spicy

https://github.com/amaltawfik/spicy

Repository

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# spicy 


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spicy adds a dash of heat to data analysis, giving insights a whole new flavour!
It is designed to make variable exploration and descriptive statistics fast, expressive, and easy to use.


## What is spicy?

spicy is an R package for quick, consistent, and elegant exploration of data frames. It helps you:

- Extract variable metadata and display compact summaries of dataset variables using `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.
- Compute frequency tables with `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.
- Explore relationships between categorical variables using `cross_tab()` for contingency tables and `cramer_v()` for association strength.
- Copy data frames or result tables directly to the clipboard using `copy_clipboard()` for fast export to spreadsheets or text editors.
- Handle `labelled`, `factor`, `Date`, `POSIXct`, and other commonly used variable types.  

All with intuitive functions that return clean, structured outputs.

---

## Installation

For the stable version, install from CRAN.
```r
install.packages("spicy")
```

You can install the development version of spicy from GitHub with:

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

---

## Example usage

Here are some quick examples using built-in datasets:
```{r eval=FALSE}
library(spicy)
library(dplyr)

# Get a summary of all variables in the Viewer
varlist(iris)

```


```{r eval=FALSE}
# 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"))
```


```{r echo=FALSE, message=FALSE}
library(spicy)
library(dplyr)

```

```{r eval=TRUE}
# Get a summary of all variables as a tibble
varlist(iris, tbl = TRUE)

# Tabulate frequencies with sort alphabetically (Z-A)
freq(iris, Species, sort = "name-")

# Cross-tab with column percentages
cross_tab(mtcars, cyl, gear)

# Cross-tab with row percentages
cross_tab(mtcars, cyl, gear, rowprct = TRUE)

# Cross-tab with column percentages grouped by a single variable
cross_tab(mtcars, cyl, gear, by = am)

# 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
mean_n(df)
sum_n(df)
count_n(df, count = 10)
count_n(df, special = "NA")
df |> mutate(count30 = count_n(count = 30))

```

> All functions can be directly used in pipelines.

---

## Why use `spicy`?

- Clean, expressive output  
- Works well with labelled survey data  
- Handles weights, percentages, NA counts  
- Great for exploring data and variables, teaching, or reporting 

---

## Citation

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:

```r
citation("spicy")
```

Or [download the BibTeX citation](inst/citation.bib) directly.

---

## License

This package is licensed under the MIT license. See [`LICENSE`](LICENSE) for details.

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