The scf R package provides a structured, reproducible,
and pedagogically-aware toolkit for analyzing the U.S. Federal Reserve’s
Survey of Consumer Finances (SCF), one of the
highest-quality data sources for information on U.S. households’ balance
sheets and income statements.
It wraps replicate-weighted, multiply-imputed SCF data into a
consistent object class (scf_mi_survey) and offers
end-to-end support for weighted descriptive statistics, hypothesis
testing, regression modeling, and high-quality visualizations—while
transparently incorporating Rubin’s Rules and complex sample design.
scf_download(): Downloads and preprocesses SCF
microdata, including all five implicates and 999 replicate weights.scf_load(): Loads .rds files into
structured scf_mi_survey objects ready for analysis.scf_update(): Adds or transforms variables across
implicates.scf_subset(): Subsets the data consistently across all
implicates.scf_freq(): Weighted frequency tables for categorical
variables.scf_xtab(): Cross-tabulations by row, column, or cell
percentages.scf_mean(), scf_median(),
scf_percentile(): Computes groupwise or overall statistics
using Rubin’s Rules.scf_corr(): Weighted Pearson correlations.scf_ttest(): One-sample and two-sample t-tests for
continuous variables.scf_prop_test(): One-sample and two-sample proportion
tests for binary variables.scf_MIcombine(): Combines estimates across imputations
using Rubin’s Rules (internal to most functions).scf_ols(): Linear regression with pooled estimates and
implicate diagnostics.scf_glm(): Generalized linear models (e.g., logistic,
Poisson).scf_logit(): Wrapper for logistic regression with
optional odds ratio output.scf_plot_dist(): Kernel density plots for visualizing
and comparing distributions by group.scf_plot_dbar(): Bar plots of categorical variable
distributions.scf_plot_bbar(): Stacked bar plots for two categorical
variables.scf_plot_cbar(): Bar plots for continuous variable
summaries by group.scf_plot_smooth(): Smoothed line plots for continuous
distributions.scf_plot_hist(): Weighted histograms of continuous
variables.scf_plot_hex(): Weighted hexbin plots for bivariate
continuous data.scf_implicates(): Extracts implicate-level results from
SCF objects.print(), summary(): Custom methods for
clean, interpretable output in analysis and teaching.The scf package is not yet on CRAN. To install the
development version from GitHub:
# Install devtools if you don't already have it
install.packages("devtools")
# Install the SCF package from GitHub
devtools::install_github("jncohen/scf")The package requires R ≥ 3.6 and the following packages:
survey (for replicate-weighted designs)ggplot2 (for plotting)httr, haven (for downloading and reading
SCF data)mitools, stats, utils,
methods, and others (loaded automatically)Use install.packages() to install any missing
dependencies manually if needed.
``` r, eval = F # Download SCF data for 2022: scf_download(2022)
scf2022 <- scf_load(2022)
```r
# Using mock data for CRAN compliance
scf2022 <- readRDS(system.file("extdata", "mock_scf2022.rds", package = "scf"))
# NOTE: This is mock data for demonstration only.
# Use `scf_download()` and `scf_load()` for full SCF datasets.# Frequency of education categories
scf_freq(scf2022, ~edcl)
# Median household net worth
scf_median(scf2022, ~networth)
# 90th percentile of income
scf_percentile(scf2022, ~income, q = 0.9)
# Histogram of net worth distribution
scf_plot_hist(scf2022, ~networth)
# Smoothed density plot of income
scf_plot_smooth(scf2022, ~income)# Cross-tabulation of education and homeownership
scf_xtab(scf2022, ~edcl, ~own)
# Stacked bar chart: homeownership by education
scf_plot_bbar(scf2022, ~edcl, ~own)
# Weighted bar chart: mean net worth by education
scf_plot_cbar(scf2022, ~networth, ~edcl, stat = "mean")
# Grouped median income by race
scf_median(scf2022, ~income, by = ~racecl)
# Correlation between income and net worth
scf_corr(scf2022, ~income, ~networth)
# Hexbin plot: income vs. net worth
scf_plot_hex(scf2022, ~income, ~networth)# One-sample proportion test: Is more than 10% of households rich?
scf_prop_test(scf2022, ~I(networth > 1e6), p = 0.10, alternative = "greater")
# Two-sample proportion test: Are women less likely to be rich?
scf_prop_test(scf2022, ~I(networth > 1e6), ~factor(hhsex, labels = c("Male", "Female")), alternative = "less")
# One-sample t-test: Is mean income different from $75,000?
scf_ttest(scf2022, ~income, mu = 75000)
# Two-sample t-test: Are older households wealthier?
scf_ttest(scf2022, ~networth, ~I(age > 50), alternative = "greater")# Linear regression: Predict net worth from income and education
scf_ols(scf2022, networth ~ income + factor(edcl))
# Generalized linear model: Predict borrowing with logistic regression
scf_glm(scf2022, hborrff ~ income + age + factor(edcl), family = binomial())
# Logit wrapper: Predict probability of owning stocks
scf_logit(scf2022, ~I(owns_stocks == 1) ~ age + income + factor(edcl))
# Bar chart of a single categorical variable
scf_plot_dbar(scf2022, ~edcl)
# Stacked bar chart comparing education by race
scf_plot_bbar(scf2022, ~edcl, ~racecl, scale = "percent", percent_by = "row")
# Smoothed line plot of net worth distribution
scf_plot_smooth(scf2022, ~networth, xlim = c(0, 2e6), method = "loess")
# Histogram of income distribution
scf_plot_hist(scf2022, ~income, bins = 40, xlim = c(0, 300000))
# Bar chart of mean net worth by education level
scf_plot_cbar(scf2022, ~networth, ~edcl, stat = "mean")
# Hexbin plot: net worth vs. income
scf_plot_hex(scf2022, ~income, ~networth, bins = 60)# Create new variables across all implicates
scf2022 <- scf_update(scf2022,
rich = networth > 1e6,
senior = age >= 65,
log_income = log(income + 1)
)
# Subset to working-age households with positive net worth
scf_sub <- scf_subset(scf2022, age >= 25 & age < 65 & networth > 0)
# Extract implicate-level estimates from a frequency table
freq <- scf_freq(scf_sub, ~own)
scf_implicates(freq, long = TRUE)For detailed examples, function documentation, and usage guides, consult the package vignettes and reference manual.
This package includes a small mock dataset
(mock_scf2022.rds) for testing purposes.
It includes only 75 rows and select variables. It is structurally
valid,
but not suitable for analytical use or inference.
If you use scf in published work, please cite it as:
Joseph N. Cohen (2025). scf: Tools for Analyzing the Survey of Consumer Finances. R package. ver. 1.0.3. https://github.com/jncohen/scf
Use citation("scf") in R for formatted references.
Joseph N. Cohen
Department of Sociology & Program in Data Analytics
Queens College, City University of New York
joseph.cohen@qc.cuny.edu
https://jncohen.commons.gc.cuny.edu