To download a directory of SAS files using SAS Studio or SAS on Demand, you can use the following steps:   Open SAS Studio or SAS on Demand in your web browser. Navigate to the directory that contains the SAS files you want to download. Select the SAS files you want to download by clicking on the checkbox next to each file. Click on the "Download" button in the toolbar at the top of the page. In the "Download" window that appears, select the "Zip" option and click on the "Download" button. The zip file containing the SAS files will be downloaded to your computer. Alternatively, you can use the "zip" command in SAS to create a zip file containing the SAS files. To do this, you will need to use the following code:   filename myfiles pipe "zip -r - myfiles.zip *.sas"; data null; infile myfiles recfm=f lrecl=32767; input; file myfiles; put infile; run; Replace "myfiles" with the name of the directory that contains the SAS files you want to download. This code will create a zip file called "myfiles.zip" that contains all the SAS files in the specified directory. You can then download the zip file to your computer using the "Download" button in SAS Studio or SAS on Demand. ... View more

To move a file from the SAS ODA server to Azure blob storage, you will need to use the Azure Databricks File System (DBFS) commands. First, you will need to mount the Azure blob storage location as a DBFS directory using the following code:   dbutils.fs.mount( source = "wasbs://<CONTAINER_NAME>@<STORAGE_ACCOUNT_NAME>.blob.core.windows.net", mount_point = "/mnt/<MOUNT_NAME>", extra_configs = {"fs.azure.account.key.<STORAGE_ACCOUNT_NAME>.blob.core.windows.net": "<ACCESS_KEY>"}) Replace <CONTAINER_NAME>, <STORAGE_ACCOUNT_NAME>, <MOUNT_NAME>, and <ACCESS_KEY> with the appropriate values for your Azure blob storage location.   Once the Azure blob storage location has been mounted as a DBFS directory, you can use the dbutils.fs.cp command to copy the file from the SAS ODA server to the Azure blob storage location. The code for this would be:   dbutils.fs.cp("/mnt/sasoda/output_file.csv", "/mnt/<MOUNT_NAME>/output_file.csv") Replace <MOUNT_NAME> with the mount name you specified in the previous step.   After running this code, the file should be successfully copied from the SAS ODA server to the Azure blob storage location. You can verify this by checking the Azure blob storage location in the Azure Portal.   ... View more

There could be a few reasons why the proc sort statement is not finding duplicate observations with the same PIN and Start Date. One reason could be that the Start Date variable is not formatted as a date. In order to properly compare dates, the Start Date variable should be formatted as a date in SAS using the date9. format. This can be done with the following code: data final; set final; format start_date date9.; run; Another reason could be that the Start Date variable has different date formats across observations. For example, some observations may have the date formatted as "Jan 1 2014" while others may have it formatted as "01JAN2014". In order to properly compare dates, all observations should have the same date format. This can be done with the following code: data final; set final; start_date = input(start_date, date9.); run; Once the Start Date variable has been properly formatted as a date, you can try running the proc sort statement again to see if it finds duplicate observations with the same PIN and Start Date. ... View more

To export the first 5 observations using PROC EXPORT, you can use the following code:   PROC EXPORT data = sashelp.cars(where=(_N_<=5)) outfile="path/dd.csv"; run;   To export observations 6-10 using PROC EXPORT, you can use the following code: PROC EXPORT data = sashelp.cars(where=(_N_>5) and (_N_<=10)) outfile="path/dd.csv"; run;   In both cases, you need to specify the name of the SAS dataset you want to export (in this case, sashelp.cars ), the file path and name for the output CSV file (in this case, path/dd.csv ), and the WHERE clause that specifies which observations you want to export.   ... View more

To extract a sample in SAS, you can use the SAMPLE statement in a PROC SURVEYSELECT procedure. Here is an example of how you might use it to extract your sample:   PROC SURVEYSELECT SAMPLE=200 SEED=12345 OUT=sample_data METHOD=SRS NOPRINT; STRATA SizeOfNodule (01: 0.375, 02: 0.375, 03: 0.25) DIAGNOSTIC (Cancer: 0.3333, Benign: 0.6667); RUN; This will create a new dataset called sample_data that contains a sample of 200 patients, with the proportions of nodule sizes and diagnostic outcomes that you specified. The SEED option allows you to specify a seed value for the random number generator, so that you can obtain the same sample each time you run the code. You can change the seed value to any integer that you like. The METHOD=SRS option specifies that the sample will be selected using simple random sampling without replacement. This means that each patient has an equal probability of being selected, and that each patient can only be selected once. The STRATA statement allows you to specify the proportions of the sample that should be allocated to each combination of nodule size and diagnostic outcome. In this case, the sample will be stratified by nodule size and diagnostic outcome, with the proportions that you specified for each stratum. I hope this helps! Let me know if you have any other questions. ... View more

Some tips for being an efficient SAS programmer include: Writing well-structured, modular code that is easy to read and understand. This means using clear and descriptive variable and function names, as well as using appropriate indentation and commenting your code to explain what it does and how it works. Leveraging existing SAS procedures and functions wherever possible, rather than reinventing the wheel. This will save you time and effort, and also ensure that your code is more robust and reliable. Testing and debugging your code thoroughly before running it on your final data. This will help you catch and fix any errors or issues before they become major problems. Staying organized and keeping track of your work. This means using a consistent file naming convention, keeping your code and data files in separate folders, and using a project management tool or workflow diagram to plan and manage your tasks. In addition to practicing regularly, there are several other ways to improve your SAS programming skills: Seek out opportunities to work on challenging projects that will push you to learn new techniques and approaches. This could be a real-world problem that you encounter in your job, or a personal project that you undertake in your free time. Join online forums or user groups where you can ask questions, share your work, and get feedback from other SAS programmers. This will help you learn from others, as well as connect with like-minded individuals who can provide support and guidance. Take online courses or workshops on SAS programming. Some popular platforms for learning SAS include Coursera, Udemy, LinkedIn Learning, and SAS Academy for Data Science. These courses will provide you with structured learning materials, as well as opportunities to practice and apply your skills in real-world scenarios. Read books and other reference materials on SAS programming. Some good books to consider include "The SAS Programming Language" by Ron Cody, "SAS for Dummies" by Stephen McDaniel and Chris Hemedinger, and "SAS Programming by Example" by Ron Cody and Ray Pass. These books will provide you with a solid foundation in SAS programming concepts and techniques, as well as examples and case studies that will help you see how these concepts are applied in practice. One key concept that is essential for effective SAS programming is understanding how to manipulate data using data steps and procedures. This includes concepts such as data types, variables, expressions, loops, conditional statements, and functions. Once you have a strong foundation in these concepts, you can start exploring more advanced techniques such as arrays, hash tables, and other data structures that will enable you to work with large and complex datasets more efficiently. Overall, the best way to improve your SAS programming skills is to practice regularly, seek out opportunities to learn from others, and stay up to date with the latest developments in the field. With time and effort, you will be able to develop the skills and expertise you need to become a more efficient and effective SAS programmer. ... View more

One way to fix this problem is to include an additional condition to check for missing values in the if statement. In SAS, missing values are represented by a dot ( . ), so you can add a condition to check if the value of bmi_v1 is equal to . and assign the missing value to iBMI_v1 in that case. Here is an example of how you can modify your code to handle missing values: data patient.guidelines; set patient.health; if bmi_v1 = . then iBMI_v1 = .; else if (bmi_v1) lt 25 then iBMI_v1 = 100; else if (bmi_v1) lt 30 then iBMI_v1 = 70; else if (bmi_v1) lt 35 then iBMI_v1 = 30; else if (bmi_v1) lt 40 then iBMI_v1 = 15; else if (bmi_v1) ge 40 then iBMI_v1 = 0; run; In the code above, the first if statement checks if the value of bmi_v1 is equal to . and assigns the missing value to iBMI_v1 if that is the case. This ensures that any missing values in bmi_v1 will be preserved in iBMI_v1 and not assigned to one of the categories. ... View more

This one works as well, thank you. And I agree with your suggestion about "summary" and "sum", indeed, they are different things. ... View more

Thank you. ... View more

Thank you ... View more

Take the %else out of this line. And remove %else later as well All these three conditions - %if &flagone.=1, %if &flagtwo.=1 and %if &flagone.^=1 and &flagtwo.^=1 are mutually exclusive but dependent from each other. If I remove the %else, then they will be executed independently from each other correct?  second(&flagtwo) condition should be executed when the &flagone. condition is false, and the third condition should be executed when the first two conditions are not met. So at least(and only) one of them should be executed.     ... View more

Thank you for your answer @PaigeMiller  I applied the code to my program, but I see some issues. Hope you can help me to figure it out.     %macro prcdth(); %let flagone=0; %let flagtwo=0; data _null_; set dd2; if ddtestcd='PRCDTH' then do; call symputx('flagone',1); stop; end; run; data _null_; set dd2; if DDTESTCD="CANDUSO" and DDORRES="UNEQUIVOCALLY DUE TO DISEASE UNDER STUDY" then do; call symputx('flagtwo',1); stop; end; run; %if &flagone.=1 %then %do; /* Create data set with zero rows */ data dd3_1; set dd2(obs=0); run; %end; %else %if &flagtwo.=1 %then %do; data dd3_2; set dd2; AVALC = "CANCER"; aval=.; PARAMCD="PRCDTH"; AVALCAT1='NON-CARDIOVASCULAR CAUSE'; if AVALCAT1='CARDIOVASCULAR CAUSE' then AVALCA1N=1; else if AVALCAT1='NON-CARDIOVASCULAR CAUSE' then AVALCA1N=2; output; run; %end; %else %if &flagone.^=1 and &flagtwo.^=1 %then %do; data dd3_3; set dd2; if DTHFL='Y' then do; AVALC="Unknown"; aval=.; PARAMCD="PRCDTH"; AVALCAT1=DDSCAT; if AVALCAT1='CARDIOVASCULAR CAUSE' then AVALCA1N=1; else if AVALCAT1='NON-CARDIOVASCULAR CAUSE' then AVALCA1N=2; output; end; run; %end; %mend prcdth; %prcdth(); 1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 61 62 63 %macro prcdth(); 64 %let flagone=0; %let flagtwo=0; 65 data _null_; 66 set dd2; 67 if ddtestcd='PRCDTH' then do; 68 call symputx('flagone',1); 69 stop; 70 end; 71 run; 72 data _null_; 73 set dd2; 74 if DDTESTCD="CANDUSO" and DDORRES="UNEQUIVOCALLY DUE TO DISEASE UNDER STUDY" then do; 75 call symputx('flagtwo',1); 76 stop; 77 end; 78 run; 79 80 %if &flagone.=1 %then %do; /* Create data set with zero rows */ 81 data dd3_1; 82 set dd2(obs=0); 83 run; 84 %end; 85 %else %if &flagtwo.=1 %then %do; 86 data dd3_2; 87 set dd2; 88 89 AVALC = "CANCER"; 90 91 aval=.; 92 PARAMCD="PRCDTH"; 93 AVALCAT1='NON-CARDIOVASCULAR CAUSE'; 94 if AVALCAT1='CARDIOVASCULAR CAUSE' then AVALCA1N=1; 95 else if AVALCAT1='NON-CARDIOVASCULAR CAUSE' then AVALCA1N=2; 96 output; 97 run; 98 %end; 99 %else %if &flagone.^=1 and &flagtwo.^=1 %then %do; 100 data dd3_3; 101 set dd2; 102 103 if DTHFL='Y' then do; 104 105 AVALC="Unknown"; 106 aval=.; 107 PARAMCD="PRCDTH"; 108 AVALCAT1=DDSCAT; 109 if AVALCAT1='CARDIOVASCULAR CAUSE' then AVALCA1N=1; 110 else if AVALCAT1='NON-CARDIOVASCULAR CAUSE' then AVALCA1N=2; 111 output; 112 end; 113 run; 114 %end; 115 %mend prcdth; 116 %prcdth(); MPRINT(PRCDTH): data _null_; MPRINT(PRCDTH): set dd2; MPRINT(PRCDTH): if ddtestcd='PRCDTH' then do; MPRINT(PRCDTH): call symputx('flagone',1); MPRINT(PRCDTH): stop; MPRINT(PRCDTH): end; MPRINT(PRCDTH): run; NOTE: There were 15 observations read from the data set WORK.DD2. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds MPRINT(PRCDTH): data _null_; MPRINT(PRCDTH): set dd2; MPRINT(PRCDTH): if DDTESTCD="CANDUSO" and DDORRES="UNEQUIVOCALLY DUE TO DISEASE UNDER STUDY" then do; MPRINT(PRCDTH): call symputx('flagtwo',1); MPRINT(PRCDTH): stop; MPRINT(PRCDTH): end; MPRINT(PRCDTH): run; NOTE: There were 42 observations read from the data set WORK.DD2. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.01 seconds MPRINT(PRCDTH): data dd3_1; MPRINT(PRCDTH): set dd2(obs=0); MPRINT(PRCDTH): run; NOTE: There were 0 observations read from the data set WORK.DD2. NOTE: The data set WORK.DD3_1 has 0 observations and 47 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds 117 118 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 131     So I have added two additional derivations and &flagtwo macro variable. The second derivation (dd3_2 dataset) I need to execute when the &flagtwo is 1 and after the &flagone else condition (i.e there is no ddtestcd='PRCDTH' in dd2 dataset, but there is an observation satisfying  DDTESTCD="CANDUSO" and DDORRES="UNEQUIVOCALLY DUE TO DISEASE UNDER STUDY" condition). The third derivation (dd3_3 dataset)  I need to execute when the &flagone is ^=1 and the &flagtwo is ^=1, after the &flagtwo else condition (i.e. there is no ddtestcd='PRCDTH' in dd2 dataset and also there is no observation satisfying  DDTESTCD="CANDUSO" and DDORRES="UNEQUIVOCALLY DUE TO DISEASE UNDER STUDY" condition, but there is an observation satisfying DTHFL='Y' condition).   Before %prcdth macro execution the input dataset dd2 has 1170 observations(checked manually). But during the macro execution it takes only 15 and 42 observations, and dd3_2 and dd3_3 datasets are not created. Could you please take a look and give some advice on what need to be adjusted, I have attached the log as well.   Thank you in advance. ... View more