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This document provides an overview of SAS programming concepts and techniques for working with data. It discusses reading raw data using DATA steps and PROC IMPORT, selecting and transforming data, merging datasets, handling missing values, and working with dates. Functions for character manipulation, arithmetic, ranking and summarizing data are also explained. Overall, the document serves as a helpful cheat sheet for common SAS programming tasks.
Understanding SAS Data Step Processingguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document provides guidance on loading and unloading data from Snowflake using the COPY command. It discusses best practices for file formats, creating tables, loading data from CSV and JSON files located in an S3 bucket, validating data files before loading, and splitting large files into smaller files for improved load performance. Recommendations are provided for debugging data load issues, including using different ON_ERROR parameters to skip files or continue loading on errors.
The document describes Snowflake's extended JSON syntax for parsing JSON data using SQL. It discusses features like JSON arrays and objects, and functions like FLATTEN, PARSE_JSON, and GET_PATH that allow extracting nested JSON data in a relational format. Sample queries are provided that leverage these functions to parse example JSON files containing employee records and related dependent data.
SAS is a programming language that can be learned quickly. New users can write simple SAS programs within hours. SAS programs involve DATA and PROC steps - DATA steps input and process data, while PROC steps perform operations and output. The example SAS program reads student data from cards, assigns the values to variables in a DATA step, and prints the output in a PROC PRINT step. Additional PROC SORT and formatting options are demonstrated. The log file provides feedback on program execution and errors.
The SQL Loader utility can be used to load external data from files into Oracle tables. It uses a control file to describe the loading process. The control file specifies the data file, table, column definitions, field delimiters and other loading options. SQL Loader then loads the data according to the specifications in the control file. Logs and error files can be generated to monitor and debug the load process. Data can be loaded into single or multiple tables based on conditions specified in the control file.
SQL Loader is a utility used to load data from flat files into Oracle tables. It can load data from other databases by first converting the data to a flat file format. The document provides steps for using SQL Loader including writing a control file to describe the data file and load options, creating Oracle tables, and running SQL Loader to import the data. SQL Loader can load data into multiple tables at once using WHEN conditions and supports both conventional and direct path loading methods.
Data set options allow features during dataset processing and control variables, observations, security, and attributes. They are specified in parentheses after a SAS data set name and include options like DROP, KEEP, RENAME, FIRSTOBS, and LABEL. Data set options apply to input datasets before programming statements and to output datasets after statements are processed.
This document provides an overview of SAS programming concepts and techniques for working with data. It discusses reading raw data using DATA steps and PROC IMPORT, selecting and transforming data, merging datasets, handling missing values, and working with dates. Functions for character manipulation, arithmetic, ranking and summarizing data are also explained. Overall, the document serves as a helpful cheat sheet for common SAS programming tasks.
Understanding SAS Data Step Processingguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document provides guidance on loading and unloading data from Snowflake using the COPY command. It discusses best practices for file formats, creating tables, loading data from CSV and JSON files located in an S3 bucket, validating data files before loading, and splitting large files into smaller files for improved load performance. Recommendations are provided for debugging data load issues, including using different ON_ERROR parameters to skip files or continue loading on errors.
The document describes Snowflake's extended JSON syntax for parsing JSON data using SQL. It discusses features like JSON arrays and objects, and functions like FLATTEN, PARSE_JSON, and GET_PATH that allow extracting nested JSON data in a relational format. Sample queries are provided that leverage these functions to parse example JSON files containing employee records and related dependent data.
SAS is a programming language that can be learned quickly. New users can write simple SAS programs within hours. SAS programs involve DATA and PROC steps - DATA steps input and process data, while PROC steps perform operations and output. The example SAS program reads student data from cards, assigns the values to variables in a DATA step, and prints the output in a PROC PRINT step. Additional PROC SORT and formatting options are demonstrated. The log file provides feedback on program execution and errors.
The SQL Loader utility can be used to load external data from files into Oracle tables. It uses a control file to describe the loading process. The control file specifies the data file, table, column definitions, field delimiters and other loading options. SQL Loader then loads the data according to the specifications in the control file. Logs and error files can be generated to monitor and debug the load process. Data can be loaded into single or multiple tables based on conditions specified in the control file.
SQL Loader is a utility used to load data from flat files into Oracle tables. It can load data from other databases by first converting the data to a flat file format. The document provides steps for using SQL Loader including writing a control file to describe the data file and load options, creating Oracle tables, and running SQL Loader to import the data. SQL Loader can load data into multiple tables at once using WHEN conditions and supports both conventional and direct path loading methods.
Data set options allow features during dataset processing and control variables, observations, security, and attributes. They are specified in parentheses after a SAS data set name and include options like DROP, KEEP, RENAME, FIRSTOBS, and LABEL. Data set options apply to input datasets before programming statements and to output datasets after statements are processed.
This document discusses SAS data libraries and how to explore them using PROC CONTENTS. Key points include:
1) SAS data libraries allow you to assign nicknames (librefs) to file paths to reference SAS data sets more efficiently.
2) By default, SAS provides a temporary WORK library and a permanent SASUSER library when a session starts.
3) The LIBNAME statement assigns a libref to a file path, making that library available for referencing data sets.
4) PROC CONTENTS lists information about SAS data sets within a library. The NODS option lists only file names without descriptor details.
This document discusses various methods for moving data in and out of Oracle databases, including:
1) Using SQL*Loader to load data from files into Oracle tables, external tables to access file data, and Oracle Data Pump for full database exports and imports.
2) Oracle Data Pump allows high-speed movement of data and metadata and can be called from the command line or Enterprise Manager. It supports features like parallel processing, encryption, and fine-grained object selection.
3) Methods like SQL*Loader and external tables read data from files while Data Pump uses the direct path API for fast loading and unloading of data directly to and from disk.
This document provides an introduction and outline for using SAS software. It covers basic SAS windows and rules, loading and viewing data, manipulating data by selecting subsets, adding or deleting variables, sorting, summarizing data with procedures, and creating plots and outputting results to Word. Examples are provided for common procedures like SORT, MEANS, UNIVARIATE, FREQ, CORR and PLOT. Practice exercises are included to try these skills on a sample dataset.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Vibrant Technologies is headquarted in Mumbai,India.We are the best SAS training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Statistical Analysis System classes in Mumbai according to our students and corporates
This document provides an overview of SAS (Statistical Analysis Software). It describes how SAS can handle large datasets with millions or billions of records. It also lists some common SAS modules and provides examples of DATA and PROC steps to create and process SAS datasets. Finally, it discusses the SAS programming environment and how to submit and run SAS programs.
The document lists various commands and functions available in FoxPro, including:
- Date, time, and mathematical functions to return values like the current date, square root, natural logarithm, etc.
- String functions to manipulate and retrieve parts of character strings like LEFT, RIGHT, LEN, SUBSTR, etc.
- Financial functions to calculate loan payments, present value, etc.
- System information functions to get the cursor position, disk space, last update date of a table, and more.
Import and Export Excel Data using openxlsx in R StudioRupak Roy
This document discusses using the openxlsx package in R to import and export Excel files without relying on Java. It covers functions for loading and reading Excel files, adding and writing data to worksheets, and saving workbooks. Functions covered include loadWorkbook(), readWorkbook(), addWorksheet(), writeData(), and saveWorkbook(). The document provides code examples for using each function to load, manipulate, and save Excel data in R.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
This document provides information about FoxPro, a database management system. It discusses the different versions of FoxPro including FoxPro 2.0, 2.6, and Visual FoxPro. It then explains some key FoxPro commands like CREATE, LIST, APPEND, USE, QUIT, and DELETE. It also covers FoxPro operators, file extensions, and ways to return to DOS from FoxPro.
FoxPro is a powerful database management system with an integrated development environment. It allows for both interpreted and compiled programming. As an interpreted language, each command is translated and executed as it runs, which is slower than compilation. A compiler translates commands to machine instructions once, improving performance. FoxPro supports mathematical and financial functions and works with databases containing records made up of fields. Common commands configure the environment, create and modify databases, add/edit/delete records, and sort/index records for easier querying and reporting.
This document provides guidance on using PROC COMPARE in SAS to compare SAS data files. It discusses using PROC COMPARE to compare the contents and values of two files, options for controlling the amount of output when differences are found, comparing multiple files, and addressing discrepancies between files such as different variable names, types, or lengths.
1. Using a SAS Catalog allows a programmer to store and manage all SAS code and project components in a centralized location.
2. Key SAS statements like FILENAME and %INCLUDE allow easy access and execution of catalog entries containing SAS programs, data, formats, and other code objects.
3. Procedures like PROC CATALOG facilitate tasks like listing, copying, renaming, and moving catalog entries.
Oxygen Compare and Merge Scripts
In this presentation I cover in detail this compare and merge scripting support, as well as showing you examples on how to use it efficiently, showing you the following:
- The Compare and Merge Files Command-Line Script - used to compare and merge files and get the comparison results in various formats (YAML, JSON, XML, HTML).
- The Compare and Merge Directories Command-Line Script - with many options to choose from, such as the comparison mode (content, binary, timestamp), the algorithm to be used for the comparison, the "strength" of the comparison algorithm, various "include/exclude" type file filters, various "ignore" type options to refine the comparison results.
- Generating File or Directory Comparison HTML Reports - the support to save the comparison made with Oxygen XML Editor in HTML format.
https://www.oxygenxml.com/events/2021/webinar_the_new_oxygen_compare_and_merge_scripts.html
This document provides an overview and introduction to the SAS statistical software system. It discusses the origins and development of SAS, describes some of the main SAS products and their uses, and provides resources for further learning including introductory books and online materials. The document also outlines the basic structure of SAS programs including data steps, procedure steps, and accessing SAS on UNIX systems. It provides explanations of key concepts such as the structure of the data step and using the INPUT statement.
This document provides information about configuring Log4j logging framework. It discusses setting up Log4j with email, file and stdout appenders. It compares XML and properties configuration files and shows how to change log levels for a running application. The document explains best practices for logging and exception handling. It provides details on the log4j.properties file, log4j XML configuration, log levels, appenders, layouts and conversion patterns.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document discusses SAS training which includes:
1) Reading data from raw data files, formatting the data, and performing statistical analysis and data manipulation.
2) Combining and subsetting different data sets.
3) Processing data iteratively through loops and producing final reports.
Calling r from sas (msug meeting, feb 17, 2018) revisedBarry DeCicco
This document discusses how to call R from SAS/IML. It explains that R has over 10,000 packages with vast capabilities, more than SAS. It provides instructions on setting up SAS to use R, exporting/importing data between the programs, submitting R code from SAS using PROC IML, and basic R commands for file paths and comments. Examples are given of R code for plotting and exporting graphics that is submitted from SAS.
This document discusses SAS data libraries and how to explore them using PROC CONTENTS. Key points include:
1) SAS data libraries allow you to assign nicknames (librefs) to file paths to reference SAS data sets more efficiently.
2) By default, SAS provides a temporary WORK library and a permanent SASUSER library when a session starts.
3) The LIBNAME statement assigns a libref to a file path, making that library available for referencing data sets.
4) PROC CONTENTS lists information about SAS data sets within a library. The NODS option lists only file names without descriptor details.
This document discusses various methods for moving data in and out of Oracle databases, including:
1) Using SQL*Loader to load data from files into Oracle tables, external tables to access file data, and Oracle Data Pump for full database exports and imports.
2) Oracle Data Pump allows high-speed movement of data and metadata and can be called from the command line or Enterprise Manager. It supports features like parallel processing, encryption, and fine-grained object selection.
3) Methods like SQL*Loader and external tables read data from files while Data Pump uses the direct path API for fast loading and unloading of data directly to and from disk.
This document provides an introduction and outline for using SAS software. It covers basic SAS windows and rules, loading and viewing data, manipulating data by selecting subsets, adding or deleting variables, sorting, summarizing data with procedures, and creating plots and outputting results to Word. Examples are provided for common procedures like SORT, MEANS, UNIVARIATE, FREQ, CORR and PLOT. Practice exercises are included to try these skills on a sample dataset.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Vibrant Technologies is headquarted in Mumbai,India.We are the best SAS training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Statistical Analysis System classes in Mumbai according to our students and corporates
This document provides an overview of SAS (Statistical Analysis Software). It describes how SAS can handle large datasets with millions or billions of records. It also lists some common SAS modules and provides examples of DATA and PROC steps to create and process SAS datasets. Finally, it discusses the SAS programming environment and how to submit and run SAS programs.
The document lists various commands and functions available in FoxPro, including:
- Date, time, and mathematical functions to return values like the current date, square root, natural logarithm, etc.
- String functions to manipulate and retrieve parts of character strings like LEFT, RIGHT, LEN, SUBSTR, etc.
- Financial functions to calculate loan payments, present value, etc.
- System information functions to get the cursor position, disk space, last update date of a table, and more.
Import and Export Excel Data using openxlsx in R StudioRupak Roy
This document discusses using the openxlsx package in R to import and export Excel files without relying on Java. It covers functions for loading and reading Excel files, adding and writing data to worksheets, and saving workbooks. Functions covered include loadWorkbook(), readWorkbook(), addWorksheet(), writeData(), and saveWorkbook(). The document provides code examples for using each function to load, manipulate, and save Excel data in R.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
This document provides information about FoxPro, a database management system. It discusses the different versions of FoxPro including FoxPro 2.0, 2.6, and Visual FoxPro. It then explains some key FoxPro commands like CREATE, LIST, APPEND, USE, QUIT, and DELETE. It also covers FoxPro operators, file extensions, and ways to return to DOS from FoxPro.
FoxPro is a powerful database management system with an integrated development environment. It allows for both interpreted and compiled programming. As an interpreted language, each command is translated and executed as it runs, which is slower than compilation. A compiler translates commands to machine instructions once, improving performance. FoxPro supports mathematical and financial functions and works with databases containing records made up of fields. Common commands configure the environment, create and modify databases, add/edit/delete records, and sort/index records for easier querying and reporting.
This document provides guidance on using PROC COMPARE in SAS to compare SAS data files. It discusses using PROC COMPARE to compare the contents and values of two files, options for controlling the amount of output when differences are found, comparing multiple files, and addressing discrepancies between files such as different variable names, types, or lengths.
1. Using a SAS Catalog allows a programmer to store and manage all SAS code and project components in a centralized location.
2. Key SAS statements like FILENAME and %INCLUDE allow easy access and execution of catalog entries containing SAS programs, data, formats, and other code objects.
3. Procedures like PROC CATALOG facilitate tasks like listing, copying, renaming, and moving catalog entries.
Oxygen Compare and Merge Scripts
In this presentation I cover in detail this compare and merge scripting support, as well as showing you examples on how to use it efficiently, showing you the following:
- The Compare and Merge Files Command-Line Script - used to compare and merge files and get the comparison results in various formats (YAML, JSON, XML, HTML).
- The Compare and Merge Directories Command-Line Script - with many options to choose from, such as the comparison mode (content, binary, timestamp), the algorithm to be used for the comparison, the "strength" of the comparison algorithm, various "include/exclude" type file filters, various "ignore" type options to refine the comparison results.
- Generating File or Directory Comparison HTML Reports - the support to save the comparison made with Oxygen XML Editor in HTML format.
https://www.oxygenxml.com/events/2021/webinar_the_new_oxygen_compare_and_merge_scripts.html
This document provides an overview and introduction to the SAS statistical software system. It discusses the origins and development of SAS, describes some of the main SAS products and their uses, and provides resources for further learning including introductory books and online materials. The document also outlines the basic structure of SAS programs including data steps, procedure steps, and accessing SAS on UNIX systems. It provides explanations of key concepts such as the structure of the data step and using the INPUT statement.
This document provides information about configuring Log4j logging framework. It discusses setting up Log4j with email, file and stdout appenders. It compares XML and properties configuration files and shows how to change log levels for a running application. The document explains best practices for logging and exception handling. It provides details on the log4j.properties file, log4j XML configuration, log levels, appenders, layouts and conversion patterns.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document discusses SAS training which includes:
1) Reading data from raw data files, formatting the data, and performing statistical analysis and data manipulation.
2) Combining and subsetting different data sets.
3) Processing data iteratively through loops and producing final reports.
Calling r from sas (msug meeting, feb 17, 2018) revisedBarry DeCicco
This document discusses how to call R from SAS/IML. It explains that R has over 10,000 packages with vast capabilities, more than SAS. It provides instructions on setting up SAS to use R, exporting/importing data between the programs, submitting R code from SAS using PROC IML, and basic R commands for file paths and comments. Examples are given of R code for plotting and exporting graphics that is submitted from SAS.
The document provides an overview of the SAS system and its components. It describes the four main data-driven tasks of data access, data management, data analysis, and data presentation. It also outlines the structure of SAS programs and data sets, and how to run and submit SAS programs. Key concepts covered include DATA and PROC steps, the SAS log and output, browsing descriptor and data portions of SAS data sets, and SAS syntax rules.
Draft sas and r and sas (may, 2018 asa meeting)Barry DeCicco
This document discusses using SAS and R together. It describes how to call R from SAS using SAS/IML, and how to call SAS from R using knitr and Markdown. Key steps include setting up SAS to use R, exporting/importing datasets between the programs, and submitting R code from SAS. Limitations include running each chunk as a separate SAS job and needing permanent files to pass data between chunks.
This document provides an introduction and overview of the SAS statistical software system. It discusses that SAS was originally developed in the 1970s for agricultural research, but is now widely used statistical software. It also summarizes the main SAS product lines, resources for learning SAS including introductory books and online documentation, and provides a basic overview of the SAS programming language including data steps, procedure steps, and accessing SAS.
This document provides a list of SAS programs covering various topics related to data management using SAS, including:
1) Reading and importing different data file types such as CSV, TAB, and delimited files;
2) Merging and concatenating datasets;
3) Converting between numeric and character values; and
4) Working with dates in SAS including calculating durations between dates.
The programs demonstrate techniques for inputting, manipulating, and outputting data in SAS using data steps and procedures like PROC PRINT and PROC IMPORT.
This document provides an overview of the Statistical Analysis System (SAS) software. It discusses what SAS is used for, including data management, statistical analysis, reporting, and more. It also covers SAS components and their usage, how to install and use the SAS studio interface, basic SAS syntax like variables and data sets, and common statistical procedures in SAS like PROC MEANS, PROC FREQ, and PROC UNIVARIATE to produce measures, frequencies and graphs.
This document provides an overview of basic SAS commands for inputting and analyzing data. It describes the SAS data step for inputting and manipulating data to create SAS datasets. It then summarizes commonly used SAS statistical procedures like ANOVA, CHART, CORR, and REG for analyzing SAS dataset. It includes syntax examples and explanations of options for these procedures.
Improving Effeciency with Options in SASguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
This document provides a summary of the SAS programming language and various SAS procedures. It describes the basic structure of a SAS job, SAS language elements like statements, comments, and variables. It also summarizes how to work with SAS data sets, the DATA and PROC steps for data manipulation and analysis, and some common statistical and graphical procedures.
MySQL is a database management system where data is stored in tables which consist of columns and rows. The document provides instructions on installing MySQL on Linux using RPM files and setting the root password. It also describes some basic MySQL concepts like queries, creating/modifying tables, and joining tables.
The document discusses basic commands in MySQL including CREATE, SELECT, DELETE, INSERT, UPDATE, and DROP commands. It also covers procedures and functions. Finally, it provides examples of how to export a MySQL database using mysqldump and import a database using the mysql command.
This document discusses bringing OpenClinica clinical trial data into SAS. It describes developing a Java utility to convert OpenClinica export files into an XML format that can be read by SAS. The utility standardizes names, adds metadata like labels and formats, and structures the data into a tall, thin format suitable for SAS. It allows OpenClinica data to be easily imported into SAS for analysis while preserving metadata.
MySQL is an open-source relational database management system that uses SQL and runs a server providing multi-user access to databases. It allows users to perform queries and make changes to data through commands like SELECT, INSERT, UPDATE, DELETE. Stored procedures and functions allow users to write and save blocks of SQL code for repeated execution with consistent results.
The document provides an overview of SAS 9.3 and the SAS environment. It discusses what SAS is, who uses it, and its evolution. It then outlines the SAS user interface and describes the main windows, including the editor window, explorer window, log window, output window, and results window. The remainder of the document focuses on SAS programs, data sets, libraries, and procedures for working with data like PROC PRINT, PROC PLOT, PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC SQL.
This document provides guidelines for developing databases and writing SQL code. It includes recommendations for naming conventions, variables, select statements, cursors, wildcard characters, joins, batches, stored procedures, views, data types, indexes and more. The guidelines suggest using more efficient techniques like derived tables, ANSI joins, avoiding cursors and wildcards at the beginning of strings. It also recommends measuring performance and optimizing for queries over updates.
SAS is a software suite for advanced analytics. It was developed in the 1960s and includes components for statistical analysis, graphics, predictive modeling, and more. The main components of SAS are the data step for data manipulation and procedure steps for analysis. Common procedures include PROC PRINT, PROC MEANS, PROC FREQ and PROC REG. SAS programs are written in the SAS code editor and results are displayed in the SAS results window.
This document provides a summary of SAS language elements, procedures, functions, formats, and the macro language. It includes brief descriptions of commonly used statements, such as DATA, SET, IF/THEN, FORMAT, and PROC, as well as summaries of various procedures like FREQ, MEANS, REPORT and SORT. It also outlines important macro language elements such as %DO, %LET, and macro quoting functions.
1. The document describes the prerequisites for setting up output in Excel using PL/SQL with XML, including designing an Excel layout, defining a stored procedure and concurrent program, and setting up required profiles.
2. It provides steps to create a PL/SQL package to generate the XML code for the Excel output, including functions to print the header, data rows, and footer.
3. Running the package main procedure will execute a cursor to fetch data and write it to the XML output, which can then be viewed in Excel when selecting that option.
Vibrant Technologies is headquarted in Mumbai,India.We are the best Business Analyst training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Business Analyst classes in Mumbai according to our students and corporators
This presentation is about -
History of ITIL,
ITIL Qualification scheme,
Introduction to ITIL,
For more details visit -
http://vibranttechnologies.co.in/itil-classes-in-mumbai.html
This presentation is about -
Create & Manager Users,
Set organization-wide defaults,
Learn about record accessed,
Create the role hierarchy,
Learn about role transfer & mass Transfer functionality,
Profiles, Login History,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, non-volatile, and time-variant collection of data used to support management decision making. It describes the data warehouse architecture including extract-transform-load processes, OLAP servers, and metadata repositories. Finally, it outlines common data warehouse applications like reporting, querying, and data mining.
This presentation is about -
Based on as a service model,
• SAAS (Software as a service),
• PAAS (Platform as a service),
• IAAS (Infrastructure as a service,
Based on deployment or access model,
• Public Cloud,
• Private Cloud,
• Hybrid Cloud,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This presentation is about -
Introduction to the Cloud Computing ,
Evolution of Cloud Computing,
Comparisons with other computing techniques fetchers,
Key characteristics of cloud computing,
Advantages/Disadvantages,
For more details you can visit -
http://vibranttechnologies.co.in/salesforce-classes-in-mumbai.html
This document provides an introduction to PL/SQL, including what PL/SQL is, why it is used, its basic structure and components like blocks, variables, and types. It also covers key PL/SQL concepts like conditions, loops, cursors, stored procedures, functions, and triggers. Examples are provided to illustrate how to write and execute basic PL/SQL code blocks, programs with variables, and stored programs that incorporate cursors, exceptions, and other features.
This document provides an introduction to SQL (Structured Query Language) for manipulating and working with data. It covers SQL fundamentals including defining a database using DDL, working with views, writing queries, and establishing referential integrity. It also discusses SQL data types, database definition, creating tables and views, and key SQL statements for data manipulation including SELECT, INSERT, UPDATE, and DELETE. Examples are provided for creating tables and views, inserting, updating, and deleting data, and writing queries using functions, operators, sorting, grouping, and filtering.
The document introduces relational algebra, which defines a set of operations that can be used to combine and manipulate relations in a database. It describes four broad classes of relational algebra operations: set operations like union and intersection, selection operations that filter tuples, operations that combine tuples from two relations like join, and rename operations. It provides examples of how these operations can be applied to relations and combined to form more complex queries.
This presentation is about -
Designing the Data Mart planning,
a data warehouse course data for the Orion Star company,
Orion Star data models,
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
This presentation is about -
Working Under Change Management,
What is change management? ,
repository types using change management
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
This presentation is about -
Overview of SAS 9 Business Intelligence Platform,
SAS Data Integration,
Study Business Intelligence,
overview Business Intelligence Information Consumers ,navigating in SAS Data Integration Studio,
For more details Visit :-
http://vibranttechnologies.co.in/sas-classes-in-mumbai.html
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
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How to Manage Reception Report in Odoo 17Celine George
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3. The topics
An overview of the SAS system
Reading raw data/ create SAS data set
Combining SAS data sets & Match merging
SAS Data Sets
Formatting data
Introduce some simple regression procedure
Summary report procedures
4. Basic Screen Navigation
Main:
Editor
contains the SAS program to be submitted.
Log
contains information about the processing of the SAS
program, including any warning and error messages
Output
contains reports generated by SAS procedures and
DATA steps
Side:
Explore
navigate to other objects like libraries
Results
navigate your Output window
5. SAS programs
A SAS program is a sequence of steps that the user
submits for execution.
Data steps are typically used to create SAS data sets
PROC steps are typically used to process SAS data
sets (that is, generate reports and graphs, edit
data, sort data and analyze data
6. SAS Data Libraries
A SAS data library is a collection of SAS files that are
recognized as a unit by SAS
A SAS data set is one type of SAS file stored in a data
library
Work library is temporary library, when SAS is closed, all
the datasets in the Work library are deleted; create a
permanent SAS dataset via your own library.
7. SAS Data Libraries
Identify SAS data libraries by assigning each a library reference
name (libref) with LIBNAME statement
LIBNAME libref “file-folder-location”;
Eg: LIBNAME readData 'C:tempsas classreadData‘;
Rules for naming a libref:
The name must be 8 characters or less
The name must begin with a letter or underscore
The remaining characters must be letters, numbers or
underscores.
8. Reading raw data set into SAS
system
In order to create a SAS data set from a raw
data file, you must
Start a DATA step and name the SAS data set
being created (DATA statement)
Identify the location of the raw data file to read
(INFILE statement)
Describe how to read the data fields from the raw
data file (INPUT statement)
9. Reading external raw data file into
SAS system
LIBNAME readData 'C:tempsas classreadData‘;
DATA readData.wa80;
INFILE “k:censusstf2_wa80.txt”;
INPUT @10 SUMRYLVL $2. @40 COUNTY $3.
@253 TABA1 9.0 @271 TABA1 9.0;
RUN;
The LIBNAME statement assigns a libref ‘readData ’ to a data library.
The DATA statement creates a permanent SAS data set named ‘wa80’.
The INFILE statement points to a raw data file.
The INPUT statement
- name the SAS variables
- identify the variables as character or numeric ($ indicates character data)
- specify the locations of the fields in the raw data
- can be specified as column, formatted, list, or named input
The RUN statement detects the end of a step
10. Example 1
Reading raw data separated by spaces
/* Create a SAS permanent data set named HighLow1;
Read the data file temperature1.dat using listing input */
DATA readData.HighLow1;
INFILE ‘C:sas classreadDatatemperature1.dat’;
INPUT City $ State $ NormalHigh NormalLow
RecordHigh RecordLow;
RUN;
/* The PROC PRINT step creates a isting report of the
readData.HighLow1 data set */
PROC PRINT DATA = readData.highlow1;
TITLE ‘High and Low Temperatures for July’;
RUN;
temperature1.dat:
Nome AK 55 44 88 29
Miami FL 90 75 97 65
Raleign NC 88 68 105 50
11. Example 2
Reading multiple lines of raw data per observation
/* Read the data file using line pointer, slash(/) and pount-n (#n).
The slash(/) indicates next line, the #n means to go to the n line
for that observation. Slash(/) can be replaced by #2 here */
DATA readData.highlow2;
INFILE ‘C:sas classreadDatatemperature2.dat’;
INPUT City $ State $
/ NormalHigh NormalLow
#3 RecordHigh RecordLow;
PROC PRINT DATA = readData.highlow2;
TITLE ‘High and Low Temperatures for July’;
RUN;
temperature2.dat:
Nome AK
55 44
88 29
Miami FL
90 75
97 65
Raleign NC
88 68
105 50
12. Example 3
Reading multiple observations per line of raw data
temperature3.dat:
Nome AK 55 44 88 29 Miami FL 90 75 97 65 Raleign NC 88
68 105 50
/* To read multiple observations per line of raw data,use double railing at
signs (@@) at the end of INPUT statement */
DATA readData.highlow3;
INFILE ‘C:sas classreadDatatemperature3.dat’;
INPUT City $ State $ NormalHigh NormalLow RecordHigh
RecordLow @@;
PROC PRINT DATA = readData.highlow3;
TITLE ‘High and Low Temperatures for July’;
RUN;
13. Reading external raw data file into
SAS system
Reading raw data arranged in columns
INPUT FILEID $ 1-5 RECTYP $ 6-9 SUMRYLVL $ 10-11
URBARURL $ 12-13 SMSACOM $ 14-15;
Reading raw data mixed in columns
INPUT FILEID $ 1-5 @10 SUMRYLVL $ 2. @253 TABA1 9.0
@271 TABA1 9.0;
/* The @n is the column pointer, where n is the number of the column
SAS should move to. The $w. reads standard character data, and
w.d reads standard numeric data, where w is the total width and d
is the number of decimal places. */
14. Reading Delimited or PC Database
Files with the IMPORT Procedure
If your data file has the proper extension, use the simplest form of
the IMPORT procedure:
PROC IMPORT DATA FILE = ‘filename’ OUT = data-set
Type of File Extension DBMS Identifier
Comma-delimited .csv CSV
Tab-delimited .txt TAB
Excel .xls EXCEL
Lotus Files .wk1, .wk3, .wk4 WK1,WK3,WK4
Delimiters other than commas or tabs DLM
Examples:
1. PROC IMPORT DATAFILE=‘c:tempsale.csv’ OUT=readData.money; RUN;
2. PROC IMPORT DATAFILE=‘c:tempbands.xls’ OUT=readData.music; RUN;
15. Reading Files with the IMPORT
Procedure
If your file does not have the proper extension, or your
file is of type with delimiters other than commas or tabs,
then you must use the DBMS= and DELIMITER= option
PROC IMPORT DATAFILE = ‘filename’ OUT = data-set
DBMS = identifier;
DELIMITER = ‘delimiter-character’;
RUN;
Example:
PROC IMPORT DATAFILE = ‘C:sas classreadDataimport2.txt’
OUT =readData.sasfile DBMS =DLM;
DELIMITER = ‘&’;
RUN;
16. Format in SAS data set
Standard Formats (selected):
Character: $w.
Date, Time and Datetime:
DATEw., MMDDYYw., TIMEw.d, ……
Numeric: COMMAw.d, DOLLARw.d, ……
Use FORMAT statement
PROC PRINT DATA=sales;
VAR Name DateReturned CandyType Profit;
FORMAT DateReturned DATE9. Profit DOLLAR 6.2;
RUN;
17. Format in SAS data set
Create your own custom formats with two steps:
Create the format using PROC FORMAT and VALUE statement.
Assign the format to the variable using FORMAT statement.
General form of a simple PROC FORMAT steps:
PROC FORMAT;
VALUE name range-1=‘formatted-text-1’
range-2=‘formatted-text-2’ ……;
RUN;
The name in VALUE statement is the name of the format you are
creating, which can’t be longer than eight characters, must not start or
end with a number. If the format is for character data, it must start with
a $.
18. Format in SAS data set
Exmaple:
/* Step1: Create the format for certain variables */
PROC FORMAT;
VALUE genFmt 1 = 'Male'
2 = 'Female';
VALUE money
low-<25000='Less than 25,000'
25000-50000='25,000 to 50,000'
50000<-high='More than 50,000';
VALUE $codeFmt
'FLTA1'-'FLTA3'='Flight Attendant'
'PILOT1'-'PILOT3'='Pilot';
RUN;
/* Step2: Assign the variables */
DATA fmtData.crew1;
SET fmtData.crew;
FORMAT Gender genFmt. Salary money. JobCode $codeFmt.;
RUN;
19. Format in SAS data set
Permanently store formats in a SAS catalog by
Creating a format catalog file with LIB in PROC
FORMAT statement
Setting the format search options
Example:
LIBNAME class ‘C:sas classFormat’;
OPTIONS FMTSEARCH=(fmtData.fmtvalue); RUN;
PROC FORMAT LIB=fmtData.fmtvalue;
VALUE genFmt 1 = ‘Male’ 2=‘Female’;
RUN;
20. Combining SAS Data Sets:
Concatenating and Interleaving
Use the SET statement in a DATA step to
concatenate SAS data sets.
Use the SET and BY statements in a DATA
step to interleave SAS data sets.
21. Combining SAS Data Sets:
Concatenating and Interleaving
General form of a DATA step concatenation:
DATA SAS-data-set;
SET SAS-data-set1 SAS-data-set2 …;
RUN;
Example:
DATA stack.allEmp;
SET stack.emp1 stack.emp2 stack.emp3;
RUN;
22. Combining SAS Data Sets:
Concatenating and Interleaving
General form of a DATA step interleave:
DATA SAS-data-set;
SET SAS-data-set1 SAS-data-set2 …;
BY BY-variable;
RUN;
Sort all SAS data set first by using PROC SORT
Example:
PROC SORT data=stack.emp2 OUT=stack.emp2_sorted; BY Salary;
RUN;
DATA stack.allEmp;
SET stack.emp1 stack.emp2 stack.emp3;
BY salary;
RUN;
23. Match-Merging SAS Data Sets
One-to-one match merge
One-to-many match merge
Many-to-many match merge
The SAS statements for all three types of match
merge are identical in the following form:
DATA new-data-set;
MERGE data-set-1 data-set-2 data-set-3 …;
BY by-variable(s); /* indicates the variable(s) that control
which observations to match */
RUN;
24. Merging SAS Data Sets: A More
Complex Example
Example: Merge two data sets acquire the names of the group
team that is scheduled to fly next week.
combData.employee combData.groupsched
EmpID LastName
E00632 Strauss
E01483 Lee
E01996 Nick
E04064 Waschk
/* To match-merge the data sets by common variables - EmpID, the data sets
must be ordered by EmpID */
PROC SORT data=combData.Groupsched;
BY EmpID;
RUN;
EmpID FlightNum
E04064 5105
E0632 5250
E01996 5501
25. Merging SAS Data Sets: A More
Complex Example
/* simply merge two data sets */
DATA combData.nextweek;
MERGE combData.employee combData.groupsched;
BY EmpID;
RUN;
EmpID LastJName FlightNum
E00632 Strauss 5250
E01483 Lee
E01996 Nick 5501
E04064 Waschk 5105
26. Merging SAS Data Sets: A More
Complex Example
Eliminating Nonmatches
Use the IN= data set option to determine which
dataset(s) contributed to the current observation.
General form of the IN=data set option:
SAS-data-set (IN=variable)
Variable is a temporary numeric variable that has two
possible values:
0 indicates that the data set did not contribute to the
current observation.
1 indicates that the data set did contribute to the
current observation.
27. Merging SAS Data Sets: A More
Complex Example
/*Exclude from the data set employee who are scheduled to fly next
week. */
LIBNAME combData “K:sas classmerge”;
DATA combData.nextweek;
MERGE combData.employee
combData.groupsched (in=InSched);
BY EmpID;
IF InSched=1; True
RUN;
EmpID LastJName FlightNum
E00632 Strauss 5250
E01996 Nick 5501
E04064 Waschk 5105
28. Merging SAS Data Sets: A More
Complex Example
/* Find employees who are not in the flight scheduled group. */
LIBNAME combData “K:sas classmerge”;
DATA combData .nextweek;
MERGE combData .employee (in=InEmp)
combData.groupsched (in=InSched);
BY EmpID;
IF InEmp=1; True
IF InSched=0; False
RUN;
EmpID LastJName FlightNum
E01483 Lee
29. Different Types of Merges in SAS
One-to-Many Merging
DATA work.three;
MERGE work.one work.two;
BY X;
RUN;
X Y
1 A
2 B
3 C
Work.two
X E
1 A1
1 A2
2 B1
3 C1
3 C2
Work.three
X Y Z
1 A A1
1 A A2
2 B B1
3 C C1
3 C C2
Work.one
30. Different Types of Merges in SAS
Many-to-Many Merging
DATA work.three;
MERGE work.one work.two;
BY X;
RUN;
X Y
1 A1
1 A2
2 B1
2 B2
Work.two
X Z
1 AA1
1 AA2
1 AA3
2 BB1
2 BB2
Work.three
X Y Z
1 A1 AA1
1 A2 AA2
1 A2 AA3
2 B1 BB1
2 B2 BB2
Work.one
32. The REG procedure
The REG procedure is one of many regression
procedures in the SAS System.
The REG procedure allows several MODEL
statements and gives additional regression
diagnostics, especially for detection of collinearity. It
also creates plots of model summary statistics and
regression diagnostics.
PROC REG <options>;
MODEL dependents=independents </options>;
PLOT <yvariable*xvariable>;
RUN;
33. An example
PROC REG DATA=water;
MODEL Water = Temperature Days Persons / VIF;
MODEL Water = Temperature Production Days / VIF;
RUN;
PROC REG DATA=water;
MODEL Water = Temperature Production Days;
PLOT STUDENT.* PREDICTED.;
PLOT STUDENT.* NPP.;
PLOT NPP.*r.;
PLOT r.*NQQ.;
RUN;
34. The LOGISTIC procedure
The binary or ordinal responses with continuous
independent variables
PROC LOGISTIC < options > ;
MODEL dependents=independents < / options > ;
RUN;
The binary or ordinal responses with categorical
independent variables
PROC LOGISTIC < options > ;
CLASS categorical variables < / option > ;
MODEL dependents=independents < / options > ;
RUN;
35. Example
PROC LOGISTIC data=Neuralgia;
CLASS Treatment Sex;
MODEL Pain= Treatment Sex Treatment*Sex Age Duration;
RUN;
36. Overview Summary Report
Procedures
PROC FREQ: produce frequency counts
PROC TABULATE: produce one- and two-dimensional tabular
reports
PROC REPORT: produce flexible detail and summary reports
37. The FREQ Procedure
The FREQ procedure display frequency counts
of the data values in a SAS data set.
General form of a simple PROC FREQ steps:
PROC FREQ DATA = SAS-data-set;
TABLE SAS-variables </options>;
RUN;
38. The FREQ Procedure
Example:
PROC FREQ DATA = class.crew ;
FORMAT JobCode $codefmt. Salary money.;
TABLE JobCode*Salary /NOCOL NOROW OUT =freqTable;
RUN;
39. The TABULATE Procedure
PROC TABULATE displays descriptive
statistics in tabular format.
General form of a simple PROC TABULATE
steps:
PROC TABULATE DATA=SAS-data-set;
CLASS class-variables;
VAR analysis-variables;
TABLE row-expression,
column-expression</options>;
RUN;
40. The TABULATE Procedure
Example:
TITLE 'Average Salary for Cary and Frankfurt';
PROC TABULATE DATA= class.crew FORMAT=dollar12.;
WHERE Location IN ('Cary','Frankfurt');
CLASS Location JobCode;
VAR Salary;
TABLE JobCode, Location*Salary*mean;
RUN;
41. The REPORT procedure
REPORT procedure combines features of the
PRINT, MEANS, and TABULATE procedures.
It enables you to
create listing reports
create summary reports
enhance reports
request separate subtotals and grand totals
42. The REPORT procedure
Example
PROC REPORT DATA =class.crew nowd HEADLINE HEADSKIP;
COLUMN JobCode Location Salary;
DEFINE JobCode / GROUP WIDTH= 8 'Job Code';
DEFINE Location / GROUP 'Home Base';
DEFINE Salary / FORMAT=dollar10. 'Average Salary‘ MEAN ;
RBREAK AFTER / SUMMARIZE DOL;
RUN;
Editor's Notes
- SAS use data libraries to store data sets.
- You can think of a SAS data library as a drawer in a filling cabinet and a SAS data set as one of the file folders in the drawer.
- The Work library is temporary. When SAS is closed, all the datasets in the Work library are deleted. if you want to save a dataset to continue to work with it later, create a permanent SAS dataset via a library.
You identify SAS data libraries by assigning each a library reference name (libref).
The name must be 8 characters or less, must begin with a letter or underscore and the remaining characters must be letters, numbers, or underscores.
When you have multiple observations per line of raw data, you can use double railing at signs (@@) at the end of your INPUT statement.
Create our own custom formats when you use a lot of coded data.
Formats can remind you of the meaning behind the category. Note that formats do not change the actual value of the variable, just how it’s displayed.
If the format is for character data, it must start with a $
the keyword NPP. or NQQ., which can be used with any of the preceding variables to construct normal P-P or Q-Q plots,
Binary responses (for example, success and failure), and ordinal responses (for example, normal, mild, and severe