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.
Inspect your data
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 0Frequency tables
freq() 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-tabulations
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 = 0.20Association measures
For 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:
cramer_v(tbl, detail = TRUE)
#> Estimate CI lower CI upper p
#> 0.136 0.079 0.191 <.001Summary tables
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"
)| Variable | Lower secondary | Upper secondary | Tertiary | Total | p | Cramer's V | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | |||
| smoking | <.001 | .14 | ||||||||
| No | 179 | 69.6 | 415 | 78.7 | 332 | 84.9 | 926 | 78.8 | ||
| Yes | 78 | 30.4 | 112 | 21.3 | 59 | 15.1 | 249 | 21.2 | ||
| physical_activity | <.001 | .21 | ||||||||
| No | 177 | 67.8 | 310 | 57.5 | 163 | 40.8 | 650 | 54.2 | ||
| Yes | 84 | 32.2 | 229 | 42.5 | 237 | 59.2 | 550 | 45.8 | ||
| dentist_12m | <.001 | .22 | ||||||||
| No | 113 | 43.3 | 174 | 32.3 | 67 | 16.8 | 354 | 29.5 | ||
| Yes | 148 | 56.7 | 365 | 67.7 | 333 | 83.2 | 846 | 70.5 | ||
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 standard errors or case weights:
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.
Row-wise summaries
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 0Learn more
- See
?varlistto inspect variables, labels, values, and missing data. - See
?cross_tabfor the full list of arguments (weights, simulation, association measures). - See
?table_categoricalfor grouped or one-way categorical tables. - See
?table_continuousfor continuous summaries and group comparisons. - See
?table_continuous_lmfor model-based mean-comparison tables with robust standard errors or case weights. - See
?assoc_measuresfor the complete list of association statistics. - See
?code_bookto generate an interactive HTML codebook.