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vannstats: simplifying statistical procedures in the social sciences

Overview

vannstats is an R package designed to simplify the statistical workflows commonly used in undergraduate and graduate-level social science courses — particularly those transitioning from SPSS to R. The package provides intuitive wrappers for normality diagnostics, descriptive statistics, bivariate analyses, and multivariate techniques, producing output that mirrors the look and feel of SPSS results.

Whether you are assessing distributional assumptions, running crosstabs, computing correlations, or fitting ANOVA models, vannstats reduces the complexity of base R syntax into concise, readable function calls.

Installation

Install the released version from CRAN:

install.packages("vannstats")

Then load the package:

Functions

Diagnostic Plots

Function Description
hst() Histogram — can be parsed by up to 2 grouping variables
box() Box-and-Whisker plot — can be parsed by up to 2 grouping variables
qq() Q-Q (Quantile-Quantile) plot — can be parsed by up to 2 grouping variables
residplot() Residuals plot for a regression equation
scatter() Scatterplot with an overlaid fit line
bar.chart() Bar chart for a given variable

Descriptive Statistics

Function Description
univ.desc() Univariate descriptive statistics, optionally parsed by a grouping variable
mode() Mode of a variable
z.calc() Z-score for a given value relative to a variable’s mean and SD
se() Standard error of a variable
ci() Confidence interval for a variable at a specified confidence level

Bivariate Comparisons & Analyses

Function Description
tab() Crosstab of observed and expected frequencies for two categorical variables
chi.sq() Pearson’s Chi-Square test (with optional post-hoc comparisons and Cramér’s V)
p.corr() Pearson’s Product-Moment Correlation Coefficient
cormat() Correlation matrix for variables in a formula
ow.anova() One-Way ANOVA (with optional Tukey’s HSD post-hoc test and means plot)
rm.anova() One-Way Repeated Measures ANOVA (with sphericity correction and test)

Data Management

Function Description
dummy() Creates dummy-coded columns for the values of a categorical variable
revcode() Reverse-codes a scale variable
stata.plm.margins() Predictive margins from a panel linear model, mirroring STATA’s margins output

Examples

Histogram 

data <- mtcars
hst(data, mpg, cyl)

Box-and-Whisker Plot 

data <- mtcars
box(data, mpg, cyl)

Q-Q Plot 

data <- mtcars
qq(data, mpg, cyl)

Crosstab 

data <- mtcars
tab(data, vs, am)

Chi-Square Test 

data <- mtcars
x2 <- chi.sq(data, vs, am)
summary(x2)

Pearson’s Correlation 

data <- mtcars
p.corr(data, mpg, wt)

Correlation Matrix 

data <- mtcars
cormat(data, mpg ~ wt + am)

One-Way ANOVA 

data <- mtcars
ow <- ow.anova(data, mpg, cyl)
summary(ow)

Repeated Measures ANOVA 

# Wide format
data <- howell_aids_wide
rm.anova(data, student, c("t1", "t2", "t3"))

# Long format
data2 <- howell_aids_long
rm.anova(data2, student, time, scores = knowledge)

Residuals Plot 

data <- mtcars
residplot(data, mpg ~ wt + cyl)

Included Datasets

vannstats ships with several built-in datasets useful for teaching and demonstration purposes:

Dataset Description
GSS2014 General Social Survey, 2014 — individual survey responses on a range of social topics (NORC at the University of Chicago)
UCR2015 Uniform Crime Reports, 2015 — county-level crime data
howell_aids_wide Howell Student AIDS Knowledge Data in wide format (3 time points)
howell_aids_long Howell Student AIDS Knowledge Data in long format
Defendants2025 Simulated data representing individual defendants at the Richard J. Donovan Correctional Facility, San Diego, CA (1,738 observations, 11 variables)

Dependencies

vannstats builds on a suite of well-established R packages including ggplot2, dplyr, tidyverse, car, rstatix, plm, purrr, gplots, rlang, stringr, MASS, formula.tools, gdata, and stats.