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The scf R package provides a structured, reproducible,
and pedagogically-conscious 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 custom
data object (scf_mi_survey) with which users can implement
custom easy-to-use functions for generating proper population estimates
for descriptive statistics, hypothesis testing, regression modeling, and
high-quality visualizations.
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 uniformly
across all implicates.scf_update_by_implicate(): Applies a user-defined
transformation to each implicate’s data frame separately. Use when a
computation depends on the within-implicate distribution (e.g.,
implicate-specific ranks or percentile thresholds).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 or a commensurate methodology.scf_pctile_sum(): Creates percentile-based grouping
variables for a continuous variable and optionally computes a summary
statistic within each group. Supports an implicate-specific
survey-weighted method (default) and the Federal Reserve’s published
stacking convention.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_quantreg(): Weighted quantile regression with
pooled coefficients across implicates. Supports multiple quantiles in a
single call.All model functions return objects of class
scf_model_result, with methods for coef(),
vcov(), predict(), AIC(),
residuals(), and summary().
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_deflate(): Converts nominal dollar estimates from
scf_mean(), scf_median(),
scf_percentile(), and scf_ttest() to real
dollars using CPI-U-RS deflation factors from the Federal Reserve’s SCF
Bulletin SAS macro.scf_regtable(): Produces formatted regression tables
from one or more scf_model_result objects, with options for
console, HTML, and CSV output.print(), summary(): Custom methods for
clean, interpretable output in analysis and teaching.Install the latest version of the package through CRAN:
install.packages("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, include = F
# This document will use mock data for CRAN compliance
# use the above method to download and load data in your analysis instead of:
scf2022 <- readRDS(system.file("extdata", "scf2022_mock_raw.rds", package = "scf"))
# NOTE: 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 $75,000?
scf_ttest(scf2022, ~income, mu = 75000)
# Two-sample t-test: Are older households wealthier?
scf_ttest(scf2022, ~networth, ~I(age > 50), alternative = "greater")# Linear regression: Predict net worth from income and education
scf_ols(scf2022, networth ~ income + factor(edcl))
# Generalized linear model: Predict borrowing with logistic regression
scf_glm(scf2022, hborrff ~ income + age + factor(edcl), family = binomial())
# Logit wrapper: Predict probability of owning stocks
scf_logit(scf2022, ~I(owns_stocks == 1) ~ age + income + factor(edcl))
# Bar chart of a single categorical variable
scf_plot_dbar(scf2022, ~edcl)
# Stacked bar chart comparing education by race
scf_plot_bbar(scf2022, ~edcl, ~racecl, scale = "percent", percent_by = "row")
# Smoothed line plot of net worth distribution
scf_plot_smooth(scf2022, ~networth, xlim = c(0, 2e6), method = "loess")
# Histogram of income distribution
scf_plot_hist(scf2022, ~income, bins = 40, xlim = c(0, 300000))
# Bar chart of mean net worth by education level
scf_plot_cbar(scf2022, ~networth, ~edcl, stat = "mean")
# Hexbin plot: net worth vs. income
scf_plot_hex(scf2022, ~income, ~networth, bins = 60)# Create new variables across all implicates
scf2022 <- scf_update(scf2022,
rich = networth > 1e6,
senior = age >= 65,
log_income = log(income + 1)
)
# Apply implicate-specific transformations (e.g., implicate-specific ranks)
scf2022 <- scf_update_by_implicate(scf2022, function(df) {
threshold <- quantile(df$networth, probs = 0.90, na.rm = TRUE)
df$top10nw <- df$networth >= threshold
df
})
# 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)# Mean net worth by decile (implicate method, statistically preferred)
scf_pctile_sum(scf2022, ~networth)
# Top 10% vs. bottom 90%, stack method (fast; replicates Fed convention)
scf_pctile_sum(scf2022, ~networth,
probs = c(0, 0.9, 1),
labels = c("bottom90", "top10"),
method = "stack")
# Add a percentile grouping variable to the object for use in other functions
scf2022 <- scf_pctile_sum(scf2022, ~networth,
probs = c(0, 0.5, 0.9, 1),
labels = c("bottom50", "next40", "top10"),
stat = "none")
scf_median(scf2022, ~income, by = ~networth_pctile)# Convert nominal mean income estimates across years to 2022 dollars
m2016 <- scf_mean(scf2016, ~income)
m2022 <- scf_mean(scf2022, ~income)
m2016_real <- scf_deflate(m2016, from_year = 2016, to_year = 2022)
m2022_real <- scf_deflate(m2022, from_year = 2022) # to_year defaults to 2022# Median regression: net worth on income and education
scf_quantreg(scf2022, networth ~ income + factor(edcl), tau = 0.5)
# Multiple quantiles in one call
scf_quantreg(scf2022, networth ~ income + factor(edcl),
tau = c(0.25, 0.5, 0.75, 0.9))# Compare OLS and logit models in a single formatted table
m_ols <- scf_ols(scf2022, networth ~ income + factor(edcl))
m_logit <- scf_logit(scf2022, ~I(networth > 1e6) ~ income + factor(edcl))
scf_regtable(m_ols, m_logit) # console output
scf_regtable(m_ols, m_logit, output = "html") # HTML fragment
scf_regtable(m_ols, m_logit, output = "csv",
file = "results/table1.csv") # CSV fileFor detailed examples, function documentation, and usage guides, consult the package vignettes and reference manual.
This package includes a small mock dataset
(scf2022_mock_raw.rds) for testing purposes.
It includes only 75 rows and select variables. It is structurally
valid,
but not suitable for analytical use or inference. Mock
data objects carry a “mock” = TRUE attribute and may trigger warnings in
functions to discourage interpretive use.
If you use scf in published work, please cite it as:
Joseph N. Cohen (2026). scf: Analyzing the Survey of Consumer Finances. R package. ver. 1.0.7. 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
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.
Health stats visible at Monitor.