spicy brings polished, console-first data exploration to R for everyday analysis workflows.
spicy is an R package for the first phase of data analysis: inspecting variables, checking distributions, exploring associations, and moving quickly toward usable outputs. It helps you:
- Inspect variables quickly with
varlist()(and its shortcutvl()), including names, labels, representative values, classes, number of distinct non-missing values, number of valid observations, and number of missing observations. Similar to the “Variable View” in SPSS or the “Variables Manager” in Stata. - Explore distributions with
freq(), and inspect associations withcross_tab()andcramer_v(), all with readable console output. - Compute row-wise summaries with
mean_n(),sum_n(), andcount_n()with automatic handling of missing data. - Build publication-ready APA cross-tab reports with
table_apa()and export to multiple formats (wide,long,tinytable,flextable,excel,clipboard,word). - Generate an interactive codebook with
code_book(), extendingvarlist()with searchable summaries and built-in export options (Copy, CSV, Excel, PDF, Print) viaDT::datatable. - Copy data frames or result tables directly to the clipboard with
copy_clipboard()for fast export to spreadsheets or text editors. - Extract and assign variable labels from column headers with
label_from_names(), especially useful for LimeSurvey CSV exports where headers follow a “name [separator] label” pattern – any string can be used as the separator (e.g., “.”, ” - “,”:“, etc.). - Work comfortably with
labelled,factor,Date,POSIXct, and other commonly used variable types.
The goal is simple: make early data exploration faster, clearer, and more pleasant in everyday analysis and data science workflows.
For the stable version, install from CRAN.
install.packages("spicy")You can install the development version of spicy from GitHub with:
# install.packages("pak")
pak::pak("amaltawfik/spicy")
# Get a summary of all variables as a tibble
varlist(iris, tbl = TRUE)
#> # A tibble: 5 × 7
#> Variable Label Values Class N_distinct N_valid NAs
#> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 Sepal.Length <NA> 4.3, 4.4, 4.5, ..., 7.9 nume… 35 150 0
#> 2 Sepal.Width <NA> 2, 2.2, 2.3, ..., 4.4 nume… 23 150 0
#> 3 Petal.Length <NA> 1, 1.1, 1.2, ..., 6.9 nume… 43 150 0
#> 4 Petal.Width <NA> 0.1, 0.2, 0.3, ..., 2.5 nume… 22 150 0
#> 5 Species <NA> setosa, versicolor, virgini… fact… 3 150 0
# Tabulate frequencies with sort alphabetically (Z-A)
freq(iris, Species, sort = "name-")
#> Frequency table: Species
#>
#> Category │ Values Freq. Percent
#> ──────────┼────────────────────────────
#> Valid │ virginica 50 33.3
#> │ versicolor 50 33.3
#> │ setosa 50 33.3
#> ──────────┼────────────────────────────
#> Total │ 150 100.0
#>
#> Class: factor
#> Data: iris
# Cross-tab with frequencies
cross_tab(mtcars, cyl, gear)
#> Crosstable: cyl x gear (N)
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼──────────────────────────┼────────────
#> 4 │ 1 8 2 │ 11
#> 6 │ 2 4 1 │ 7
#> 8 │ 12 0 2 │ 14
#> ─────────────┼──────────────────────────┼────────────
#> Total │ 15 12 5 │ 32
#>
#> Chi-2: 18.0 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 6 expected cells < 5 (66.7%). Minimum expected = 1.09. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Cross-tab with column percentages
cross_tab(mtcars, cyl, gear, percent = "column")
#> Crosstable: cyl x gear (Column %)
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────────────────┼────────────
#> 4 │ 6.7 66.7 40.0 │ 34.4
#> 6 │ 13.3 33.3 20.0 │ 21.9
#> 8 │ 80.0 0.0 40.0 │ 43.8
#> ─────────────┼────────────────────────────────────┼────────────
#> Total │ 100.0 100.0 100.0 │ 100.0
#> N │ 15 12 5 │ 32
#>
#> Chi-2: 18.0 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 6 expected cells < 5 (66.7%). Minimum expected = 1.09. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Cross-tab with row percentages
cross_tab(mtcars, cyl, gear, percent = "row")
#> Crosstable: cyl x gear (Row %)
#>
#> Values │ 3 4 5 │ Total N
#> ─────────────┼─────────────────────────────────┼─────────────────────
#> 4 │ 9.1 72.7 18.2 │ 100.0 11
#> 6 │ 28.6 57.1 14.3 │ 100.0 7
#> 8 │ 85.7 0.0 14.3 │ 100.0 14
#> ─────────────┼─────────────────────────────────┼─────────────────────
#> Total │ 46.9 37.5 15.6 │ 100.0 32
#>
#> Chi-2: 18.0 (df = 4), p = 0.001
#> Cramer's V: 0.53
#> Warning: 6 expected cells < 5 (66.7%). Minimum expected = 1.09. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Cross-tab grouped by a single variable
cross_tab(mtcars, cyl, gear, by = am)
#> Crosstable: cyl x gear (N) | am = 0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼─────────────────────────┼────────────
#> 4 │ 1 2 0 │ 3
#> 6 │ 2 2 0 │ 4
#> 8 │ 12 0 0 │ 12
#> ─────────────┼─────────────────────────┼────────────
#> Total │ 15 4 0 │ 19
#>
#> Chi-2: 9.0 (df = 2), p = 0.011
#> Cramer's V: 0.69
#> Warning: 5 expected cells < 5 (83.3%). 2 expected cells < 1. Minimum expected = 0.63. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | am = 1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 6 2 │ 8
#> 6 │ 0 2 1 │ 3
#> 8 │ 0 0 2 │ 2
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 8 5 │ 13
#>
#> Chi-2: 3.8 (df = 2), p = 0.146
#> Cramer's V: 0.54
#> Warning: 6 expected cells < 5 (100%). 1 expected cell < 1. Minimum expected = 0.77. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
# Cross-tab grouped by two variables
cross_tab(mtcars, cyl, gear, by = interaction(vs, am))
#> Crosstable: cyl x gear (N) | vs x am = 0.0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼─────────────────────────┼────────────
#> 4 │ 0 0 0 │ 0
#> 6 │ 0 0 0 │ 0
#> 8 │ 12 0 0 │ 12
#> ─────────────┼─────────────────────────┼────────────
#> Total │ 12 0 0 │ 12
#>
#> Crosstable: cyl x gear (N) | vs x am = 1.0
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 1 2 0 │ 3
#> 6 │ 2 2 0 │ 4
#> 8 │ 0 0 0 │ 0
#> ─────────────┼────────────────────────┼────────────
#> Total │ 3 4 0 │ 7
#>
#> Chi-2: 0.2 (df = 1), p = 0.659
#> Cramer's V: 0.17
#> Warning: 4 expected cells < 5 (100%). Minimum expected = 1.29. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | vs x am = 0.1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 0 1 │ 1
#> 6 │ 0 2 1 │ 3
#> 8 │ 0 0 2 │ 2
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 2 4 │ 6
#>
#> Chi-2: 3.0 (df = 2), p = 0.223
#> Cramer's V: 0.71
#> Warning: 6 expected cells < 5 (100%). 3 expected cells < 1. Minimum expected = 0.33. Consider `simulate_p = TRUE` or set globally via `options(spicy.simulate_p = TRUE)`.
#>
#> Crosstable: cyl x gear (N) | vs x am = 1.1
#>
#> Values │ 3 4 5 │ Total
#> ─────────────┼────────────────────────┼────────────
#> 4 │ 0 6 1 │ 7
#> 6 │ 0 0 0 │ 0
#> 8 │ 0 0 0 │ 0
#> ─────────────┼────────────────────────┼────────────
#> Total │ 0 6 1 │ 7
# Compute row-wise mean/sum (all values must be valid by default) or specific value
df <- data.frame(
var1 = c(10, NA, 30, 40, 50),
var2 = c(5, NA, 15, NA, 25),
var3 = c(NA, 30, 20, 50, 10)
)
df
#> var1 var2 var3
#> 1 10 5 NA
#> 2 NA NA 30
#> 3 30 15 20
#> 4 40 NA 50
#> 5 50 25 10
mean_n(df)
#> [1] NA NA 21.66667 NA 28.33333
sum_n(df)
#> [1] NA NA 65 NA 85
count_n(df, count = 10)
#> [1] 1 0 0 0 1
count_n(df, special = "NA")
#> [1] 1 2 0 1 0
df |> mutate(count30 = count_n(count = 30))
#> var1 var2 var3 count30
#> 1 10 5 NA 0
#> 2 NA NA 30 1
#> 3 30 15 20 1
#> 4 40 NA 50 0
#> 5 50 25 10 0
# Extract labels from column names like "varname. label"
# This format ("name. label") is the default in LimeSurvey CSV exports
# when using: Export results -> Export format: CSV -> Headings: Question code & question text.
# It uses ". " (dot + space) as the default separator between the question code and question text.
df <- tibble::tibble(
"age. Age of respondent" = c(25, 30),
"score. Total score. Manually computed." = c(12, 14)
)
out <- label_from_names(df)
# View assigned labels
labelled::var_label(out)
#> $age
#> [1] "Age of respondent"
#>
#> $score
#> [1] "Total score. Manually computed."Additional exported helpers:
# Association strength from a contingency table
cramer_v(table(mtcars$cyl, mtcars$gear))
# Interactive codebook (requires DT)
code_book(iris)
# APA-ready table builder
table_apa(
data = mtcars,
row_vars = c("vs", "am"),
group_var = "gear",
labels = c("Engine", "Transmission"),
output = "long",
style = "raw"
)
# Low-level ASCII builders used by print methods
tab <- cross_tab(mtcars, cyl, gear)
spicy_print_table(tab)
build_ascii_table(tab)
# Clipboard export helper (interactive session)
copy_clipboard(head(mtcars))All functions can be directly used in pipelines.
- Clean, expressive output
- Works well with labelled survey data
- Handles weights, percentages, NA counts
- Great for exploring data and variables, teaching, or reporting
If you use spicy in a publication or teaching material:
- Use
citation("spicy")for the exact BibTeX entry of your installed version. - CRAN does not assign package DOI values.
- The archival DOI for
spicyis: https://doi.org/10.5281/zenodo.15397865.
You can also view the source citation file: https://github.com/amaltawfik/spicy/blob/main/inst/CITATION
This package is licensed under the MIT license. See LICENSE
for details.