Cast function
Cast a molten data frame into the reshaped or aggregated form you want
cast(data, formula = ... ~ variable, fun.aggregate=NULL, ..., margins=FALSE, subset=TRUE, df=FALSE, fill=NULL, add.missing=FALSE, value = guess_value(data))
data |
molten data frame, see |
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 |
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 ~ .
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
Hadley Wickham <h.wickham@gmail.com>
#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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.