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spicy

spicy brings polished, console-first data exploration to R for everyday analysis workflows.

What is spicy?

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 APA-style reporting — offering functionality similar to Stata or SPSS but within a tidyverse-friendly R environment. It helps you:

  • Inspect variables with varlist() — names, labels, values, classes, missings. Similar to SPSS “Variable View” or Stata “Variables Manager”.
  • Explore distributions with freq() and associations with cross_tab().
  • Measure associations with cramer_v(), phi(), gamma_gk(), kendall_tau_b(), somers_d(), and more — all with confidence intervals and p-values.
  • Build APA tables with table_apa() — export to gt, tinytable, flextable, Excel, Word, or clipboard.
  • Compute row-wise summaries with mean_n(), sum_n(), and count_n().
  • Generate codebooks with code_book() — interactive HTML with search, sort, and export.
  • Extract labels from column headers with label_from_names() — useful for LimeSurvey CSV exports.

Works with labelled, factor, ordered, Date, POSIXct, and other common variable types. See vignette("spicy") for a full tour.

Installation

Install the stable version from CRAN:

Or from r-universe:

install.packages("spicy", repos = c("https://amaltawfik.r-universe.dev", "https://cloud.r-project.org"))

Or the development version from GitHub:

# install.packages("pak")
pak::pak("amaltawfik/spicy")

Quick tour

Inspect variables

varlist demo with labelled data

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

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 < 0.001
#> Cramer's V = 0.14

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  < 0.001

See vignette("association-measures") for a guide on choosing the right measure.

APA tables 

table_apa(
  sochealth,
  row_vars = c("smoking", "physical_activity"),
  group_var = "education",
  labels = c("Current smoker", "Physical activity"),
  output = "wide",
  style = "report"
)
#>            Variable Lower secondary n Lower secondary % Upper secondary n
#> 1    Current smoker                                                      
#> 2                No               179              69.6               415
#> 3               Yes                78              30.4               112
#> 4 Physical activity                                                      
#> 5                No               177              67.8               310
#> 6               Yes                84              32.2               229
#>   Upper secondary % Tertiary n Tertiary % Total n Total %      p Cramer's V
#> 1                                                         < .001        .14
#> 2              78.7        332       84.9     926    78.8                  
#> 3              21.3         59       15.1     249    21.2                  
#> 4                                                         < .001        .21
#> 5              57.5        163       40.8     650    54.2                  
#> 6              42.5        237       59.2     550    45.8

See vignette("table-apa") for all output formats, weights, CI, and export options.

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 0

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"

Citation

If you use spicy in a publication or teaching material:

License

MIT. See LICENSE for details.