R: Cast functions Cast a molten data frame into an array or data...

cast {reshape2} | R Documentation

## Cast functions Cast a molten data frame into an array or data frame.

### Description

Use `acast` or `dcast` depending on whether you want vector/matrix/array output or data frame output. Data frames can have at most two dimensions.

### Usage

    dcast(data, formula, fun.aggregate = NULL, ..., margins = NULL,
      subset = NULL, fill = NULL, drop = TRUE,
      value.var = guess_value(data))

    acast(data, formula, fun.aggregate = NULL, ..., margins = NULL,
      subset = NULL, fill = NULL, drop = TRUE,
      value.var = guess_value(data))

### Arguments

`data` | molten data frame, see `melt`.
`formula` | casting formula, see details for specifics.
`fun.aggregate` | aggregation function needed if variables do not identify a single observation for each output cell. Defaults to length (with a message) if needed but not specified.
`...` | 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 compute all margins . Any variables that can not be margined over will be silently dropped.
`subset` | quoted expression used to subset data prior to reshaping, e.g. `subset = .(variable=="length")`.
`fill` | value with which to fill in structural missings, defaults to value from applying `fun.aggregate` to 0 length vector
`drop` | should missing combinations dropped or kept?
`value.var` | name of column which stores values, see `guess_value` for default strategies to figure this out.

### Details

The cast formula has the following format: `x_variable + x_2 ~ y_variable + y_2 ~ z_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 ~ .`.

Alternatively, you can supply a list of quoted expressions, in the form `list(.(x_variable, x_2), .(y_variable, y_2), .(z))`. The advantage of this form is that you can cast based on transformations of the variables: `list(.(a + b), (c = round(c)))`. See the documentation for `.` for more details and alternative formats.

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 single summary statistic.

### See Also

`melt`, http://had.co.nz/reshape/

### Examples

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

    acast(aqm, day ~ month ~ variable)
    acast(aqm, month ~ variable, mean)
    acast(aqm, month ~ variable, mean, margins = TRUE)
    dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))

    library(plyr) # needed to access . function
    acast(aqm, variable ~ month, mean, subset = .(variable == "ozone"))
    acast(aqm, variable ~ month, mean, subset = .(month == 5))

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

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

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

    acast(chick_m, time ~ diet, length)

    dcast(chick_m, diet + chick ~ time)
    acast(chick_m, diet + chick ~ time)
    acast(chick_m, chick ~ time ~ diet)
    acast(chick_m, diet + chick ~ time, length, margins="diet")
    acast(chick_m, diet + chick ~ time, length, drop = FALSE)

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

    ff_d <- melt(french_fries, id=1:4, na.rm=TRUE)
    acast(ff_d, subject ~ time, length)
    acast(ff_d, subject ~ time, length, fill=0)
    dcast(ff_d, treatment ~ variable, mean, margins = TRUE)
    dcast(ff_d, treatment + subject ~ variable, mean, margins="treatment")
    if (require("lattice")) {
     lattice::xyplot(`1` ~ `2` | variable, dcast(ff_d, ... ~ rep), aspect="iso")
    }

[Package reshape2 version 1.4.3 Index]