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spicy is an R package for descriptive statistics and data analysis,
designed for data science and survey research workflows. It covers
variable inspection, frequency tables, cross-tabulations with
chi-squared tests and effect sizes, and publication-ready summary
tables, offering functionality similar to Stata or SPSS but within a
tidyverse-friendly R environment. This vignette walks through the core
workflow using the bundled sochealth dataset, a
simulated social-health survey with 1200 respondents and 24
variables.
varlist() (or its shortcut vl()) gives a
compact overview of every variable in a data frame: name, label,
representative values, class, number of distinct values, valid
observations, and missing values. In RStudio or Positron, calling
varlist() without arguments opens an interactive viewer -
this is the most common usage in practice. Here we use
tbl = TRUE to produce static output for the vignette:
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 rowsYou can also select specific columns with tidyselect syntax:
varlist(sochealth, starts_with("bmi"), income, weight, tbl = TRUE)
#> # A tibble: 4 × 7
#> Variable Label Values Class N_distinct N_valid NAs
#> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 bmi Body mass index 16, 1… nume… 177 1188 12
#> 2 bmi_category BMI category Norma… orde… 3 1188 12
#> 3 income Monthly household income (… 1000,… nume… 1052 1200 0
#> 4 weight Survey design weight 0.294… nume… 794 1200 0freq() produces frequency tables with counts,
percentages, and (optionally) valid and cumulative percentages.
freq(sochealth, education)
#> Frequency table: education
#>
#> Category │ Values Freq. Percent
#> ────────────┼───────────────────────────────────────
#> Valid │ Lower secondary 261 21.8
#> │ Upper secondary 539 44.9
#> │ Tertiary 400 33.3
#> ────────────┼───────────────────────────────────────
#> Total │ 1200 100.0
#>
#> Label: Highest education level
#> Class: ordered, factor
#> Data: sochealthWeighted frequencies use the weights argument. With
rescale = TRUE, the total weighted N matches the unweighted
N:
freq(sochealth, education, weights = weight, rescale = TRUE)
#> Frequency table: education
#>
#> Category │ Values Freq. Percent
#> ────────────┼───────────────────────────────────────
#> Valid │ Lower secondary 259 21.6
#> │ Upper secondary 546 45.5
#> │ Tertiary 395 32.9
#> ────────────┼───────────────────────────────────────
#> Total │ 1200 100.0
#>
#> Label: Highest education level
#> Class: ordered, factor
#> Data: sochealth
#> Weight: weight (rescaled)cross_tab() crosses two categorical variables. By
default it shows counts, a chi-squared test, and Cramer’s V:
cross_tab(sochealth, smoking, education)
#> Crosstable: smoking x education (N)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> No │ 179 415 332 │ 926
#> Yes │ 78 112 59 │ 249
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> Total │ 257 527 391 │ 1175
#>
#> Chi-2(2) = 21.6, p <.001
#> Cramer's V = 0.14Add percentages with percent:
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 = 0.14Group by a third variable with by:
cross_tab(sochealth, smoking, education, by = sex)
#> Crosstable: smoking x education (N) | sex = Female
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> No │ 95 220 160 │ 475
#> Yes │ 38 62 31 │ 131
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> Total │ 133 282 191 │ 606
#>
#> Chi-2(2) = 7.1, p = .029
#> Cramer's V = 0.11
#>
#> Crosstable: smoking x education (N) | sex = Male
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> No │ 84 195 172 │ 451
#> Yes │ 40 50 28 │ 118
#> ──────────┼──────────────────────────────────────────────────┼─────────
#> Total │ 124 245 200 │ 569
#>
#> Chi-2(2) = 15.6, p <.001
#> Cramer's V = 0.17When both variables are ordered factors, cross_tab()
automatically selects an ordinal measure (Kendall’s Tau-b) instead of
Cramer’s V:
cross_tab(sochealth, self_rated_health, education)
#> Crosstable: self_rated_health x education (N)
#>
#> Values │ Lower secondary Upper secondary Tertiary │ Total
#> ─────────────┼──────────────────────────────────────────────────┼─────────
#> Poor │ 28 28 5 │ 61
#> Fair │ 86 118 62 │ 266
#> Good │ 102 263 193 │ 558
#> Very good │ 44 118 133 │ 295
#> ─────────────┼──────────────────────────────────────────────────┼─────────
#> Total │ 260 527 393 │ 1180
#>
#> Chi-2(6) = 73.2, p <.001
#> Kendall's Tau-b = 0.20For a quick overview of all available association statistics, pass a
contingency table to assoc_measures():
tbl <- xtabs(~ smoking + education, data = sochealth)
assoc_measures(tbl)
#> Measure Estimate SE CI lower CI upper p
#> Cramer's V 0.136 -- 0.079 0.191 <.001
#> Contingency Coefficient 0.134 -- -- -- <.001
#> Lambda symmetric 0.000 0.000 0.000 0.000 --
#> Lambda R|C 0.000 0.000 0.000 0.000 --
#> Lambda C|R 0.000 0.000 0.000 0.000 --
#> Goodman-Kruskal's Tau R|C 0.018 0.008 0.003 0.034 .023
#> Goodman-Kruskal's Tau C|R 0.008 0.003 0.001 0.014 .022
#> Uncertainty Coefficient symmetric 0.011 0.005 0.002 0.021 .021
#> Uncertainty Coefficient R|C 0.018 0.008 0.003 0.032 .021
#> Uncertainty Coefficient C|R 0.009 0.004 0.001 0.016 .021
#> Goodman-Kruskal Gamma -0.268 0.056 -0.378 -0.158 <.001
#> Kendall's Tau-b -0.126 0.027 -0.180 -0.073 <.001
#> Kendall's Tau-c -0.117 0.026 -0.167 -0.067 <.001
#> Somers' D R|C -0.091 0.020 -0.131 -0.052 <.001
#> Somers' D C|R -0.175 0.038 -0.249 -0.101 <.001Individual functions such as cramer_v(),
gamma_gk(), or kendall_tau_b() return a scalar
by default. Pass detail = TRUE for the confidence interval
and p-value:
table_categorical() covers grouped or one-way summary
tables for categorical variables:
table_categorical(
sochealth,
select = c(smoking, physical_activity, dentist_12m),
by = education,
output = "tinytable"
)table_continuous() summarizes continuous variables,
either overall or by a categorical by variable, and can
also add group-comparison tests:
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
#> │ Tertiarytable_continuous_lm() covers the same reporting
territory when you want to stay in a linear-model framework, for example
with robust or cluster-robust standard errors, case weights, or additive
covariate adjustment:
table_continuous_lm(
sochealth,
select = c(wellbeing_score, bmi),
by = sex,
vcov = "HC3"
)
#> Continuous outcomes by Sex
#>
#> Variable │ M (Female) M (Male) Δ (Male - Female)
#> ───────────────────────────────┼─────────────────────────────────────────
#> WHO-5 wellbeing index (0-100) │ 67.16 71.05 3.89
#> Body mass index │ 25.69 26.20 0.51
#>
#> Variable │ 95% CI LL 95% CI UL p R² n
#> ───────────────────────────────┼─────────────────────────────────────────
#> WHO-5 wellbeing index (0-100) │ 2.12 5.65 <.001 0.02 1200
#> Body mass index │ 0.09 0.93 .018 0.00 1188For detailed guidance, see the dedicated articles on
table_categorical(), table_continuous(),
table_continuous_lm(), and the final reporting overview for
APA-style summary tables.
mean_n(), sum_n(), and
count_n() compute row-wise statistics across selected
columns, with automatic handling of missing values.
sochealth |>
dplyr::mutate(
mean_sat = mean_n(select = starts_with("life_sat")),
sum_sat = sum_n(select = starts_with("life_sat"), min_valid = 2),
n_missing = count_n(select = starts_with("life_sat"), special = "NA")
) |>
dplyr::select(starts_with("life_sat"), mean_sat, sum_sat, n_missing) |>
head() |>
as.data.frame()
#> life_sat_health life_sat_work life_sat_relationships life_sat_standard
#> 1 5 3 5 5
#> 2 4 4 5 5
#> 3 3 2 5 3
#> 4 3 4 3 2
#> 5 4 5 4 4
#> 6 5 5 5 3
#> mean_sat sum_sat n_missing
#> 1 4.50 18 0
#> 2 4.50 18 0
#> 3 3.25 13 0
#> 4 3.00 12 0
#> 5 4.25 17 0
#> 6 4.50 18 0?varlist to inspect variables, labels, values, and
missing data.?freq for one-way frequency tables (weights,
sorting, custom missing values, labelled-data display modes).?cross_tab for the full list of arguments (weights,
simulation, association measures).?assoc_measures for the complete list of
association statistics; ?cramer_v for the canonical entry
point.?table_categorical for grouped or one-way
categorical tables.?table_continuous for continuous summaries and
group comparisons.?table_continuous_lm for model-based
mean-comparison tables with robust / cluster-robust / bootstrap /
jackknife SE, case weights, or additive covariate adjustment.?mean_n, ?sum_n, ?count_n
for row-wise summaries with optional minimum-valid-values rules.?code_book to generate an interactive HTML
codebook; ?label_from_names to derive variable labels from
"code. label"-style column names (e.g., LimeSurvey
exports).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.