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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Adding new records


For those of you familiar with databases, you may already know how to perform an insert operation to append a new record to the dataset. Alternatively, you can use an alter operation to add a new column (attribute) into a table. In R, you can also perform insert and alter operations but much more easily. We will introduce the rbind and cbind function in this recipe so that you can easily append a new record or new attribute to the current dataset with R.

Getting ready

Refer to the Converting data types recipe and convert each attribute of imported data into the proper data type. Also, rename the columns of the employees and salaries datasets by following the steps from the Renaming the data variable recipe.

How to do it…

Perform the following steps to add a new record or new variable into the dataset:

  1. First, use rbind to insert a new record to employees:

    > employees <- rbind(employees, c(10011, '1960-01-01', 'Jhon', 'Doe', 'M', '1988-01-01'))
    
  2. We can then reassign the...

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