---
title: "Transformation Information Report"
author: "Report by dlookr package"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    number_sections: true
    theme: cayman
    highlight: github
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(warn = -1)
```

```{r environment, echo=FALSE, warning=FALSE, message=FALSE}
library(xtable)
library(magrittr)
library(dlookr)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(moments)
library(mice)
library(knitr)
library(kableExtra)
```

```{r init, echo=FALSE, warning=FALSE, message=FALSE}
library(smbinning)
library(xtable)

edaData <- as.data.frame(get("edaData", .dlookrEnv))
targetVariable <- get("targetVariable", .dlookrEnv)

if (length(targetVariable) == 0) targetVariable <- NULL

names(edaData) <- gsub("[[:punct:][:space:]]", "_", names(edaData))

idx.numeric <- find_class(edaData, type = "numerical")
varname <- names(edaData)[idx.numeric]

varname <- setdiff(varname, targetVariable)

setUnder <- function(x) {
  gsub("_", ".", x)
}
```

# Imputation
## Missing Values
### Missing values imputation information
The variables for imputate missing values are as follows.
```{r nalist, echo=FALSE, warning=FALSE, message=FALSE, comment=""}
nalist <- find_na(edaData, index = FALSE)

if (length(nalist) > 0) {
  naclass <- get_class(edaData) %>% 
    filter(variable %in% nalist) %>% 
    select(class) %>% 
    pull() %>% 
    as.character()
  
  cat(paste(nalist, collapse = ", "))
} else {
  cat("There are no variables including missing values.")
}
```

```{r Imputate_function, echo=FALSE}
numerics <- c("mean", "median", "mode", "knn", "rpart", "mice")
categories <- c("mode", "rpart", "mice")

imputae_summary <- function(object) {
  type <- attr(object, "type")
  method <- attr(object, "method")
  var_type <- attr(object, "var_type")

  original <- object

  if (type == "missing values") {
    na_pos <- attr(object, "na_pos")
    seed <- attr(object, "seed")

    original[na_pos] <- NA
  } else if (type == "outliers") {
    outlier_pos <- attr(object, "outlier_pos")
    outliers <- attr(object, "outliers")

    original[outlier_pos] <- outliers
  }

  if (var_type == "numerical") {
    original <- as.numeric(original)
    object <- as.numeric(object)
  } else if (var_type == "categorical") {
    original <- factor(original)
    object <- factor(object)
  }

  dframe <- data.frame(original = original,
    imputation = object) %>%
    tidyr::gather()

  if (var_type == "numerical") {
    smmry <- dframe %>%
      group_by(key) %>%
      describe("value") %>%
      select(-variable, -key) %>%
      t
    
    smmry <- smmry[, 2:1]
    colnames(smmry) <- c("Original", "Imputation")
  } else if (var_type == "categorical") {
    tab_freq <- xtabs(~ value + key, dframe, addNA = TRUE)
    tab_relat <- round(prop.table(tab_freq, 2) * 100, 2)

    smmry <- cbind(tab_freq, tab_relat)
    smmry <- smmry[, c(2, 1, 4, 3)]
    colnames(smmry) <- c("original", "imputation",
      "original_percent", "imputation_percent")
  }

  if (method %in% c("knn", "rpart", "mice")) {
    if (method == "knn") {
      met <- "K-Nearest Neighbors"
    } else if (method == "rpart") {
      met <- "Recursive Partitioning and Regression Trees"
    } else if (method == "mice") {
      met <- "Multivariate Imputation by Chained Equations"
      met <- sprintf("%s\n - random seed : %s", met, seed)
    }
    cat(sprintf("* Imputate %s based on %s\n\n", type, met))
  }

  cat("* Information of Imputation (before vs after)\n")

  invisible(smmry)
}
```


```{r Imputate_na, echo=FALSE, warning=FALSE, message=FALSE, comment="", fig.height=5, fig.width=7, results='asis'}

if (length(nalist) > 0) {
  for (i in 1:length(nalist)) {
    cat("\n")
    cat("###", setUnder(nalist[i]), "\n")
    
    if (naclass[i] %in% c("integer", "numeric")) {
      method <- numerics
    } else if (naclass[i] %in% c("factor", "ordered")) {
      method <- categories
    }
    
    if (is.null(targetVariable)) {
      method <- setdiff(method, c("knn", "rpart", "mice"))
    }
    
    for (j in 1:length(method)) {
      cat("\n")
      cat("#### Imputate missing values with '", method[j], "'\n")
      
      impu <- imputate_na(edaData, nalist[i], targetVariable, method = method[j], print_flag = FALSE)
      tab <- imputae_summary(impu)
      
      kable(tab, digits = 2, format = "html",
            format.args = list(big.mark = ",")) %>% 
        kable_styling(full_width = FALSE, font_size = 13, position = "left") %>% 
        row_spec(NROW(tab), bold = TRUE, color = "white", background = "#FFA349") %>% 
        print()
  
      cat("\n")
      cat("<br><br><br>\n")
      
      grid.draw(plot(impu))
      cat("\n")
    } 
  }
}
```

## Outliers
### Outliers imputation information
The numerical variables for imputate outliers are as follows.

```{r outlist, echo=FALSE, warning=FALSE, message=FALSE, comment=""}
outlist <- find_outliers(edaData, index = FALSE)

if (length(outlist) > 0) {
  cat(paste(outlist, collapse = ", "))
} else {
  cat("There are no variables including outliers.")
}
```

```{r Imputate_outliers, echo=FALSE, warning=FALSE, message=FALSE, comment="", fig.height=5, fig.width=7, results='asis'}

if (length(outlist) > 0) {
  for (i in 1:length(outlist)) {
    cat("\n")
    cat("###", setUnder(outlist[i]), "\n")
    
    method <- c("mean", "median", "mode", "capping")
    
    for (j in 1:length(method)) {
      cat("\n")
      cat("#### Imputate outliers with '", method[j], "'\n")
      
      impu <- imputate_outlier(edaData, outlist[i], method = method[j])
      tab <- imputae_summary(impu)
      
      kable(tab, digits = 2, format = "html",
            format.args = list(big.mark = ",")) %>% 
        kable_styling(full_width = FALSE, font_size = 13, position = "left") %>% 
        row_spec(NROW(tab), bold = TRUE, color = "white", background = "#FFA349") %>% 
        print()
  
      cat("\n")
      cat("<br><br><br>\n")
      
      grid.draw(plot(impu))
      cat("\n")
    } 
  }
}
```

# Resolving Skewness
## Skewed variables information
The numerical variables for resolving skewness are as follows.
```{r, echo=FALSE}
summary_transform <- function(object, ...) {
  method <- attr(object, "method")
  origin <- attr(object, "origin")

  suppressWarnings({dframe <- data.frame(original = origin,
    trans = object) %>%
    tidyr::gather()})

  smmry <- dframe %>%
    group_by(key) %>%
    describe("value") %>%
    select(-variable, -key) %>%
    t
  colnames(smmry) <- c("Original", "Transformation")
  invisible(smmry)
}
```


```{r skewlist, echo=FALSE, warning=FALSE, message=FALSE, comment=""}
skewlist <- find_skewness(edaData, index = FALSE)
skew <- find_skewness(edaData, value = TRUE)

if (length(skewlist) > 0) {
  cat(paste(skewlist, collapse = ", "))
  skew <- skew[skewlist]
} else {
  cat("There are no variables including skewed.")
}
```

```{r resolve_skewness, echo=FALSE, warning=FALSE, message=FALSE, comment="", fig.height=4, fig.width=8, results='asis'}

if (length(skewlist) > 0) {
  for (i in 1:length(skewlist)) {
    cat("\n")
    cat("###", setUnder(skewlist[i]), "\n")
    
    if (skew[i] <= 0) {
      method <- c("1/x", "x^2", "x^3")
    } else {
      method <- c("log", "log+1", "sqrt")
    }
      
    for (j in 1:length(method)) {
      cat("\n")
      cat("#### Resolving skewness with '", method[j], "'\n")
      
      trans <- transform(pull(edaData, skewlist[i]), method = method[j])
      tab <- summary_transform(trans)
      
      kable(tab, digits = 2, format = "html",
            format.args = list(big.mark = ",")) %>% 
        kable_styling(full_width = FALSE, font_size = 13, position = "left") %>% 
        row_spec(NROW(tab), bold = TRUE, color = "white", background = "#FFA349") %>% 
        print()
  
      cat("\n")
      cat("<br><br><br>\n")
      
      grid.draw(plot(trans))
      cat("\n")
    } 
  }
}
```

# Binning
## Numerical Variables for Binning
The numerical variables for binning are as follows.
```{r, echo=FALSE}
idx.numeric <- find_class(edaData, type = "numerical")
var_numeric <- names(edaData)[idx.numeric]

var_numeric <- setdiff(var_numeric, targetVariable)
```

```{r target, echo=FALSE, warning=FALSE, message=FALSE, comment=""}
var_numeric
```

## Binning
```{r binning, echo=FALSE, warning=FALSE, message=FALSE, comment="", fig.height=5, fig.width=6, results='asis'}
if (length(var_numeric) > 0) {
  for (i in 1:length(var_numeric)) {
    cat("\n")
    cat("###", setUnder(var_numeric[i]), "\n")
    
    method <- c("quantile", "equal", "pretty", "kmeans", "bclust")
    
    for (j in 1:length(method)) {
      cat("\n")
      cat("#### Binning with '", method[j], "'\n")
      
      binn <- binning(pull(edaData, var_numeric[i]), type = method[j])
      tab <- summary(binn)
      
      kable(tab, digits = 4, format = "html",
            format.args = list(big.mark = ",")) %>% 
        kable_styling(full_width = FALSE, font_size = 13, position = "left") %>% 
        row_spec(NROW(tab), bold = TRUE, color = "white", background = "#FFA349") %>% 
        print()
  
      cat("\n")
      cat("<br><br><br>\n")
      
      plot(binn)
      cat("\n")
    } 
  }
}
```

## Optimal Binning
For the scoring modeling, optimal binning is performed considering the characteristics of numerical data.
This work uses the recursive partitioning technique known as supervised discretization when categorizing numeric data as categorical data.

```{r optimal, echo=FALSE, warning=FALSE, message=FALSE, comment="", fig.height=6, fig.width=7, results='asis'}
if (!is.null(targetVariable)) {
  n_levles <- length(table(pull(edaData, targetVariable)))

  if (n_levles == 2) {
    # Optimal Binning for Scoring Modeling
    results <- lapply(var_numeric, function(x)
      binning_by(df = edaData, y = targetVariable, x = x, p = 0.05))
    
    # could also use tapply here
    for (i in 1:length(var_numeric)) {
      cat("\n")
      cat("###", setUnder(var_numeric[i]), "\n")

      result <- results[[i]]

      if (is.character(result)) {
        cat(result)
        cat("\n")
        next
      }
      
      cat("\n")

      tab <- attr(result, "ivtable")
      kable(tab, digits = 2, format = "html",
        format.args = list(big.mark = ",")) %>% 
        kable_styling(full_width = FALSE, font_size = 10, position = "left") %>% 
        row_spec(NROW(tab), bold = TRUE, color = "white", background = "#FFA349") %>% 
        print()
  
      cat("\n")
      cat(sprintf("Information value = %s", attr(result, "iv")))
      cat("<br><br><br>\n")
  
      plot(result)
      cat("\n")
    } 
  } else {
    cat("The target variable is not a binary class.\n\n")
  }
} else {
  cat("There is no target variable.\n\n")
}
```

```{r, echo=FALSE}
options(warn = 0)
```


