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The SUBSTR function is used for extracting a string or replacing the contents of a character value.
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TRANSLATE Function: With this function, the characters specified in a string are replaced by the characters specified by users.
PROC SORT sorts SAS data sets by variables so that a new data set can be prepared for further use.
PROC UNIVARIATE is used for the elementary numeric analysis, and it examines how data is distributed.
The term ‘append’ means adding at the end.
In SAS, we can say that the APPEND procedure is a procedure adding one SAS data set to another SAS data set.
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For analyzing data, the BMPD procedure is used.
RUN-group processing is used to submit a PROC step using the RUN statement without ending the procedure.
The BY statement is used by the BY-group processing so that it can process data that are indexed, grouped, or ordered based on variables.
The CALENDAR procedure shows data in a monthly calendar format from a SAS data set.
UPCASE and LOWCASE, known as the character functions, are used for character handling in SAS.
The DIVIDE function is used to return the division result.
The BOR function is a bitwise logical operation used to return bitwise logical OR between two statements.
CALL PRXFREE routine is used for character string matching and for the allocation of free memory for Perl regular expression.
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CALL PRXCHANGE routine is used for performing the replacement of pattern matching.
The ANYDIGIT function is used to search for the first occurrence of a digit (numeral) in a string. It returns the position of the digit. If no digit is found, it returns a ‘0’. By using an optional parameter, the ANYDIGIT function can begin the search at any given position in the string.
The character-value is any SAS character expression, and the term start is an optional parameter that specifies the position within the string to begin the search.
The character or numeric variables that are specified can be assigned missing values through the CALL MISSING routine.
It is used for assigning an ALTER password, which will stop users from changing the file.
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It is used for compressing the data into new output.
Instructions used by SAS for writing data values are known as Formats.
Variable formats are handled by PROC COMPARE as it is used for comparing unformatted values.
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It provides IPv6 support, new TrueType fonts, extended time notations, the restart mode, universal printing, the checkpoint mode, and ISO 8601 support.
By using $BASE64X encoding, the character data is converted into ASCII text.
The VFORMATX function is used to return the format that is assigned with the value of a given statement.
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With the help of the STD function, the standard deviation will be returned for the nonmissing statements.
Debugging is a technique for testing the program logic, and this can be done with the help of Debugger.
When a data set is closed, its tape positioning is defined by FILECLOSE.
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ODS stands for the Output Delivery System.
CDISC stands for Clinical Data Interchange Standards Consortium.
The method used for copying blocks of data is defined as the block I/O method.
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The copy statement should be followed by an input data library and an output data library.
The max() function is used to return the largest value.
It is a function that provides a system error number.
SAS, i.e., Statistical Analysis System, is a combined set of software solutions that helps users analyze data.
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A SAS program consists of:
The main function of a DATA step is to create SAS data sets by manipulating data.
Program Data Vector (PDV) is the area of memory where data sets are created through the SAS system, one at a time. When a program is executed, an Input Buffer is created that reads data values and makes them assigned to their respective variables.
With WHERE statements, automatic conversions cannot be performed because WHERE statement variables exist in the data set.
Identical observations are checked and removed through the NODUP option. On the other hand, the NODUPKEY option checks for all BY variable values and if found, it will eliminate those.
PROC SUMMARY is the same as PROC MEANS, i.e., it will give descriptive statistics but will not give output as default. We have to give an option ‘print’, and then it will give the output.
PROC PRINT outputs a list of the values of some or all variables in a SAS data set. PROC CONTENTS tells the structure of the data set rather than the data values.
The functions of PROC GLM are covariance analysis, variance analysis, multivariate, and repeated analysis of variance.
Informat is an instruction that SAS uses to read data values. It is used to read or input data from the external files.
CATX syntax inserts delimiters, removes trailing and leading blanks, and returns a concatenated character string.
PROC GPLOT identifies the data set that contains the plot variables. It has more options and, therefore, can create more colorful and fancier graphics.
By using the DESCENDING keyword in the PROC SORT code, we can sort in descending order.
A single dash specifies the consecutively numbered variables. A double dash specifies the variables available within the data set.
Example: Data Set: ID NAME B1 B2 C1 B3
Important points for running a SAS program are:
Input delimiters are DLM and DSD.
TRIM: TRIM removes the trailing blanks from a character expression.
Str1 = ‘my’;
Str2 = ‘dog’;
Result = TRIM (Str1)(Str2);
Result = ‘mydog’
Program Data Vector (PDV) is a logical area in memory.
Each package offers its own unique strengths and weaknesses. As a whole, SAS, Stata, and SPSS form a set of tools that can be used for a wide variety of statistical analyses. With Stat/Transfer, it is very easy to convert data files from one package to another in just a matter of seconds or minutes.
Therefore, there can be quite an advantage switching from one analysis package to another depending on the nature of our problem. For example, if we are performing analysis using mixed models, we might choose SAS, but if we are doing logistic regression we might choose Stata. Moreover, if we are doing an analysis of variance, then we might choose SPSS.
If we are frequently performing statistical analysis, it is strongly recommended to consider making each one of these packages part of our toolkit for data analysis.
SAS/ETS software provides tools for a wide variety of applications in business, government, and academia. Major uses of SAS/ETS procedures are economic analysis, forecasting, economic and financial modeling, time series analysis, financial reporting, and manipulation of time-series data.
The common theme relating to many applications of the software is time-series data. SAS/ETS software is useful whenever it is necessary to analyze or predict processes that take place over time or to analyze models that involve simultaneous relationships.
Although SAS/ETS software is most closely associated with business, finance, and economics, time-series data also arise in many other fields. SAS/ETS software is useful whenever time dependencies, simultaneous relationships, or dynamic processes complicate data analysis. For example, an environmental quality study might use SAS/ETS software’s time-series analysis tools to analyze pollution emissions data. A pharmacokinetic study might use SAS/ETS software’s features for nonlinear systems to model the dynamics of drug metabolism in different tissues.
To create a compressed SAS data set, we use the COMPRESS=YES option as an output DATA set option or in an OPTIONS statement. Compressing a data set reduces its size by reducing repeated consecutive characters or numbers to 2-byte or 3-byte representations.
To uncompress observations, we must use a DATA step to copy the data set and use the option COMPRESS=NO for the new data set.
The advantages of using a SAS compressed data set are that there would be reduced storage requirements for the data set and only fewer input/output operations would be necessary to read from and write to the data set during processing.
The disadvantages include not being able to use the SAS observation number to access an observation. The CPU time required to prepare compressed observations for input/output observations is increased because of the overhead of compressing and expanding the observations. We have to remember that if there are a few repeated characters, a data set can occupy more space in the compressed form than in the uncompressed form, due to the higher overhead per observation. For more details on SAS compression see SAS Language: Reference, Version 6, First Edition, Cary, NC: SAS Institute Inc., 1990.
When we are working with large data sets, we can do the following steps to reduce space requirements:
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