Simplified Statistical Analysis Tools for Social Science • jstats

Simplified statistical-analysis tools for social-science researchers and teachers, with output styled after commercial statistical software.

jstats provides a small, consistent set of functions for the analyses social scientists run most often — frequencies and descriptives, t-tests, ANOVA, cross-tabulations, correlation, linear and logistic regression, and scale reliability — with sensible defaults and clean, recognizable output. It reads data exported from commercial statistical software such as SPSS, Stata, and SAS (via haven) directly, value and variable labels included, and stays close enough to base R that the skills you build transfer.

It is designed for researchers moving into R from SPSS, Stata, or SAS who want to get their analyses done without first learning a new dialect — and it doubles as teaching infrastructure for students meeting statistics and R at the same time.

Installation

jstats installs as a pre-built binary from its R-universe repository, so there is no compiler or build toolchain to set up:

install.packages("jstats",
  repos = c("https://jma61.r-universe.dev", "https://cloud.r-project.org"))

Then load it as usual:

library(jstats)

Requires R >= 4.2.0.

A quick taste

Every install bundles community, a small synthetic dataset, so these examples run as-is:

library(jstats)

# Frequency table for a categorical variable
jfreq(community, Region)

# Descriptive statistics
jdesc(community, Age, WellbeingScore, CommuteTime)

# Independent-samples t-test (formula interface)
jt(WellbeingScore ~ Volunteer, community)

# Linear regression -- categorical predictors are dummy-coded for you
jlm(WellbeingScore ~ Age + CommuteTime + Region, community)

Two patterns cover the whole package: descriptive functions take the data first, then unquoted variable names — jfreq(community, Region); group-comparison and modeling functions take a formula, then the data — jt(y ~ group, community), just as in base R.

What’s included

Every user-facing function is prefixed j:

Describe — jdesc, jfreq
Compare groups — jt (t-test), jaov (ANOVA), jcrosstab (cross-tabulation)
Relate & model — jcorr (correlation), jlm (linear regression), jlogistic (logistic regression)
Scale reliability — jalpha (Cronbach’s alpha)
Prepare & screen — jscreen, jrecode, jrelabel, jsubset
Import & export — jload, jsave, jconvert (round-trip between SPSS, Stata, SAS, Excel, and CSV)
Plot — jplot

Functions accept haven-labelled or plain numeric variables directly — there is normally no need to convert anything to factors yourself. (A second bundled dataset, clinic, offers messier data for practicing cleaning and missing-value handling.)

Staying up to date

jstats is under active development, and new versions appear regularly. To update, run:

jupdate()

It fetches and installs the latest version in one step; restart R afterward to load it.

Documentation

Function reference — https://jma61.github.io/jstats/
Getting-started guides (no prior R assumed) — https://jma61.github.io/jstats-guides/

License

jstats is free, open-source software, released under the MIT License.

Created and maintained by Jeff Ackerman.