spicy is an R package for frequency tables, cross-tabulations, association measures, summary tables, and labelled survey data workflows.
spicy helps you explore categorical and labelled data in R with readable, console-first outputs. It is designed for survey research, descriptive statistics, and reporting workflows, with tools for frequency tables, cross-tabulations with chi-squared tests and effect sizes, categorical and continuous summary tables, variable inspection, and codebooks.
With spicy, you can:
varlist() and
vl() for names, labels, values, classes, and missing
data.freq().cross_tab(), including percentages, chi-squared tests, and
effect sizes.cramer_v(),
phi(), gamma_gk(),
kendall_tau_b(), somers_d(), and related
functions.table_categorical() for gt, tinytable, flextable, Excel,
Word, or clipboard export.table_continuous() for console, gt, tinytable, flextable,
Excel, Word, or clipboard output.code_book()
for labelled and survey-style datasets.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.
Install the current CRAN release:
install.packages("spicy")Install the latest build from r-universe:
install.packages("spicy", repos = c("https://amaltawfik.r-universe.dev", "https://cloud.r-project.org"))This may be newer than the current CRAN release.
Install the development version from GitHub with
pak:
# install.packages("pak")
pak::pak("amaltawfik/spicy")The examples below use the bundled sochealth
dataset.
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 rowsSee Explore
variables and build codebooks in R for more on
varlist(), vl(), and
code_book().
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
#> ─────────────┼───────────────────────────────────────────────────────────
#> No │ 69.6 78.7 84.9
#> Yes │ 30.4 21.3 15.1
#> ─────────────┼───────────────────────────────────────────────────────────
#> Total │ 100.0 100.0 100.0
#> N │ 257 527 391
#>
#> Values │ Total
#> ─────────────┼────────────
#> No │ 78.8
#> Yes │ 21.2
#> ─────────────┼────────────
#> Total │ 100.0
#> N │ 1175
#>
#> Chi-2(2) = 21.6, p < 0.001
#> Cramer's V = 0.14See Frequency
tables and cross-tabulations in R for freq(),
cross_tab(), percentages, weights, and tests.
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 < 0.001See Cramer’s V, Phi, and association measures in R for a guide on choosing the right measure.
table_categorical(
sochealth,
select = c(smoking, physical_activity),
labels = c("Current smoker", "Physical activity")
)
#> Categorical table
#>
#> Variable │ n %
#> ────────────────────────┼─────────────────────
#> Current smoker │
#> No │ 926 78.8
#> Yes │ 249 21.2
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Physical activity │
#> No │ 650 54.2
#> Yes │ 550 45.8table_categorical(
sochealth,
select = c(smoking, physical_activity),
by = education,
labels = c("Current smoker", "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 │ 45.8table_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 1192table_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 399See Categorical summary tables in R for categorical summaries, Continuous summary tables in R for continuous summaries and group comparisons, and Summary tables for APA-style reporting for an overview of summary tables.
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().
# 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().
If you are looking for a specific workflow, start with these vignettes:
Key reference pages:
varlist()code_book()label_from_names()freq()cross_tab()cramer_v()table_categorical()table_continuous()mean_n()sum_n()count_n()To cite spicy in a publication or teaching material:
citation("spicy") to generate the current BibTeX
entry.MIT. See LICENSE for details.