spicy is designed to make variable exploration and descriptive statistics fast, expressive, and easy to use.
spicy is an R package for quick, consistent, and elegant exploration of data frames. It helps you:
varlist() (with vl() as a
convenient shortcut), including names, labels, values, classes, number
of distinct non-missing values, number of valid observations, number of
missing observations. Similar to the “Variable View” in SPSS or the
“Variables Manager” in Stata.freq(), row-wise means
with mean_n(), row-wise sums with sum_n(), and
counts of specific values using count_n() — all with
automatic handling of missing data.cross_tab() for contingency tables and
cramer_v() for association strength.copy_clipboard() for fast export to spreadsheets or text
editors.label_from_names(), especially useful for LimeSurvey CSV
exports where headers follow the "name. label"
convention.labelled, factor,
Date, POSIXct, and other commonly used
variable types.All with intuitive functions that return clean, structured outputs.
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")Here are some quick examples using built-in datasets:
library(spicy)
library(dplyr)
# Get a summary of all variables in the Viewer
varlist(iris)
# Get a summary of the variables that start with "d" in the Viewer
# Asterisks (*) in the title indicate that the data frame has been subsetted
vl(mtcars, starts_with("d"))
# Get a summary of all variables as a tibble
varlist(iris, tbl = TRUE)
#> # A tibble: 5 × 7
#> Variable Label Values Class Ndist_val 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, virginica fact… 3 150 0
# Tabulate frequencies with sort alphabetically (Z-A)
freq(iris, Species, sort = "name-")
#> Frequency table: Species
#> ────────────────────────────
#> Values N % Valid%
#> ────────────────────────────
#> virginica 50 33.3 33.3
#> versicolor 50 33.3 33.3
#> setosa 50 33.3 33.3
#> Total 150 100.0 100.0
#> ────────────────────────────
#> Class: factor
#> Data: iris
# Cross-tab with column percentages
cross_tab(mtcars, cyl, gear)
#> Crosstable: cyl x gear (%)
#> ─────────────────────────────────────────
#> Values 3 4 5 Row_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
#> Column_Total 100.0 100.0 100.0 100.0
#> N 15.0 12.0 5.0 32.0
#> ─────────────────────────────────────────
#> Chi-2 = 18 (df = 4), p = 0.00121, Cramer's V = 0.53
# Cross-tab with row percentages
cross_tab(mtcars, cyl, gear, rowprct = TRUE)
#> Crosstable: cyl x gear (%)
#> ─────────────────────────────────────────
#> Values 3 4 5 Row_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
#> Column_Total 46.9 37.5 15.6 100.0 32
#> ─────────────────────────────────────────
#> Chi-2 = 18 (df = 4), p = 0.00121, Cramer's V = 0.53
# Cross-tab with column percentages grouped by a single variable
cross_tab(mtcars, cyl, gear, by = am)
#> $`0`
#> Crosstable: cyl x gear | am = 0 (%)
#> ───────────────────────────────────
#> Values 3 4 Row_Total
#> ───────────────────────────────────
#> 4 6.7 50.0 15.8
#> 6 13.3 50.0 21.1
#> 8 80.0 0.0 63.2
#> Column_Total 100.0 100.0 100.0
#> N 15.0 4.0 19.0
#> ───────────────────────────────────
#> Chi-2 = 9 (df = 2), p = 0.0113, Cramer's V = 0.69
#>
#> $`1`
#> Crosstable: cyl x gear | am = 1 (%)
#> ───────────────────────────────────
#> Values 4 5 Row_Total
#> ───────────────────────────────────
#> 4 75.0 40.0 61.5
#> 6 25.0 20.0 23.1
#> 8 0.0 40.0 15.4
#> Column_Total 100.0 100.0 100.0
#> N 8.0 5.0 13.0
#> ───────────────────────────────────
#> Chi-2 = 3.8 (df = 2), p = 0.146, Cramer's V = 0.54
# 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 0All functions can be directly used in pipelines.
spicy?If you use spicy in a publication or teaching material,
please cite it as:
Tawfik, A. (2025). spicy: Tools for Data Management and Variable Exploration (Version 0.1.0) [R package]. https://doi.org/10.5281/zenodo.15397865
You can also get the citation in R format by typing:
citation("spicy")Or download the BibTeX citation directly.
This package is licensed under the MIT license. See LICENSE for details.