How to Learn Data Manipulation Techniques Using SAS Programming 2
Data manipulation is the process of transforming, summarizing, and combining data to create new information and insights. Data manipulation techniques are essential for data analysis, reporting, and decision making. SAS is a powerful software that
provides many tools and functions for data manipulation. One of the best ways to learn data manipulation techniques using SAS is to take the course SAS Programming 2: Data Manipulation Techniques.

Sas Programming 2 Data Manipulation Techniques Pdf 17
Download https://t.co/2pjQ0tRQWB



This course is designed for business analysts and SAS programmers who need to learn how to use the SAS DATA step and procedures to access, transform, and summarize data. The course covers topics such as:


Understanding and controlling DATA step processing
Creating an accumulating column and processing data in groups
Manipulating data with functions
Converting column type
Creating custom formats
Concatenating and merging tables
Processing repetitive code
Restructuring tables

The course also prepares you for the following certification exams:


SAS Certified Professional: Clinical Trials Programming Using SAS 9.4
SAS Certified Specialist: Base Programming Using SAS 9.4
SAS Certified Associate: Programming Fundamentals Using SAS 9.4

The course duration is 2 days for classroom delivery or 5 half-day sessions for live web delivery. The course requires at least six months of experience writing SAS programs or completion of the SAS Programming 1: Essentials course. You can find more
information about the course content, prerequisites, schedule, and registration at [1] or [2].

If you want to learn data manipulation techniques using SAS, this course is a great opportunity to enhance your skills and knowledge. You will be able to apply what you learn to your own data analysis projects and achieve better results.


To give you a glimpse of what you can learn in the course, here are some examples of data manipulation techniques using SAS:



Creating an Accumulating Column
An accumulating column is a column that contains the cumulative sum of another column. For example, if you have a column that shows the monthly sales of a product, you can create an accumulating column that shows the total sales up to each month. This
can help you see the growth trend and compare different periods. To create an accumulating column using SAS, you can use the RETAIN statement and the SUM function. For example:

data sales;
input month $ sales;
retain total_sales 0; /* initialize the accumulating column */
total_sales = sum(total_sales,sales); /* update the accumulating column */
datalines;
Jan 100
Feb 120
Mar 150
Apr 130
May 160
;
run;

proc print data=sales;
run;

The output will look like this:

month sales total_sales
Jan 100 100
Feb 120 220
Mar 150 370
Apr 130 500
May 160 660

Manipulating Data with Functions
SAS provides many functions that can help you manipulate data in various ways. For example, you can use functions to extract or modify parts of a string, perform calculations, convert data types, handle missing values, and more. Some of the common
functions are:


SUBSTR: extracts a substring from a string
SCAN: extracts a word from a string
CAT: concatenates strings
TRIM: removes trailing blanks from a string
UPCASE: converts a string to uppercase
LOWCASE: converts a string to lowercase
INPUT: converts a character value to a numeric value
PUT: converts a numeric value to a character value
INT: returns the integer part of a numeric value
ROUND: rounds a numeric value to a specified number of decimal places
MEAN: returns the mean of a list of numeric values
SUM: returns the sum of a list of numeric values
MAX: returns the maximum of a list of values
MIN: returns the minimum of a list of values
N: returns the number of non-missing values in a list
NMISS: returns the number of missing values in a list
COALESCE: returns the first non-missing value in a list
.: represents a missing numeric value
"": represents a missing character value

For example, suppose you have a data set that contains the names and salaries of some employees. You can use functions to manipulate the data as follows:

data employees;
input name $ salary;
datalines;
John Smith 50000
Mary Jones 45000
Peter Lee 55000
Jane Doe .
;
run;

data employees2;
set employees;
first_name = scan(name,1); /* extract the first name */
last_name = scan(name,2); /* extract the last name */
initial = upcase(substr(first_name,1,1)); /* get the first initial */
salary = input(put(salary,best12.),comma12.); /* add commas to salary */
run;

proc print data=employees2;
run;

The output will look like this:

name salary first_name last_name initial
John Smith 50,000 John Smith J
Mary Jones 45,000 Mary Jones M
Peter Lee 55,000 Peter Lee P
Jane Doe . Jane Doe J

35727fac0c

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)