SAS for R Users

SAS for R Users

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data mi...

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Bibliographic Details
Main Author: Ohri, Ajay
Format: eBook
Language:English
Published: Somerset : John Wiley & Sons, Incorporated, 2019.
Subjects:
SAS (Computer program language)
Statistics > Data processing.
R (Computer program language)
Statistics > Data processing
Online Access:Click for online access
Table of Contents:
  • Cover; Title Page; Copyright Page; Contents; Preface; Scope; Chapter 1 About SAS and R; 1.1 About SAS; 1.1.1 Installation; 1.2 About R; 1.2.1 The R Environment; 1.2.2 Installation of R; 1.3 Notable Points in SAS and R Languages; 1.4 Some Important Functions with Comparative Comparisons Respectively; 1.5 Summary; 1.6 Quiz Questions; Quiz Answers; Chapter 2 Data Input, Import and Print; 2.1 Importing Data; 2.1.1 Packages in R; 2.2 Importing Data in SAS; 2.2.1 Data Input in SAS; 2.2.2 Using Proc Import to Import a Raw File; 2.2.3 Creating a temporary dataset from a permanent one using "set."
  • 2.3 Importing Data in R2.3.1 Importing from Comma Separated Value (CSV) Files; 2.3.2 Importing from Excel Files; 2.3.3 Importing from SAS; 2.3.4 Importing from SPSS and STATA; 2.3.5 Assigning the Values Imported to a Data Object in R; 2.4 Providing Data Input; 2.4.1 Data Input in R; 2.4.1.1 Using the c() function is the simplest way to create a list in R; 2.4.1.2 Providing missing values to the vector; 2.4.1.3 To Input multiple columns of data; 2.4.1.4 Using loops to input; 2.5 Data Input in SAS; 2.6 Printing Data; 2.6.1 Print in SAS; 2.6.2 Print in R; 2.7 Summary; 2.8 Quiz Questions
  • Quiz AnswersChapter 3 Data Inspection and Cleaning; 3.1 Introduction; 3.2 Data Inspection; 3.2.1 Data Inspection in SAS; 3.2.2 Data Inspection in R; 3.3 Missing Values; 3.3.1 Missing Values in SAS; 3.3.2 Missing Values in R; 3.4 Data Cleaning; 3.4.1 Data Cleaning in SAS; 3.4.2 Data Cleaning in R; 3.5 Quiz Questions; Quiz Answers; Chapter 4 Handling Dates, Strings, Numbers; 4.1 Working with Numeric Data; 4.1.1 Handling Numbers in SAS; 4.1.2 Numeric Data in R; 4.2 Working with Date Data; 4.2.1 Handling Dates in SAS; 4.2.2 Handling Dates in R; 4.3 Handling Strings Data
  • 4.3.1 Handling Strings Data in SAS4.3.2 Handling Strings Data in R; 4.4 Quiz Questions; Quiz Answers; Chapter 5 Numerical Summary and Groupby Analysis; 5.1 Numerical Summary and Groupby Analysis; 5.2 Numerical Summary and Groupby Analysis in SAS; 5.3 Numerical Summary and Group by Analysis in R; 5.3.1 Hmisc and Data. Table Packages; 5.3.2 Dplyr Package; 5.4 Quiz Questions; Quiz Answers; Chapter 6 Frequency Distributions and Cross Tabulations; 6.1 Frequency Distributions in SAS; 6.2 Frequency Distributions in R; 6.2.1 Frequency Tabulations in R
  • 6.2.2 Frequency Tabulations in R with Other Variables StatisticsChapter 7 Using SQL with SAS and R; 7.1 What is SQL?; 7.1.1 Basic Terminology; 7.1.2 CAP Theorem; 7.1.3 SQL in SAS and R; 7.2 SQL Select; 7.2.1 SQL WHERE; 7.2.2 SQL Order By; 7.2.3 AND, OR, NOT in SQL; 7.2.4 SQL Select Distinct; 7.2.5 SQL INSERT INTO; 7.2.6 SQL Delete; 7.2.7 SQL Aggregate Functions; 7.2.8 SQL ALIASES; 7.2.9 SQL ALTER TABLE; 7.2.10 SQL UPDATE; 7.2.11 SQL IS NULL; 7.2.12 SQL LIKE and BETWEEN; 7.2.13 SQL GROUP BY; 7.2.14 SQL HAVING; 7.2.15 SQL CREATE TABLE and SQL CONSTRAINTS; 7.2.16 SQL UNION; 7.2.17 SQL JOINS

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