However, if someone else wants to look at your data, the variable names aren't descriptive enough to impart their full meaning. At the time, it might be perfectly clear to you what the distinction is between the two variables. If you want to create a variable for their current age and also a variable for the age they were when they started at the college, you might name the variables “age” and “age_start” respectively. For example, in our sample data of students we have a variable for the student’s birth date and a variable for the student’s enrollment date. A label can provide information about a variable that you might not be able to incorporate into the variable name. User-defined Formats (adding value labels): How to define your own formats (covers value labels for numerically-coded categorical variables)īesides formatting a variable, adding a variable label is another way to make your dataset and output easier to read and interpret.Formats and Informats: How to apply SAS's built-in formats (covers most quantitative numeric variables and character variables).Variable formats are detailed enough that we've split them into their own tutorials. Value labels for numerically-coded categorical variables (e.g., 1 = Male, 2 = Female)įormats are crucial for helping readers understand your data and your output.Inclusion of commas, dollar signs, or other "prettifying" marks for numeric variables.Number of decimal places to show for numeric variables (e.g., 1 versus 1.00).Variable formatĪ variable's format describes how it should be displayed in the SAS output. The default number of bytes for both numeric and character variables is 8. The "length" of a variable in SAS corresponds to the number of bytes for storing variables (source: SAS 9.2 Language Reference: LENGTH Statement). For example, zip codes cannot be added or multiplied even though they are made up of numbers, but they are useful when treated as categories.) Missing values for character variables appear as a blank (""). (Numeric values can be treated as characters if the numbers are used as labels and would not be used for meaningful mathematical calculations. This can include letters, special characters (such as parentheses or pound signs), and even numbers. Missing values for numeric variables appear as a period (.).Ĭharacter variables (also known as string variables) contain information that the system recognizes as text. Additionally, date-time variables are also considered numeric in SAS. However, numeric variables can also be used as indicator variables to represent categorical data, especially if the categories are ordinal. Typically, these are variables that you’ll want to perform arithmetic calculations on, like addition and subtraction. Numeric variables are variables that store numbers. In SAS, there are two types of variables: numeric and character. Additionally, it is also good practice to assign a label to each of your variables. Variable labels provide information about a variable that might not fit into the variable name itself. When possible, avoid generic variable names like x1 that don't provide any information about what the variable represents. It is good practice to give your variables descriptive yet succinct names. Instead, use “FirstName” or "first_name". For example, you cannot name a variable “First Name” because SAS will not recognize the blank. Numbers can be used after the first character. The name can start with a letter or an underscore (_), but cannot start with a number. The name cannot contain more than 32 characters.When naming a variable in SAS, there are a few rules you must follow: Variable names are just that: they are a name used to refer to a variable in a dataset. In a SAS dataset, variables themselves have five important properties: name, type, length, format, and label. For example, the rows (observations) in your data set might represent people, and the columns in your data set would contain characteristics about the people (like gender, age, height, etc). Variables correspond to characteristics of the data that are documented for all (or most) of the observations. A SAS dataset consists of columns of variables and rows of observations.
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