[EBOOK] The Little SAS Book: A Primer, Fifth Edition Full PDF,Living in the Light: A guide to personal transformation
12/10/ · The fifth edition of The Little SAS Book has been completely updated to reflect the new default output introduced with SAS A classic that just keeps getting better, The Little 22/04/ · Hello Friends, If you want to download free Ebook, you are in the right place to download Ebook. Ebook The Little SAS Book: A Primer, Fifth Edition EBOOK ONLINE 26/03/ · Hello Guys, If you want to download free Ebook, you are in the right place to download Ebook. Ebook The Little SAS Book: A Primer, Fifth Edition EBOOK ONLINE Introducing SAS Software x About This Book xi About These Authors xv Chapter 1 Getting Started Using SAS Software 1 The SAS Language 2 SAS Data Sets 4 DATA and This edition of The Little SAS Book is designed to work with all of the interfaces that are included with Base SAS: SAS Studio, SAS Enterprise Guide, and the SAS windowing environment ... read more
The following PROC CONTENTS produces a report about the CARS data set in the SASHELP data library. Because the SASHELP library is defined by default, no LIBNAME statement is needed. s s7bdat Release Created 9. Engine Size L Length IN MPG City MPG Highway 1 Make 2 Model 4 Origin 3 Type 13 Weight 14 Wheelbase Char 13 Char 40 Char 6 Char 8 Num 8 Num 8 Sort Information Sortedby Make Type Validated YES Character Set ANSI The output starts with information about your data set and then describes each variable. In fact, entire books have been written about exchanging data between SAS and Excel. SAS Studio, SAS Enterprise Guide and the SAS windowing environment each have point-and-click windows that will import Excel files for you, but writing a program gives you more control and is easier to repeat. PROC IMPORT will scan your Excel worksheet and automatically determine the variable types character or numeric , will assign lengths to the character variables, and can recognize most date formats.
Also, if you want, you can use the first row in your worksheet for the variable names. DBMS identifiers There are several DBMS identifiers you can use to read Excel files. Three commonly used identifiers are: EXCEL, XLS, and XLSX. In the UNIX operating environment, use the XLS identifier for older style files. xls extension , and the XLSX identifier for newer style files. xlsx extension. In the Windows operating environment, in addition to the XLS and XLSX identifiers, you can use the EXCEL identifier to read all types of Excel files. The EXCEL identifier uses different technology to read files than do the XLS and XLSX identifiers, so the results may be different. By default, the XLS and XLSX identifiers look at more data rows to determine the column type than does the EXCEL identifier.
Not all of these identifiers may work for you if your Windows computer has a mixture of bit and bit applications. In addition, some computer configurations may require that a PC Files Server be installed. The PC Files Server uses the EXCELCS identifier. See the SAS Documentation for more information. If you do not want this, then you can add the following statement to the procedure and SAS will give your variables names like A, B, C, and so on. For each type of tree the file includes the scientific and common names, maximum height, age at first blooming when planted from seed, whether evergreen or deciduous, and color of flowers. The following program reads the Microsoft Excel file using the IMPORT procedure with the XLSX DBMS identifier and the REPLACE option, and creates a permanent data set named MAGNOLIA. You can view the data in the Output Data tab in SAS Enterprise Guide or SAS Studio, or in the Viewtable window in the SAS windowing environment.
ScientificNam e CommonName 1 Southern Magnolia M. grandiflora 2 M. campbellii 3 Lily Magnolia M. liliiflora 4 MaxHeigh t 5 Star Magnolia M. stellata AgeBloo m 80 15 80 20 12 M. soulangiana Saucer Magnolia 25 10 Notice that the variable names were taken from the first row in the spreadsheet. If you have spaces or special characters in the first row, see Section 3. You should always check the SAS log when you create data sets to make sure nothing went wrong. These notes tell you that the MAGNOLIA data set has five observations and six variables. NOTE: The import data set has 5 observations and 6 variables. NOTE: SASFILES. MAGNOLIA was successfully created. The XLSX LIBNAME engine goes a step further. With the XLSX LIBNAME engine, you can read from and write to an Excel file without converting it to a SAS data set.
This engine works for files created by any version of Microsoft Excel or later on the Windows or UNIX operating environments. You must have SAS 9. With this engine, you can read an existing worksheet, replace a worksheet, or add a new worksheet, but you cannot update individual values inside a worksheet. The XLSX LIBNAME engine uses the first line in your data file for the variable names, scans each full column to determine the variable type character or numeric , assigns lengths to character variables, and recognizes dates, and numeric values containing commas or dollar signs. While the XLSX LIBNAME engine does not offer many options, because you are using an Excel worksheet like a SAS data set, you can use many standard data set options. See Section 6. Here is the general form of a LIBNAME statement with the XLSX engine option: LIBNAME libref XLSX 'filename'; Notice that the only difference between this LIBNAME statement and one used to access a SAS data library Section 2.
The libref is a name you make up and must follow the rules for names of SAS data libraries eight characters or fewer, start with a letter or underscore; and contain only letters, numerals or underscores. The filename is the name used by your operating environment. Keep in mind that, unlike some other LIBNAME engines, the XLSX LIBNAME engine only locks your Excel file while it actively accesses it. The file quickly becomes available to other people and processes that can read and edit it. You do not need to release the file, but if you know you are done with it, it is a good practice to clear the libref with this statement: LIBNAME libref CLEAR; Example This example uses the same Excel spreadsheet as the previous section. The spreadsheet contains data about magnolia trees: the scientific and common names, maximum height, age at first blooming when planted from seed, whether evergreen or deciduous, and color of flowers.
With the XLSX LIBNAME engine, SAS can read the spreadsheet directly, without first converting it to a SAS data set. Here is a PROC PRINT that prints the data from the Excel spreadsheet. A simple PROC PRINT like this one prints the values for all variables and all observations. sheet1; TITLE 'PROC PRINT of Excel File'; RUN; Here are the results of the PROC PRINT. Notice that the variable names were taken from the first row in the spreadsheet. PROC PRINT of Excel File ScientificNam Obs e CommonName MaxHeigh t AgeBloom 1 M. campbellii 3 M. liliiflora Southern Magnolia 80 5 M. stellata Star Magnolia 20 12 4 M. soulangiana Saucer Magnolia 15 80 Lily Magnolia 25 10 If you want to convert an Excel spreadsheet to a SAS data set, you can do that too. Here is a DATA step that reads the Excel spreadsheet using a SET statement in a DATA step, which is covered in more detail in Section 3. magnolia; SET exfiles.
sheet1; RUN; Here is the MAGNOLIA data set: ScientificNam e CommonName 1 Southern Magnolia M. soulangiana Saucer Magnolia 25 10 The SAS log shows that no observations or variables were lost: NOTE: The import data set has 5 observations and 6 variables. NOTE: There were 5 observations read from the data set EXFILES. NOTE: The data set SASFILES. MAGNOLIA has 5 observations and 6 variables. Delimited files are raw data files that have a special character separating data values. Many types of software can save data as delimited files, often with commas or tab characters for delimiters. The IMPORT procedure for reading delimited files is part of Base SAS in the UNIX and Windows operating environments. You can also read delimited files with a DATA step Section 2. PROC IMPORT will scan your data file the first 20 rows by default and automatically determine the variable types character or numeric , will assign lengths to the character variables, and can recognize some date formats.
PROC IMPORT will treat two consecutive delimiters in your data file as a missing value, will read values enclosed in quotation marks, and assign missing values to variables when it runs out of data on a line. Also, if you want, you can use the first line in your data file for the variable names. SAS will determine the file type based on the extension of the file. Type of File Extension DBMS Identifier Comma-delimited. csv CSV Tab-delimited. If the data do not start in the first line of the file, use the DATAROWS statement. If the delimiter is not a comma, tab, or space, use the DELIMITER statement. Default is 1. Default is space. Default is YES. If NO, then variables are named VAR1, VAR2, VAR3, and so on.
Default is Jerry keeps records of the number of customers present for each band. The data are the band name, and the number of customers at 8 p. When a data value contains the delimiter, then the value must be enclosed in quotation marks. Notice that PROC IMPORT used the first row of data for variable names. BandName 1 2 3 5 Catalina Converts 63 17 28 34 The Silveyville Jazz Quartet 45 Stop, Drop, and Rock-N-Roll 4 Awesome Octaves EightPM NinePM TenPM Lupine Lights 62 38 30 The minimum record length was The maximum record length was NOTE: The data set WORK. MUSIC has 5 observations and 5 variables. If your data are already stored as SAS data sets, as Excel spreadsheets, as CSV or tab-delimited files, or in a database such as Oracle, then you can probably skip this topic. However, if your data are in raw data files also referred to as text, ASCII, sequential, or flat files , then you can use the power and flexibility of the DATA step to read your data.
A raw data file can be viewed using simple text editors or system commands. On personal computers, raw data files will either have no program associated with them, or they will be associated with simple editors like Microsoft Notepad. In some operating environments, you can use commands to list the file, such as the cat or more commands in UNIX. Spreadsheet files are examples of data files that are not raw data. It may cause your computer to beep and chirp, making you wish you had that private office down the hall. It looks nothing like the nice neat rows and columns you see when you use your spreadsheet software to view the same file. The first step toward reading raw data files is telling SAS where to find the raw data. Your raw data may be either internal to your SAS program also called instream , or in a separate file.
Either way, you must tell SAS where to find your data. Internal raw data If you type raw data directly into your SAS program, then the data are internal to your program. You may want to do this when you have small amounts of data, or when you are testing a program with a small test data set. Use the DATALINES statement to indicate internal data. The DATALINES statement must be the last statement in the DATA step. All lines in the SAS program following the DATALINES statement are considered data until SAS encounters a semicolon. The semicolon can be on a line by itself or at the end of a SAS statement that follows the data lines. Any statements following the data are part of a new step. The CARDS statement is synonymous with the DATALINES statement. The following SAS program illustrates the use of the DATALINES statement. The INPUT statement is discussed in Sections 2.
External raw data files Usually you will want to keep data in external files, separating the data from the program. This eliminates the chance that you might accidentally alter the data when you are editing your SAS program. Use the INFILE statement to tell SAS the filename and path, if appropriate of the file containing the data. The INFILE statement follows the DATA statement and must precede the INPUT statement. After the INFILE keyword, enclose the file path and name in quotation marks. Here is the general form for different operating environments. If you are using SAS University Edition or SAS OnDemand for Academics, then use the Linux style of statement. DAT'; Suppose the following data are in a file called President. Always check this information as it could indicate problems.
The following is an excerpt from the SAS log after reading the external file. A simple comparison of the number of records read by the INFILE statement four in this case with the number of observations also four in the SAS data set can tell you a lot about whether SAS read your data correctly. The minimum record length was 9. List input is an easy way to read raw data into SAS, but with ease come a few limitations. By default, you must read all the data in a record—no skipping over unwanted values. Any missing data must be indicated with a period. Character data, if present, must be simple—no embedded spaces, and no values greater than 8 characters in length. If the data file contains dates or other values that need special treatment, then list input would not be appropriate. This may sound like a lot of restrictions, but a surprising number of data files can be read using list input.
The INPUT statement, which is part of the DATA step, tells SAS how to read your raw data. To write an INPUT statement using list input, simply list the variable names after the keyword INPUT in the order they appear in the data file. Generally, variable names must be 32 characters or fewer, start with a letter or an underscore, and contain only letters, underscores, or numerals. Leave at least one space between names, and remember to place a semicolon at the end of the statement. If your data file does not quite satisfy the rules for list input, you will be glad to know that there are various ways to work around the limitations. Many of those are discussed later in this chapter. One of the most common problems is character variables with lengths longer than 8. You can tell SAS to use a longer length with a LENGTH statement. So if a LENGTH statement comes before an INPUT statement, then SAS will use that length.
Example Your hometown has been overrun with toads this year. A local resident, having heard of frog jumping in California, had the idea of organizing a toad jump to cap off the annual town fair. If the toad is disqualified for any jump, then a period is used to indicate missing data. Here is what the data file ToadJump. dat looks like: Lucky 2. This data file does not look very neat, but it does meet most of the requirements for list input: no embedded spaces, all values are separated by at least one space, and missing data are indicated by a period. However, there is one problem. The name of one toad, Toadzilla, is longer than 8 characters. dat'; INPUT ToadName Weight Jump1 Jump2 Jump3; RUN; The LENGTH statement creates a variable named ToadName that is character indicated by the dollar sign and has a length of 9. Then the INPUT statement reads the variables ToadName, Weight, Jump1, Jump2, and Jump3. All the other variables are numeric.
It is always important to check data sets you create to make sure they are correct. Here is the TOADS data set. ToadName 1 Lucky Weight Jump1 2. TOADS has 6 observations and 5 variables. You can use list input to read files with other delimiters too, such as commas or tabs. Standard numeric data contain only numerals, decimal points, plus and minus signs, and E for scientific notation. Numbers with embedded commas or dates, for example, are not standard. The street Martin Luther King Jr. Boulevard should be read as one variable not five, as it would be with list input. Data which can be read with column input can often also be read with formatted input discussed in Sections 2. The columns are positions of the characters or numbers in the data line and are not to be confused with columns like those you see in a spreadsheet. Repeat this for all the variables you want to read. The third variable, Height, is also numeric and is in columns 14 through Example The local minor league baseball team, the Walla Walla Sweets, is keeping records about concession sales.
A ballpark favorite are the sweet onion rings which are sold at the concession stands and also by vendors in the bleachers. The ballpark owners have a feeling that in games with lots of hits and runs more onion rings are sold in the bleachers than at the concession stands. They think they should send more vendors out into the bleachers when the game heats up, but need more evidence to back up their feelings. For each home game they have the following information: name of opposing team, number of onion ring sales at the concession stands and in the bleachers, the number of hits for each team, and the final score for each team. The following is a sample of the data file named OnionRing. All the values line up in columns, the team names have embedded blanks, missing values are blank, and in one case there is no space between data values. Those Gilroy Garlics fans must really love onion rings. The variables CSales and BSales read the concession and bleacher sales in columns 21 through 24 and 25 through 28, respectively.
The number of hits for the home team, OurHits, and the visiting team, TheirHits, are in columns 29 through 31 and 32 through 34, respectively. The number of runs for the home team, OurRuns, is in columns 35 through 37, while the number of runs for the visiting team, TheirRuns, is in columns 38 through Here is the SALES data set. VisitingTeam 1 3 35 These notes appear in the log: 1. SALES has 4 observations and 7 variables. For example, we humans easily read the number 1,, as one million and one, but your trusty computer sees it as a character string. While the embedded commas make the number easier for us to interpret, they make the number impossible for the computer to recognize without some instructions.
In SAS, informats are used to tell the computer how to interpret these types of data. Informats are useful anytime you have nonstandard data. Numbers with embedded commas or dollar signs are nonstandard data. Other examples include data in hexadecimal or packed decimal formats. SAS has informats for reading these types of data as well. Dates are perhaps the most common nonstandard data. Using date informats, SAS will convert conventional forms of dates like or 31OCT23 into a number, the number of days since January 1, This number is referred to as a SAS date value.
Why January 1, ? Who knows? Maybe was a good year for the SAS founders. This turns out to be extremely useful when you want to do calculations with dates. For example, you can easily find the number of days between two dates by subtracting one from the other. There are three general types of informats: character, numeric, and date. A table of selected SAS informats appears in the next section. d Date informatw. The period is an important part of the informat name. Without a period, SAS may try to interpret the informat as a variable name, which by default, cannot contain any special characters except the underscore. d, which reads standard numeric data. Use informats by placing the informat after the variable name in the INPUT statement; this is called formatted input. Age 3. Height 5. Now the starting point for the second variable is column 11, and SAS reads values for Age in columns 11 through The values for the third variable, Height, are in columns 14 through Those five columns include the decimal place and the decimal point itself The values for the last variable, BirthDate, are in columns 19 through Example This example illustrates the use of informats for reading data.
The data contain the results from a pumpkincarving contest. Alicia Grossman 13 c 7. dat, and uses a LIBNAME statement to create a permanent SAS data set named CONTEST. dat using formatted input; DATA pump. Score1 Score2 Score3 4. Variable Age has an informat of 3. Variable Type is character, and it is one column wide. Variable Date has an informat MMDDYY The remaining variables, Score1 through Score3, all require the same informat, 4. Putting the variables and the informat in separate sets of parentheses saves typing. Here is the CONTEST data set. Name 1 9 D 10 C 6 D Jose Martinez 6 13 c Lori Newcombe 5 Elizabeth Garcia 4 Matthew Lee 3 Type Alicia Grossman 2 Age 7 d Brian Williams 11 C Date Score1 8. Section 3. Score2 7. CONTEST has 6 observations and 7 variables.
Reads character data—trims leading blanks 1—32, Date, Time, and Datetime1 ANYDTD TEw. Reads dates in various date forms 5—32 DATEw. Reads dates in form: ddmmmyy or ddmmmyyyy 7—32 DATETIME Reads datetime values in the w. form: ddmmmyy hh:mm:ss. ss 13—40 DDMMYYw Reads dates in form: ddmmyy or 6— ddmmyyyy JULIANw. Reads Julian dates in form: yyddd or yyyyddd 5—32 MMDDYYw Reads dates in form: mmddyy or 6— mmddyyyy STIMERw Reads time in form: hh:mm:ss. ss 1— or mm:ss. ss, or ss. ss TIMEw. d Reads time in form: hh:mm:ss. ss 5—32 or hh:mm using a hour clock Numeric COMMAw Removes embedded commas 1— d but reverses w. d role of comma and period 1—32 PERCENT w. Converts percentages to proportions 1—32 w. d Reads standard numeric data 1—32 1 SAS date values are the number of days since January 1, Time values are the number of seconds past midnight, and datetime values are the number of seconds past midnight January 1, d INPUT Time TIME8.
d 1, INPUT Income COMMA INPUT Value PERCENT5. d List style is the easiest; column style is a bit more work; and formatted style is the hardest of the three. However, column and formatted styles do not require spaces or other delimiters between variables and can read embedded blanks. Formatted style can read special data such as dates. Sometimes you use one style, sometimes another, and sometimes the easiest way is to use a combination of styles. SAS is so flexible that you can mix and match any of the input styles for your own convenience. Example The following raw data contain information about U. When SAS reads a line of raw data, it uses a pointer to mark its place, but each style of input uses the pointer a little differently. With list input, SAS automatically scans to the next non-blank field and starts reading. With column style input, SAS starts reading in the exact column you specify. But with formatted input, SAS just starts reading—wherever the pointer is, that is where SAS reads.
Sometimes you need to move the pointer explicitly, and you can do that by using the column pointer, n, where n is the number of the column SAS should move to. informat told SAS to read nine columns, and SAS did that even when those columns were completely blank. The column pointer, n, has other uses, too, and can be used anytime you want SAS to skip backward or forward within a line of data. You could use it, for example, to skip over unneeded data, or to read a variable twice using different informats. You need more tools in your bag: tools like the 'character' column pointer, and the colon and ampersand modifiers. The 'character' column pointer In the preceding section we showed how you can use the column pointer to move to a particular column before reading data. For these types of situations, you can use the 'character' column pointer. For example, suppose you have a data file that has information about dog ownership.
Nothing in the file lines up, but you know that the breed of the dog always follows the word Breed. in the INPUT statement, then SAS will read for 20 columns whether or not there is a space in those columns. If you want SAS to read only until it encounters a space or the end of the data line, you can use a colon modifier on the informat. This works with other delimiters besides spaces. If you have embedded spaces in your data values, you may still be able to read your data. Insert the ampersand after the name of the variable with the embedded spaces. Part of the competition involves racing the canoes. The data lines start with the name of the canoe, followed by the school, and the time. Bellatorum School CSULA Time Because the canoe names have different lengths, the school names do not line up in the same column.
Also, the time values are sometimes six characters and sometimes seven. to read the values of CanoeName. The result is that SAS will read CanoeName until it encounters two or more spaces and then stop even if the length of that CanoeName is less than the specified informat. Then the INPUT statement uses 'School' and 'Time' to position the column pointer to read the school name and time. The school names are all less than the default length of 8 characters so no informat is needed to read them, but times are non-standard data and require an informat.
Because the time is not always the same number of characters, a colon modifier is added to the informat :STIMER8. Without the colon modifier, SAS would go to a new data line to try to read the time values when it ran out of characters on a line of data. Since SAS will automatically go to the next line if it runs out of data before it has read all the variables in an INPUT statement, you could just let SAS take care of figuring out when to go to a new line. But if you know that your data file has multiple lines of raw data per observation, it is better for you to explicitly tell SAS when to go to the next line than to make SAS figure it out. To tell SAS when to skip to a new line, you simply add line pointers to your INPUT statement.
The n line pointer performs the same action except that you specify the line number. The n in n stands for the number of the line of raw data for that observation; so 2 means to go to the second line for that observation, and 4 means go to the fourth line. You can even go backward using the n line pointer, reading from line 4 and then from line 3, for example. The slash is simpler, but n is more flexible. Example A colleague is planning his next summer vacation, and he wants to go someplace where the weather is just right. He obtains data from a meteorological database. Unfortunately, he has not quite figured out how to export from this database and makes a rather odd file. The file contains information about temperatures for the month of July for Alaska, Florida, and North Carolina. If your colleague chooses the last state, maybe he can visit SAS headquarters. The first line contains the city and state, the second line lists the normal high temperature and normal low in degrees Fahrenheit , and the third line contains the record high and low: Nome AK 55 44 88 29 Miami FL 90 75 97 65 Raleigh NC 88 68 50 The following program reads the weather data from a file named Temperature.
Then the slash tells SAS to move to column 1 of the next line of data before reading NormalHigh and NormalLow. Likewise, the 3 tells SAS to move to column 1 of the third line of data for that observation before reading RecordHigh and RecordLow. As usual, there is more than one way to write this INPUT statement. You could replace the slash with 2 or replace 3 with a slash. The minimum record length was 5. HIGHLOW has 3 observations and 6 variables. Notice that while nine records were read from the INFILE statement, the SAS data set contains just three observations. Usually, this would set off alarms in your mind, but here it confirms that indeed three data lines were read for every observation just as planned. You should always check your log, particularly when using line pointers. Normally SAS assumes that each line of raw data represents no more than one observation.
When you have multiple observations per line of raw data, you can use double trailing at signs at the end of your INPUT statement. Example Suppose you have a colleague who is planning a vacation and has obtained a file containing data about rainfall for the three cities he is considering. The file contains the name of each city, the state, average rainfall in inches for the month of July, and average number of days with measurable precipitation in July. The raw data look like this: Nome AK 2. The following program reads these data from a file named Precipitation. dat' The minimum record length was NOTE: SAS went to a new line when INPUT statement reached past the end of a line. RAINFALL has 3 observations and 4 variables.
While only two records were read from the raw data file, the RAINFALL data set contains three observations. The log also includes a note saying SAS went to a new line when the INPUT statement reached past the end of a line. This means that SAS came to the end of a line in the middle of an observation and continued reading with the next line of raw data. Normally these messages would indicate a problem, but in this case they are exactly what you want. For example, you might be reading U. You could read all the records in the data file and then throw out the unneeded ones, but that would waste time. Instead, you can read just enough variables to decide whether to keep the current observation, and then end the INPUT statement with an at sign , called a trailing at. This tells SAS to hold that line of raw data. If it is, then you can read data for the remaining variables with a second INPUT statement. Without the trailing , SAS would automatically start reading the next line of raw data with each INPUT statement.
The trailing is similar to the column pointer, n, introduced in Section 2. By specifying a number after the sign, you tell SAS to move to a particular column. Example You want to read part of a raw data file containing local traffic data for freeways and surface streets. The data include information about the type of street, name of street, the average number of vehicles per hour traveling that street during the morning, and the average number of vehicles per hour for the evening. Here are the raw data: freeway surface Martin Luther King Jr. The first reads the character variable Type and then ends with an.
The trailing holds each line of data while the IF statement tests it. The second INPUT statement reads Name in columns 9 through 38 , AMTraffic, and PMTraffic. If an observation has a value of surface for the variable Type, then the second INPUT statement never executes. Instead, SAS returns to the beginning of the DATA step to process the next observation and does not add the unwanted observation to the FREEWAYS data set. FREEWAYS has 3 observations and 4 variables. The other five observations had a value of surface for the variable Type and were deleted by the IF statement. This example used a DELETE statement to delete observations for surface streets. Instead of specifying which observations to delete, you could specify which observations to keep. To keep only observations for freeways, you would use a subsetting IF statement.
Trailing versus double trailing The double trailing , discussed in the previous section, is similar to the trailing. Both are line-hold specifiers; the difference is how long they hold a line of data for input. The trailing holds a line of data for subsequent INPUT statements, but releases that line of data when SAS returns to the top of the DATA step to begin building the next observation. The double trailing holds a line of data for subsequent INPUT statements even when SAS starts building a new observation. In both cases, the line of data is released if SAS reaches a subsequent INPUT statement that does not contain a line-hold specifier.
When reading raw data files, SAS makes certain assumptions. For example, SAS starts reading with the first data line and, if SAS runs out of data on a line, it automatically goes to the next line to read values for the rest of the variables. The options in the INFILE statement change the way SAS reads raw data files. The following options are useful for reading particular types of data files. Place these options after the filename in the INFILE statement. This is useful if you have a data file that contains descriptive text or header information at the beginning, and you want to skip over these lines before reading the data. It tells SAS to stop reading when it gets to that line in the raw data file. Note that it does not necessarily correspond to the number of observations. For example, suppose the ice-cream sales data had a remark at the end of the file that was not part of the data.
The MISSOVER option tells SAS not to go to the next line of data when it runs out of data. Instead, assign missing values to any remaining variables. The following data file illustrates where this option may be useful. This file contains test scores for a self-paced course. Since not all students complete all the tests, some have more scores than others. This option tells SAS to read data for the variable until it reaches the end of the data line, or the last column specified in the format or column range, whichever comes first.
The next file contains addresses and must be read using column or formatted input because the street names have embedded blanks. Note that the data lines have different lengths: John Garcia Maple Ave. Sylvia Chung Washington Drive Martha Newton 45 S. This program uses column input to read the address file. Without the TRUNCOVER option, SAS would try to go to the next line to read the data for Street on the first and third records. But when the data line ends in the middle of a variable field, TRUNCOVER will take as much as is there, whereas MISSOVER will assign the variable a missing value. Section 2. By default, SAS interprets two or more delimiters in a row as a single delimiter. If your file has missing values, and two delimiters in a row indicate a missing value, then you will also need the DSD option in the INFILE statement.
The DSD option The DSD Delimiter-Sensitive Data option for the INFILE statement does three things for you. First, it ignores delimiters in data values enclosed in quotation marks. Second, it does not read quotation marks as part of the data value. Third, it treats two delimiters in a row as a missing value. The DSD option assumes that the delimiter is a comma. Many programs, such as Microsoft Excel, can save data in CSV format. These files have commas for delimiters and consecutive commas for missing values; if there are commas in any of the data values, then those values are enclosed in quotation marks.
Example The following example illustrates how to read a CSV file using the DSD option. Jerry keeps records of the number of customers for each band, for each night they play in his shop. Also, the last group has a missing data point for the 9 p. hour as indicated by two consecutive commas. EightPM NinePM TenPM ElevenPM; RUN; Notice that for BandName the INPUT statement uses an informat with a colon modifier. The colon modifier tells SAS to read for the length of the informat 30 for BandName , or until it encounters a delimiter, whichever comes first. This data file is missing one data value at the end of the first line. By default, SAS would go to the next line of data to find the last data value. The MISSOVER option on the INFILE statement tells SAS not to go to the next line of data when it runs out of values. It is prudent when using the DSD option to add the MISSOVER option if there is any chance that you have missing data values at the end of your data lines.
Here is the MUSIC data set: BandName 1 2 3 5 Catalina Converts 63 17 28 34 The Silveyville Jazz Quartet 45 Stop, Drop, and Rock-N-Roll 4 Awesome Octaves EightPM NinePM TenPM Lupine Lights 62 38 30 CHAPTER 3 Working with Your Data 3. Using the DATA step, you can read raw data files, modify existing SAS data sets, and combine SAS data sets. You can also create new variables making use of many available operators and SAS functions, use conditional logic, subset data, and a whole host of other things. There is so much you can do with the DATA step that we could easily write an entire book on the topic. This chapter discusses many of the common operations that you might want to perform on your data. Chapter 6 covers various methods for combining data sets. DATA steps with INPUT statements We have already seen many examples in Chapter 2 of using the INPUT statement to read raw data files.
If you want to do any other data manipulation, such as create new variables, you can do that in the same DATA step as the INPUT statement. Just remember that DATA steps execute line by line, so most other statements need to come after the INPUT statement. The following shows the general form of the DATA step when reading raw data files: DATA new-data-set; INFILE raw-data-file; INPUT variables; Other DATA step statements go here; DATA steps with SET statements When your data are already in a SAS data set, but you want to manipulate the data more, you use a SET statement. The SET statement brings the data into the DATA step one observation at a time, and processes all the observations automatically. To read a SAS data set, start with the DATA statement specifying the name of the new data set.
Then follow with the SET statement specifying the name of the old data set you want to read. Then the results of the DATA step will overwrite the old data set named in the SET statement as long as the DATA step does not have any errors. The following shows the general form of the DATA and SET statements: DATA new-data-set; SET old-data-set; Other DATA step statements go here; Any assignment, subsetting IF, or other DATA step statements usually follow the SET statement. The hotel name is followed by the nightly rate for two people in yen and the distance from Kyoto Station in kilometers. The Grand West Arashiyama 9. dat and creates a permanent SAS data set. After the INPUT statement is a simple assignment statement covered in more detail in the next section that creates a new variable, USD, which multiplies the value of the variable Yen by the exchange rate of 0.
Then, after the SET statement, a simple assignment statement creates a new variable, Miles, which multiplies the value of the variable Kilometers by 0. Fortunately, SAS is flexible and uses a common-sense approach to these tasks. On the right side of the equal sign may appear a constant, another variable, or a mathematical expression. When the expression is numeric, Qwerty will be numeric. When it is character, Qwerty will be character. When deciding how to interpret your expression, SAS follows the standard mathematical rules of precedence: SAS performs exponentiation first, then multiplication and division, followed by addition and subtraction. You can use parentheses to override that order. If you use parentheses, your programs will be a lot easier to read. Example The following comma-separated values CSV data are from a survey of home gardeners.
Gardeners were asked to estimate the number of pounds they harvested for four crops: tomatoes, zucchini, peas, and grapes. Name,Tomato,Zucchini,Peas,Grapes Gregor,10,2,40,0 Molly,15,5,10, Luther,50,10,15,50 Susan,20,0,. The first creates a new variable, Zone, equal to a numeric constant, The variable Type is set equal to a character constant, home. The variable Zucchini is multiplied by 10 because that just seems natural for the prolific zucchini. Total is the sum for all the types of plants. PerTom is not a genetically engineered tomato but the percentage of harvest which were tomatoes.
The other four assignment statements each created a new variable. When a variable is new, SAS adds it to the data set you are creating. When a variable already exists, SAS replaces the original value with the new one. Using an existing name has the advantage of not cluttering your data set with a lot of similar variables. The variable Peas had a missing value for the last observation. Because of this, the variables Total and PerTom, which are calculated from Peas, were also set to missing and this message appeared in the log: NOTE: Missing values were generated as a result of performing an operation on missing values. This message is a flag that often indicates an error. However, in this case it is not an error but simply the result of incomplete data collection.
If you want to add only nonmissing values, you can use the SUM function discussed in Section This is where functions are handy, simplifying your task because SAS has already done the programming for you. All you need to do is plug the right values into the function and out comes the result—like putting a dollar in a change machine and getting back four quarters. SAS has hundreds of functions in general areas including: Character Macro Character String Matching Mathematical Date and Time Probability Descriptive Statistics Random Number Distance State and ZIP Code Financial Variable Information The next two sections list the most common SAS functions along with examples.
Functions perform a calculation on, or a transformation of, the arguments given in parentheses following the function name. SAS functions have the following general form: function-name argument, argument, Arguments are separated by commas and can be variable names, constant values such as numbers or characters enclosed in quotation marks, or expressions. The following statement computes DateOfBirth as a SAS date value using the function MDY and the variables MonthBorn, DayBorn, and YearBorn. Just be careful when nesting functions that each parenthesis has a mate. Example Data from a pumpkin carving contest illustrate the use of several functions. Here is the SAS data set CONTEST that was created in Section 2. This differs from simply adding the arguments together and dividing by their number, which would return a missing value if any of the arguments were missing.
The variable DayEntered is created using the DAY function, which returns the day of the month. The variable Type is transformed using the UPCASE function. SAS is case sensitive when it comes to variable values; a 'd' is not the same as 'D'. The data file has both lowercase and uppercase letters for the variable Type, so the function UPCASE is used to make all the values uppercase. Here is the SAS data set PUMPKIN created in the above program: Name 1 Alicia Grossman 2 Matthew Lee 3 Elizabeth Garcia 4 Lori Newcombe 5 Jose Martinez 6 Brian Williams Ag Typ Score Score Score AvgSco e e Date 1 2 3 re 13 C 6 9 D 6 D 11 C 7 4. Section 4. This is called conditional logic, and you do it with IF-THEN statements: IF condition THEN action; The condition is an expression comparing one thing to another, and the action is what SAS should do when the expression is true, often an assignment statement.
The terms on either side of the comparison may be constants, variables, or expressions. Those terms are separated by a comparison operator, which may be either symbolic or mnemonic. The decision of whether to use symbolic or mnemonic operators depends on your personal preference. IN compares the value of a variable to a list of values. A single IF-THEN statement can only have one action. If you add the keywords DO and END, then you can execute more than one action. Together, the DO statement, the END statement, and all the statements in between are called a DO group.
AND all comparisons must be true OR at least one comparison must be true Be careful with long strings of comparisons; they can be a logical maze. Example The following tab-delimited data show information about rare antique cars sold at auction. The data values are the make, model, the year the car was made, the number of seats , and the selling price in millions of dollars: Make Model YearMade Seats Millions Paid DeDion LaMarquise 4 4. txt, then a DATA step makes some modifications to the data. For example, you might have data for each day but need a report by season, or perhaps you have data for each census tract but want to analyze it by state.
There are several ways to create a grouping variable including using a PUT function with a user-defined format, covered in Section 4. By adding the keyword ELSE to your IF statements, you can tell SAS that these statements are related. You can have any number of these statements. First, it is more efficient, using less computer time; once an observation satisfies a condition, SAS skips the rest of the series. Sometimes the last ELSE statement in a series is a little different, containing just an action, with no IF or THEN. Note the final ELSE statement in this series: IF condition THEN action; ELSE IF condition THEN action; ELSE action; An ELSE of this kind becomes a default, which is automatically executed for all observations failing to satisfy any of the previous IF statements.
This would lead to the value Cold being truncated to Col. Adding the following LENGTH statement before the first occurrence of the Status varable fixes this problem. Al backyard gazebo txt using PROC IMPORT then assigns a grouping variable called CostGroup in a DATA step. The first statement deals with observations that have missing data for the variable Cost. Without this first statement, observations with a missing value for Cost would be incorrectly assigned a CostGroup of low. SAS considers missing values to be smaller than nonmissing values, smaller than any printable character for character variables, and smaller than negative numbers for numeric variables. TBD A common way to do this is with a subsetting IF statement in a DATA step. But it is really a special case of the standard IF-THEN statement. In this case, the action is merely implied.
If the expression is true, then SAS continues with the DATA step. If the expression is false, then no further statements are processed for that observation; that observation is not added to the data set being created; and SAS moves on to the next observation. You can think of the subsetting IF as a kind of on-off switch. If the condition is true, then the switch is on and the observation is processed. If the condition is false, then that observation is turned off. DELETE statements do the opposite of subsetting IFs. The WHERE statement is similar to the IF statement and can be more efficient.
But you can only use the WHERE statement when selecting observations from existing SAS data sets and only for variables that already exist in the data set. The WHERE statement has the following general form: WHERE expression; Example A local zoo maintains a database about the feeding of the animals. A portion of the data appears below. For each group of animals the data include the scientific class, the enclosure those animals live in, and whether they get fed in the morning, afternoon, or both: Animal,Class,Enclosure,FeedTime bears,Mammalia,E2,both elephants,Mammalia,W3,am flamingos,Aves,W1,pm frogs,Amphibia,E8,pm kangaroos,Mammalia,W4,am lions,Mammalia,W6,pm snakes,Reptilia,E9,pm tigers,Mammalia,W9,both zebras,Mammalia,W2,am This program reads the data from a comma-delimited data file called Zoo. csv, creating a permanent SAS data set, ZOO. Note that the Area variable appears in the resulting data set even though the statements creating it come after the subsetting IF statement.
Observations are written to the data set at the end of the DATA step unless there is an OUTPUT statement discussed in Sections 3. The MAMMALS data set looks like this: Animal Class Enclosure FeedTime 1 bears Mammalia E2 both 2 elephants Mammalia W3 am 3 kangaroos Mammalia W4 am 4 lions Mammalia W6 pm 5 tigers Mammalia W9 both 6 zebras Mammalia W2 am These notes appear in the log stating that although nine observations were read from the original data set, the resulting data set MAMMALS contains only six observations: NOTE: There were 9 observations read from the data set FEED. MAMMALS has 6 observations and 5 variables. It is always a good idea to check the SAS log when you subset observations to make sure that you ended up with what you expected.
Generally, you use the subsetting IF when it is easier to specify a condition for including observations, and use the DELETE statement when it is easier to specify a condition for excluding observations. A common task in SQL is querying data and producing subsets of data. In SQL documentation, data sets are called tables, observations are called rows, and variables are called columns, but they are the same thing. The basic form of PROC SQL for subsetting data is: PROC SQL; CREATE TABLE new-data-set AS SELECT variable-list FROM old-data-set WHERE expression; QUIT; The procedure starts with the keywords PROC SQL followed by a semicolon.
Next comes the SQL statement containing four clauses. The CREATE TABLE … AS clause denotes the name of the new SAS data set that you will create. The SELECT clause lists the variables or columns you want to keep. Books That Will Help You Love and Accept Yourself. The Gifts of Imperfection: Embrace Who You Are 98 Pages · · 1. The Choice: Embrace the Possible Pages · · 1. Best Motivational Books to Take Charge of Your Life. Boundaries: When to Say Yes, How to Say No to Take Control of Your Life Pages · · 1. The 5 Second Rule: Transform your Life, Work, and Confidence with Everyday Courage Pages · · The Daily Stoic: Meditations on Wisdom, Perseverance, and the Art of Living Pages · · 2. Load more files. Educational Videos. Listen to this before you start your day! Pdfdrive:hope Give books away. Get books you want. Ask yourself: Am I following the crowd or am I listening to my own heart and intuition? Get Top Trending Free Books in Your Inbox.
Delwiche Page Find all the books, read about the author, and more. See search results for this author Are you an author? Learn about Author Central Lora D. Slaughter Page Find all the books, read about the author, and more. Learn about Author Central Susan J. Slaughter Author. Ebook EPUB The Little SAS Book: A Primer, Fifth Edition EBOOK ONLINE DOWNLOAD Hello Friends, If you want to download free Ebook, you are in the right place to download Ebook. Slaughter Author Author. A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming. Lora Delwiche and Susan Slaughter offer a user-friendly approach so readers can quickly and easily learn the most commonly used features of the SAS language. Each topic is presented in a self-contained two-page layout complete with examples and graphics.
The fifth edition has been completely updated to reflect the new default output introduced with SAS 9. In addition, there is a now a full chapter devoted to ODS Graphics including the SGPLOT and SGPANEL procedures. Other changes include expanded coverage of linguistic sorting and a new section on concatenating macro variables with other text. This book is a great tool for users of SAS 9. This title belongs on every SAS programmer's bookshelf. It's a resource not just to get you started, but one you'll return to as you continue to improve your programming skills. This book is part of the SAS Press program. Let's be real: has been a nightmare.
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Apr 23, , AM Apr Reply to author. Report message as abuse. Show original message. Either email addresses are anonymous for this group or you need the view member email addresses permission to view the original message. Download Link : DOWNLOAD The Little SAS Book: A Primer, Fifth Edition Read More : READ The Little SAS Book: A Primer, Fifth Edition Ebook EPUB The Little SAS Book: A Primer, Fifth Edition EBOOK ONLINE DOWNLOAD Hello Friends, If you want to download free Ebook, you are in the right place to download Ebook. Description A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming.
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SAS considers missing values to be smaller than nonmissing values, smaller than any printable character for character variables, and smaller than negative numbers for numeric variables. sas, then it is a good bet that your log file will be Marathon. The second INPUT statement reads Name in columns 9 through 38 , AMTraffic, and PMTraffic. In the preceding data set, Name is obviously a character variable, and Height and Weight are numeric. If your program creates a new SAS data set, then by default SAS Enterprise Guide will automatically display it. Unfortunately, he has not quite figured out how to export from this database and makes a rather odd file.
With ANY, variable names can use special characters including spaces, but names containing special characters must use a name literal of t form 'variable-name'N. d Reads time in form: hh:mm:ss. I get used to download ebooks from Torlock, with pandavpn from pandavpnpro. Now the starting point for the second variable is column 11, and SAS reads values for Age in columns 11 through Variable Type is character, and it is one column wide.
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