使用caret::preProcess缩放全部列时为何部分列被忽略？

Got it, let’s unpack this issue—this is a super common gotcha when working with SPSS data in R using haven and caret! Here’s the root cause:

• When you import a .sav file with haven::read_sav(), any numeric column that had value labels in SPSS gets imported as a haven_labelled object (a custom class from the haven package), not a base R numeric or integer type.
• By default, caret::preProcess() only applies standardization (or any preprocessing method) to columns that are explicitly base numeric/integer. It doesn’t recognize the haven_labelled class, so those columns get skipped entirely.

You’ve got a couple of easy ways to make sure all your numeric columns get scaled:

1. Strip Labels to Convert to Base Numeric Types #

If you don’t need to keep the SPSS value labels (which makes sense for scaling, since scaled values won’t match the original labels anyway), use haven::zap_labels() to convert labelled columns to plain numeric/integer columns:

# Load libraries and data
library(haven)
library(caret)

my_data <- read_sav("your_dataset.sav")

# Remove labels and convert to base numeric types
my_data_clean <- zap_labels(my_data)

# Now run preProcess on all numeric columns
preproc_obj <- preProcess(my_data_clean, method = c("center", "scale"))
my_data_scaled <- predict(preproc_obj, my_data_clean)

zap_labels() preserves the underlying numeric values while stripping the label metadata—exactly what you need for standardization.

2. Keep Labels (If Needed) and Coerce Column Classes #

If you want to retain the labels (though scaled values might not align with the original labels logically), you can manually convert haven_labelled columns to numeric while keeping the label attributes. Use dplyr for a clean approach:

library(dplyr)
library(haven)
library(caret)

my_data <- read_sav("your_dataset.sav")

# Convert all labelled columns to numeric (keeps labels if you want them)
my_data <- my_data %>%
  mutate(across(where(is.labelled), as.numeric))

# Now preProcess will handle all numeric columns
preproc_obj <- preProcess(my_data, method = c("center", "scale"))
my_data_scaled <- predict(preproc_obj, my_data)

The is.labelled() function checks for haven’s custom labelled columns, and as.numeric() converts them to base numeric without losing the original values.

Before scaling, double-check that your labelled columns are actually continuous numeric values (not categorical codes in disguise). If they’re categorical, scaling them isn’t appropriate—but since you specified you want to standardize all numeric columns, this fix should work perfectly.

内容的提问来源于stack exchange，提问作者user3245256