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Introduction

stddiff.spark provides Spark-compatible implementations of the standardized difference calculations from the stddiff package. The interface is identical to stddiff, so you can swap your existing calls in-place without changing your workflow.

Functions automatically dispatch to the stddiff package when non-Spark data is supplied, so the same code works seamlessly on both local R data frames and Spark DataFrames.

Ordering of groups and levels

Spark DataFrames have no factor type, so values are ordered the way stddiff’s default as.factor() conversion orders them: numbers in numeric order, strings alphabetically, dates and date-times in time order, and FALSE before TRUE.

• Group column (gcol): the first value is reported as the control group (the .c columns) and the second as the treatment group (the .t columns).
• Binary variables: the first value is coded 0 and the second 1, so p.c and p.t are the proportions of the second value.
• Categorical variables: levels are listed in this order, and the first level is the reference.

The ordering only decides how results are labeled. The standardized differences and their confidence intervals are the same either way: the numeric and binary differences are absolute values, and the categorical difference is a Mahalanobis distance, which doesn’t depend on which group comes first or which level is the reference.

[!Note] To report a different group as the control group, recode the group column so that group sorts first. For example, if the groups are “drug” and “placebo”, “drug” sorts first and is reported as the control group. To make placebo the control group instead:

library(dplyr)
spark_df <- spark_df %>%
  mutate(group = ifelse(group == "placebo", 0L, 1L))

Placebo is now 0, which sorts before 1, so it is reported in the .c columns and the drug group in the .t columns.

Differences from stddiff

On Spark data the results replicate stddiff, including its behaviour on unusual input, except:

• Errors for one variable: where stddiff stops with an error because of one variable, such as a singular matrix in stddiff.category(), the Spark version returns NA for that variable with a warning, and the other variables still get results. Local data frames are passed to stddiff, so there the error remains.
• Exactly two groups: the Spark version stops with an error unless the group column has exactly two values. stddiff doesn’t check this: with more than two groups, stddiff.binary() and stddiff.numeric() silently compare the first two, and stddiff.category() stops with an unrelated error.
• NaN counts as missing: in every function, including stddiff.binary() and stddiff.category(), where stddiff keeps NaN as a value. sparklyr’s copy_to() turns some R NAs into NaN, such as those from arithmetic like (bmi >= 30) * 1, and NaN is never a real category.
• Large groups: with more than about 46,000 rows per group, stddiff’s integer counts overflow and its confidence limits are NA, with the warning “NAs produced by integer overflow”. The Spark version computes them. Local data frames are passed to stddiff, so they get NA.
• Unusual number formats: stddiff.numeric() converts strings as as.numeric() does for decimals, Inf and NaN, but unusual formats that as.numeric() also reads, such as hexadecimal ("0x1A"), become NA with its coercion warning.
• Custom level order: Spark has no factor type, so the data can’t carry a custom level order. Groups and levels follow their sorted order, which matches stddiff’s default (as.factor()). On a local data frame, stddiff follows the level order of a factor column.

[!Note] Like stddiff, stddiff.binary() doesn’t check that variables are binary. With more than two distinct values the results aren’t meaningful: the proportions can exceed 1, and the standardized difference can be NaN, with R’s “NaNs produced” warning.

Installation

CRAN

install.packages("stddiff.spark")

GitHub

# install.packages("remotes") # if you don’t have it
remotes::install_github("alicja-januszkiewicz/stddiff.spark")

Usage

library(sparklyr)
library(dplyr)
library(stddiff.spark)

# connect to Spark
sc <- spark_connect(master = "local")

# create example local data
my_data <- data.frame(
  treatment = c(1, 0, 1, 0, 1, 0),
  age       = c(34, 28, 45, 30, 50, 33),
  bmi       = c(22.1, 24.3, 27.8, 23.5, 28.2, 25.0),
  weight    = c(70, 65, 85, 68, 90, 72)
)

# copy data to Spark
spark_df <- copy_to(sc, my_data, overwrite = TRUE)

# compute standardized differences for numeric variables
stddiff.numeric(spark_df, gcol = 1, vcol = 2:4)

# disconnect Spark
spark_disconnect(sc)

Requirements

• Apache Spark (tested with 3.4.4 and 4.2.0)
• sparklyr (>= 1.8.4). sparklyr versions before 1.9.5 need dbplyr below 2.6.0.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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