Package 'catcont'

categorical or continuous variables

Description

These functions facilitate working with variables as categorical or continous rather than logical, integer, numeric, factor, character, ..

Usage

cat_cont(x)

is_cat(x)

## Default S3 method:
is_cat(x)

## S3 method for class 'ordered'
is_cat(x)

## S3 method for class 'factor'
is_cat(x)

## S3 method for class 'logical'
is_cat(x)

is_cont(x)

## Default S3 method:
is_cont(x)

## S3 method for class 'logical'
is_cont(x)

## S3 method for class 'factor'
is_cont(x)

## S3 method for class 'ordered'
is_cont(x)

which_cat(x, ..., names = FALSE)

which_cont(x, ..., names = FALSE)

Arguments

x	object
...	arguments passed to other functions.
names	logical; whether to return the names of the variables instead of their index?

Details

These functions are used to test and identify which/if a variable or variables are categorical or continuos. is_cat and is_cont take single variable arguments.

Mostly, the categorical and continuos assessment is straight- forward. Continuous variables are respresented by integer, double or complex types. All other types are categorical. There are a few opinionated exceptions:

• factors are categorical (though typed 'integer')

• ordered factors are (though typed 'integer')

• logical are categorical

For simplicity, it is assumed that a vector cannot be simultaneous categorical and continous, though in some cases (e.g. ordered factors) this may be the case.

Value

cat_cont returns a named character with values either "cat" or "cont". If x is a atomic vector, a single string is given. If x is recursive, a "cat"/"cont" value is given for each element. Names correspond to the names of the element.

is_cat and is_cont return logical.

which_cat and which.cont report which variables in an object are categorical and continuous. By default, interger indices are return. If names=TRUE, the names of the variables are returned instead.

See Also

• base::typeof()

• base::is.numeric() methods::is()

• base::which()

Examples

data(iris)
  cat_cont(iris)


  is_cat(letters)          # TRUE
  is_cat(factor(letters))  # TRUE
  is_cat(TRUE)             # TRUE
  is_cat(FALSE)            # TRUE
  is_cat(1:10)             # FALSE
  is_cat(rnorm(10))        # FALSE
  is_cat( Sys.Date() )     # FALSE
  is_cat( complex(1,2) )   # FALSE

  is_cont(letters)         # FALSE
  is_cont(factor(letters)) # FALSE
  is_cont(TRUE)            # FALSE
  is_cont(FALSE)           # FALSE
  is_cont(1:10)            # TRUE
  is_cont(rnorm(10))       # TRUE
  is_cont( Sys.Date() )    # TRUE
  is_cont( complex(1,2) )  # TRUE

  which_cat(iris)
  which_cat( iris, names=TRUE )

  which_cont(iris)
  which_cont( iris, names=TRUE )

---

mutate_if_cat, mutate_if_cont

Description

mutates only categorical|continuous columns

Usage

mutate_if_cat(.tbl, .funs, ...)

## Default S3 method:
mutate_if_cat(.tbl, .funs, ...)

## S3 method for class 'data.table'
mutate_if_cat(.tbl, .funs, ...)

mutate_if_cont(.tbl, .funs, ...)

## Default S3 method:
mutate_if_cont(.tbl, .funs, ...)

## S3 method for class 'data.table'
mutate_if_cont(.tbl, .funs, ...)

Arguments

.tbl	table
.funs	functions see dplyr::mutate_if()
...	additional parameters

Details

Mutates categorical or continuous columns.

The data.table variants do this as

Value

An object of class .tbl in with columns mutated according to .funs

See Also

Similar to dplyr::mutate_if()

Examples

data(iris)

## Not run: 
  iris %>% mutate_if_cat( as.character )

  library(data.table)
  setDT(iris)
  class(iris$Species)
  iris %>% mutate_if_cat( as.character )
  class(iris1$Species)  # character
  class(iris2)

  iris %>% mutate_if_cont( add, 2 )

## End(Not run)

---

select_cat, select_cont

Description

Select columns by type

Usage

select_cat(.data)

## Default S3 method:
select_cat(.data)

## S3 method for class 'data.table'
select_cat(.data)

select_cont(.data)

## Default S3 method:
select_cont(.data)

## S3 method for class 'data.table'
select_cont(.data)

Arguments

.data

table

Details

select_cat() and select_cont() return only the categorical and continuous types respectively. This is closely mirrors the dplyr function select but works with non-table values as well.

Value

Returns a table-like object of the same class as data unless there are no columns in which case 'NULL' is returned

Examples

data(iris)
  select_cat(iris)
  select_cont(iris)

## Not run: 
  setDT(iris)
  select_cat(iris)
  select_cont(iris)

## End(Not run)

---