uci-ml-to-r/readme-make.Rmd

---
title: "UCI Machine Learning datasets for R"
author: "Andrzej Wójtowicz"
output: 
  html_document: 
    keep_md: yes
---

```{r global-options, include=FALSE}
knitr::opts_chunk$set(comment="", echo=FALSE, 
                      warning=FALSE, message=FALSE)
source('config.R')
```

Document generation date: `r Sys.time()`.


```{r show-datasets, results='asis'}
library(yaml)

cat("\n# Table of Contents\n\n")

for (dir.name in dir(PATH_DATASETS))
{
    config.yaml.file.path = paste0(PATH_DATASETS, dir.name, "/", FILE_CONFIG_YAML)
    config.yaml = yaml.load_file(config.yaml.file.path)
    
    anchor = gsub(" ", "-", gsub("[[:punct:]]", "", 
                                 tolower(config.yaml$name)))
    cat(paste0("1. [", config.yaml$name, "](#", anchor, ")\n" ))
    
}

cat("\n---\n\n")

for (dir.name in dir(PATH_DATASETS))
{
    config.yaml.file.path = paste0(PATH_DATASETS, dir.name, "/", FILE_CONFIG_YAML)
    config.yaml = yaml.load_file(config.yaml.file.path)
    
    cat(paste("#", config.yaml$name, "\n\n"))
    
    cat(paste("**Local directory**:", dir.name, "\n\n"))
    
    cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n"))
    
    cat(paste("**Source data files**:\n\n"))
    for (file.url in config.yaml$urls)
    {
        cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n"))
    }
    cat("\n")
    
    cat(paste0("**Cite**:\n```nohighlight\n", config.yaml$cite, "\n```\n\n"))
    
    cat(paste("**Dataset**:\n\n"))
    
    preprocessed.dir = gsub("\\*", dir.name, PATH_DATASET_PREPROCESSED)
    preprocessed.file.path = paste0(preprocessed.dir, FILE_PREPROCESSED_OUTPUT)
    
    dataset = readRDS(preprocessed.file.path)
    
    cat("```nohighlight\n")
    cat(str(dataset))
    cat("\n```\n\n")
    
    cat("**Predictors**:\n\n")
    
    df.pred = data.frame(table(sapply(dataset[, 1:(ncol(dataset)-1)], 
                               function(f){paste(class(f), collapse=" ")})))
    colnames(df.pred) = c("Type", "Frequency")
    
    cat(knitr::kable(df.pred, format="markdown"), sep="\n")
    cat("\n")
    
    perc.classes = sort(round(100*as.numeric(
        table(dataset[, ncol(dataset)]))/nrow(dataset), 0))
    num.classes = sort(as.numeric(table(dataset[, ncol(dataset)])))
    
    cat("**Class imbalance**:\n\n")
    
    cat(knitr::kable(data.frame(A=c(paste(perc.classes[1], "%"), num.classes[1]), 
                                B=c(paste(perc.classes[2], "%"), num.classes[2])), 
                     format="markdown", col.names=c("class A", " class B"),
                     align=c("c", "c")), 
        sep="\n")
    

    cat("\n---\n\n")
}
```