###
###  UCI Ionosphere Data
###

Local copy:
  ionosphere.csv

Downloaded From:
  https://www.openml.org/d/59
  (CSV file: https://api.openml.org/data/v1/get_csv/59)

Original Source:
  UCI Machine Learning Repository
  https://archive.ics.uci.edu/dataset/52/ionosphere

Variable Information:

  Name -- Data Type -- Measurement -- Description
  a01 ... a34 -- quantitative -- unitless, -1 to 1 -- autocorrelation values, 2 per pulse number (a01-a02 for pulse 1, a03-a04 for pulse 2, ..., a33-a34 for pulse 17)
  class       -- binary (g, b) -- none -- g = good radar return, b = bad radar return -- response variable

Summary:
  Number of instances (observations): 351
  Number of Attributes: 35
  Attribute breakdown: 34 quantitative feature variables, and 1 binary response variable
  Missing Attribute Values: None
  Class distribution: g (good) = 225 (64.1%), b (bad) = 126 (35.9%)


Abstract:
  Classification of radar returns from the ionosphere. "Good" returns show
  evidence of some type of structure in the ionosphere; "bad" returns do not,
  and their signals pass through the ionosphere.

Data Characteristics:
  The radar data was collected by a system in Goose Bay, Labrador. The system
  consists of a phased array of 16 high-frequency antennas with a total
  transmitted power on the order of 6.4 kilowatts. The targets were free
  electrons in the ionosphere. Received signals were processed with an
  autocorrelation function whose arguments are the time of a pulse and the pulse
  number. There were 17 pulse numbers for the Goose Bay system, and each instance
  is described by 2 attributes per pulse number, corresponding to the complex
  value returned by the function.

Notes:
  The data was created by V. Sigillito, S. Wing, L. Hutton and K. Baker (Space
  Physics Group, Applied Physics Laboratory, Johns Hopkins University, 1989) and
  donated by Vince Sigillito.
  The first attribute (a01) takes only the values 0 and 1, and the second
  attribute (a02) equals 0 in every row, so it carries no information and can be
  dropped (it also makes standardizing the columns divide by zero).

Reference:
  Sigillito, V. G., Wing, S. P., Hutton, L. V., & Baker, K. B. (1989).
  Classification of radar returns from the ionosphere using neural networks.
  Johns Hopkins APL Technical Digest, 10, 262-266.


----------
library(tidyverse)
DF <- read_csv("ionosphere.csv")

DF