spicy is an R package for frequency tables, cross-tabulations, association measures, summary tables, and labelled survey data workflows.
What is spicy?
spicy helps you explore categorical, continuous, and labelled survey data in R. It provides readable, console-first outputs for survey research, descriptive statistics, and reporting workflows, including frequency tables, cross-tabulations with chi-squared tests and effect sizes, categorical and continuous summary tables, variable inspection, and codebooks.
With spicy, you can:
-
Inspect variables with
varlist()andvl()for names, labels, values, classes, and missing data. -
Create frequency tables in R with
freq(). -
Create cross-tabulations in R with
cross_tab(), including percentages, chi-squared tests, and effect sizes. -
Measure associations with
cramer_v(),phi(),gamma_gk(),kendall_tau_b(),somers_d()and 8 other coefficients; useassoc_measures()to compute the full set at once. -
Build categorical summary tables in R with
table_categorical()for gt, tinytable, flextable, Excel, Word, or clipboard export. -
Build continuous summary tables in R with
table_continuous()for console, gt, tinytable, flextable, Excel, Word, or clipboard output. -
Build model-based continuous summary tables in R with
table_continuous_lm()for linear regression reporting: classical / HC* / cluster-robust / bootstrap / jackknife variance, four effect-size families (f², Cohen’s d, Hedges’ g, Hays’ omega²) with noncentral CIs, optional additive covariate adjustment with G-computation (Statamarginsstyle) or equal-weight (emmeansstyle) marginal means, weighted comparisons, and console, gt, tinytable, flextable, Excel, Word, or clipboard output. -
Generate interactive and exportable codebooks with
code_book()for labelled and survey-style datasets. -
Extract variable labels with
label_from_names(), including LimeSurvey-style headers.
Works with labelled, factor, ordered, Date, POSIXct, and other common variable types. For a full introduction, see Getting started with spicy.
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")Quick tour
The examples below use the bundled sochealth dataset.
Inspect variables
varlist(sochealth, tbl = TRUE)
#> # A tibble: 24 × 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 14 more rows
code_book(
sochealth,
starts_with("bmi"),
values = TRUE,
include_na = TRUE
)See Explore variables and build codebooks in R 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
#> Cramer's V = 0.14See Frequency tables and cross-tabulations in R 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 CI lower CI upper p
#> 0.176 0.120 0.231 <.001See Cramer’s V, Phi, and association measures in R 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
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 650 54.2
#> Yes │ 550 45.8
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
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> 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
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 57.5 163 40.8 650
#> Yes │ 42.5 237 59.2 550
#>
#> Variable │ Total % p Cramer's V
#> ───────────────────┼────────────────────────────
#> Current smoker │ <.001 .14
#> No │ 78.8
#> Yes │ 21.2
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │ <.001 .21
#> No │ 54.2
#> Yes │ 45.8
table_continuous(
sochealth,
select = c(bmi, life_sat_health)
)
#> Descriptive statistics
#>
#> Variable │ M SD Min Max 95% CI LL
#> ────────────────────────────────┼──────────────────────────────────────
#> Body mass index │ 25.93 3.72 16.00 38.90 25.72
#> Satisfaction with health (1-5) │ 3.55 1.25 1.00 5.00 3.48
#>
#> Variable │ 95% CI UL n
#> ────────────────────────────────┼─────────────────
#> Body mass index │ 26.14 1188
#> Satisfaction with health (1-5) │ 3.62 1192
table_continuous(
sochealth,
select = c(bmi, life_sat_health),
by = education
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ────────────────────────────────┼────────────────────────────────────────────
#> Body mass index │ Lower secondary 28.09 3.47 18.20 38.90
#> │ Upper secondary 26.02 3.43 16.00 37.10
#> │ Tertiary 24.39 3.52 16.00 33.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.71 1.20 1.00 5.00
#> │ Upper secondary 3.53 1.19 1.00 5.00
#> │ Tertiary 4.11 1.04 1.00 5.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n
#> ────────────────────────────────┼────────────────────────────────────────────
#> Body mass index │ Lower secondary 27.66 28.51 260
#> │ Upper secondary 25.73 26.31 534
#> │ Tertiary 24.04 24.74 394
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.57 2.86 259
#> │ Upper secondary 3.43 3.63 534
#> │ Tertiary 4.01 4.21 399
#>
#> Variable │ Group p
#> ────────────────────────────────┼────────────────────────
#> Body mass index │ Lower secondary <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary <.001
#> │ Upper secondary
#> │ Tertiary
table_continuous_lm(
sochealth,
select = c(wellbeing_score, bmi),
by = sex,
vcov = "HC3",
output = "data.frame"
)
#> Variable M (Female) M (Male)
#> wellbeing_score WHO-5 wellbeing index (0-100) 67.16194 71.04879
#> bmi Body mass index 25.68506 26.19685
#> Δ (Male - Female) 95% CI LL 95% CI UL p R²
#> wellbeing_score 3.8868576 2.12265210 5.6510631 1.670572e-05 0.015475137
#> bmi 0.5117882 0.08904596 0.9345305 1.769614e-02 0.004728908
#> n
#> wellbeing_score 1200
#> bmi 1188See Categorical summary tables in R for categorical summaries, Continuous summary tables in R for continuous summaries and group comparisons, Model-based continuous summary tables in R for weighted or robust linear-model reporting, and Summary tables for APA-style reporting for an overview of summary tables.
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 in R for more on label_from_names(), varlist(), and code_book().
Learn by task
If you are looking for a specific workflow, start with these vignettes:
- Getting started with spicy
- Explore variables and build codebooks in R
- Frequency tables and cross-tabulations in R
- Cramer’s V, Phi, and association measures in R
- Categorical summary tables in R
- Continuous summary tables in R
- Model-based continuous summary tables in R
- Summary tables for APA-style reporting
Key reference pages:
- Reference for
varlist() - Reference for
code_book() - Reference for
label_from_names() - Reference for
freq() - Reference for
cross_tab() - Reference for
cramer_v() - Reference for
table_categorical() - Reference for
table_continuous() - Reference for
table_continuous_lm() - Reference for
mean_n() - Reference for
sum_n() - Reference for
count_n()
Citation
To cite spicy in a publication or teaching material:
- Use
citation("spicy")to generate the current BibTeX entry. - 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.