1 / 95

Presentation and Data http:// www.lisa.stat.vt.edu Short Courses Intro to SAS Download Data to Desktop

Presentation and Data http:// www.lisa.stat.vt.edu Short Courses Intro to SAS Download Data to Desktop. Introduction to SAS. Mark Seiss , Dept. of Statistics. Reference Material. The Little SAS Book – Delwiche and Slaughter SAS Programming I: Essentials

Télécharger la présentation

Presentation and Data http:// www.lisa.stat.vt.edu Short Courses Intro to SAS Download Data to Desktop

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

  1. Presentation and Data • http://www.lisa.stat.vt.edu • Short Courses • Intro to SAS • Download Data to Desktop

  2. Introduction to SAS Mark Seiss, Dept. of Statistics

  3. Reference Material • The Little SAS Book – Delwiche and Slaughter • SAS Programming I: Essentials • SAS Programming II: Manipulating Data with the DATA Step • Presentation and Data • http://www.lisa.stat.vt.edu

  4. Presentation Outline 1. Introduction to the SAS Environment 2. Working With SAS Data Sets 3. Summary Procedures 4. Basic Statistical Analysis Procedures

  5. Presentation Outline • Questions/Comments • Individual Goals/Interests

  6. 1. SAS Programs 2. SAS Data Sets and Data Libraries 3. SAS System Help 4. Creating SAS Data Sets Introduction to the SAS Environment

  7. File extension - .sas Editor window has four uses: Access and edit existing SAS programs Write new SAS programs Submitting SAS programs for execution Saving SAS programs SAS program – sequence of steps that the user submits for execution Submitting SAS programs Entire program Selection of the program SAS Programs

  8. Syntax Rules for SAS statements Free-format – can use upper or lower case Usually begin with an identifying keyword Can span multiple lines Always end with a semicolon Multiple statements can be on the same line Errors Misspelled key words Missing or invalid punctuation (missing semi-colon common) Invalid options Indicated in the Log window SAS Programs

  9. 2 Basic steps in SAS programs: Data Steps Typically used to create SAS datasets and manipulate data, Begins with DATA statement Proc Steps Typically used to process SAS data sets Begins with PROC statement The end of the data or proc steps are indicated by: RUN statement – most steps QUIT statement – some steps Beginning of another step (DATA or PROC statement) SAS Programs

  10. Output generated from SAS program – 2 Windows SAS log Information about the processing of the SAS program Includes any warnings or error messages Accumulated in the order the data and procedure steps are submitted SAS output Reports generated by the SAS procedures Accumulates output in the order it is generated SAS Programs

  11. SAS Data Set Specifically structured file that contains data values. File extension - .sas7bdat Rows and Columns format – similar to Excel Columns – variables in the table corresponding to fields of data Rows – single record or observation Two types of variables Character – contain any value (letters, numbers, symbols, etc.) Numeric – floating point numbers Located in SAS Data Libraries SAS Data Sets and Data Libraries

  12. SAS Data Libraries Contain SAS data sets Identified by assigning a library reference name – libref Temporary Work library SAS data files are deleted when session ends Library reference name not necessary Permanent SAS data sets are saved after session ends SASUSER library You can create and access your own libraries SAS Data Sets and Data Libraries

  13. SAS Data Libraries cont. Assigning library references Syntax LIBNAME libref ‘SAS-data-library’; Rules for Library References 8 characters or less Must begin with letter or underscore Other characters are letters, numbers, or under scores SAS Data Sets and Data Libraries

  14. SAS Data Libraries cont. Identifying SAS data sets within SAS Data Libraries libref.filename Accessing SAS data sets within SAS Data Libraries Example: DATA new_data_set; set libref.filename; run; Creating SAS data sets within SAS Data Libraries Example: DATA libref.filename; set old_data_set; run; SAS Data Sets and Data Libraries

  15. SAS Help and Documentation Help  SAS Help and Documentation Red Book Icon SAS Online Help http://support.sas.com/ SAS System Help

  16. Creating a SAS data sets from raw data 4 methods 1. Importing existing data sets using Import menu option 2. Importing existing raw data in SAS program 3. Manually entering raw data in SAS program 4. Manually entering raw data using Table Editor Creating SAS Data Sets

  17. Using the import data menu option 1. File  Import Data 2. Standard data source  select the file format 3. Specify file location or Browse to select file 4. Create name for the new SAS data set and specify location Creating SAS Data Sets

  18. Compatible file formats Microsoft Excel Spreadsheets Microsoft Access Databases Comma Separate Files (.csv) Tab Delimited Files (.txt) dBASE Files (.dbf) JMP data sets SPSS Files Lotus Spreadsheets Stata Files Paradox Files Creating SAS Data Sets

  19. Example Data Sets Excel File – State_SAT_data.xls http://www.stat.ucla.edu/labs/datasets/sat.dat Extracted from 1997 Digest of Education Statistics, an annual publication of the U.S. Department of Education Contains variables that show the relationship between public school expenditure and SAT performance Variables: State (state) Current expenditure per pupil (expend) Average pupil to teacher ratio (PT_ratio) Estimated annual salary of teachers (salary) Percentage of eligible students taking the SAT (students) Average verbal SAT score (verbal) Average math SAT Score (math) Average total score (total) Creating SAS Data Sets

  20. Example Data Sets Cont. Text file – State_region_data.txt Contains region assignments for each state 1 = New England 2 = Middle Atlantic 3 = East North Central 4 = West North Central 5 = South Atlantic 6 = East South Central 7 = West South Central 8 = Mountain 9 = Pacific Creating SAS Data Sets

  21. Creating SAS Data Sets • Import State_SAT_data.xls  Assign as work.state_sat_data.sas7bdat • Import State_region_data.txt  Assign as work.state_region_data.sas7bdat

  22. Introduction to the SAS Environment • Questions/Comments

  23. 1. Data Set Information 2. Data Set Manipulation 3. Data Set Processing 4. Combining Data Sets A. Concatenating/Appending B. Merging 5. Saving Data Sets Working With SAS Data Sets

  24. Proc Contents Output contains a table of contents of the specified data set Data Set Information Data set name Number of observations Number of Variables Variable Information Type (numeric or character) Length Syntax: PROC CONTENTS DATA=input_data_set; RUN; Data Set Information

  25. Assignment Obtain Data Set Information for work.state_sat_data and work.state_region_data Data Set Information

  26. Solution proc contents data=state_sat_data; run; proc contents data=state_region_data; run; Data Set Information

  27. Create a new SAS data set using an existing SAS data set as input Specify name of the new SAS data set after the DATA statement Use SET statement to identify SAS data set being read Syntax: DATA output_data_set; SET input_data_set; <additional SAS statements>; RUN; By default the SET statement reads all observations and variables from the input data set into the output data set. Data Set Manipulation

  28. Assignment Statements Evaluate an expression Assign resulting value to a variable General Form: variable = expression; Example: miles_per_hour = distance/time; SAS Functions Perform arithmetic functions, compute simple statistics, manipulate dates, etc. General Form: variable=function_name(argument1, argument2,…); Example: Time_worked = sum(Day1,Day2, Day3, Day4, Day5); Data Set Manipulation

  29. Selecting Variables Use DROP and KEEP to determine which variables are written to new SAS data set. 2 Ways DROP and KEEP as statements Form: DROP Variable1 Variable2; KEEP Variable3 Variable4 Variable5; DROP and KEEP options in SET statement Form: SET input_data_set (KEEP=Var1); Data Set Manipulation

  30. Conditional Processing Uses IF-THEN-ELSE logic General Form: IF <expression1> THEN <statement>; ELSE IF <expression2> THEN <statement>; ELSE <statement>; <expression> is a true/false statement, such as: Day1=Day2, Day1 > Day2, Day1 < Day2 Day1+Day2=10 Sum(day1,day2)=10 Day1=5 and Day2=5 Data Set Manipulation

  31. Conditional Processing Data Set Manipulation

  32. Conditional Processing cont. If <expression1> is true, <statement> is processed ELSE IF and ELSE are only processed if <expression1> is false Only one statement specified using this form Use DO and END statements to execute group of statements General Form: IF <expression> THEN DO; <statements>; END; ELSE DO; <statements>; END; Data Set Manipulation

  33. Subsetting Rows (Observations) We will look at two ways Using IF statement Using WHERE option in SET statement IF statement Only writes observations to the new data set in which an expression is true; General Form: IF <expression>; Example: IF career = ‘Teacher’; IF sex ne ‘M’; In the second example, only observations where sex is not equal to ‘M’ will be written to the output data set Data Set Manipulation

  34. Subsetting Rows (Observations) cont. Where Option in SET statement Use option to only read rows from the input data set in which the expression is true General Form: SET input_data_set (where=(<expression>)); Example: SET vacation (where=(destination=‘Bermuda’)); Only observations where the destination equals ‘Bermuda’ will be read from the input data set Comparison Resulting output data set is equivalent IF statement – all rows read from the input data set Where option – only rows where expression is true are read from input data set Difference in processing time when working with big data sets Data Set Manipulation

  35. Assignments 1. Create new dataset work.state_SAT_data2 from work.state_SAT_data Assign new variable  upper_ind If total > 1000 then upper_ind=1 Otherwise upper_ind=0 2. Create new dataset work.south from work.state_region_data Specify work.south contains only records from regions 5, 6, or 7 Specify work.south only contains the state variable Data Set Manipulation

  36. Solutions 1. data state_sat_data2; set state_sat_data; if total>1000 then upper_ind=1; else upper_ind=0; run; Data Set Manipulation

  37. Solutions 2. data south; set state_region_data; if region=5 or region=6 or region=7; keep state; run; OR data south; set state_region_data(where=(region=5 or region=6 or region=7)); keep state; run; Data Set Manipulation

  38. PROC SORT sorts data according to specified variables General Form: PROC SORT DATA=input_data_set <options>; BY Variable1 Variable2; RUN; Sorts data according to Variable1 and then Variable2; By default, SAS sorts data in ascending order Number low to high A to Z Use DESCENDING statement for numbers high to low and letters Z to A BY City DESCENDING Population; SAS sorts data first by city A to Z and then Population high to low Data Set Manipulation

  39. Some Options NODUPKEY Eliminates observations that have the same values for the BY variables OUT=output_data_set By default, PROC SORT replaces the input data set with the sorted data set Using this option, PROC SORT creates a newly sorted data set and the input data set remains unchanged Data Set Manipulation

  40. Data Set Processing DATA steps read in data from existing data sets or raw data files one row at a time, like a loop DATA step reads data from the input data set in the following way: 1. Read in current row from input data set to Program Data Vector (PDV) 2. Process SAS statements 3. PDV to output data set 4. Set current row to the next row in the input data set 5. Iterate to Step 1 One row at a time is processed Thus we cannot simply add the value of a variable in one row to the value in another row Data Set Processing

  41. Concatenating (or Appending) Stacks each data set upon the other If one data set does not have a variable that the other datasets do, the variable in the new data set is set to missing for the observations from that data set. General Form: DATA output_data_set; SET data1 data2; run; PROC APPEND may also be used Combining Data Sets

  42. Merging Data Sets One-to-One Match Merge A single record in a data set corresponds to a single record in all other data sets Example: Patient and Billing Information One-to-Many Match Merge Matching one observation from one data set to multiple observations in other data sets Example: County and State Information Note: Data must be sorted before merging can be done (PROC SORT) Combining Data Sets

  43. One-to-One Match Merge Usually need at least one common variable between data sets – matching purposes For the example, a patient ID would be needed Do not need common variable if all data sets are in exactly the same order General Form: DATA output_data_set; MERGE input_data_set1 input_data_set2; By variable1 variable2; RUN; Combining Data Sets

  44. One-to-One Match Merge Example: Performance Goals Code: DATA compare; MERGE performance goals; BY month; difference=sales-goal; RUN; Combining Data Sets

  45. One-to-One Match Merge Example cont.: Compare Combining Data Sets

  46. One-to-Many Match Merge Requires at least one common variable in the data sets for matching purposes For the example, State information is in both the state and county files If two data sets have variables with the same name, the variables in the second data set will overwrite the variable in the first. General Form: DATA output_data_set; MERGE Data1 Data2 Data3; BY Variable1 Variable2; RUN: Combining Data Sets

  47. One-to-Many Match Merge Example: Videos Adjustment Code: DATA prices; MERGE videos adjustment BY category; NewPrice=(1-adjustment)*sales; RUN; Combining Data Sets

  48. One-to-One Many Merge Example cont.: Videos Combining Data Sets

  49. Assignment Create the dataset work.state_data Merge work.state_sat_data2 with work.state_region_data by the state variable Combining Data Sets

  50. Solution procsort data=state_sat_data2; by state; run; procsort data=state_region_data; by state; run; data state_data; merge state_sat_data2 state_region_data; by state; run; Combining Data Sets

More Related