reshape (version 0.8.9)

cast: 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

See Also

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

Examples

# NOT RUN {
#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")
# }