SAS FAQ. Sometimes, you might need to know the path to the temporary work directory that SAS uses. There are at least two ways to do. SAS (previously "Statistical Analysis System") is a statistical software suite developed by SAS Institute for data management, advanced analytics. Offered by SAS. This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is.|#|
A better way to do this would be through the use of a DO group which allows for multiple statements. This is followed by a set of statements to be executed. Functions accepts arguments and then produce a particular value numeric or character based on those arguments. Arguments are enclosed within parentheses and each argument is separated by a comma.
SAS has a wide array of different functions depending on the needs of the user and can be used in Data step. We will cover a few examples of basic mathematical functions, common date functions, and some additional functions useful for specific data management tasks.
There are many different way of creating the sum and mean variables that we need. Alternatively, we can use the SUM function, the arguments are the variables you wish to sum together. The difference between using the function versus manually adding together each variable is the treatment of missing. If we use the SUM function, any missing values will be treated as though they were zero, and the new variable will be equal to missing only if all of the variables listed are missing.
Which method is most appropriate depends on the situation and what you are trying to achieve. Last, if you have a lot of variables to be summed you can specify a SAS variable list. This syntax works since the variable being specified are consecutive in the data.
We also use similar syntax to demonstrate how to estimate the average or mean budget variables. SAS has a number of additional mathematical functions including absolute value, maximum, minimum and square root that can be used in a similar manner.
One of the more challenging data types to deal with in any data analysis package are date values. Thankfully, SAS has some built-in functions that can assist users with managing this data type. SAS stores date information as numeric values representing the number days before or after Jan 1, SAS can also recognize 2 or 4 digit year values.
The third argument specifies the method for calculating the days. INTCK counts the number of intervals between two dates, in our example we asked SAS to output the number of years between an employees data of birth and when they were hired which we would be equivalent to an employees age at the time of hire.
Here we want to calculate when an employee with be eligible for their next bonus. In our example, employees are eligible 6 months after their hire date. As mentioned before, SAS stores date information as numeric information in days. Thus if you do not format date with a format statement discussed further in the next section , it will display as just a number. SAS includes several other types of functions designed for specific types of needs many of these functions are helpful for data management of character or string information.
You will notice a few things about the output above. The SUBSTR function takes 3 arguments, the name of variable with the information you want to extract, the character position you want to start from and then the number of character to extract. Unfortunately, this means whatever value we are extracting must always be of the same length. What if we have product names of different lengths. In the previous examples, we were extracting values from a string, but what if we wanted to combine string variables.
A useful function would be CATX. Additionally, the function also requires the specification of variables that includes information on the delimiter of choice. In the first example, the delimiter is just a blank while in the second example the delimiter is a comma. Note: The order in which the variables are specified in the CATX function governs the order in which they will be combined. The are many instances when having your data sorted in a particular way will be helpful for visualizing your data.
Additionally, certain types of data management needs like merging datasets or grouping observations by a particular characteristic require sorting. Sorting data by a single variable in SAS is the most simple. By default SAS sorts data ascending with the smaller values first. One data management task that requires proper sorting is merging. Merging involves matching one observation in a dataset to one observation One-to-One or multiple observations One-to-Many in a second dataset.
In order for this to be done properly in SAS, the datasets to be merged must be sorted by the same variable s. In the example below, we will merge a dataset that has employee payroll information with a second dataset with employee addresses. A few things to take note of. In general, you do not want to merge datasets that include variables with the same names.
SAS can only use one set of values and will arbitrarily choose the values from the last dataset read. Thus, you should rename variables before attempting the merge. The variable after the BY statement is the same unique identifier that we just used for sorting. Below is a subset of variables from the newly merged data. The first set of data provides information on order and delivery dates.
In the second set of data we have information on the product or products ordered. Below is our syntax to merge the two data sets. Notice we also used a KEEP statement. This allows us to merge the data and control the number variables present in the final merged dataset. Above is a selected portion of the merged data. SAS executed the merge without an error but it appears that we have some missing data as a result. This is an indication that we perhaps have some non-matches. There are a couple ways you can deal with this issue.
First, you can leave the data as is with missing information for non-matches. Alternatively, you can choose to control the observations output to the new merged data set by using the IN option on the MERGE statement. The IN option a creates variable indicating which dataset s contributed to forming the observation in the final merge dataset. It is a temporary variable used in the merging process that is given a 0 value if did not provide information or a 1 if it did.
We could then use this variable to select observations in the newly merged data that come from one dataset or both. Thus, you will not end up with any missing values. Now, one thing you generally want to avoid is many-to-many merges. When neither dataset has a unique identifier that will allow for proper matching of records the result is a somewhat unpredictable and often undesirable assorting of observations. Appending or concatenating observations is the process of adding rows or observations to a dataset as opposed to merging which adds variables.
This can also be accomplished using a Data step. SAS will stack the columns to together by matching the names across datasets. We will append three datasets that include information on orders from 3 consecutive months July-September in Below is snapshot of the first two records from each of the datasets to be appended. The syntax to conduct the append is quite simple.
All you have to do is list the datasets to be appended on the SET statement line. The ordering and the number of datasets does not matter.
This example worked perfectly because the three datasets shared the exact same variables. But what happens when you append the datasets that do not contain the same variables? Below we have two sets of data, one for Eclipse shoes and one for Tracker Shoes. The append still executes without error. As a researcher it is important to know how to manipulate and change your output to convey important information to your audience.
We will begin by exploring some ways of enhancing the output from this procedure. Whenever you are presenting tables of information, the first item most people look for is a title.
All currently-employed faculty and staff, as well as currently-enrolled students are eligible to request a copy of SAS at no charge. SAS licenses must be renewed yearly.
Note that SAS is only available for Windows operating systems. Please contact your local support person to receive a copy of the software or to renew your existing license. Visit software. Mac users who do not wish to use virtualization software to run the Windows operating system on their Macs may be interested in SAS OnDemand for Academics. Search this Guide Search. Why use SAS?
Enormous array of statistical methods and algorithms, especially for advanced statistics. Highly customizable analysis options and output options. Publication-quality graphics with ODS.
Widely used in many fields, including business and medicine. Large, active online community. What file types are typically associated with SAS? How can I access SAS software? While processing the data, this analytics software is considered as a very helpful tool for generating information. Again, due to the simple interface, the business analyst is not required to learn to programme and it makes the work very easy and efficient for them as well.
SAS applications provide us a platform where we can work on millions of different data to transform into business useful information. The usage of this system is not limited to any particular industry. It works well in order to process data that belong to any industry.
For example, it could be used in Finance with the same efficiency as it could be used in the human resource industry. Any person regardless of the technical background can work with it. The interface is very simple in the analytics software that makes it more convenient for the users to interact with. To write a program in SAS, one must have to be aware of the fundamentals of programming. There has to be used the same way as the keywords are used in other languages.
The basic understanding of coding will be many files to write your code in the editor tab. In my understanding, till now you should be knowing about why you should use this system. But again to refresh your learning or to give you a heads up, we can repeat the notion again.
So SAS can be used when we have a need to process the data that is available with a lot of variety and also in a very large amount. It uses the data and helps to convert it into some useful information. Also, if compared with other methods of working with data, SAS is always preferred due to its tendency to work with the pool of data.
Microsoft Excel could also be used sometimes while working with less data but when it comes on handling records of millions of customers, using their data to generate the report, SAS is the best option available to us. The SAS is used vigorously to enhance the decision making the quality of any business. Anyone with a good knowledge of the analysis system could be a business analyst and grow their career in multinational organizations. This is a hypothetical question and totally based on what a person wants, knowledgeable and willing to learn.
This system is open for all kinds of people regardless of their technical background. If someone wants to work in data analysis then they can make the use of the simple graphics interface in order to process data.
While someone who has programming skills and uses to develop their customized analysis solution. This system gives the opportunity to a broad set of people to work with it. Also, those people who have worked in other technologies and now want willing to switch in this system can also be a part of it due to the simple interaction interface it has. It simply makes the work very easy for all of us. The SAS provides a very easy mechanism to work with a huge amount of data.
SAS software is available in the market that helps to implement this system. This has been a guide to What is SAS. Here we discussed the working, definition, skills, and advantages of SAS. You can also go through our other suggested articles to learn more —.
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