{"id":2074,"date":"2015-02-21T13:27:04","date_gmt":"2015-02-21T18:27:04","guid":{"rendered":"http:\/\/blogs.bu.edu\/ellisrp\/?p=2074"},"modified":"2015-07-23T11:00:33","modified_gmt":"2015-07-23T15:00:33","slug":"ellis-sas-tips-for-new-sas-programmers","status":"publish","type":"post","link":"https:\/\/blogs.bu.edu\/ellisrp\/2015\/02\/ellis-sas-tips-for-new-sas-programmers\/","title":{"rendered":"Ellis SAS tips for New SAS programmers"},"content":{"rendered":"

There is also a posting on Ellis SAS tips for Experienced SAS programmers<\/a><\/p>\n

It focuses on issues when using large datasets.<\/p>\n

 <\/p>\n

Randy\u2019s SAS hints for New SAS programmers, updated Feb 21, 2015<\/p>\n

    \n
  1. \n

    ALWAYS<\/span><\/em><\/strong><\/span><\/h3>\n

    begin and intermix your programs with internal documentation. (Note how I combined six forms of emphasis in ALWAYS: color, larger font, caps, bold, italics, underline.) Normally I recommend only one, but documenting your programs is really important. (Using only one form of emphasis is also important, just not really important.)
    \n<\/strong><\/li>\n<\/ol>\n

    A simple example to start your program in SAS is<\/p>\n

    ******************
    \n* Program = test1, Randy Ellis, first version: March 8, 2013 \u2013 test program on sas features
    \n***************;<\/p>\n

    Any comment starting with an asterisk and ending in a semicolon is ignored;<\/p>\n

     <\/p>\n

      \n
        \n
      1. Most common errors\/causes of wasted time while programming in SAS.<\/strong><\/li>\n<\/ol>\n<\/ol>\n

        a. Forgetting semicolons at the end of a line<\/p>\n

        b. Omitting a RUN statement, and then waiting for the program to run.<\/p>\n

        c. Unbalanced single or double quotes.<\/p>\n

        d. Unintentionally commenting out more code than you intend to.<\/p>\n

        e. Foolishly running a long program on a large dataset that has not first been tested on a tiny one.<\/p>\n

        f. Trying to print out a large dataset which will overflow memory or hard drive space.<\/p>\n

        g. Creating an infinite loop in a datastep; Here is one silly one. Usually they can be much harder to identify.<\/p>\n

        data infinite_loop;
        \nx=1;
        \nnevertrue=0;
        \ndo while x=1;
        \nif nevertrue =1 then x=0;
        \nend;
        \nrun;<\/p>\n

        h. There are many other common errors and causes of wasted time. I am sure you will find your own<\/p>\n

         <\/p>\n

          \n
        1. With big datasets, 99 % of the time it pays to use the following system OPTIONS:<\/strong><\/li>\n<\/ol>\n

           <\/p>\n

          options compress =yes nocenter;<\/p>\n

          or<\/p>\n

          options compress =binary nocenter;<\/p>\n

          binary compression works particularly well with many binary dummy variables and sometimes is spectacular in saving 95%+ on storage space and hence speed.<\/p>\n

           <\/p>\n

          \/* mostly use *\/
          \noptions nocenter \/* SAS sometimes spends many seconds figuring out how to center large print outs of
          \ndata or results. *\/
          \nps=9999 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\/* avoid unneeded headers and page breaks that split up long tables in output *\/
          \nls=200; \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\/* some procs like PROC MEANS give less output if a narrow line size is used *\/
          \n <\/p>\n

          *other key options to consider;<\/p>\n

          Options obs = max\u00a0\u00a0 \/* or obs=100, Max= no limit on maximum number of obs processed *\/
          \nNodate nonumber \/* useful if you don\u2019t want SAS to embed headers at top of each page in listing *\/
          \nMacrogen\u00a0\u00a0\u00a0\u00a0 \/* show the SAS code generated after running the Macros. *\/
          \nMprint\u00a0\u00a0 \/* show how macro code and macro variables resolve *\/
          \nnosource \/* suppress source code from long log *\/
          \nnonotes\u00a0\u00a0 \/* be careful, but can be used to suppress notes from log for long macro loops *\/<\/p>\n

          ;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 *remember to always end with a semicolon!;<\/p>\n

           <\/p>\n

            \n
          1. Use these three key procedures regularly<\/strong><\/li>\n<\/ol>\n

            Proc contents data=test; run; \/* shows a summary of the file similar to Stata\u2019s DESCRIBE *\/
            \nProc means data = test (obs=100000); run; \/* set a max obs if you don\u2019t want this to take too long *\/
            \nProc print data = test (obs=10); run;<\/p>\n

             <\/p>\n

            I recommend you create and use regularly a macro that does all three easily:<\/p>\n

            %macro cmp(data=test);
            \nProc Contents data=&data; Proc means data = &data (obs=1000); Proc print data = &data (obs=10); run;
            \n%end;<\/p>\n

            Then do all three (c<\/span>ontents, m<\/span>eans, p<\/span>rint ten obs) with just<\/p>\n

            %cmp(data = mydata);<\/p>\n

             <\/p>\n

              \n
            1. Understand temporary versus permanent files;<\/strong><\/li>\n<\/ol>\n

              Data one;\u00a0\u00a0 creates a work.one temporary dataset that disappears when SAS terminates;<\/p>\n

              Data out.one; creates a permanent dataset in the out directory that remains even if SAS terminates;<\/p>\n

               <\/p>\n

              Define libraries (or directories):<\/p>\n

              Libname out \u201cc:\/data\/marketscan\/output\u201d;
              \nLibname in \u201cc:\/data\/marketscan\/MSdata\u201d;
              \n <\/p>\n

               <\/p>\n

              Output or data can be written into external files:<\/p>\n

              Filename textdata \u201cc:\/data\/marketscan\/textdata.txt\u201d;<\/p>\n

               <\/p>\n

                \n
              1. Run tests on small samples to develop programs and then Toogle between tiny and large samples when debugged.<\/strong><\/li>\n<\/ol>\n

                A simple way is<\/p>\n

                Options obs =10;
                \n*options obs = max; *only use this when you are sure your programs run.
                \n <\/p>\n

                OR, some procedures and data steps using End= dataset option do not work well on partial samples. For those I often toggle between two different input libraries. Create a subset image of all of your data in a separate directory and then toggle using the libname commands;<\/p>\n

                 <\/p>\n

                *Libname in \u2018c:\/data\/projectdata\/fulldata\u2019;
                \nLibname in \u2018c:\/data\/projectdata\/testsample\u2019;<\/p>\n

                 <\/p>\n

                Time spent creating a test data set is time well spent.<\/p>\n

                You could even write a macro to make it easy. (I leave it as an exercise!)<\/p>\n

                 <\/p>\n

                  \n
                1. Use arrays abundantly. You can use different array names to reference the same set of variables. This is very convenient;<\/strong><\/li>\n<\/ol>\n

                   <\/p>\n

                  %let rhs=x1 x2 y1 y2 count more;
                  \nData _null_;
                  \nArray X {100} X001-X100; *usual form;
                  \nArray y {100} ;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 * creates y1-y100;
                  \nArray xmat {10,10} X001-X100; *matrix notation allows two dimensional indexes;
                  \nArray XandY {*} X001-X100 y1-y100 index ; *useful when you don\u2019t know the count of variables in advance;
                  \nArray allvar &rhs. ;\u00a0\u00a0\u00a0\u00a0 *implicit arrays can use implicit indexes;
                  \n <\/p>\n

                  *see various ways of initializing the array elements to zero;<\/p>\n

                  Do i = 1 to 100; x{i} = 0; end;
                  \n <\/p>\n

                  Do i = 1 to dim(XandY); XandY{i} = 0; end;<\/p>\n

                   <\/p>\n

                  Do over allvar; allvar = 0; end;\u00a0\u00a0 *sometimes this is very convenient;<\/p>\n

                   <\/p>\n

                  Do i=1 to 100 while (y(i) = . );
                  \ny{i} = 0;\u00a0\u00a0 *do while and do until are sometimes useful;
                  \nend;<\/p>\n

                   <\/p>\n

                  run;<\/p>\n

                    \n
                  1. For some purposes naming variables in arrays using leading zeros improves sort order of variables<\/strong><\/li>\n<\/ol>\n

                    Use:
                    \nArray x {100} X001-X100;
                    \nnot
                    \nArray x {100} X1-X100;<\/p>\n

                    With the second, the alphabetically sorted variables are x1,x10,x100, x11, x12,..,x19, x2,x20 , etc.<\/p>\n

                     <\/p>\n

                      \n
                    1. Learn Set versus Merge command (Update is for rare, specialized use)<\/strong><\/li>\n<\/ol>\n

                       <\/p>\n

                      Data three;\u00a0\u00a0 *information on the same person combined into a single record;
                      \nMerge ONE TWO;
                      \nBY IDNO;
                      \nRun;<\/p>\n

                       <\/p>\n

                        \n
                      1. Learn key dataset options like<\/strong><\/li>\n<\/ol>\n

                        Obs=
                        \nKeep=
                        \nDrop=
                        \nIn=
                        \nFirstobs=
                        \nRename=(oldname=newname)
                        \nEnd=<\/p>\n

                         <\/p>\n

                          \n
                        1. Keep files being sorted \u201cskinny\u201d by using drop or keep statements<\/strong><\/li>\n<\/ol>\n

                          Proc sort data = IN.BIG(keep=IDNO STATE COUNTY FROMDATE) out=out.bigsorted;
                          \nBY STATE COUNTY IDNO FROMDATE;
                          \nRun;<\/p>\n

                          Also consider NODUP and NODUPKEY options to sort while dropping duplicate records, on all or on BY variables, respectively.<\/p>\n

                           <\/p>\n

                            \n
                          1. Take advantage of BY group processing<\/strong><\/li>\n<\/ol>\n

                            Use FIRST.var and LAST.var abundantly.<\/p>\n

                             <\/p>\n

                            USE special variables
                            \n_N_ = current observation counter
                            \n_ALL_ set of all variables such as Put _all_. Or when used with PROC CONTENTS, set of all datasets.<\/p>\n

                             <\/p>\n

                            Also valuable is<\/p>\n

                            PROC CONTENTS data = in._all_; run;<\/p>\n

                             <\/p>\n

                              \n
                            1. Use lots of comments<\/strong><\/li>\n<\/ol>\n

                               <\/p>\n

                              * this is a standard SAS comment that ends with a semicolon;<\/p>\n

                               <\/p>\n

                              \/*\u00a0\u00a0 a PL1 style comment can comment out multiple lines including ordinary SAS comments;<\/p>\n

                              * Like this; *\/<\/p>\n

                               <\/p>\n

                              %macro junk; Macros can even comment out other macros or other pl1 style comments;<\/p>\n

                              \/*such as this; *\/ * O Boy!; %macro ignoreme;\u00a0\u00a0 mend; *very powerful;<\/p>\n

                               <\/p>\n

                              %mend; * end macro junk;<\/p>\n

                               <\/p>\n

                                \n
                              1. Use meaningful file names!<\/strong><\/li>\n<\/ol>\n

                                Data ONE TWO THREE can be useful.<\/p>\n

                                 <\/p>\n

                                  \n
                                1. Put internal documentation about what the program does, who did it and when.<\/strong><\/li>\n
                                2. Learn basic macro language;<\/strong> See SAS program demo for examples. Know the difference between executable<\/strong> and declarative <\/strong>statements used in DATA step<\/li>\n<\/ol>\n

                                  \u00a0<\/strong><\/p>\n

                                  17. EXECUTABLE COMMANDS USED IN DATA STEP (Actually DO something, once for every record)<\/strong><\/p>\n

                                   <\/p>\n

                                  Y=y+x (assignment. In STATA you would use GEN y=x or REPLACE Y=X)
                                  \n 
                                  \nDo I = 1 to 10;
                                  \nEnd; (always paired with DO, can be nested nearly unlimited deepness)<\/p>\n

                                   <\/p>\n

                                  INFile in \u2018c:\/data\/MSDATA\/claimsdata.txt\u2019;\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 define where input statements read from;
                                  \nFile out \u2018c:\/data\/MSDATA\/mergeddata.txt\u2019;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 define where put statements write to;<\/p>\n

                                  \u00a0<\/em><\/p>\n

                                  Goto johnny; \u00a0\u00a0\u00a0\u00a0\u00a0* always avoid. Use do groups instead;<\/em><\/p>\n

                                   <\/p>\n

                                  IF a=b THEN y=0 ;
                                  \nELSE y=x; * be careful when multiple if statements;
                                  \nCALL subroutine();<\/em> (Subroutines are OK, Macros are better)<\/em><\/p>\n

                                  \u00a0<\/em><\/p>\n

                                  INPUT\u00a0\u00a0 X ; (read in one line of X as text data from INFILE)
                                  \nPUT\u00a0\u00a0 x y= \/ z date.; (Write out results to current LOG or FILE file)<\/p>\n

                                   <\/p>\n

                                  MERGE IN.A IN.B ;
                                  \nBY IDNO;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 *\u00a0\u00a0 Match up with BY variable IDNO as you simultaneously read in A&B;<\/p>\n

                                  Both files must already be sorted by IDNO.<\/p>\n

                                  SET A B;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0* read in order, first all of A, and then all of B;<\/p>\n

                                  UPDATE\u00a0\u00a0 A B; *replace variables with new values from B only if non missing in B;<\/p>\n

                                   <\/p>\n

                                  OUTPUT out.A;\u00a0\u00a0 \u00a0\u00a0\u00a0*Write out one obs to out.A SAS dataset;
                                  \nOUTPUT; \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0*Writes out one obs of every output file being created;<\/p>\n

                                  DELETE;\u00a0\u00a0 * do not output this record, and return to the top of the datastep;<\/p>\n

                                  STOP;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 * ends the current SAS datastep;<\/p>\n

                                  \u00a0<\/strong><\/p>\n

                                  18. Assignment commands for DATA Step are <\/strong><\/p>\n

                                  only done once at the start of the data step<\/strong><\/p>\n

                                   <\/p>\n

                                  DATA ONE TWO IN.THREE;<\/p>\n

                                  *This would create three data sets, named ONE TWO and IN.THREE<\/p>\n

                                  Only the third one will be kept once SAS terminates.;<\/p>\n

                                  Array x {10} x01-x10;
                                  \nATTRIB x length =16 Abc length=$8;
                                  \nRETAIN COUNT 0;
                                  \nBY state county IDNO;
                                  \nAlso consider\u00a0\u00a0
                                  \nBY DESCENDING IDNO; or BY IDNO UNSORTED; if grouped but not sorted by IDNO;
                                  \nDROP i;\u00a0\u00a0 * do not keep i in final data set, although it can still be used while the data step is running
                                  \nKEEP IDNO AGE SEX; *this will drop all variables from output file except these three;
                                  \nFORMAT x date.;\u00a0\u00a0 *permanently link the format DATE. To the variable link;<\/p>\n

                                  INFORMAT ABC $4.;<\/p>\n

                                  LABEL AGE2010 = \u201cAge on December 31 2010\u201d;
                                  \nLENGTH x 8; *must be assigned the first time you reference the variable;
                                  \nRENAME AGE = AGE2010; After this point you must use the newname (AGE2010);
                                  \nOPTIONS NOBS=100; One of many options. Note done only once.<\/p>\n

                                   <\/p>\n

                                  19. Key Systems language commands<\/strong><\/p>\n

                                  LIBNAME to define libraries
                                  \nFILENAME to define specific files, such as for text data to input or output text<\/p>\n

                                  TITLE THIS TITLE WILL APPEAR ON ALL OUTPUT IN LISTING until a new title line is given;<\/p>\n

                                  %INCLUDE<\/p>\n

                                  %LET year=2011;<\/p>\n

                                  %LET ABC = \u201cRandy Ellis\u201d;<\/p>\n

                                   <\/p>\n

                                  20. Major procs you will want to master<\/strong><\/p>\n

                                  DATA step !!!!! Counts as a procedure;<\/p>\n

                                  PROC CONTENTS<\/p>\n

                                  PROC PRINT<\/p>\n

                                  PROC MEANS<\/p>\n

                                  PROC SORT<\/p>\n

                                  PROC FREQ \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0frequencies<\/p>\n

                                  PROC SUMMARY \u00a0\u00a0\u00a0\u00a0\u00a0(Can be done using MEANS, but easier)<\/p>\n

                                  PROC CORR (Can be done using Means or Summary)<\/p>\n

                                  PROC REG\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 OLS or GLS<\/p>\n

                                  PROC GLM\u00a0\u00a0 General Linear Models with automatically created fixed effects<\/p>\n

                                  PROC FORMAT \/INFORMAT<\/p>\n

                                  PROC UNIVARIATE<\/p>\n

                                  PROC GENMOD nonlinear models<\/p>\n

                                  PROG SURVEYREG clustered errors<\/p>\n

                                  None of the above will execute unless a new PROC is started OR you include a RUN; statement.<\/p>\n

                                  21. Formats are very powerful.<\/strong> Here is an example from the MarketScan data. One use is to simply recode variables so that richer labels are possible.<\/p>\n

                                   <\/p>\n

                                  Another use is to look up or merge on other information in large files.<\/p>\n

                                   <\/p>\n

                                  Proc format;
                                  \nvalue $region
                                  \n1=\u20191-Northeast Region\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n2=\u20192-North Central Region\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n3=\u20193-South Region\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n4=\u20194-West Region\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n5=\u20195-Unknown Region\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n;<\/p>\n

                                   <\/p>\n

                                  value $sex<\/p>\n

                                  1=\u20181-Male\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \n2=\u20182-Female\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u2018
                                  \nother=\u2018 Missing\/Unknown\u2019<\/p>\n

                                  ;<\/p>\n

                                   <\/p>\n

                                  *Three different uses of formats;<\/p>\n

                                  Data one ;
                                  \nsex=\u20191\u2019;
                                  \nregion=1;
                                  \nLabel sex = \u2018patient sex =1 if male\u2019;
                                  \nlabel region = census region;
                                  \nrun;<\/p>\n

                                  Proc print data = one;<\/p>\n

                                  Run;<\/p>\n

                                   <\/p>\n

                                  data two;
                                  \nset one;
                                  \nFormat sex $sex.;<\/strong> * permanently assigns sex format to this variable and stores format with the dataset;
                                  \nRun;<\/p>\n

                                  Proc print data = two;
                                  \nRun;<\/p>\n

                                  Proc contents data = two;
                                  \nRun;<\/p>\n

                                  *be careful if the format is very long!;<\/p>\n

                                   <\/p>\n

                                  Data three;
                                  \nSet one;
                                  \nCharsex=put(sex,$sex.);<\/strong>
                                  \nRun;<\/p>\n

                                  *maps sex into the label, and saves a new variable as the text strings. Be careful can be very long;<\/p>\n

                                  Proc print data =three;
                                  \nRun;<\/p>\n

                                   <\/p>\n

                                  Proc print data = one;
                                  \nFormat sex $sex.; <\/strong>
                                  \n*this is almost always the best way to use formats: Only on your results of procs, not saved as part of the datasets;
                                  \nRun;<\/p>\n

                                   <\/p>\n

                                  If you are trying to learn SAS on your own, then I recommend you buy:<\/p>\n

                                  \n

                                  The Little SAS Book: A Primer, Fifth Edition (or an earlier one)<\/h2>\n

                                  Nov 7, 2012<\/p>\n

                                  by Lora Delwiche and Susan Slaughter<\/div>\n<\/div>\n

                                  Beginners introduction to SAS. Probably the best single book to buy when learning SAS.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  There is also a posting on Ellis SAS tips for Experienced SAS programmers It focuses on issues when using large datasets.   Randy\u2019s SAS hints for New SAS programmers, updated Feb 21, 2015 ALWAYS begin and intermix your programs with internal documentation. (Note how I combined six forms of emphasis in ALWAYS: color, larger font, … Continue reading Ellis SAS tips for New SAS programmers<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":2755,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[185,7238,10931,7241,7242,7243],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/posts\/2074"}],"collection":[{"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/users\/2755"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/comments?post=2074"}],"version-history":[{"count":4,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/posts\/2074\/revisions"}],"predecessor-version":[{"id":2193,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/posts\/2074\/revisions\/2193"}],"wp:attachment":[{"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/media?parent=2074"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/categories?post=2074"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.bu.edu\/ellisrp\/wp-json\/wp\/v2\/tags?post=2074"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}