Hands-On SAS for Data Analysis by Gulati, Harish -- Read -- Imperial Library of Trantor

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Title Page Copyright and Credits

Hands-On SAS For Data Analysis

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Preface

Who this book is for What this book covers To get the most out of this book

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Section 1: SAS Basics Introduction to SAS Programming

SAS dataset fundamentals

Creating an SAS table

Compile phase Execution phase

Dataset creation example

SAS programming language – basic syntax

Data step Proc SQL

SAS LOG

Naming conventions in SAS

Naming conventions for Teradata in SAS

Dataset options

Compression Encryption Indexing

SAS operators

Arithmetic operators Comparison operators Logical operators

Formats

Formatting to make the data readable Specifying a format to make it meaningful Altering the data type

Subsetting datasets

WHERE and IF statements Using OPTIONS DROP or KEEP options Viewing properties

Dictionary tables

Role of _ALL_ and _IN_

Summary

Data Manipulation and Transformation

Length of a variable Case conversion and alignment

LowCase, PropCase, and UpCase AnyUpper, AnyLower, and NoTupper Left and right

String identification

The Scan function Index, Indexc, and Indexw Find

Dealing with blanks

Compress, Strip, and Trim

Missing and multiple values

COALESCE and COALESCEC

Interval calculations

INTNX and INTCK

Concatenation

CAT CATS, CATT, and CATX LAGS

Logic and control

IFC and IFN WHICHC or WHICHN CHOOSEC or CHOOSEN

Number manipulation

CEIL, FLOOR, INT, and ROUND

Summary

Section 2: Merging, Optimizing, and Descriptive Statistics Combining, Indexing, Encryption, and Compression Techniques Simplified

Introduction to combining

Concatenating Interleaving Merging Updating Modifying

Concatenation

Different variable length and additional variables Duplicate values Different data types Leveraging the temporary variable PROC APPEND

Interleaving Merging

By Matching Overlapping variables One-to-many merging Program data vector Many-to-many merging

Indexing

Unique values Missing values

Encryption Summary

Power of Statistics, Reporting, Transforming Procedures, and Functions

Proc Freq

Cross tabulation Restricting Proc Freq output Cross tabulation with a controlling variable Proc Freq and statistical tests

Proc Univariate

Basic statistics and extreme observations Tests of normality Tests for location

Proc Means and Summary

Proc Means Proc Summary

Proc Corr Proc REG Proc Transpose Summary

Section 3: Advanced Programming Advanced Programming Techniques - SAS Macros

What are macros? Macro variable processing Macro resolution tracking Macro definition processing Comparing positional and keywords parameters Data-driven programming Leveraging automatic global macro variables Macros that evaluate Writing efficient macros Summary

Powerful Functions, Options, and Automatic Variables Simplified

NOMPREPLACE and MREPLACE NOMCOMPILE and NCOMPILE MCOMPILENOTE NOMEXECNOTE and MEXECNOTE MAUTOCOMPLOC MACRO and NOMACRO

Available macro functions

Exchanging values between the DATA step and macro variables

Choosing between CALL SYMGET and CALL SYMPUT

CALL EXECUTE Altering the CALL SYMPUT example Resolving macro variables

Macro variable names within text Macro variables and libraries Indirect macro referencing Series of macro variable references with a single macro call Multiple ampersands

Macro quoting

Using the %STR quote Using the %NRSTR quote Using the %BQOUTE and %NRBQOUTE quotes

Summary

Section 4: SQL in SAS Advanced Programming Techniques Using PROC SQL

Comparing data steps and Proc SQL Proc SQL joins

Inner join Left join Right join Full join One-to-many join Many-to-many join

Proc SQL essentials

Subsetting Grouping and summarizing

Dictionary tables Summary

Deep Dive into PROC SQL

SAS views in Proc SQL

SQL views syntax Describing views Optimizing performance using views

Making changes with Proc SQL

Deleting Altering

Identifying duplicates using Proc SQL Creating an index in Proc SQL Macros and Proc SQL

Creating a macro variable using Into Creating multiple macro variables using Into

Summary

Section 5: Data Visualization and Reporting Data Visualization

The role of data visualization in analytics Histograms Line plots Vertical and horizontal bar charts Scatter charts Box plot Summary

Reporting and Output Delivery System

Proc Tabulate

Comparing multiple Proc Means and Proc Tabulates Multiple tables using Proc Tabulate Choosing the statistics Formatting the output Two-dimensional output

Specifying the ODS destination Formatting ODS files

Multiple sheets Applying filters Controlling the print options Changing the default cells

ODS Excel charts

Color-coding the output Copying over the formula

Summary

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