---
title: "Create pdqr-functions with `new_*()`"
output:
  rmarkdown::html_vignette:
    fig_width: 6.5
    fig_height: 4
vignette: >
  %\VignetteIndexEntry{Create pdqr-functions with `new_*()`}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(pdqr)

set.seed(101)
```

Package 'pdqr' supports two types of distributions:

- **Type "discrete"**: random variable has finite number of output values. It is explicitly defined by the collection of its values with their corresponding probability.
- **Type "continuous"**: there are infinite number of output values in the form of continuous random variable. It is explicitly defined by piecewise-linear density function.

**Note** that all distributions assume **finite support** (output values are bounded from below and above) and **finite values of density function** (density function in case of "continuous" type can't go to infinity).

All `new_*()` functions create a pdqr-function of certain type ("discrete" or "continuous") based on sample or data frame of appropriate structure:

- **Sample input** is processed based on type. For "discrete" type it gets tabulated with frequency of unique values serving as their probability. For "continuous" type distribution density is estimated using [`density()`](https://rdrr.io/r/stats/density.html) function if input has at least 2 elements. For 1 element special "dirac-like" pdqr-function is created: an *approximation single number* with triangular distribution of very narrow support (1e-8 of magnitude). Basically, sample input is converted into data frame of appropriate structure that defines distribution (see next list item).
- **Data frame input** should completely define distribution. For "discrete" type it should have "x" and "prob" columns for output values and their probabilities. For "continuous" type - "x" and "y" columns for points, which define piecewise-linear continuous density function. Columns "prob" and "y" will be automatically normalized to represent proper distribution: sum of "prob" will be 1 and total square under graph of piecewise-linear function will be 1.

We will use the following data frame inputs in examples:

```{r setup_data-frame-inputs}
# For type "discrete"
dis_df <- data.frame(x = 1:4, prob = 4:1 / 10)
# For type "continuous"
con_df <- data.frame(x = 1:4, y = c(0, 1, 1, 1))
```

This vignette is organized as follows:

- Four sections about how to create p-, d-, q-, and r-functions (both from sample and data frame).
- Section "Special cases", which describes two special cases of pdqr-functions: dirac-like and boolean.
- Section "Using `density()` arguments" describes how to use `density()` arguments to tweak smoothing during creation of "continuous" pdqr-functions.
- "Metadata of pdqr-functions" describes the concept of metadata of pdqr-functions.

## P-functions

P-function (analogue of `p*()` functions in base R) represents a cumulative distribution function of distribution.

### From sample

```{r p-fun_sample}
# Treating input as discrete
p_mpg_dis <- new_p(mtcars$mpg, type = "discrete")
p_mpg_dis

# Treating input as continuous
p_mpg_con <- new_p(mtcars$mpg, type = "continuous")
p_mpg_con

# Outputs are actually vectorized functions
p_mpg_dis(15:20)
p_mpg_con(15:20)

# You can plot them directly using base `plot()` and `lines()`
plot(p_mpg_con, main = "P-functions from sample")
lines(p_mpg_dis, col = "blue")
```

### From data frame

```{r p-fun_data-frame}
p_df_dis <- new_p(dis_df, type = "discrete")
p_df_dis

p_df_con <- new_p(con_df, type = "continuous")
p_df_con

plot(p_df_con, main = "P-functions from data frame")
lines(p_df_dis, col = "blue")
```

## D-functions

D-function (analogue of `d*()` functions in base R) represents a probability mass function for "discrete" type and density function for "continuous":

### From sample

```{r d-fun_sample}
# Treating input as discrete
d_mpg_dis <- new_d(mtcars$mpg, type = "discrete")
d_mpg_dis

# Treating input as continuous
d_mpg_con <- new_d(mtcars$mpg, type = "continuous")
d_mpg_con

# Outputs are actually vectorized functions
d_mpg_dis(15:20)
d_mpg_con(15:20)

# You can plot them directly using base `plot()` and `lines()`
op <- par(mfrow = c(1, 2))
plot(d_mpg_con, main = '"continuous" d-function\nfrom sample')
plot(d_mpg_dis, main = '"discrete" d-function\nfrom sample', col = "blue")
par(op)
```

### From data frame

```{r d-fun_data-frame}
d_df_dis <- new_d(dis_df, type = "discrete")
d_df_dis

d_df_con <- new_d(con_df, type = "continuous")
d_df_con

op <- par(mfrow = c(1, 2))
plot(d_df_con, main = '"continuous" d-function\nfrom data frame')
plot(d_df_dis, main = '"discrete" d-function\nfrom data frame', col = "blue")
par(op)
```

## Q-functions

Q-function (analogue of `q*()` functions in base R) represents a quantile function, an inverse of corresponding p-function:

### From sample

```{r q-fun_sample}
# Treating input as discrete
q_mpg_dis <- new_q(mtcars$mpg, type = "discrete")
q_mpg_dis

# Treating input as continuous
q_mpg_con <- new_q(mtcars$mpg, type = "continuous")
q_mpg_con

# Outputs are actually vectorized functions
q_mpg_dis(c(0.1, 0.3, 0.7, 1.5))
q_mpg_con(c(0.1, 0.3, 0.7, 1.5))

# You can plot them directly using base `plot()` and `lines()`
plot(q_mpg_con, main = "Q-functions from sample")
lines(q_mpg_dis, col = "blue")
```

### From data frame

```{r q-fun_data-frame}
q_df_dis <- new_q(dis_df, type = "discrete")
q_df_dis

q_df_con <- new_q(con_df, type = "continuous")
q_df_con

plot(q_df_con, main = "Q-functions from data frame")
lines(q_df_dis, col = "blue")
```

## R-functions

R-function (analogue of `r*()` functions in base R) represents a random generation function. For "discrete" type it will generate only values present in input. For "continuous" function it will generate values from distribution corresponding to one estimated with `density()`.

### From sample

```{r r-fun_sample}
# Treating input as discrete
r_mpg_dis <- new_r(mtcars$mpg, type = "discrete")
r_mpg_dis

# Treating input as continuous
r_mpg_con <- new_r(mtcars$mpg, type = "continuous")
r_mpg_con

# Outputs are actually functions
r_mpg_dis(5)
r_mpg_con(5)

# You can plot them directly using base `plot()` and `lines()`
op <- par(mfrow = c(1, 2))
plot(r_mpg_con, main = '"continuous" r-function\nfrom sample')
plot(r_mpg_dis, main = '"discrete" r-function\nfrom sample', col = "blue")
par(op)
```

### From data frame

```{r r-fun_data-frame}
r_df_dis <- new_r(dis_df, type = "discrete")
r_df_dis

r_df_con <- new_r(con_df, type = "continuous")
r_df_con

op <- par(mfrow = c(1, 2))
plot(r_df_con, main = '"continuous" r-function\nfrom data frame')
plot(r_df_dis, main = '"discrete" r-function\nfrom data frame', col = "blue")
par(op)
```

## Special cases

### Dirac-like

When creating "continuous" pdqr-function with `new_*()` from single number, a special "dirac-like" pdqr-function is created. It is an *approximation of single number* with triangular distribution of very narrow support (1e-8 of magnitude):

```{r dirac}
r_dirac <- new_r(3.14, type = "continuous")
r_dirac
r_dirac(4)

  # Outputs aren't exactly but approximately equal
dput(r_dirac(4))
```

### Boolean

Boolean pdqr-function is a special case of "discrete" function, which values are exactly 0 and 1. Those functions are usually created after transformations involving logical operators (see vignette on transformation for more details). It is assumed that 0 represents that some expression is false, and 1 is for being true. Corresponding probabilities describe distribution of expression's logical values. The only difference from other "discrete" pdqr-functions is in more detailed printing.

```{r boolean}
new_d(data.frame(x = c(0, 1), prob = c(0.25, 0.75)), type = "discrete")
```

## Using `density()` arguments

When creating pdqr-function of "continuous" type, `density()` is used to estimate density. To tweak its performance, supply its extra arguments directly to `new_*()` functions. Here are some examples:

```{r density-args}
plot(
  new_d(mtcars$mpg, "continuous"), lwd = 3,
  main = "Examples of `density()` options"
)

# Argument `adjust` of `density()` helps to define smoothing bandwidth
lines(new_d(mtcars$mpg, "continuous", adj = 0.3), col = "blue")

# Argument `n` defines number of points to be used in piecewise-linear
# approximation
lines(new_d(mtcars$mpg, "continuous", n = 5), col = "green")

# Argument `cut` defines the "extending" property of density estimation.
# Using `cut = 0` assumes that density can't go outside of input's range
lines(new_d(mtcars$mpg, "continuous", cut = 0), col = "magenta")
```

## Metadata of pdqr-functions

Every pdqr-function has metadata, information which describes underline distribution and pdqr-function. Family of `meta_*()` functions are implemented to extract that information:

- **"x_tbl" metadata** (returned by `meta_x_tbl()`) completely defines distribution. It is a data frame with structure depending on type of pdqr-function:
    - For "discrete" type it has columns "x" (output values), "prob" (their probability), and "cumprob" (their cumulative probability). 
    - For "continuous" type it has columns "x" (knots of piecewise-linear density), "y" (density values at those points), "cumprob" (their cumulative probability).
- **Pdqr class** (returned by `meta_class()`) - class of pdqr-function. This can be one of "p", "d", "q", "r". Represents how pdqr-function describes underlying distribution.
- **Pdqr type** (returned by `meta_type()`) - type of pdqr-function. This can be one of "discrete" or "continuous". Represents type of underlying distribution.
- **Pdqr support** (returned by `meta_support()`) - support of distribution. This is a range of "x" column from "x_tbl" metadata.

```{r meta_x_tbl}
# Type "discrete"
d_dis <- new_d(1:4, type = "discrete")
meta_x_tbl(d_dis)
meta_class(d_dis)
meta_type(d_dis)
meta_support(d_dis)

# Type "continuous"
p_con <- new_p(1:4, type = "continuous")
head(meta_x_tbl(p_con))
meta_class(p_con)
meta_type(p_con)
meta_support(p_con)

# Dirac-like "continuous" function
r_dirac <- new_r(1, type = "continuous")
dput(meta_x_tbl(r_dirac))
dput(meta_support(r_dirac))

# `meta_all()` returns all metadata in a single list
meta_all(d_dis)
```

For more details go to help page of `meta_all()`.