Create Dummy Variables from Categorical Data

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.

DESCRIPTION

A function to make dummy variables for R.

Usage

makedummies(dat, basal_level = FALSE, col = NULL, numerical = NULL, as.is = NULL)

• dat: data.frame
• basal_level
  • 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
• sep: obsoluted

Examples

Simple usages

factor

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

[1] a a a b b b c c c
Levels: a b c

  x_b x_c
1   0   0
2   0   0
3   0   0
4   1   0
5   1   0
6   1   0
7   0   1
8   0   1
9   0   1

makedummies(dat, basal_level = TRUE)

  x_a x_b x_c
1   1   0   0
2   1   0   0
3   1   0   0
4   0   1   0
5   0   1   0
6   0   1   0
7   0   0   1
8   0   0   1
9   0   0   1

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)

[1] a a a b b b c c c
Levels: a < c < b

  x_c x_b
1   0   0
2   0   0
3   0   0
4   0   1
5   0   1
6   0   1
7   1   0
8   1   0
9   1   0

numeric

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

  x
1 1
2 1
3 1
4 2
5 2
6 2
7 3
8 3
9 3

factor and numeric

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

  x_b x_c y
1   0   0 1
2   0   0 1
3   0   0 1
4   1   0 2
5   1   0 2
6   1   0 2
7   0   1 3
8   0   1 3
9   0   1 3

factors

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

  x_b x_c y_2 y_3
1   0   0   0   0
2   0   0   0   0
3   0   0   0   0
4   1   0   1   0
5   1   0   1   0
6   1   0   1   0
7   0   1   0   1
8   0   1   0   1
9   0   1   0   1

Options

"col" option

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

  x_b x_c
1   0   0
2   0   0
3   0   0
4   1   0
5   1   0
6   1   0
7   0   1
8   0   1
9   0   1

"numerical" option

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

  x y_2 y_3
1 1   0   0
2 1   0   0
3 1   0   0
4 2   1   0
5 2   1   0
6 2   1   0
7 3   0   1
8 3   0   1
9 3   0   1

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, numerical = c("x", "y"))

  x y
1 a 4
2 a 4
3 a 4
4 b 5
5 b 5
6 b 5
7 c 6
8 c 6
9 c 6

[1] a a a b b b c c c
Levels: a < c < b

  x y
1 1 4
2 1 4
3 1 4
4 3 5
5 3 5
6 3 5
7 2 6
8 2 6
9 2 6

"as.is" option

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

  x y_2 y_3
1 a   0   0
2 a   0   0
3 a   0   0
4 b   1   0
5 b   1   0
6 b   1   0
7 c   0   1
8 c   0   1
9 c   0   1

makedummies(dat, as.is = c("x", "y"))

  x y
1 a 1
2 a 1
3 a 1
4 b 2
5 b 2
6 b 2
7 c 3
8 c 3
9 c 3