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R Data Visualization Cookbook

You're reading from  R Data Visualization Cookbook

Product type Book
Published in Jan 2015
Publisher
ISBN-13 9781783989508
Pages 236 pages
Edition 1st Edition
Languages

Table of Contents (17) Chapters

R Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. A Simple Guide to R 2. Basic and Interactive Plots 3. Heat Maps and Dendrograms 4. Maps 5. The Pie Chart and Its Alternatives 6. Adding the Third Dimension 7. Data in Higher Dimensions 8. Visualizing Continuous Data 9. Visualizing Text and XKCD-style Plots 10. Creating Applications in R Index

Special values in R


R comes with some special values. Some of the special values in R are NA, Inf, -Inf, and NaN.

How to do it…

The missing values are represented in R by NA. When we download data, it may have missing data and this is represented in R by NA:

z = c( 1,2,3, NA,5,NA) # NA in R is missing Data

To detect missing values, we can use the install.packages() function or is.na(), as shown:

complete.cases(z) # function to detect NA
is.na(z) # function to detect NA

To remove the NA values from our data, we can type the following in our active R session console window:

clean <- complete.cases(z)
z[clean] # used to remove NA from data

Please note the use of square brackets ([ ]) instead of parentheses.

In R, not a number is abbreviated as NaN. The following lines will generate NaN values:

##NaN
0/0
m <- c(2/3,3/3,0/0)
m

The is.finite, is.infinite, or is.nan functions will generate logical values (TRUE or FALSE).

is.finite(m)
is.infinite(m)
is.nan(m)

The following line will generate inf as a special value in R:

## infinite
k = 1/0

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

How it works…

complete.cases(z) is a logical vector indicating complete cases that have no missing value (NA). On the other hand, is.na(z) indicates which elements are missing. In both cases, the argument is our data, a vector, or a matrix.

R also allows its users to check if any element in a matrix or a vector is NA by using the anyNA() function. We can coerce or assign NA to any element of a vector using the square brackets ([ ]). The [3] input instructs R to assign NA to the third element of the dk vector.

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R Data Visualization Cookbook
Published in: Jan 2015 Publisher: ISBN-13: 9781783989508
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