expss (version 0.10.1)

as.category: Convert dichotomy data.frame/matrix to data.frame with category encoding

Description

Convert dichotomy data.frame/matrix to data.frame with category encoding

Usage

as.category(x, prefix = NULL, counted_value = 1, compress = FALSE)
is.category(x)

Arguments

x

Dichotomy data.frame/matrix (usually with 0,1 coding).

prefix

If is not NULL then column names will be added in the form prefix+column number.

counted_value

Vector. Values that will be considered as indicator of category presence. By default it equals to 1.

compress

Logical. Should we drop columns with all NA? FALSE by default. TRUE significantly decreases performance of the function.

Value

data.frame of class category with numeric values that correspond to column numbers of counted values. Column names of x or variable labels are added as value labels.

See Also

as.dichotomy for reverse conversion, mrset, mdset for usage multiple-response variables with tables.

Examples

# NOT RUN {
set.seed(123)

# Let's imagine it's matrix of consumed products
dichotomy_matrix = matrix(sample(0:1,40,replace = TRUE,prob=c(.6,.4)),nrow=10)
colnames(dichotomy_matrix) = c("Milk","Sugar","Tea","Coffee")

as.category(dichotomy_matrix, compress = TRUE) # compressed version
category_encoding = as.category(dichotomy_matrix)

 # should be TRUE
identical(val_lab(category_encoding), c(Milk = 1L, Sugar = 2L, Tea = 3L, Coffee = 4L))
all(as.dichotomy(category_encoding, use_na = FALSE) == dichotomy_matrix)

# with prefix
as.category(dichotomy_matrix, prefix = "products_")

# data.frame with variable labels
dichotomy_dataframe = as.data.frame(dichotomy_matrix)
colnames(dichotomy_dataframe) = paste0("product_", 1:4)
var_lab(dichotomy_dataframe[[1]]) = "Milk"
var_lab(dichotomy_dataframe[[2]]) = "Sugar"
var_lab(dichotomy_dataframe[[3]]) = "Tea"
var_lab(dichotomy_dataframe[[4]]) = "Coffee"

as.category(dichotomy_dataframe, prefix = "products_")

# }