aggregate data using just two functions: melt and cast.
Authors: Hadley Wickham [aut, cre]
Maintainer: Hadley Wickham <[email protected]>
License: MIT + file LICENSE
Version: 0.8.9
Built: 2024-10-31 02:47:45 UTC
Source: https://github.com/cran/reshape

Help Index


Cast function

Description

Cast a molten data frame into the reshaped or aggregated form you want

Usage

cast(data, formula = ... ~ variable, fun.aggregate=NULL, ...,
  margins=FALSE, subset=TRUE, df=FALSE, fill=NULL, add.missing=FALSE,
  value = guess_value(data))

Arguments

data

molten data frame, see melt

formula

casting formula, see details for specifics

fun.aggregate

aggregation function

add.missing

fill in missing combinations?

value

name of value column

...

further arguments are passed to aggregating function

margins

vector of variable names (can include "grand\_col" and "grand\_row") to compute margins for, or TRUE to computer all margins

subset

logical vector to subset data set with before reshaping

df

argument used internally

fill

value with which to fill in structural missings, defaults to value from applying fun.aggregate to 0 length vector

Details

Along with melt and recast, this is the only function you should ever need to use. Once you have melted your data, cast will arrange it into the form you desire based on the specification given by formula.

The cast formula has the following format: x_variable + x_2 ~ y_variable + y_2 ~ z_variable ~ ... | list_variable + ... The order of the variables makes a difference. The first varies slowest, and the last fastest. There are a couple of special variables: "..." represents all other variables not used in the formula and "." represents no variable, so you can do formula=var1 ~ .

Creating high-D arrays is simple, and allows a class of transformations that are hard without apply and sweep

If the combination of variables you supply does not uniquely identify one row in the original data set, you will need to supply an aggregating function, fun.aggregate. This function should take a vector of numbers and return a summary statistic(s). It must return the same number of arguments regardless of the length of the input vector. If it returns multiple value you can use "result\_variable" to control where they appear. By default they will appear as the last column variable.

The margins argument should be passed a vector of variable names, eg. c("month","day"). It will silently drop any variables that can not be margined over. You can also use "grand\_col" and "grand\_row" to get grand row and column margins respectively.

Subset takes a logical vector that will be evaluated in the context of data, so you can do something like subset = variable=="length"

All the actual reshaping is done by reshape1, see its documentation for details of the implementation

Author(s)

Hadley Wickham <[email protected]>

See Also

reshape1, http://had.co.nz/reshape/

Examples

#Air quality example
names(airquality) <- tolower(names(airquality))
aqm <- melt(airquality, id=c("month", "day"), na.rm=TRUE)

cast(aqm, day ~ month ~ variable)
cast(aqm, month ~ variable, mean)
cast(aqm, month ~ . | variable, mean)
cast(aqm, month ~ variable, mean, margins=c("grand_row", "grand_col"))
cast(aqm, day ~ month, mean, subset=variable=="ozone")
cast(aqm, month ~ variable, range)
cast(aqm, month ~ variable + result_variable, range)
cast(aqm, variable ~ month ~ result_variable,range)

#Chick weight example
names(ChickWeight) <- tolower(names(ChickWeight))
chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE)

cast(chick_m, time ~ variable, mean) # average effect of time
cast(chick_m, diet ~ variable, mean) # average effect of diet
cast(chick_m, diet ~ time ~ variable, mean) # average effect of diet & time

# How many chicks at each time? - checking for balance
cast(chick_m, time ~ diet, length)
cast(chick_m, chick ~ time, mean)
cast(chick_m, chick ~ time, mean, subset=time < 10 & chick < 20)

cast(chick_m, diet + chick ~ time)
cast(chick_m, chick ~ time ~ diet)
cast(chick_m, diet + chick ~ time, mean, margins="diet")

#Tips example
cast(melt(tips), sex ~ smoker, mean, subset=variable=="total_bill")
cast(melt(tips), sex ~ smoker | variable, mean)

ff_d <- melt(french_fries, id=1:4, na.rm=TRUE)
cast(ff_d, subject ~ time, length)
cast(ff_d, subject ~ time, length, fill=0)
cast(ff_d, subject ~ time, function(x) 30 - length(x))
cast(ff_d, subject ~ time, function(x) 30 - length(x), fill=30)
cast(ff_d, variable ~ ., c(min, max))
cast(ff_d, variable ~ ., function(x) quantile(x,c(0.25,0.5)))
cast(ff_d, treatment ~ variable, mean, margins=c("grand_col", "grand_row"))
cast(ff_d, treatment + subject ~ variable, mean, margins="treatment")

Split a vector into multiple columns

Description

This function can be used to split up a column that has been pasted together.

Usage

colsplit(x, split="", names)

Arguments

x

character vector or factor to split up

split

regular expression to split on

names

names for output columns

Author(s)

Hadley Wickham <[email protected]>


Combine factor levels

Description

Convenience function to make it easy to combine multiple levels

Usage

combine_factor(fac, variable=levels(fac), other.label="Other")

Arguments

fac

factor variable

variable

either a vector of . See examples for more details.

other.label

label for other level

Author(s)

Hadley Wickham <[email protected]>

Examples

df <- data.frame(a = LETTERS[sample(5, 15, replace=TRUE)], y = rnorm(15))  
combine_factor(df$a, c(1,2,2,1,2))
combine_factor(df$a, c(1:4, 1))
(f <- reorder(df$a, df$y))
percent <- tapply(abs(df$y), df$a, sum)
combine_factor(f, c(order(percent)[1:3]))

Condense a data frame

Description

Condense

Usage

condense.df(data, variables, fun, ...)

Arguments

data

data frame

variables

character vector of variables to condense over

fun

function to condense with

...

arguments passed to condensing function

Author(s)

Hadley Wickham <[email protected]>


Expand grid

Description

Expand grid of data frames

Usage

expand.grid.df(..., unique=TRUE)

Arguments

...

list of data frames (first varies fastest)

unique

only use unique rows?

Details

Creates new data frame containing all combination of rows from data.frames in ...

Author(s)

Hadley Wickham <[email protected]>

Examples

expand.grid.df(data.frame(a=1,b=1:2))
expand.grid.df(data.frame(a=1,b=1:2), data.frame())
expand.grid.df(data.frame(a=1,b=1:2), data.frame(c=1:2, d=1:2))
expand.grid.df(data.frame(a=1,b=1:2), data.frame(c=1:2, d=1:2), data.frame(e=c("a","b")))

Sensory data from a french fries experiment

Description

This data was collected from a sensory experiment conducted at Iowa State University in 2004. The investigators were interested in the effect of using three different fryer oils had on the taste of the fries.

Variables:

  • time in weeks from start of study.

  • treatment (type of oil),

  • subject,

  • replicate,

  • potato-y flavour,

  • buttery flavour,

  • grassy flavour,

  • rancid flavour,

  • painty flavour

Usage

data(french_fries)

Format

A data frame with 696 rows and 9 variables


Aggregate multiple functions into a single function

Description

Combine multiple functions to a single function returning a named vector of outputs

Usage

funstofun(...)

Arguments

...

functions to combine

Details

Each function should produce a single number as output

Author(s)

Hadley Wickham <[email protected]>

Examples

funstofun(min, max)(1:10)
funstofun(length, mean, var)(rnorm(100))

Melt

Description

Melt an object into a form suitable for easy casting.

Usage

melt(data, ...)

Arguments

data

Data set to melt

...

Other arguments passed to the specific melt method

Details

This the generic melt function. See the following functions for specific details for different data structures:

Author(s)

Hadley Wickham <[email protected]>


Melt an array

Description

This function melts a high-dimensional array into a form that you can use cast with.

Usage

## S3 method for class 'array'
melt(data, varnames = names(dimnames(data)), ...)

Arguments

data

array to melt

varnames

variable names to use in molten data.frame

...

other arguments ignored

Details

This code is conceptually similar to as.data.frame.table

Author(s)

Hadley Wickham <[email protected]>

Examples

a <- array(1:24, c(2,3,4))
melt(a)
melt(a, varnames=c("X","Y","Z"))
dimnames(a) <- lapply(dim(a), function(x) LETTERS[1:x])
melt(a)
melt(a, varnames=c("X","Y","Z"))
dimnames(a)[1] <- list(NULL)
melt(a)

Melt a data frame

Description

Melt a data frame into form suitable for easy casting.

Usage

## S3 method for class 'data.frame'
melt(data, id.vars, measure.vars,
  variable_name = "variable", na.rm = !preserve.na, preserve.na = TRUE, ...)

Arguments

data

Data set to melt

id.vars

Id variables. If blank, will use all non measure.vars variables. Can be integer (variable position) or string (variable name)

measure.vars

Measured variables. If blank, will use all non id.vars variables. Can be integer (variable position) or string (variable name)

variable_name

Name of the variable that will store the names of the original variables

na.rm

Should NA values be removed from the data set?

preserve.na

Old argument name, now deprecated

...

other arguments ignored

Details

You need to tell melt which of your variables are id variables, and which are measured variables. If you only supply one of id.vars and measure.vars, melt will assume the remainder of the variables in the data set belong to the other. If you supply neither, melt will assume factor and character variables are id variables, and all others are measured.

Value

molten data

Author(s)

Hadley Wickham <[email protected]>

See Also

http://had.co.nz/reshape/

Examples

head(melt(tips))
names(airquality) <- tolower(names(airquality))
melt(airquality, id=c("month", "day"))
names(ChickWeight) <- tolower(names(ChickWeight))
melt(ChickWeight, id=2:4)

Merge all

Description

Merge together a series of data.frames

Usage

merge_all(dfs, ...)

Arguments

dfs

list of data frames to merge

...

other arguments passed on to merge

Details

Order of data frames should be from most complete to least complete

Author(s)

Hadley Wickham <[email protected]>

See Also

merge_recurse


Name rows

Description

Add variable to data frame containing rownames

Usage

namerows(df, col.name = "id")

Arguments

df

data frame

col.name

name of new column containing rownames

Details

This is useful when the thing that you want to melt by is the rownames of the data frame, not an explicit variable

Author(s)

Hadley Wickham <[email protected]>


Recast

Description

melt and cast data in a single step

Usage

recast(data, formula, ..., id.var, measure.var)

Arguments

data

Data set to melt

formula

Casting formula, see cast for specifics

...

Other arguments passed to cast

id.var

Identifying variables. If blank, will use all non measure.var variables

measure.var

Measured variables. If blank, will use all non id.var variables

Details

This conveniently wraps melting and casting a data frame into one step.

Author(s)

Hadley Wickham <[email protected]>

See Also

http://had.co.nz/reshape/

Examples

recast(french_fries, time ~ variable, id.var=1:4)

Rename

Description

Rename an object

Usage

rename(x, replace)

Arguments

x

object to be renamed

replace

named vector specifying new names

Details

The rename function provide an easy way to rename the columns of a data.frame or the items in a list.

Author(s)

Hadley Wickham <[email protected]>

Examples

rename(mtcars, c(wt = "weight", cyl = "cylinders"))
a <- list(a = 1, b = 2, c = 3)
rename(a, c(b = "a", c = "b", a="c")) 

# Example supplied by Timothy Bates
names <- c("john", "tim", "andy")
ages <- c(50, 46, 25)
mydata <- data.frame(names,ages)
names(mydata) #-> "name",  "ages"

# lets change "ages" to singular.
# nb: The operation is not done in place, so you need to set your 
# data to that returned from rename

mydata <- rename(mydata, c(ages="age"))
names(mydata) #-> "name",  "age"

Rescaler

Description

Convenient methods for rescaling data

Usage

rescaler(x, type="sd", ...)

Arguments

x

object to rescale

type

type of rescaling to use (see description for details)

...

other options (only pasesed to rank)

Details

Provides methods for vectors, matrices and data.frames

Currently, five rescaling options are implemented:

  • I: do nothing

  • range: scale to [0, 1]

  • rank: convert values to ranks

  • robust: robust version of sd, substract median and divide by median absolute deviation

  • sd: subtract mean and divide by standard deviation

Author(s)

Hadley Wickham <[email protected]>

See Also

rescaler.default


Demo data describing the Smiths

Description

A small demo dataset describing John and Mary Smith. Used in the introductory vignette.

Usage

data(smiths)

Format

A data frame with 2 rows and 5 variables


Sort data frame

Description

Convenience method for sorting a data frame using the given variables.

Usage

sort_df(data, vars=names(data))

Arguments

data

data frame to sort

vars

variables to use for sorting

Details

Simple wrapper around order

Author(s)

Hadley Wickham <[email protected]>


Apply a Function to a Data Frame split by levels of indices

Description

Function sparseby is a modified version of by for tapply applied to data frames. It always returns a new data frame rather than a multi-way array.

Usage

sparseby(data, INDICES = list(), FUN, ..., GROUPNAMES = TRUE)

Arguments

data

an R object, normally a data frame, possibly a matrix.

INDICES

a variable or list of variables indicating the subgroups of data

FUN

a function to be applied to data frame subsets of data.

...

further arguments to FUN.

GROUPNAMES

a logical variable indicating whether the group names should be bound to the result

Details

A data frame or matrix is split by row into data frames or matrices respectively subsetted by the values of one or more factors, and function FUN is applied to each subset in turn.

sparseby is much faster and more memory efficient than by or tapply in the situation where the combinations of INDICES present in the data form a sparse subset of all possible combinations.

Value

A data frame or matrix containing the results of FUN applied to each subgroup of the matrix. The result depends on what is returned from FUN:

If FUN returns NULL on any subsets, those are dropped.

If it returns a single value or a vector of values, the length must be consistent across all subgroups. These will be returned as values in rows of the resulting data frame or matrix.

If it returns data frames or matrices, they must all have the same number of columns, and they will be bound with rbind into a single data frame or matrix.

Names for the columns will be taken from the names in the list of INDICES or from the results of FUN, as appropriate.

Author(s)

Duncan Murdoch

See Also

tapply, by

Examples

x <- data.frame(index=c(rep(1,4),rep(2,3)),value=c(1:7))
x
sparseby(x,x$index,nrow)

# The version below works entirely in matrices
x <- as.matrix(x)
sparseby(x,list(group = x[,"index"]), function(subset) c(mean=mean(subset[,2])))

Stamp

Description

Stamp is like reshape but the "stamping" function is passed the entire data frame, instead of just a few variables.

Usage

stamp(data, formula = . ~ ., fun.aggregate, ..., margins=NULL,
  subset=TRUE, add.missing=FALSE)

Arguments

data

data.frame (no molten)

formula

formula that describes arrangement of result, columns ~ rows, see reshape for more information

fun.aggregate

aggregation function to use, should take a data frame as the first argument

...

arguments passed to the aggregation function

margins

margins to compute (character vector, or TRUE for all margins), can contain grand_row or grand_col to inclue grand row or column margins respectively.

subset

logical vector by which to subset the data frame, evaluated in the context of the data frame so you can

add.missing

fill in missing combinations?

Details

It is very similar to the by function except in the form of the output which is arranged using the formula as in reshape

Note that it's very easy to create objects that R can't print with this function. You will probably want to save the results to a variable and then use extract the results. See the examples.

Author(s)

Hadley Wickham <[email protected]>


Tipping data

Description

One waiter recorded information about each tip he received over a period of a few months working in one restaurant. He collected several variables:

  • tip in dollars,

  • bill in dollars,

  • sex of the bill payer,

  • whether there were smokers in the party,

  • day of the week,

  • time of day,

  • size of the party.

In all he recorded 244 tips. The data was reported in a collection of case studies for business statistics (Bryant & Smith 1995).

Usage

data(tips)

Format

A data frame with 244 rows and 7 variables

References

Bryant, P. G. and Smith, M (1995) Practical Data Analysis: Case Studies in Business Statistics. Homewood, IL: Richard D. Irwin Publishing:


Unique default

Description

Convenience function for setting default if not unique

Usage

uniquedefault(values, default)

Arguments

values

vector of values

default

default to use if values not uniquez

Details

Used by ggplot2

Author(s)

Hadley Wickham <[email protected]>


Untable a dataset

Description

Inverse of table

Usage

untable(df, num)

Arguments

df

matrix or data.frame to untable

num

vector of counts (of same length as df)

Details

Given a tabulated dataset (or matrix) this will untabulate it by repeating each row by the number of times it was repeated

Author(s)

Hadley Wickham <[email protected]>