makedummies (version 1.2.1)

makedummies: Create Dummy Variables from Categorical Data

Description

Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. This package enables to select whether a dummy variable for base group is included (for principal component analysis/factor analysis) or excluded (for regression analysis) by an option. makedummies function accepts data.frame, matrix, and tbl (tibble) class (by tibble package). matrix class data is automatically converted to data.frame class.

Usage

makedummies(dat, ...)
# S3 method for default
makedummies(dat, basal_level = FALSE, col = NULL,
  numerical = NULL, as.is = NULL, ...)
# S3 method for matrix
makedummies(dat, ...)
# S3 method for tbl
makedummies(dat, basal_level = FALSE, col = NULL,
  numerical = NULL, as.is = NULL, ...)

Arguments

dat

data of data.frame, matrix, or tbl class

…

arguments to makedummies.data.frame (tbl class)

basal_level

logical

TRUE

: include a dummy variable for base group

FALSE

(default) : exclude a dummy variable for base group

col

Columns vector (all columns are used if NULL is given)

numerical

Columns vector converting from factor/ordered to numeric (ignore if column is numeric)

as.is

Columns vector not converting

Value

return as data.frame or tbl class

Examples

# NOT RUN {
#### 'data.frame' class
## factor
dat <- data.frame(x = factor(rep(c("a", "b", "c"), each = 3)))
dat$x
makedummies(dat)

## ordered
dat <- data.frame(x = factor(rep(c("a", "b", "c"), each = 3)))
dat$x <- ordered(dat$x, levels = c("a" ,"c" ,"b"))
dat$x
makedummies(dat)

## numeric
dat <- data.frame(x = rep(1:3, each = 3))
makedummies(dat)

## factor and numeric
dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = rep(1:3, each = 3)
)
makedummies(dat)

## factors
dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = factor(rep(1:3, each = 3))
)
makedummies(dat)

## data including NA

dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = rep(1:3, each = 3)
)
dat$x[4] <- NA; dat$y[6] <- NA
makedummies(dat)

## "col" option
dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = factor(rep(1:3, each = 3))
)
makedummies(dat, col = "x")

## "numerical" option
dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = factor(rep(1:3, each = 3))
)
makedummies(dat, numeric = "x")

dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = rep(4:6, each = 3)
)
dat$x <- ordered(dat$x, levels = c("a" ,"c" ,"b"))
dat
dat$x
makedummies(dat, numeric = c("x", "y"))

## "as.is" option
dat <- data.frame(
    x = factor(rep(c("a", "b", "c"), each = 3)),
    y = factor(rep(1:3, each = 3))
)
dat
makedummies(dat, as.is = "x")
makedummies(dat, as.is = c("x", "y"))

#### 'tibble' class
if (require(tibble)) {
  dat <- as_tibble(iris)
  makedummies(dat[46:55,], col = "Species", basal_level = TRUE)

  # non-standard variable name
  dat2 <- tibble(
      `1` = factor(rep(c("c", "a", "b"), each = 3)),
      `@` = factor(rep(1:3, each = 3)),
      `&` = rep(4:6, each = 3)
  )
  dat2

  makedummies(dat2, basal_level = TRUE)
  makedummies(dat2, as.is = "@", basal_level = TRUE)
  makedummies(dat2, numerical = "1", basal_level = TRUE)
}

# }