# Previous functions of the day

## with {base}

Evaluate an R expression in an environment constructed from data,

possibly modifying the original data.

## subset {base}

Return subsets of vectors, matrices or data frames which meet conditions.

## transform {base}

`transform`

is a generic function, which---at least

currently---only does anything useful with

data frames. `transform.default`

converts its first argument to

a data frame if possible and calls `transform.data.frame`

.

## do.call {base}

`do.call`

constructs and executes a function call from a name or

a function and a list of arguments to be passed to it.

## traceback {base}

By default `traceback()`

prints the call stack of the last

uncaught error, i.e., the sequence of calls that lead to the error.

This is useful when an error occurs with an unidentifiable error

message. It can also be used to print the current stack or

arbitrary lists of deparsed calls.

## CrossTable {gmodels}

An implementation of a cross-tabulation function with output

similar to S-Plus crosstabs() and SAS Proc Freq (or SPSS format)

with Chi-square, Fisher and McNemar tests of the independence

of all table factors.

## createDataPartition {caret}

A series of test/training partitions are created using

`createDataPartition`

while `createResample`

creates one or

more bootstrap samples. `createFolds`

splits the data into

`k`

groups while `createTimeSlices`

creates cross-validation

sample information to be used with time series data.

## confusionMatrix {caret}

Calculates a cross-tabulation of observed and predicted classes with associated statistics.

## train {caret}

This function sets up a grid of tuning parameters for a number of classification and regression routines,

fits each model and calculates a resampling based performance measure.

## expand.grid {base}

Create a data frame from all combinations of the supplied vectors or

factors. See the description of the return value for precise details of

the way this is done.