expss (version 0.5.5)

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

Description

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

Usage

category(x, prefix = NULL, use_var_lab = TRUE, counted_value = 1, compress = TRUE)
category_df(x, prefix = NULL, use_var_lab = TRUE, counted_value = 1, compress = TRUE)

Arguments

x

Dichotomy matrix (usually with 0,1 coding).

prefix

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

use_var_lab

logical If TRUE then we will try to use variable labels as value labels instead of column names.

counted_value

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

compress

Logical. Should we drop columns with all NA?

Value

Matrix or data.frame 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

dichotomy for reverse conversion.

Examples

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")
category(dichotomy_matrix,compress=FALSE) # uncompressed version
category_matrix=category(dichotomy_matrix)

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

# with prefix
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"

category_df(dichotomy_dataframe, prefix = "products_")