That is the difference between numeric and character variables. Because it is a character variable, SAS will know you can't calculate the average of sex. SAS knows that the sex variable is character because of the quotes. So this line sets the character variable sex to the letters Male: Every statement in SAS ends in a semicolon. To examine or change values of a character variable you will use quotes. SAS can not do math on character variables. A missing number in a numeric variable is shown as just a decimal place and a missing character variable is displayed as an empty cell in the grid. When you load data into SAS it will guess if the variable is character or numeric based on what it sees. This “camel” case style makes it relatively easy to decode the meaning of variables like dateOfRadiation. Because SAS does not want you to use spaces in the variable names I suggest you make the first letter of each word, after the first word, in a variable uppercase. While SAS can be flexible about the names you use for your variables, begin variable names, with a letter of the alphabet and include nothing other than letters or numbers in the names. The columns holding the characteristics you are assessing (i.e., the variables) can be either numeric or character types and should have names that are reasonably short (SAS will let you use 32 letters but 5 to 15 letters are usually enough to be descriptive). Those features which you are measuring are called variables. That is, you typically will have one line per subject (or one line per patient visit) and one column for each thing you are assessing. These notes will appear as green text between * or between /* */.ĭatasets look like a grid of data in a Microsoft Excel workbook. You will also see comments added to SAS programs.
#SAS LIBNAME FOR EXCEL MAC CODE#
SAS does not pay attention to capitalization or spacing (unless the code is inside of quotes or if spaces create new words) but you will see code with white spaces added using the space key or tab to make the programs easier to read.
Real projects can use hundreds or even many thousands of lines to do complex tasks. The details will be explained later but below I am making a temporary dataset called older, which only includes the people in a permanent dataset called rhinitis who are greater than 80 years old: SAS requires only a few lines to create a new dataset.
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