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Summary Data Mining introduction into R + solutions exercices (introduction) $3.76
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  3. Universiteit Gent (UGent)
  4.  
  5. Handelsingenieur
  6.  
  7. Data Mining (FOOO759A)

Summary

Summary Data Mining introduction into R + solutions exercices (introduction)
 0 purchase
  • Course
  • Data Mining (FOOO759A)
  • Institution
  • Universiteit Gent (UGent)
  • Book
  • An Introduction to Statistical Learning

This document contains a brief summary of all codes covered in the introduction. In addition, this document contains the solutions to the exercises in this “introchapter”.

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Document information

  • No
  • Introduction to r + solutions exercises
  • July 31, 2023
  • 17
  • 2022/2023
  • Summary

Subjects

  • introductie in r de basics
  • oplossingen oefeningen van introductie

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Content preview

RStudio: functions and definitions
Introduction in R
<-: assign values to variables

print(a): shows us the value stored in a

ls (): you can see all the variables saved in your workspace

rm (): you can delete any variable that you don’t want

rm (list=ls ()): remove all objects at once

#: commenting your code

install. Packages (): are installed in R not RStudio.

installed. Packages (): to see which packages you already have installed.

library (): loading a package.

data (): you can see all the available datasets.

seq (): creates a vector of numbers

seq (1, 10) or 1:10 1 defines start, 10 defines end

seq (1, 10, 2) 1 defines start, 10 defines end, 2 how much to jump by

1 3 5 7 9

seq (1, 15, length = 5) sequences of 5 numbers equally spaced between 1 and 15

1.0 4.5 8.0 11.5 15.0

sum (): adds numbers up

c (): concatenate: it will join the numbers 1,3,2,5 and save them as a vector named x

when type x, it gives us back the vector

> x <- c (1, 3, 2, 5)

>x

[1] 1 3 2 5

abs (): absolute value

class (): helps determine what type of object we have (vb. numeric, character, data. frame…)

str (): finding out more about the structure of an object

head (): the first n rows present in the input data frame

$: accessor: to access different variables represented by columns included in this data frame

Record$student_id or record [[“student_id”]]

Murders$population

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