In mlr-org/mlr: Machine Learning in R

library("mlr")
library("BBmisc")
library("ParamHelpers")
urlMlrFunctions = "https://mlr-org.github.io/mlr/reference/"
ext = ".html"
library("pander")

# show grouped code output instead of single lines
knitr::opts_chunk$set(collapse = TRUE)

For your convenience mlr provides pre-defined Task()s for each type of learning problem. These are used throughout this tutorial in order to get shorter and more readable code.

# urlMlrFunctions, ext are defined in build
linkTask = function(x) {
  collapse(sprintf("[%1$s](%2$s%1$s%3$s)", x, urlMlrFunctions, ext), sep = "<br />")
}
d = data(package = "mlr")
d = d$results
types = sapply(d[, "Item"], function(x) getTaskDesc(eval(parse(text = x)))$type)
names(types) = NULL
ord = order(types)
d = d[ord, ]
types = types[ord]
types[duplicated(types)] = ""
df = data.frame(Type = types, Task = d[, "Item"], Description = d[, "Title"])
df$Task = sapply(df$Task, linkTask)
pandoc.table(df, style = "rmarkdown", split.tables = Inf, split.cells = Inf,
  justify = rep("left", 3))