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R for Data Science Cookbook (n)

You're reading from  R for Data Science Cookbook (n)

Product type Book
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)

Table of Contents (19) Chapters

R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. Functions in R 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

Merging data


Merging data enables us to understand how different data sources relate to each other. The merge operation in R is similar to the join operation in a database, which combines fields from two datasets using values that are common to each.

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 merge salaries and employees:

  1. As employees and salaries are common in emp_no, we can merge these two datasets using emp_no as the join key:

    > employees_salary  <- merge(employees, salaries, by="emp_no")
    > head(employees_salary,3)
      emp_no birth_date first_name last_name salary  from_date    to_date
    1  10001 1953-09-02     Georgi   Facello  60117 1986-06-26 1987-06-26
    2  10001 1953-09-02     Georgi   Facello  62102 1987-06-26 1988-06-25
    3  10001...
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