spicy is an R package for publication-ready tables: frequency tables and cross-tabulations, association measures, categorical and continuous summary tables, regression tables for 39 model classes, and labelled survey data workflows.
Features
Every summary and regression table prints as readable text in the console and exports to gt, tinytable, flextable, Excel, Word, or the clipboard, following rigorous reporting conventions – APA by default, with named journal styles. Around the tables, spicy provides the survey-data tools that feed them: variable inspection, codebooks, label extraction, and row-wise summaries.
-
Frequency tables with
freq(). -
Cross-tabulations with
cross_tab(): percentages, weights, chi-squared tests, and effect sizes. -
Association measures:
cramer_v(),phi(),gamma_gk(),kendall_tau_b(),somers_d()and 6 other coefficients;assoc_measures()computes the full set at once. -
Categorical and continuous summary tables with
table_categorical()andtable_continuous(), overall or by group. -
Multiple-response tables with
table_multiresponse(): “check all that apply” items read as one question, overall or by group, with a global test that allows for several answers per respondent. The function is experimental in this release. -
Outcome-first summary tables with
table_outcome(): one continuous outcome described across several groupings, one block of rows per grouping variable, a group comparison per block, and a marginalOverallrow – the transpose oftable_continuous(), with the same statistic vocabulary and output engines. -
Design-based summary tables with
table_continuous_svy()andtable_categorical_svy()on asurvey::svydesignobject: estimates, SEs and CIs from thesurveypackage (Taylor linearization or replicate weights), design degrees of freedom, observed and weighted counts – the design twins of the two families above, with the same statistic vocabulary and output engines. -
Model-based continuous summary tables with
table_continuous_lm()for linear-model reporting: classical / HC* / cluster-robust / bootstrap / jackknife variance, four effect-size families (f², Cohen’s d, Hedges’s g, Hays’s omega²) with noncentral CIs, additive covariate adjustment with G-computation (Statamarginsstyle) or equal-weight (emmeansstyle) marginal means, and weighted comparisons. -
Regression tables with
table_regression()for one or more fitted models side by side, across 39 model classes in 12 families (see Supported models below): classical / heteroskedasticity-robust / cluster-robust / bootstrap / jackknife variance with each class’s field-standard backend, standardized coefficients, family-awareexponentiate(OR / IRR / HR / RR / MR, link-gated), Wald or profile-likelihood CIs, average marginal effects (per-category for ordinal and multinomial models), partial f² / η² / ω² / χ² effect sizes, class-aware fit statistics (pseudo-R², Nakagawa marginal / conditional R², ICC), hierarchical model comparisons with the correct nested test per class (a Wald test on the requested matrix under a robust or cluster-robustvcov), and multiple-comparison adjustment. Mixed models report their random effects as table rows with an optional boundary-correct per-term test; ordinal models report their thresholds, and cumulative-link mixed models (ordinal::clmm(),glmmTMBwith the ordinal family) both; zero-inflated and hurdle models report every model component. Survival models go beyond hazard ratios: adjusted restricted-mean-survival-time and risk differences by g-computation (tau/at_time), forcoxphfits (stratified included) andsurvregAFT fits, in single tables and in the univariable screen. -
Univariable screening with
table_regression_uv(): one-predictor-at-a-time models merged with the multivariable fit, per-predictor N and events, forglm,lm, and Cox outcomes. -
Journal styles with
spicy_style():style = "jama","nejm","lancet","annals","apa", or"aer"restyles any table, every rule sourced from each style’s published guidelines. For a French table,options(spicy.language = "fr")gives the words and the French typography together. -
Variable inspection with
varlist()andvl(): names, labels, values, classes, distinct values (N_distinct), valid observations (N_valid), and missing data. -
Codebooks with
code_book(): variables, values, and unweighted counts, printed in the console, written to Excel, or compiled to a PDF with a cover, notes (paragraphs, lists, italics, bold, code, links), one sheet per variable, and an index. -
Label extraction with
label_from_names(), including LimeSurvey-style headers. -
Row-wise summaries with
mean_n(),sum_n(), andcount_n(), with explicit control over missing values.
Works with labelled, factor, ordered, Date, POSIXct, and other common variable types – and honors declared missing values (haven’s na_values/na_range, tagged NAs) across the descriptive functions, with disclosure notes and a user_na escape hatch. For a full introduction, see Getting started with spicy.
What makes the tables dependable
-
One table, every output, verified. The console rendering and the gt, tinytable, flextable, Word, Excel, and clipboard outputs are kept cell-for-cell identical by dedicated parity test batteries – significance stars,
events/Ncounts, reference rows, block structure, captions, and disclosure notes included. What you publish is what you saw. -
Numbers validated against reference implementations. Estimates, standard errors, and association measures are pinned, to the digit, against independent software and packages (SPSS/PSPP conventions for contingency measures, Stata conventions for cluster-robust variance,
survival::concordance(), exact order-statistic confidence intervals for medians), and each convention is documented where the number is produced. - Disclosure by default. Whatever affects the numbers is stated in the table’s own notes, on every output: removed missing values, estimation samples, standard-error types, test choices, multiple-comparison families.
-
A typed, versioned table contract.
as_structured()returns the complete typed view of a regression or summary table – values, precisions, significance markers, row roles – with an explicit contract version, so custom renderers and downstream pipelines can detect changes instead of silently mis-reading them. - Hard errors over silent guesses. Requests a model class cannot honor are refused with a classed, actionable error – never rendered as an empty column or a silently substituted statistic.
Supported models
table_regression() accepts a single fit or a list of fits from any of these classes, and renders them with the conventions of each model family:
Class-specific structure renders as labeled blocks in the same table: random effects (with SE and CI on each variance component, and an optional boundary-correct per-term likelihood-ratio test), ordinal thresholds, non-proportional effects, zero-inflation and dispersion components. Robust variance requests use each class’s field-standard backend and are refused with a clear message when no valid backend exists.
The full capability map – each family’s marginal-effects estimand, exponentiate semantics, robust-variance backends and standardized-coefficient support – is the Supported models article; table_regression_models() returns the same registry as a data frame.
Installation
Install the current CRAN release, recommended for most users:
install.packages("spicy")Install the latest r-universe build:
install.packages(
"spicy",
repos = c(
"https://amaltawfik.r-universe.dev",
"https://cloud.r-project.org"
)
)This installs spicy from r-universe when available; CRAN is included only as a fallback for dependencies. The r-universe build may be newer than the current CRAN release.
Install the development version from GitHub with pak:
# install.packages("pak")
pak::pak("amaltawfik/spicy")Or with remotes:
# install.packages("remotes")
remotes::install_github("amaltawfik/spicy")Quick tour
The examples below use the bundled sochealth dataset.
Inspect variables
varlist(sochealth, tbl = TRUE)
#> # A tibble: 29 × 7
#> Variable Label Values Class N_distinct N_valid NAs
#> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 sex Sex Femal… fact… 2 1200 0
#> 2 age Age (years) 25, 2… nume… 51 1200 0
#> 3 age_group Age group 25-34… orde… 4 1200 0
#> 4 education Highest education le… Lower… orde… 3 1200 0
#> 5 social_class Subjective social cl… Lower… orde… 5 1200 0
#> 6 region Region of residence Centr… fact… 6 1200 0
#> 7 employment_status Employment status Emplo… fact… 4 1200 0
#> 8 income_group Household income gro… Low, … orde… 4 1182 18
#> 9 income Monthly household in… 1000,… nume… 1052 1200 0
#> 10 smoking Current smoker No, Y… fact… 2 1175 25
#> # ℹ 19 more rowsCodebooks
code_book() documents a data frame: the list of variables in the console, an Excel workbook, or a PDF with a cover, a page about the data, one sheet per variable, and an index. The notes take paragraphs and list items, with italics, bold, code, and links.
code_book(
sochealth,
title = "Social health survey",
subtitle = "Simulated data shipped with spicy",
authors = list(
list(
name = "Jane Doe",
affiliation = "University of Somewhere",
orcid = "0000-0002-1825-0097"
),
list(name = "John Doe", affiliation = "Somewhere Institute of Public Health")
),
notes = c(
"Simulated data shipped with *spicy* (`?sochealth`): 1,200 respondents of a fictitious social health survey.",
"- `weight` is the survey design weight: the counts of this codebook are **unweighted**."
),
output = "sochealth_codebook.pdf"
)
The whole document: the PDF, the same in French, and the Excel workbook. See Explore variables and build codebooks for more on varlist(), vl(), and code_book().
Frequency tables and cross-tabulations
freq(sochealth, income_group)
#> Frequency table: income_group
#>
#> Category │ Values Freq. Percent Valid Percent
#> ────────────┼─────────────────────────────────────────────────────
#> Valid │ Low 247 20.6 20.9
#> │ Lower middle 388 32.3 32.8
#> │ Upper middle 328 27.3 27.7
#> │ High 219 18.2 18.5
#> Missing │ NA 18 1.5
#> ────────────┼─────────────────────────────────────────────────────
#> Total │ 1200 100.0 100.0
#>
#> Label: Household income group
#> Class: ordered, factor
#> Data: sochealth
cross_tab(sochealth, smoking, education, percent = "col")
#> Crosstable: smoking x education (Column %)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> No │ 69.6 78.7 84.9 │ 78.8
#> Yes │ 30.4 21.3 15.1 │ 21.2
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> Total │ 100.0 100.0 100.0 │ 100.0
#> N │ 257 527 391 │ 1175
#>
#> Chi-2(2) = 21.6, p < .001
#> Cramér's V = 0.14
#> Missing values removed: smoking (25).See Frequency tables and cross-tabulations for freq(), cross_tab(), percentages, weights, and tests.
Association measures
tbl <- xtabs(~ self_rated_health + education, data = sochealth)
# Quick scalar estimate
cramer_v(tbl)
#> [1] 0.1761697
# Detailed result with CI and p-value
cramer_v(tbl, detail = TRUE)
#> Estimate SE CI lower CI upper p
#> 0.176 – 0.120 0.231 <.001See Cramér’s V, Phi, and association measures for a guide on choosing the right measure.
Summary tables
table_categorical(
sochealth,
select = c(smoking, physical_activity),
labels = c(
smoking = "Current smoker",
physical_activity = "Physical activity"
)
)
#> Categorical table
#>
#> Variable │ n %
#> ─────────────────────┼───────────────
#> Current smoker │
#> No │ 926 78.8
#> Yes │ 249 21.2
#> (Missing) │ 25
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 650 54.2
#> Yes │ 550 45.8
#>
#> Note. N = 1200.
table_categorical(
sochealth,
select = c(smoking, physical_activity),
by = education,
labels = c(
smoking = "Current smoker",
physical_activity = "Physical activity"
)
)
#> Categorical table by education
#>
#> Variable │ Lower secondary n Lower secondary % Upper secondary n
#> ───────────────────┼─────────────────────────────────────────────────────────
#> Current smoker │
#> No │ 179 69.6 415
#> Yes │ 78 30.4 112
#> (Missing) │ 4 12
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 177 67.8 310
#> Yes │ 84 32.2 229
#>
#> Variable │ Upper secondary % Tertiary n Tertiary % Total n
#> ───────────────────┼────────────────────────────────────────────────────
#> Current smoker │
#> No │ 78.7 332 84.9 926
#> Yes │ 21.3 59 15.1 249
#> (Missing) │ 9 25
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 57.5 163 40.8 650
#> Yes │ 42.5 237 59.2 550
#>
#> Variable │ Total % p V
#> ───────────────────┼─────────────────────
#> Current smoker │ <.001 .14
#> No │ 78.8
#> Yes │ 21.2
#> (Missing) │
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │ <.001 .21
#> No │ 54.2
#> Yes │ 45.8
#>
#> Note. N = 1200: Lower secondary (n = 261), Upper secondary (n = 539), Tertiary (n = 400). Percentages are column percentages. P-values: Pearson chi-squared test. V = Cramér's V.
table_continuous(
sochealth,
select = c(bmi, life_sat_health)
)
#> Descriptive statistics
#>
#> Variable │ M SD Min Max 95% CI LL 95% CI UL n
#> ─────────────────┼───────────────────────────────────────────────────────
#> bmi │ 25.93 3.72 16.00 38.90 25.72 26.14 1188
#> life_sat_health │ 3.55 1.25 1.00 5.00 3.48 3.62 1192
#>
#> Note. N = 1200. Missing values removed: bmi (12), life_sat_health (8). CI = confidence interval; LL = lower limit; UL = upper limit.
table_continuous(
sochealth,
select = c(bmi, life_sat_health),
by = education
)
#> Descriptive statistics by education
#>
#> Variable │ Group M SD Min Max 95% CI LL
#> ─────────────────┼───────────────────────────────────────────────────────
#> bmi │ Lower secondary 28.09 3.47 18.20 38.90 27.66
#> │ Upper secondary 26.02 3.43 16.00 37.10 25.73
#> │ Tertiary 24.39 3.52 16.00 33.00 24.04
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> life_sat_health │ Lower secondary 2.71 1.20 1.00 5.00 2.57
#> │ Upper secondary 3.53 1.19 1.00 5.00 3.43
#> │ Tertiary 4.11 1.04 1.00 5.00 4.01
#>
#> Variable │ Group 95% CI UL n p
#> ─────────────────┼────────────────────────────────────────
#> bmi │ Lower secondary 28.51 260 <.001
#> │ Upper secondary 26.31 534
#> │ Tertiary 24.74 394
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> life_sat_health │ Lower secondary 2.86 259 <.001
#> │ Upper secondary 3.63 534
#> │ Tertiary 4.21 399
#>
#> Note. N = 1200: Lower secondary (n = 261), Upper secondary (n = 539), Tertiary (n = 400). Missing values removed: bmi (12), life_sat_health (8). P-values: Welch one-way ANOVA. CI = confidence interval; LL = lower limit; UL = upper limit.
table_continuous_lm(
sochealth,
select = c(wellbeing_score, bmi),
by = sex,
vcov = "HC3",
output = "data.frame"
)
#> variable label M (Female) M (Male) Δ 95% CI LL
#> 1 wellbeing_score wellbeing_score 67.16194 71.04879 3.8868576 2.12265210
#> 2 bmi bmi 25.68506 26.19685 0.5117882 0.08904596
#> 95% CI UL p R² n
#> 1 5.6510631 1.670572e-05 0.015475137 1200
#> 2 0.9345305 1.769614e-02 0.004728908 1188
fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
table_regression(fit)
#> Linear regression: wellbeing_score
#>
#> Variable │ B SE 95% CI p
#> ─────────────────┼──────────────────────────────────────
#> (Intercept) │ 65.20 1.66 [61.95, 68.45] <.001
#> age │ 0.05 0.03 [-0.01, 0.11] .130
#> sex: │
#> Female (ref.) │ – – – –
#> Male │ 3.86 0.91 [ 2.08, 5.63] <.001
#> smoking: │
#> No (ref.) │ – – – –
#> Yes │ -1.72 1.11 [-3.89, 0.45] .121
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> n │ 1175
#> R² │ 0.02
#> Adj. R² │ 0.02
#>
#> Note. Linear regression. Std. errors: classical (OLS). CI = confidence interval.Regression tables cover 39 model classes with the conventions of each family. A mixed-effects fit, for example, reports its random effects as rows (SD, correlation, residual – each with SE and CI), the ICC and group sizes as fit statistics, and the likelihood-ratio test of the random part with the boundary-correct chi-bar-squared p-value:
library(lme4)
fit_mixed <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
table_regression(fit_mixed)
#> Linear mixed-effects regression: Reaction
#>
#> Variable │ B SE 95% CI p
#> ─────────────────────────────────┼────────────────────────────────────────
#> (Intercept) │ 251.41 6.82 [238.03, 264.78] <.001
#> Days │ 10.47 1.55 [ 7.44, 13.50] <.001
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Random effects: │
#> σ Subject (Intercept) │ 24.74 5.84 [ 6.79, 34.32] –
#> σ Subject Days │ 5.92 1.25 [ 2.47, 8.00] –
#> ρ Subject ((Intercept), Days) │ 0.07 0.33 [ -0.57, 0.70] –
#> σ (Residual) │ 25.59 1.51 [ 22.44, 28.39] –
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> n │ 180
#> N (Subject) │ 18
#> R² (marginal) │ 0.28
#> R² (conditional) │ 0.80
#> AIC │ 1755.6
#> BIC │ 1774.8
#>
#> Note. Linear mixed-effects regression. Std. errors: Wald (model-based). P-values: Wald-z, large-sample approximation. Load `lmerTest` for Satterthwaite t-tests. Random effects (REML): LR test vs linear regression, χ̄²(3) = 150.04, p < .001. CI = confidence interval; AIC = Akaike information criterion; BIC = Bayesian information criterion.See Categorical summary tables for categorical summaries, Continuous summary tables for continuous summaries and group comparisons, Model-based continuous summary tables for weighted or robust linear-model reporting, Publication-ready regression tables for table_regression() across model families, and Summary tables for reporting for an overview of summary tables.
The same numbers can be laid out outcome-first: one outcome, several groupings, one block per grouping.
table_outcome(
sochealth,
outcome = bmi,
select = c(sex, smoking)
)
#> Descriptive statistics of bmi
#>
#> Variable │ M SD Min Max 95% CI LL 95% CI UL n p
#> ─────────────┼─────────────────────────────────────────────────────────────
#> Overall │ 25.93 3.72 16.00 38.90 25.72 26.14 1188
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> sex │ .018
#> Female │ 25.69 3.78 16.00 38.90 25.39 25.98 616
#> Male │ 26.20 3.64 16.00 37.70 25.90 26.50 572
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> smoking │ .903
#> No │ 25.96 3.76 16.00 38.90 25.72 26.21 915
#> Yes │ 25.93 3.58 16.80 35.30 25.48 26.38 248
#> (Missing) │ 24.74 3.63 17.60 32.50 23.24 26.23 25
#>
#> Note. N = 1200. Missing values removed: bmi (12). P-values: Welch t-test. Each block compares bmi across the levels of one variable, without controlling for the other variables. Overall = the whole analytic sample. CI = confidence interval; LL = lower limit; UL = upper limit.A “check all that apply” question gets a table of its own, one row per item. In sochealth, five logical items record the sources of health information a respondent used in the past 12 months. The article Multiple-response questions explains the bases, the percentages and the tests.
table_multiresponse(
sochealth,
select = starts_with("info_"),
by = sex
)
#> Multiple responses by sex
#>
#> Variable │ Female n Female % Male n Male % Total n Total % p
#> ──────────────────┼─────────────────────────────────────────────────────────────
#> info_doctor │ 377 62.9 344 60.9 721 61.9 1.000
#> info_pharmacist │ 137 22.9 119 21.1 256 22.0 1.000
#> info_internet │ 268 44.7 255 45.1 523 44.9 1.000
#> info_family │ 219 36.6 141 25.0 360 30.9 <.001
#> info_media │ 78 13.0 63 11.2 141 12.1 1.000
#> (No item chosen) │ 31 5.2 36 6.4 67 5.8 –
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Base │ 599 565 1164
#>
#> Note. Multiple answers possible: percentages can sum to more than 100%. Base: respondents who answered the question (excluded: 36 with no answer to the question). Item p-values: Pearson chi-squared test of each item by group. Adjusted for 5 tests (Holm method). Overall group comparison: Rao-Scott second-order corrected chi-squared test of multiple marginal independence, χ²(4.61) = 18.78, p = .002.Row-wise summaries
df <- data.frame(
x1 = c(10, NA, 30, 40, 50),
x2 = c(5, NA, 15, NA, 25),
x3 = c(NA, 30, 20, 50, 10)
)
mean_n(df)
#> [1] NA NA 21.66667 NA 28.33333
sum_n(df, min_valid = 2)
#> [1] 15 NA 65 90 85
count_n(df, special = "NA")
#> [1] 1 2 0 1 0See Getting started with spicy for a longer workflow using mean_n(), sum_n(), and count_n().
Label extraction
# LimeSurvey-style headers: "code. label"
df <- tibble::tibble(
"age. Age of respondent" = c(25, 30),
"score. Total score" = c(12, 14)
)
out <- label_from_names(df)
labelled::var_label(out)
#> $age
#> [1] "Age of respondent"
#>
#> $score
#> [1] "Total score"See Explore variables and build codebooks for more on label_from_names(), varlist(), and code_book().
Documentation
Each workflow has a dedicated article on the package site:
- Getting started with spicy
- Explore variables and build codebooks
- Frequency tables and cross-tabulations
- Cramér’s V, Phi, and association measures
- Categorical summary tables
- Continuous summary tables
- Model-based continuous summary tables
- One outcome across several groupings
- Multiple-response questions
- Summary tables from a survey design
- Publication-ready regression tables
- Supported models
- The structured view: programmatic access to spicy tables
- Mixed-effects regression tables
- GEE regression tables (population-averaged models)
- Count and two-part regression tables
- Survival regression tables
- Ordinal regression tables
- Multinomial regression tables
- Bayesian regression tables
- Categorical predictors
- Summary tables for reporting
Key reference pages: freq(), cross_tab(), cramer_v(), table_categorical(), table_continuous(), table_continuous_lm(), table_regression(), varlist(), code_book(), label_from_names(), mean_n(), sum_n(), and count_n() – see the full function index for everything else.
Citation
To cite spicy in a publication or teaching material (this entry is generated by citation("spicy") at build time and always refers to the current CRAN release):
Tawfik A (2026). spicy: Publication-Ready Tables for Descriptive Statistics and Regression Models. doi:10.32614/CRAN.package.spicy https://doi.org/10.32614/CRAN.package.spicy. R package version 0.14.0, https://CRAN.R-project.org/package=spicy.
@Manual{,
title = {spicy: Publication-Ready Tables for Descriptive Statistics and Regression Models},
author = {Amal Tawfik},
year = {2026},
note = {R package version 0.14.0},
url = {https://CRAN.R-project.org/package=spicy},
doi = {10.32614/CRAN.package.spicy},
}- Package DOI: https://doi.org/10.32614/CRAN.package.spicy.
- Source citation file: https://github.com/amaltawfik/spicy/blob/main/inst/CITATION
License
MIT. See LICENSE for details.