* Fetch the file from the web site */ filename probly temp Ī note for SAS University Edition users: this step requires the update released in December 2017, which includes the secure components that support HTTPS access. Here's the PROC HTTP step to download the CSV file into a temporary fileref. To download the file via a script, I need the "Raw" file URL, which I can access via the Raw button on the GitHub page. But that's not the URL that I need for PROC HTTP or any programmatic access. You can see the contents of the CSV file at this friendly URL. It runs faster, and it allows you to separate the step of fetching the file from the subsequent steps of processing that file.
You'll find lots of papers and examples that use FILENAME URL for the same job in fewer lines of code, but PROC HTTP is more robust. It's still my favorite technique for reading data from the Internet. In 2012 I first shared this method for reading data from a cloud service like DropBox and GitHub. To solve his requirements, the SAS program must connect to GitHub and download the CSV file, import the data into SAS, change the column names to comply with SAS naming rules, but retain the original column names as descriptive labels. Rick challenged me to devise a method to pull the data directly from GitHub and prep it in SAS. In his final program, Rick includes a DATA step with everything you need to reproduce his nifty plots, so you don't need to do any additional data prep. The complete rules are documented in the SAS Language Reference Guide. Traditionally, SAS variable names must adhere to a few common programming rules: they must be alphanumeric, begin with a letter, and contain no spaces or special characters.
Rick also had to rename the variables (column names) from the data table so that they are easier to code within SAS. The data was shared as a CSV file on GitHub. To prepare the article, Rick first had to download the source data from the study he cited. It's a fun, informative post - I recommend it! You'll "Almost Certainly" enjoy it. In his recent article Perceptions of probability, Rick Wicklin explores how vague statements about "likeliness" translate into probabilities that we can express numerically.
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