# Handling Binary and Categorical Variables

#### 2026-01-20

The CISS-VAE model can handle binary and categorical variables, but categorical variables must first be converted into binary dummy variables.

The Palmer Penguins dataset has both continuous (bill_length, bill_depth, flipper_length, body_mass) and categorical (species, island, sex) values so it makes a good example for this. We can use the `dummy_cols()` function from the `fastDummies` package to create dummy variables for our categories. Set `ignore_na = TRUE` and `remove_selected_columns = TRUE` to avoid creating a new column for NA values and to remove the original categoricals once the dummies are created.

    library(tidyverse)
    library(kableExtra)
    library(reticulate)
    library(rCISSVAE)
    library(fastDummies)
    library(palmerpenguins)

    ##
    ## Attaching package: 'palmerpenguins'

    ## The following objects are masked from 'package:datasets':
    ##
    ##     penguins, penguins_raw

    data(package = 'palmerpenguins')

    penguins_clean = na.omit(penguins)%>%
            select(year, everything()) ## removing existing incomplete rows for illustration purposes

    glue::glue("Dimensions: {paste0(dim(penguins), collapse = ',')}")

    ## Dimensions: 344,8

    head(penguins_clean) %>% kable()

year | species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex
--- | --- | --- | --- | --- | --- | --- | ---
2007 | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male
2007 | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female
2007 | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female
2007 | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female
2007 | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male
2007 | Adelie | Torgersen | 38.9 | 17.8 | 181 | 3625 | female

    ## create penguins_missing
    n  <- nrow(penguins_clean)
    p  <- ncol(penguins_clean)
    m  <- floor(0.20 * n * p)               # number of cells to mask
    idx <- sample.int(n * p, m)             # positions in a logical matrix

    mask <- matrix(FALSE, nrow = n, ncol = p)
    mask[idx] <- TRUE

    penguins_missing <- penguins_clean

    ## anything can be missing except the year
    for (j in seq(2, p, 1)) {
      penguins_missing[[j]][mask[, j]] <- NaN
    }

    # quick check of missingness rate
    glue::glue("\nMissingness proportion of penguins_missing: {round(mean(is.na(as.matrix(penguins_missing))), 2)}")

    ## Missingness proportion of penguins_missing: 0.17

    ## create dummy vars

    penguin_dummies_complete = penguins_clean %>%
        dummy_cols(select_columns = c("species", "island", "sex"),
        ignore_na = TRUE,
        remove_first_dummy = TRUE,
        remove_selected_columns = TRUE)

    penguin_dummies = penguins_missing %>%
        dummy_cols(select_columns = c("species", "island", "sex"),
        ignore_na = TRUE,
        remove_first_dummy = TRUE,
        remove_selected_columns = TRUE)

    head(penguin_dummies) %>% kable()

year | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | species_Chinstrap | species_Gentoo | island_Dream | island_Torgersen | sex_male
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
2007 | 39.1 | NaN | 181 | 3750 | 0 | 0 | 0 | 1 | 1
2007 | 39.5 | 17.4 | 186 | 3800 | NA | NA | NA | NA | 0
2007 | 40.3 | 18.0 | 195 | 3250 | 0 | 0 | 0 | 1 | 0
2007 | NaN | NaN | 193 | 3450 | NA | NA | NA | NA | 0
2007 | 39.3 | NaN | 190 | 3650 | 0 | 0 | NA | NA | NA
2007 | 38.9 | NaN | NaN | NaN | 0 | 0 | 0 | 1 | 0

Now that the dummy vars are created and there is missingness, we can create a `binary_feature_mask` and impute with `run_cissvae()`.

    binary_feature_mask = c(rep(FALSE, 5), rep(TRUE, 5))

    glue::glue("Binary Feature Mask: {paste0(binary_feature_mask, collapse = ', ')}")

    results = run_cissvae(
        data = penguin_dummies,
        val_proportion = 0.20, ## small dataset so using higher val proportion
        columns_ignore = "year",
        binary_feature_mask = binary_feature_mask,
        clusters = NULL,
        n_clusters = 1,
        scale_features = TRUE,
        epochs = 500,
        debug = FALSE
    )

    head(results$imputed_dataset)
    head(penguin_dummies)

    ##   year bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    ## 0 2007       39.10000       20.1713               181        3750
    ## 1 2007       39.50000       17.4000               186        3800
    ## 2 2007       40.30000       18.0000               195        3250
    ## 3 2007       36.70000       19.3000               193        3450
    ## 4 2007       40.38935       20.6000               190        3650
    ## 5 2007       38.90000       17.8000               181        3625
    ##   species_Chinstrap species_Gentoo island_Dream island_Torgersen     sex_male
    ## 0      2.210167e-15   1.156993e-15 1.339994e-03     6.544447e-07 1.0000000000
    ## 1      0.000000e+00   0.000000e+00 0.000000e+00     1.000000e+00 0.0000000000
    ## 2      0.000000e+00   0.000000e+00 4.124794e-09     4.522174e-01 0.0000000000
    ## 3      0.000000e+00   0.000000e+00 0.000000e+00     1.000000e+00 0.0002707173
    ## 4      3.508456e-16   7.418369e-16 8.792417e-04     1.695923e-06 0.9999980927
    ## 5      0.000000e+00   0.000000e+00 0.000000e+00     1.000000e+00 0.0000000000

    ## # A tibble: 6 × 10
    ##    year bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    ##   <int>          <dbl>         <dbl>             <dbl>       <dbl>
    ## 1  2007           39.1         NaN                 181        3750
    ## 2  2007           39.5          17.4               186        3800
    ## 3  2007           40.3          18                 195        3250
    ## 4  2007          NaN           NaN                 193        3450
    ## 5  2007           39.3         NaN                 190        3650
    ## 6  2007           38.9         NaN                 NaN         NaN
    ## # ℹ 5 more variables: species_Chinstrap <int>, species_Gentoo <int>,
    ## #   island_Dream <int>, island_Torgersen <int>, sex_male <int>

As we can see above, the imputed values for the binary variables are in terms of probability, not a flat 0,1 so we have to convert those values to binary. The ‘imputed_dataset’ is returned as a data.frame, so we can use tidyverse mutate to convert the binary variables.

    results$imputed_dataset <- results$imputed_dataset %>%
      mutate(across(
        .cols = matches("species|island|sex"),
        .fns = ~ case_when(
          .x > 0.5 ~ 1,
          .x <= 0.5 ~ 0,
          TRUE ~ .x
        )
      ))

    head(results$imputed_dataset)
    head(penguin_dummies)
    head(penguin_dummies_complete)

    ##   year bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    ## 0 2007       39.10000       20.1713               181        3750
    ## 1 2007       39.50000       17.4000               186        3800
    ## 2 2007       40.30000       18.0000               195        3250
    ## 3 2007       36.70000       19.3000               193        3450
    ## 4 2007       40.38935       20.6000               190        3650
    ## 5 2007       38.90000       17.8000               181        3625
    ##   species_Chinstrap species_Gentoo island_Dream island_Torgersen sex_male
    ## 0                 0              0            0                0        1
    ## 1                 0              0            0                1        0
    ## 2                 0              0            0                0        0
    ## 3                 0              0            0                1        0
    ## 4                 0              0            0                0        1
    ## 5                 0              0            0                1        0

    ## # A tibble: 6 × 10
    ##    year bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    ##   <int>          <dbl>         <dbl>             <dbl>       <dbl>
    ## 1  2007           39.1         NaN                 181        3750
    ## 2  2007           39.5          17.4               186        3800
    ## 3  2007           40.3          18                 195        3250
    ## 4  2007          NaN           NaN                 193        3450
    ## 5  2007           39.3         NaN                 190        3650
    ## 6  2007           38.9         NaN                 NaN         NaN
    ## # ℹ 5 more variables: species_Chinstrap <int>, species_Gentoo <int>,
    ## #   island_Dream <int>, island_Torgersen <int>, sex_male <int>

    ## # A tibble: 6 × 10
    ##    year bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
    ##   <int>          <dbl>         <dbl>             <int>       <int>
    ## 1  2007           39.1          18.7               181        3750
    ## 2  2007           39.5          17.4               186        3800
    ## 3  2007           40.3          18                 195        3250
    ## 4  2007           36.7          19.3               193        3450
    ## 5  2007           39.3          20.6               190        3650
    ## 6  2007           38.9          17.8               181        3625
    ## # ℹ 5 more variables: species_Chinstrap <int>, species_Gentoo <int>,
    ## #   island_Dream <int>, island_Torgersen <int>, sex_male <int>