106 lines
3.4 KiB
Plaintext
106 lines
3.4 KiB
Plaintext
---
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title: "UCI Machine Learning datasets for R"
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author: "Andrzej Wójtowicz"
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output:
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html_document:
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keep_md: yes
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---
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```{r global-options, include=FALSE}
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knitr::opts_chunk$set(comment = "", echo = FALSE, warning = FALSE, message = FALSE)
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source('init.R')
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```
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Document generation date: `r Sys.time()`.
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This project preprocesses a few datasets from [UC Irvine Machine Learning
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Repository](https://archive.ics.uci.edu/ml/) into tidy R object files.
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It focuses on the binary classification datasets and saves only complete cases
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within a dataset.
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**R software**: [Microsoft R Open](https://mran.microsoft.com/open/) (`r paste0(R.version$major, ".", R.version$minor)`)
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**Reproducibility library**: [checkpoint](https://github.com/RevolutionAnalytics/checkpoint)
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**Reproducibility procedure**:
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1. Run *s1-download-data.R* to download original datasets.
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2. Run *s2-preprocess-data.R* to preprocess the datasets.
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3. Optionally knit s*3-make-readme.Rmd* to get an overview of the preprocessed datasets.
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```{r show-datasets, results='asis'}
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cat("\n# Table of Contents\n\n")
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for (dir.name in dir(DATASETS.DIR))
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{
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config.yaml.file.path = file.path(DATASETS.DIR, dir.name, DATASET.CONFIG.FILE)
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config.yaml = yaml.load_file(config.yaml.file.path)
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anchor = gsub(" ", "-", gsub("[[:punct:]]", "",
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tolower(config.yaml$name)))
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cat(paste0("1. [", config.yaml$name, "](#", anchor, ")\n" ))
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}
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cat("\n---\n\n")
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for (dir.name in dir(DATASETS.DIR))
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{
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config.yaml.file.path = file.path(DATASETS.DIR, dir.name, DATASET.CONFIG.FILE)
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config.yaml = yaml.load_file(config.yaml.file.path)
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cat(paste("#", config.yaml$name, "\n\n"))
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cat(paste("**Local directory**:", dir.name, "\n\n"))
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cat(paste0("**Details**: [link](", config.yaml$info, ")\n\n"))
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cat(paste("**Source data files**:\n\n"))
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for (file.url in config.yaml$urls)
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{
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cat(paste0("* [", URLdecode(basename(file.url)), "](", file.url, ")\n"))
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}
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cat("\n")
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cat(paste0("**Cite**:\n```nohighlight\n", config.yaml$cite, "\n```\n\n"))
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cat(paste("**Dataset**:\n\n"))
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preprocessed.dir = gsub(DATASET.NAME.PATTERN, dir.name,
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DATASET.PREPROCESSED.DIR)
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preprocessed.file.path = file.path(preprocessed.dir,
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DATASET.PREPROCESSED.OUTPUT.FILE)
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dataset = readRDS(preprocessed.file.path)
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cat("```nohighlight\n")
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cat(str(dataset))
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cat("\n```\n\n")
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cat("**Predictors**:\n\n")
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df.pred = data.frame(table(sapply(dataset[, 1:(ncol(dataset) - 1)],
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function(f){paste(class(f), collapse = " ")})))
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colnames(df.pred) = c("Type", "Frequency")
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cat(knitr::kable(df.pred, format = "markdown"), sep = "\n")
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cat("\n")
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perc.classes = sort(round(100*as.numeric(
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table(dataset[, ncol(dataset)]))/nrow(dataset), 0))
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num.classes = sort(as.numeric(table(dataset[, ncol(dataset)])))
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cat("**Class imbalance**:\n\n")
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cat(knitr::kable(data.frame(A = c(paste(perc.classes[1], "%"),
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num.classes[1]),
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B = c(paste(perc.classes[2], "%"),
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num.classes[2])),
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format = "markdown", col.names = c("class A", "class B"),
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align = c("c", "c")),
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sep = "\n")
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cat("\n---\n\n")
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}
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```
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