In this Module 2 Discussion, we shall discuss how to use R to obtain information by…

In this Module 2 Discussion, we shall discuss how to use R to
obtain information by exploring, cleaning, and preprocessing the
data. The following is a kind of checklist of frequent steps in
data preparation. More precisely, they are also typical steps in
cleansing data. Such steps include (at least):

No.

Steps

R functions

1

Loading and looking at the dataset in R

2

Identify missing values

3

Identify outliers

4

Check for overall plausibility and errors (e.g, typos)

5

Identify highly correlated variables

6

Identify variables with (nearly) no variance

7

Identify variables with strange names or values

8

Check variable classes (eg. Characters vs factors)

9

Remove/transform some variables (maybe your model does not like
categorial variables)

10

Rename some variables or values (especially interesting if large
number)

11

Check some overall pattern (statistical/numerical
summaries/graphical illustrations)

12

Center/scale variables

In view of the above steps, please scan through the three
examples (Example 1,2,3) in Data Mining and Business Analytics with
R Chapter 2 and Data Mining for Business Analytics: Concepts,
Techniques, and Applications in R section 2.4 (found in this week’s
Reading & Resources) to find and then fill in the blanks in the
above table for those R functions we can use to handle these steps,
respectively. For example, you may put read.csv() and view() in the
first row as they are the ways to realize that specific step. You
may also refer to some open resources to find relevant R functions
to fill in those blanks and each blank can have multiple R
functions as answers.

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