README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.align = 'center', 
  fig.dim = c(7, 7), 
  out.width = "50%"
)
library("oeli")
set.seed(1)
```

# Utilities for developing R code <a href="https://loelschlaeger.de/oeli/"><img src="man/figures/logo.png" align="right" height="138" /></a>

<!-- badges: start -->
[![CRAN status](https://www.r-pkg.org/badges/version/oeli)](https://CRAN.R-project.org/package=oeli)
[![CRAN downloads](https://cranlogs.r-pkg.org/badges/last-month/oeli)](https://CRAN.R-project.org/package=oeli)
[![R-CMD-check](https://github.com/loelschlaeger/oeli/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/loelschlaeger/oeli/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/loelschlaeger/oeli/graph/badge.svg)](https://app.codecov.io/gh/loelschlaeger/oeli)
<!-- badges: end -->

This [R](https://CRAN.R-project.org) package provides helper functions I found useful when developing R code - perhaps you will too! The released package version can be installed via:

``` r
install.packages("oeli")
```

The following shows some demos. Click the headings for references on all available helpers in each category.

### [Data](https://loelschlaeger.de/oeli/reference/index.html#data)

The `hermann` data contains historical information on editions of the [Hermannslauf](https://de.wikipedia.org/wiki/Hermannslauf), including the date, temperature, and winning times for men and women:

```{r, hermann}
hermann
```

### [Distributions](https://loelschlaeger.de/oeli/reference/index.html#distribution)

The package has density, distribution, and sampling functions for some distributions not included in base R, like the Dirichlet:

```{r, dirichlet}
ddirichlet(x = c(0.2, 0.3, 0.5), concentration = 1:3)
rdirichlet(concentration = 1:3)
```

Or the mixture of Gaussian distributions:

```{r, mixnorm}
x <- c(0, 0)
mean <- matrix(c(1, 1, -1, -1), ncol = 2) # means in columns
Sigma <- matrix(c(diag(2), 0.1 * diag(2)), ncol = 2) # vectorized covariances in columns
proportions <- c(0.7, 0.3)
dmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)
pmixnorm(x = x, mean = mean, Sigma = Sigma, proportions = proportions)
rmixnorm(n = 1000, mean = mean, Sigma = Sigma, proportions = proportions) |>
  as.data.frame() |> 
  ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x = V1, y = V2))
```

### [Function helpers](https://loelschlaeger.de/oeli/reference/index.html#functional)

Retrieving default arguments of a `function`:

```{r, function_defaults} 
f <- function(a, b = 1, c = "", ...) { }
function_defaults(f)
```

### [Indexing helpers](https://loelschlaeger.de/oeli/reference/index.html#indexing)

Create all possible permutations of vector elements:

```{r, permutations}
permutations(LETTERS[1:3])
```

### [Package helpers](https://loelschlaeger.de/oeli/reference/index.html#packaging)

Quickly have a basic logo for your new package:

```{r, package_logo}
logo <- package_logo("my_package", brackets = TRUE)
print(logo)
```

How to print a `matrix` without filling up the entire console?

```{r, print_matrix}
x <- matrix(rnorm(10000), ncol = 100, nrow = 100)
print_matrix(x, rowdots = 4, coldots = 4, digits = 2, label = "what a big matrix")
```

And what about a `data.frame`?

```{r, print_data.frame}
x <- data.frame(x = rnorm(1000), y = LETTERS[1:10])
print_data.frame(x, rows = 7, digits = 0)
```

### [Simulation helpers](https://loelschlaeger.de/oeli/reference/index.html#simulation)

Let's simulate correlated regressor values from different marginal distributions:

```{r, simulate_regressors}
labels <- c("P", "C", "N1", "N2", "U")
n <- 100
marginals <- list(
  "P" = list(type = "poisson", lambda = 2),
  "C" = list(type = "categorical", p = c(0.3, 0.2, 0.5)),
  "N1" = list(type = "normal", mean = -1, sd = 2),
  "U" = list(type = "uniform", min = -2, max = -1)
)
correlation <- matrix(
  c(1, -0.3, -0.1, 0, 0.5,
    -0.3, 1, 0.3, -0.5, -0.7,
    -0.1, 0.3, 1, -0.3, -0.3,
    0, -0.5, -0.3, 1, 0.1,
    0.5, -0.7, -0.3, 0.1, 1),
  nrow = 5, ncol = 5
)
data <- correlated_regressors(
  labels = labels, n = n, marginals = marginals, correlation = correlation
)
head(data)
cor(data)
``` 

### [Transformation helpers](https://loelschlaeger.de/oeli/reference/index.html#transformation)

The `group_data.frame()` function groups a given `data.frame` based on the values in a specified column:

```{r, group_data.frame}
df <- data.frame("label" = c("A", "B"), "number" = 1:10)
group_data.frame(df = df, by = "label")
```

### [Validation helpers](https://loelschlaeger.de/oeli/reference/index.html#validation)

Is my matrix a proper transition probability matrix?

```{r, check_transition_probability_matrix}
matrix <- diag(4)
matrix[1, 2] <- 1
check_transition_probability_matrix(matrix)
```