Please see the notes for this tutorial in the first link below.
Please see the notes for this tutorial in the first link below.
Before we can start using the SAS program and learn how to write the code and match it to our data and trials, we need to be aware of different versions of SAS that we have access to.
SAS is an extremely large and complex software program with many different components. We primarily use Base SAS, SAS/STAT, SAS/ACCESS, and maybe bits and pieces of other components such as SAS/IML.
SAS University Edition and SAS OnDemand both use SAS Studio. SAS Studio is an interface to the SAS program and contains the following components:
If you are using the PC or Server SAS versions, you may have access to more than the modules listed above. To see exactly what you have access to, you can run the following code:
You will see the components available to you listed in the log window.
Also note the additional information available to you:
There are a number of components to the SAS interface:
SAS is divided into 2 areas:
DATA step is all about data manipulation – one of the key strengths to SAS
PROCs – this is where you will find most of your statistical procedures.
Notes to Reading Data into SAS are available as a PDF document. Please download and save on your laptop.
Oh yes! It is that time of year again 🙂 I have to admit that I love fall – my favourite season. The time for so many new beginnings. With this all in mind, the new schedule for F19 OACStats workshops is now open for registration at https://oacstats_workshops.youcanbook.me/. Workshops will be approximately 3 hours long with breaks and hands-on exercises – so bring your laptops with the appropriate software installed. Please note that the workshops are being held in Crop Science Building Rm 121B (room with NO computers) and will begin at 8:30am.
September 10: Introduction to SAS
September 17: Introduction to R
October 15: Getting comfortable with your data in SAS: Descriptive statistics and visualizing your data
October 29: Getting comfortable with your data in R: Descriptive statistics and visualizing your data
November 5: ANOVA in SAS
November 15: ANOVA in R
I am also trying something new this semester – to stay with the theme of new beginnings 🙂 Tutorials! These will be held on Friday afternoons from 1:30-3:30 – sorry only time I could get a lab that worked with all the schedules. They will be held in Crop Science Building Rm 121A (room with computers). Topics will jump around a bit with time to review and work on Workshop materials. To register for these please visit: https://oacstatstutorials.youcanbook.me/
September 13: Saving your code and making your research REPRODUCIBLE
Cancelled: September 20: Introduction to SPSS
September 27: Follow-up questions to Intro to SAS and Intro to R workshops
October 18: More DATA Step features in SAS
October 25: More on Tidy Data in R
November 1: Open Forum
November 15: Questions re: ANOVAs in SAS and R
November 29: Open Forum
I hope to see many of you this Fall!
One last new item – PODCASTS. I’ll be trying to record the workshops and tutorials. These will be posted on the new page and heading PODCASTS. I will also link to them in each workshops post.
Welcome back and let’s continue to make Stats FUN
A question that comes up more and more in my position. Graduate students starting their academic career or experienced researchers looking to keep up with the “trends”.
There was a recent article published on the RBloggers website, that compared the top statistical packages: R, Python (?), SAS, SPSS, and Stata. If you are interested in reading the original article I’ve linked to it here. I’d like to summarize and show a few examples as well.
R Studio is one of the more common ways that folks are using R today. It is a comfortable environment – a little bit of GUI that really doesn’t leave you hanging out in space – ok maybe a little – but you’re fine once you get comfortable with the coding.
Yes! you read that correctly – you need to write coding in R – very similar to needing to write code in SAS. The code or syntax is different for the 2 programs – but you need to write some code in order to conduct any statistical analyses in either program.
SAS as you may be aware has a few different interfaces as well. There is the SAS Studio – used with the Free University edition
Licensed version of SAS:
As I noted earlier each program has their own language or syntax. R is comprised of packages that may deal with a type of analysis. Within a package there are several functions. SAS we have PROCedures with options and lines of code that will run the analysis. Very similar concepts. Each program will have documentation. Since R is open source and community driven, the detail of the documentation will depend on the creator of the package. SAS documentation is extensive but very technical at times.
ggplot(fruit, aes(x=Yield)) +
plot(Yield ~ Variety,
col = factor(Variety),
legend = c(1, 2, 3, 4),
col = c(“black”, “red”, “green”, “blue”),
Proc sgplot data=out_asp2010_test;
scatter x=julian y=mms / group=entry yerrorlower= low4 yerrorupper = high4;
series x=julian y=mms / group=entry lineattrs=(pattern=solid);
xaxis label =”Julian Day”;
yaxis label = “Mms”;
title “Plot of Mms by Julian Day for 2010”;
As noted above R is open source and community-driven. Which also means that it is supported by the community. Any questions, challenges you may encounter, you will use a variety of sources to find help: the author of the package you are using, or a listserv.
SAS is a commercial product with professional support network to assist its users. There are listservs of users as well.
As pointed out in the R Bloggers article, they both have their strengths and their weaknesses. I’ll be honest I never through I’d see the day when banks and pharma started using R, but it’s here! The small program that folks used because it was free and accessible, has now become a major contender in the statistical analysis world.
Which program you select to use, will depend on your background – what have you used in your undergrad or in your course – the level of support available to you on your campus, maybe what program your supervisor uses or recommends. I used to recommend SAS if you were going to work in a workplace that needed standards, but after learning more about R and seeing its growth, I’m not sure that should be a reason to use SAS in academia anymore.
I, personally, believe, that we should be learning both programs – I know too much time to learn – but they both look awesome on a resume, and they both provide you with the opportunities to increase your skillset and talk stats to SAS and R users 😉
To complete the contents of the day-long R workshop offered on June 11, 2019, we will work through the following sessions: