5.8 LAB: Structuring data using mutate() The hmeq_small dataset contains information on 5960 home equity loans, including 7 features on the characteristics of the loan. • Load the hmeq_small.csv data set as a data frame. • Create a new data frame with the columns LOAN and VALUE standardized and saved as LOAN STAND and VALUE_STAND. • Create a second data frame with the columns LOAN and VALUE normalized and saved as LOAN_NORM and VALUE_NORM. • Print the summaries of the new data frames. Ex: Using the first 100 rows, found in hmeq_sample.csv, the output is:
5.8 LAB: Structuring data using mutate() The hmeq_small dataset contains information on 5960 home equity loans, including 7 features on the characteristics of the loan. • Load the hmeq_small.csv data set as a data frame. • Create a new data frame with the columns LOAN and VALUE standardized and saved as LOAN STAND and VALUE_STAND. • Create a second data frame with the columns LOAN and VALUE normalized and saved as LOAN_NORM and VALUE_NORM. • Print the summaries of the new data frames. Ex: Using the first 100 rows, found in hmeq_sample.csv, the output is:
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:5.8 LAB: Structuring data using mutate()
The hmeq_small dataset contains information on 5960 home equity loans, including 7 features on the characteristics of the loan.
• Load the hmeq_small.csv data set as a data frame.
• Create a new data frame with the columns LOAN and VALUE standardized and saved as LOAN STAND and VALUE_STAND.
• Create a second data frame with the columns LOAN and VALUE normalized and saved as LOAN_NORM and VALUE_NORM.
• Print the summaries of the new data frames.
Ex: Using the first 100 rows, found in hmeq_sample.csv, the output is:
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