Topics in Data Management SAS Data Step

Combining Data Sets I - SET Statement Data available on common variables from different sources. Multiple datasets with common variable names, possibly different sampling/experimental units –Exam scores from students in various sections of STA 2023 –County level data from different state databases –Flight departure/arrival data from different months

Combining Data Sets I - SET Statement options nodate nonumber ps=54 ls=80; data one; input student $ 1-8 idnum 9-12 exam exam exam ; section=1; cards; Amy Zed ; run; data five; input student $ 1-8 idnum 9-12 exam exam exam ; section=5; cards; Alex Zach ; run; data all; set one five; run; proc print; run; quit;

Combining Data Sets I - SET Statement The SAS System Obs student idnum exam1 exam2 exam3 section 1 Amy Zed Alex Zach

Combining Data Sets II - MERGE Statement Data on common sampling/experimental units, different variables/characteristics measured in different datasets. –County data from different government sources –Store sales data updated over time

Combining Data Sets II - MERGE Statement options nodate nonumber ps=54 ls=80; data s2003; input store $ 1-8 sales ; cards; Atlanta 1459 Zurich 1383 ; run; data s2004; input store $ 1-8 sales ; cards; Atlanta 1459 Zurich 1383 ; run; proc sort data=s2003; by store; proc sort data=s2004; by store; data s0304; merge s2003 s2004; by store; run; proc print; run; quit; The SAS System Obs store sales03 sales04 1 Atlanta Zurich

Creating New Variables From Existing Ones Creating Final Grade for Students (Exams 1 and 2 Each Count 30%, Exam 3 40%) –Total = (0.3*Exam1)+(0.3*Exam2)+(0.4*Exam3) Obtaining Sales Growth (%) for stores –Grow0403=100*(sales04-sales03)/sales03

Grades Example data all; set one five; total=(0.3*exam1)+(0.3*exam2)+(0.4*exam3); run; proc print; var student idnum total; run; quit; The SAS System Obs student idnum total 1 Amy Zed Alex Zach

Building Case Histories Have multiple observations of same variable on individual units (not necessarily the same number across individuals). Want to summarize the measurements for each individual and obtain single “record”. –Summary of all Delta flights for each ATL route to other cities for October 2004 –Arrest record for juveniles over a 5 year period –Sales histories for individual stores in a retail chain

Building Case Histories Step 1: SORT dataset on the variable(s) that define(s) the individual units/cases. Step 2: Set the previous dataset into a new one, using the same BY statement as in the SORT. –The new dataset “sees” the old dataset as a series of “blocks” of measurements by individual cases Step 3: Define any variables you want to use to summarize cases in RETAIN statement. Step 4: At beginning of each individual, reset variables in Step 3 (typically to 0) Step 5: At end of each individual OUTPUT record

Example - Brookstone Store Sales&Inventory 8 EXCEL Spreadsheets - 4 Quarters X 2 Measures 520 stores observed over 52 weeks Typical Spreadsheet Portion (4 stores X 6 weeks): Note that the company provides 13 columns representing the 13 weeks in the quarter for each store…not the way we want to analyze it. Also, got rid of commas in EXCEL before exporting to text file.

Reading the Data in SAS Data inv1; infile ‘filename’; input storeid 6-8 storename $ do week=1 to 13; input output; end; run; This creates 13 “observations” per store and single inv variable

Reading the Data in SAS SET MERGE

Building a Store Record for Year Suppose Management wants following summary measures for each store: –Total sales –Average sales to inventory ratio –Mean and standard deviation of sales –Correlation between sales and inventory We need the following quantities counted across weeks: –SALES, SALES 2, INV, INV 2, SALES*INV, SALES/INV

SAS Code to Obtain Measures by Store (P1) Data inv; set inv1-inv4; run; proc sort; by storeid; run; Data sales; set sales1-sales4; run; proc sort; by storeid; run; Data invsales; merge inv sales; by storeid; run; proc sort; by storeid; run; Data invsales1; set invsales; by storeid; retain sumsales sumsales2 suminv suminv2 salesxinv sales_inv;

If first.storeid then do; sumsales=0; sumsales2=0; suminv=0; suminv2=0; salesxinv=0; sales_inv=0; end; sumsales=sumsales+sales; sumsales2=sumsales2+(sales**2); suminv=suminv+inv; suminv2=suminv2+(inv**2); salesxinv=salesxinv+(sales*inv); sales_inv=sales_inv+(sales/inv); if last.storeid then do; totsales=sumsales; meansal_inv=sales_inv/52; meansales=totsales/52; varsales=(sumsales2-(sumsales**2)/52)/51; stdsales=sqrt(varsales); varinv=(suminv2-(suminv**2)/52)/51; stdinv=sqrt(varinv); covslinv=(salesxinv-(sumsales*suminv)/52)51; corrslinv=covslinv/(stdsales*stdinv); output; end; run;