Posted on behalf of SAS Press author Derek Morgan.

I was sitting in a model railroad club meeting when one of our more enthusiastic young members said, "Wouldn't it be cool if we could make a computer simulation, with trains going between stations and all. We could have cars and engines assigned to each train and timetables and…"

So, I thought to myself, “Timetables… I bet SAS can do that easily… sounds like something fun for Mr. Dates and Times."

As it turns out, the only easy part of creating a timetable is calculating the time. SAS handles the concept of elapsed time smoothly. It’s still addition and subtraction, which is the basis of how dates and times work in SAS. If a train starts at 6:00 PM (64,800 seconds of the day) and arrives at its destination 12 hours (43,200 seconds) later, it arrives at 6:00 AM the next day. The math is start time+duration=end time (108,000 seconds,) which is 6:00 AM the next day. It doesn’t matter which day, that train is always scheduled to arrive at 6:00 AM, 12 hours from when it left.

It got a lot more complicated when the group grabbed onto the idea. One of the things they wanted to do was to add or delete trains and adjust the timing so multiple trains don’t run over the same track at the same time. This wouldn’t be that difficult in SAS; just create an interactive application, but… I’m the only one who has SAS. So how do I communicate my SAS database with the “outside world”? The answer was Microsoft Excel, and this is where it gets thorny.

It’s easy enough to send SAS data to Excel using ODS EXCEL and PROC REPORT, but how could I get Excel to allow the club to manipulate the data I sent?
I used the COMPUTE block in PROC REPORT to display a formula for every visible train column. I duplicated the original columns (with corresponding names to keep it all straight) and hid them in the same spreadsheet. The EXCEL formula code is in line 8.

Compute Block Code:

I also added three rows to the dataset at the top. The first contains the original start time for each train, the second contains an offset, which is always zero in the beginning, while the third row was blank (and formatted with a black background) to separate it from the actual schedule.

The users can change the offset to change the starting time of a train (Column C, Figure 2.) The formula in the visible columns adds the offset to the value in each cell of the corresponding hidden column (as long as it isn’t blank.) You can’t simply add the offset to the value of the visible cell, because that would be a circular reference.

The next problem was moving a train to an earlier starting time, because Excel has no concept of negative time (or date—a date prior to the Excel reference date of January 1, 1900 will be a character value in Excel and cause your entire column to be imported into SAS as character data.) Similarly, you can’t enter -1:00 as an offset to move the starting time of our 5:35 AM train to 4:35 AM. Excel will translate “-1:00” as a character value and that will cause a calculation error in Excel. In order to move that train to 4:35 AM, you have to add 23 hours to the original starting time (Column D, Figure 2.)

After the users adjust the schedules, it’s time to return our Excel data to SAS, which creates more challenges. In the screenshot above, T534_LOC is the identifier of a specific train, and the timetable is kept in SAS time values. Unfortunately, PROC IMPORT using DBMS=XLSX brings the train columns into SAS as character data. T534_LOC also imports as the actual Excel value, time as a fraction of a day.

Figure 3: How the Schedule Adjustment File Imports to SAS

While I can fix that by converting the character data to numeric and multiplying by 86,400, I still need the original column name of T534_LOC for the simulation, so I would have to rename each character column and output the converted data to the original column name. There are currently 146 trains spread across 12 files, and that is a lot of work for something that was supposed to be easy! Needless to say, this “little” side project, like most model railroads, is still in progress. However, this exercise in moving time data between Microsoft Excel and SAS gave me even more appreciation for the way SAS handles date and time data.

Figure 4 is a partial sample of the finished timetable file, generated as an RTF file using SAS. The data for trains 534 and 536 are from the spreadsheet in Figure 1.

Figure 4: Partial Sample Timetable

Want to learn more about how to use and manipulate dates, times, and datetimes in SAS? You'll find the answers to these questions and much more in my book The Essential Guide to SAS Dates and Times, Second Edition. Updated for SAS 9.4, with additional functions, formats, and capabilities, the Second Edition has a new chapter dedicated to the ISO 8601 standard and the formats and functions that are new to SAS, including how SAS works with Universal Coordinated Time (UTC). Chapter 1 is available as a free preview here.

For updates on new SAS Press books and great discounts subscribe to the SAS Press New Book Newsletter.

Whether you are a strong believer in the power of dividing by zero, agnostic, undecided, a supporter, denier or anything in between and beyond, this blog post will bring all to a common denominator.

## History of injustice

For how many years have you been told that you cannot divide by zero, that dividing by zero is not possible, not allowed, prohibited? Let me guess: it’s your age minus 7 (± 2).

But have you ever been bothered by that unfair restriction? Think about it: all other numbers get to be divisors. All of them, including positive, negative, rational, even irrational and imaginary. Why such an injustice and inequality before the Law of Math?

We have our favorites like π, and prime members (I mean numbers), but zero is the bottom of the barrel, the lowest of the low, a pariah, an outcast, an untouchable when it comes to dividing by. It does not even have a sign in front of it. Well, it’s legal to have, but it’s meaningless.

And that’s not all. Besides not being allowed in a denominator, zeros are literally discriminated against beyond belief. How else could you characterize the fact that zeros are declared as pathological liars as their innocent value is equated to FALSE in logical expressions, while all other more privileged numbers represent TRUE, even the negative and irrational ones!

## Extraordinary qualities of zeros

Despite their literal zero value, their informational value and qualities are not less than, and in many cases significantly surpass those of their siblings. In a sense, zero is a proverbial center of the universe, as all the other numbers dislocated around it as planets around the sun. It is not coincidental that zeros are denoted as circles, which makes them forerunners and likely ancestors of the glorified π.

Speaking of π, what is all the buzz around it? It’s irrational. It’s inferior to 0: it takes 2 π’s to just draw a single zero (remember O=2πR?). Besides, zeros are not just well rounded, they are perfectly rounded.

Privacy protection experts and GDPR enthusiasts love zeros. While other small numbers are required to be suppressed in published demographical reports, zeros may be shown prominently and proudly as they disclose no one’s personally identifiable information (PII).

No number rivals zero. Zeros are perfect numerators and equalizers. If you divide zero by any non-zero member of the digital community, the result will always be zero. Always, regardless of the status of that member. And yes, zeros are perfect common denominators, despite being prohibited from that role for centuries.

Zeros are the most digitally neutral and infinitely tolerant creatures. What other number has tolerated for so long such abuse and discrimination!

Enough is enough!

## Dividing by zero opens new horizons

Can you imagine what new opportunities will arise if we break that centuries-old tradition and allow dividing by zero? What new horizons will open! What new breakthroughs and discoveries can be made!

With no more prejudice and prohibition of the division by zero, we can prove virtually anything we wish. For example, here is a short 5-step mathematical proof of “4=5”:

1)   4 – 4 = 10 – 10
2)   22 – 22 = 5·(2 – 2)
3)   (2 + 2)·(2 – 2) = 5·(2 – 2) /* here we divide both parts by (2 – 2), that is by 0 */
4)   (2 + 2) = 5
5)   4 = 5

Let’s make the next logical step. If dividing by zero can make any wish a reality, then producing a number of our choosing by dividing a given number by zero scientifically proves that division by zero is not only legitimate, but also feasible and practical.

As you will see below, division by zero is not that easy, but with the power of SAS, the power to know and the powers of curiosity, imagination and perseverance nothing is impossible.

## Division by zero - SAS implementation

Consider the following use case. Say you think of a “secret” number, write it on a piece of paper and put in a “secret” box. Now, you take any number and divide it by zero. If the produced result – the quotient – is equal to your secret number, wouldn’t it effectively demonstrate the practicality and magic power of dividing by zero?

Here is how you can do it in SAS. A relatively “simple”, yet powerful SAS macro %DIV_BY_0 takes a single number as a numerator parameter, divides it by zero and returns the result equal to the one that is “hidden” in your “secret” box. It is the ultimate, pure artificial intelligence, beyond your wildest imagination.

All you need to do is to run this code:

```  data MY_SECRET_BOX; /* you can use any dataset name here */ MY_SECRET_NUMBER = 777; /* you can use any variable name here and assign any number to it */ run;   %macro DIV_BY_0(numerator);   %if %sysevalf(&numerator=0) %then %do; %put 0:0=1; %return; %end; %else %let putn=&sysmacroname; %let %sysfunc(putn(%substr(&putn,%length(&putn)),words.))= %sysevalf((&numerator/%sysfunc(constant(pi)))**&sysrc); %let a=com; %let null=; %let nu11=%length(null); %let com=*= This is going to be an awesome blast! ; %let %substr(&a,&zero,&zero)=*Close your eyes and open your mind, then; %let imagine = "large number like 71698486658278467069846772 Bytes divided by 0"; %let O=%scan(%quote(&c),&zero+&nu11); %let l=%scan(%quote(&c),&zero); %let _=%substr(%scan(&imagine,&zero+&nu11),&zero,&nu11); %let %substr(&a,&zero,&zero)%scan(&&&a,&nu11+&nu11-&zero)=%scan(&&&a,-&zero,!b)_; %do i=&zero %to %length(%scan(&imagine,&nu11)) %by &zero+&zero; %let null=&null%sysfunc(&_(%substr(%scan(&imagine,&nu11),&i,&zero+&zero))); %end; %if &zero %then %let _0=%scan(&null,&zero+&zero); %else; %if &nu11 %then %let _O=%scan(&null,&zero); %if %qsysfunc(&O(_&can)) %then %if %sysfunc(&_0(&zero)) %then %put; %else %put; %put &numerator:0=%sysfunc(&_O(&zero,&zero)); %if %sysfunc(&l(&zero)) %then;   %mend DIV_BY_0;   %DIV_BY_0(55); /* parameter may be of any numeric value */```

When you run this code, it will produce in the SAS LOG your secret number:

55:0=777

How is that possible without the magic of dividing by zero? Note that the %DIV_BY_0 macro has no knowledge of your dataset name, nor the variable name holding your secret number value to say nothing about your secret number itself.

That essentially proves that dividing by zero can practically solve any imaginary problem and make any wish or dream come true. Don’t you agree?

There is one limitation though. We had to make this sacrifice for the sake of numeric social justice. If you invoke the macro with the parameter of 0 value, it will return 0:0=1 – not your secret number - to make it consistent with the rest of non-zero numbers (no more exceptions!): “any number, except zero, divided by itself is 1”.

## Challenge

Can you crack this code and explain how it does it? I encourage you to check it out and make sure it works as intended. Please share your thoughts and emotions in the Comments section below.

## Disclosure

This SAS code contains no cookies, no artificial sweeteners, no saturated fats, no psychotropic drugs, no illicit substances or other ingredients detrimental to your health and integrity, and no political or religious statements. It does not collect, distribute or sell your personal data, in full compliance with FERPA, HIPPA, GDPR and other privacy laws and regulations. It is provided “as is” without warranty and is free to use on any legal SAS installation. The whole purpose of this blog post and the accompanied SAS programming implementation is to entertain you while highlighting the power of SAS and human intelligence, and to fool around in the spirit of the date of this publication.

Dividing by zero with SAS was published on SAS Users.

SAS Visual Analytics supports region maps for Country, US states, and provinces out-of-the-box.  These work well for small scale maps covering the world, a continent, or a single country.  However, other regions are often needed.  Beginning in version 8.3, VA supports custom polygons to display regions such as sales territories, counties, or zip codes.

Region (choropleth) maps use a fill color to show relationships between the regions based upon a response value from your data.  Using custom polygons in VA follows the same steps outlined in previous posts for predefined or custom coordinate geography items, with just a few additional steps.  Here’s the basic flow:

• Import polygon shapefile into SAS dataset
• Import the shape dataset into VA
• Create a Custom polygon provider
• Create the geography item
• Create and customize the map

## Before we begin

VA supports two sources for creating custom polygons: Esri shapefiles and Esri Feature Services.  The goal for this post is to show how to create custom polygons using an Esri shapefile.

Typically, when working with custom polygons, you will have two datasets: the first defines the custom regions (shape data) and the second contains the data you wish to map (business data).  The shape data is derived from an Esri shapefile or feature service.  The business data can be in a shapefile or any format supported by VA (.sas7bdat, .csv, .xls, etc). It contains the information you want to analyze distributed across the regions defined by the shape data.

It is recommended that you verify the imported shape data before using it in your final map.  This will confirm the data is valid and make debugging an issue easier should you encounter any errors.  To verify, use the same dataset for both the shape and business data.  The example below will use this approach.

Access to a GIS application such as Esri’s ArcGIS or QGIS is recommended.  There are two areas where they can help you prepare to use custom polygons in your VA map:

• Creating a shapefile to define polygons specific to your business need or application
• Viewing the attribute table of existing shapefiles to determine its unique identifier column

For this example, we will be creating a map of registered Neighborhood Associations in Boise, Idaho. To follow along, download the data from the City of Boise open data site: Boise Neighborhood Associations

### Shape data

The shape data defining the custom regions needs to be in an Esri shapefile format. These files can be created in a GIS application or obtained from a wide variety of online sources such as: the US Census Bureau (http://www.census.gov); local and state municipalities; state agencies such as the Department of Transportation; and university GIS departments.  Most municipalities now have Open Data portals that provide a wealth of reliable data for public use.  These sources are maintained by dedicated staff and are updated regularly.

The business data can be specific to your company’s operation or customer base.  Or it can be broad and general using census or demographic information.  It answers the question of What you want to analyze on the map.  The business data must contain a column that aligns with your shape data.  For example: If you want to map the age distribution and spending habits of your target customers across zip codes, then your business data must have a column for zip codes that allows it to be joined to a zip code region in the shape data.

## 2. Import polygon data into a SAS dataset

VA 8.3 does not support the native shapefile format. To use a shapefile in VA, you must first import it into SAS.  Included with Viya3.4, the %shpimprt macro will convert a shapefile into a SAS dataset and load it into CAS.  You can find the documentation for it here: %shpimprt documentation.

Alternatively, the shapefile can be manually imported with these basic steps:

• Import the shapefile into SAS
• Add a sequence column to the dataset
• Reduce the density of the dataset
• Limit the dataset based on the density value

Additional details and sample code for each of these steps can be found in the text file linked here: Manual shapefile import steps.

## 3. Import the shape dataset into VA

Next, we must import the dataset into VA, if using the manual shapefile import process.  To do this, locate the data pane on the left of VA.  From the ‘Open Data Source’ window, select Import > Local File.  Navigate to the location of the SAS dataset created from Step 2 and click the Open button.

Adjust the target location as needed, based on your VA installation, and make note of the location selected.  This path will be required to configure the custom polygon provider. Review and adjust the other options as needed.  Click the blue ‘Import Item’ button at the top of the window to start the import process.  A message will appear indicating the import status. Upon successful import, click the 'OK' button to open the dataset.

Since we are using the same dataset for the shape and business data, we need to make a copy of the category variable that will be used for our map. Right click on ‘ASSOCIATIO’ and select ‘Duplicate’.  Next, let’s change the names of both variables to better distinguish them from one another:

• Change ‘ASSOCIATIO’ to ‘Business data’
• Change ‘ASSOCIATIO (1)’ to ‘Shape data’

## 4. Create the geography item

We are now ready to start creating the geography item.  With Custom polygons, an additional step is required beyond what was described in previous posts with predefined and custom coordinates geography items.  We must define a Custom Polygon provider so VA knows how to locate and display the Boise Neighborhood Associations.  This is needed only once and is part of the geography item setup you are familiar with.

Our goal is to map the regions of the Boise Neighborhood Associations, so we will use ‘Shape data’ for our geography item.  Locate it in the VA data panel and change its Classification type to ‘Geography’.  From the ‘Geography data type’ dropdown, select ‘Custom polygonal shapes’. Several new fields will be displayed.  In the ‘Custom polygon provider’ dropdown, click the ‘Define new polygon provider’ button.

A ‘New Polygon Provider’ window will appear.  All fields shown are required.  The Advanced section has additional options, but they are not needed for this example.

Configure the fields based on the following:

• Name / Label – Enter ‘Boise Neighborhoods’ for both (these values do not have to be the same)
• Type – The default CAS Table is the correct option for this example.
• Server / Library – These values must match those used for the data upload in Step 3.
• Table – Select the name of the table uploaded in Step 3 (Boise_Neighborhoods)
• ID Column – The unique identifier column of the dataset. Used to join the shape and business data together. (Select OBJECTID)
• Sequence Column – This column is created during the import process from Step 2. Needed by VA to display the custom regions. (Select SEQUENCE)

The custom polygon provider is now configured.  All that is needed to finish the geography item setup, is to identify the Region ID.  This is the crucial step that will join the shape data to the business data.  The Region ID column must match the ID Column chosen when the custom polygon provider was setup.  Since we are using the same dataset in this example, that value is the same: OBJECTID.

In cases where different datasets are used for the shape and business data, the name of Region ID and ID Column may be different.  The column labels are not important, but their content must match for the join to occur.

Notice that once you select the correct RegionID value, the preview window will display the custom regions from the imported shape data.  The Latitude and Longitude columns are not required in this example.  Click the ‘OK’ button, to finish the setup.

## 5. Create and customize the map

You are now ready to create your map.  Drag the Boise Neighborhoods geography item to the report canvas.  Let’s enhance the appearance of our map by making a few style changes:

• Set a Color role to shade the Neighborhood Association regions (Roles > Color > Business data)
• Position the legend on the left of the map (Options > Legend)
• Adjust the transparency of the fill color to 45% (Options > Map Transparency)
• Change the map service to Esri World Street Map (Options > Map service)

Final map with custom polygons.

Congratulations!  You have just created your first custom region map.  In this post we discussed how to use the Custom Polygon provider to define your own regions using an Esri shapefile.  Compared to the Predefined and Custom Coordinate options, custom polygons give you additional flexibility and control over how your spatial data is analyzed.

Creating custom region maps with SAS Visual Analytics was published on SAS Users.

In this post, we continue our discussion of geography variables, the foundation of Visual Analytics Geo maps. This time we will look at Custom Coordinates.  As with any statistical graph, understanding your data is key.  But when using Custom Coordinates for geographic maps, this understanding becomes even more important.

Use the Custom Coordinate geography variable when your data does not match one of VA’s predefined geography types (see previous post, Fundamentals of SAS Visual Analytics geo maps).  For Custom coordinates, your data set must include latitude and longitude values as separate variables.   These values should be sourced from trustworthy providers and validated for accuracy prior to loading into VA.

When using Custom Coordinates, the Coordinate Space must also be considered.  The coordinate space defines the grid used to plot your data.  The underlying map is also based on a grid.  In order for your data to display correctly on a map, these grids must match.  Visual Analytics uses the World Geodetic System (WGS84) as the default coordinate space (grid).  This will work for most scenarios, including the example below.

Once you have selected a dataset and confirmed it contains the required spatial information, you can now create a Custom Geography variable.  In this example, I am using the variable Business Address from the dataset Wake_Co_Pizza.  Let’s get started.

1. Begin by opening VA and navigate to the Data panel on the left of the application.
2. Select the dataset and locate the variable that you wish to map. Click the down arrow to the right of the variable and chose ‘Geography’ from the Classification dropdown menu.
3. The ‘Edit Geography Item’ window appears. Select Custom coordinates in the ‘Geography data type’ dropdown.   Three new dropdown lists appear that are specific to the Custom coordinates data type: ‘Latitude (y)’, ‘Longitude (x)’ and ‘Coordinate Space’.

When using the Custom coordinates data type, we must tell VA where to find the spatial data in our dataset.  We do this using the Latitude (y) and Longitude (x) dropdown lists.  They contain all measures from your dataset.  In this example, the variable ‘Latitude World Geodetic System’ contains our latitude values and the variable  ‘Longitude World Geodetic System’ contains our longitude values.   The ‘Coordinate Space’ dropdown defaults to World Geodetic System (WGS84) and is the correct choice for this example.

1. Click the OK button to complete the setup once the latitude and longitude variables have been selected from their respective dropdown lists. You should see a new ‘Geography’ section in the Data panel.  The name of the variable (or its edited value) will be displayed beside a globe icon to indicate it is a geography variable.  In this case we see the variable Business Address.

Congratulations!  You have now created a custom geography variable and are ready to display it on a map.  To do this, simply drag it from the Data panel and drop it on the report canvas.  The auto-map feature of VA will recognize it as a geography variable and display the data as a bubble map with an OpenStreetMap background.

In this post, we created a custom geography variable using the default Coordinate Space.  Using a custom geography variable gives you the flexibility of mapping data sets that contain valid latitude and longitude values.  Next time, we will take our exploration of the geography variable one step further and explore using custom polygons in your maps.

Creating a map with SAS Visual Analytics begins with the geographic variable.  The geographic variable is a special type of data variable where each item has a latitude and longitude value.  For maximum flexibility, VA supports three types of geography variables:

1. Predefined
2. Custom coordinates
3. Custom polygons

This is the first in a series of posts that will discuss each type of geography variable and their creation. The predefined geography variable is the easiest and quickest way to begin and will be the focus of this post.

SAS Visual Analytics comes with nine (9) predefined geographic lookup types.  This lookup method requires that your data contains a variable matching one of these nine data types:

• Country or Region Names – Full proper name of a country or region (ISO 3166-1)
• Country or Region ISO 2-Letter Codes – Alpha-2 country code (ISO 3166-1)
• Country or Region ISO Numeric Codes – Numeric-3 country code (ISO 3166-1)
• Country or Region SAS Map ID Values – SAS ID values from MPASGFK continent data sets
• Subdivision (State, Province) Names – Full proper name for level 2 admin regions (ISO 3166-2)
• Subdivision (State, Province) SAS Map ID Values – SAS ID values from MAPSGFK continent data sets (Level 1)
• US State Names – Full proper name for US State
• US State Abbreviations – Two letter US State abbreviation
• US Zip Codes – A 5-digit US zip code (no regions)

Once you have identified a variable in your dataset matching one of these types, you are ready to begin.  For our example map, the dataset 'Crime' and variable 'State name' will be used.  Let’s get started.

Creating a predefined geography variable in SAS Visual Analytics

1. Begin by opening VA and navigate to the Data panel on the left of the application.
2. Select the desired dataset and locate a variable that matches one of the predefined lookup types discussed above. Click the down arrow to the right of the variable and select ‘Geography’ from the Classification dropdown menu.
3. The ‘Edit Geography Item’ window will open. Depending upon the type of geography variable selected, some of the options on this dialog will vary.  The 'Name' textbox is common for all types and will contain the variable selected from your dataset.  Edit this label as needed to make it more user friendly for your intended audience.
4. The ‘Geography data type’ drop down list is where you select the desired type of geography variable.  In this example, we are using the default predefined option.
5. Locate the 'Name or code context' dropdown list.  Select the type of predefined variable that matches the data type of the variable chosen from your data.  Once selected, VA scans your data and does an internal lookup on each data item.  This process identifies latitude and longitude values for each item of your dataset.  Lookup results are shown on the right of the window as a percentage and a thumbnail size map.  The thumbnail map displays the the first 100 matches.
6. If there are any unmatched data items, the first 5 will be displayed.  This may provide a better understanding of your data.  In this example, it is clear from variable name as to what type should be selected (US State Names).  However, in most cases that choice will not be this obvious.  The lesson here, know your data!

Unmatched data items indicators

Once you are satisfied with the matched results, click the OK button to continue.  You should see a new section in the Data panel labeled ‘Geography’.  The name of the variable will be displayed beside a globe icon. This icon represents the geography variable and provides confirmation it was created successfully.

Icon change for geography variable

Now that the geography variable has been created, we are ready to create a map.  To do this, simply drag it from the Data panel and drop it on the VA report canvas.  The auto-map feature of VA will recognize the geography variable and create a bubble map with an OpenStreetMap background.  Congratulations!  You have just created your first map in VA.

Bubble map created with predefined geography variable

The concept of a geography variable was introduced in this post as the foundation for creating all maps in VA.  Using the predefined geography variable is the quickest way to get started with Geo maps.  In situations when the predefined type is not possible, using one of VA's custom geography types becomes necessary.  These scenarios will be discussed in future blog posts.

Fundamentals of SAS Visual Analytics geo maps was published on SAS Users.

There is certainly no shortage of terrific tips and tricks in various SAS blogs from some of our most distinguished SAS in-house experts. But, there's another group of equally qualified experts who don't often get to share their expertise on this channel: our customers. So, I went on a quest to get the inside scoop from various SAS users, polling Friends of SAS members to get their feedback on their favorite SAS tips.

We asked a few of these Friends of SAS members who are regular SAS users to share with us their top SAS tips and tricks for improving performance or something they wished they had known earlier in their SAS career. Based on that, we got a wide range of tips and tricks from a number of different SAS users – ranging from novice to expert and across various industries and product users. Check out some of them below:

### FUNCTIONS

Functions are either built into SAS itself or you can write your own customized code that act in the same manner, all of which help in analyzing and processing data. There are a variety of function categories that include mathematical, date and time, character, truncation, and miscellaneous. Using functions makes us more efficient, and we don’t have to re-invent the wheel every time we want to figure something out. With this being said, some of our regular SAS users have a thing or two to say about dealing with functions that may help you out:

“Before you program any complex code, look for a SAS function that will do the task for you.”
- John Ladds, Past President, OASUS

“Insert a line break in a concatenated string, such as: manylines = catx('0a'x,a,b,c);”
- Aroop Ghosh, Principal Consultant, Webtalk Communications

“Use the lag function to create time related variables, for example, in time punch data”
- Yolanda, Analyst, TD

“A good trick that I have recently learnt [sic]which can make the code less wordier is using the functions IFN and IFC as an alternative to IF THEN ELSE statements in conditional processing.”
- Sunny Giroti, Master of Business Analytics Candidate, Schulich School of Business

“IFN can be used in place of IF THEN ELSE to shorten code”
- Neil Menezes, Senior Business Anlyst, CTFS

“Ron Cody’s link from SAS.COM. It has many SAS function examples.”
- John Lam, CIBC

### SYNTAX/SHORTCUTS/EFFICIENCIES

You know what they say: time is money. So for a SAS programmer, finding shortcuts and ways to work more efficiently and faster are important to get a job done quicker. Here are a few ways SAS users think can make your life easy while working with SAS:

“Use missover to ensure no records are skipped when reading in a file”
- Scott Bellefeuille, IT Solutions Developer (Merchant Services), TD Bank

“Pressing keys 'Ctrl'+'/' to comment out a line of code.”
- Bunce Leung, Execution Manager, RBC

“Variable Lists - being able to refer to variables using double dashes to indicate all variables between first and last in a dataset is super useful for many procs. The later versions of being able to use the prefix and colon to indicate all datasets with a prefix is a great shortcut as well.”
- Fareeza Khurshed, Manager (Statistical Services), Alberta Treasury Board and Finance

“I like to use PERL in SAS for finding stuff in character variables.”
- Peter Timusk, Statistics Officer, Statistics Canada

“Title "SAS can give you an Inheritance". Have an ODBC driver on your local PC but not on a remote server? No problem. Use rsubmit with the inheritlib option. Your remote server will now inherit the ODBC driver and be able to access a database you thought you could only reach with your PC.”
- Horst Wolter, Manager, TD Bank

“If you want to speed the processing of your program. Run your join statements on the "work" library. It is must faster.”
- Estela Tavares, Economist, Statistics Canada

“When dealing with probability, can logistic be used in all cases? Trick Q - as A is N0. What about the times, probability is 0 and 1. What if the data is heavily distributed on 1s and 0s.”

“Proc tabulate can perform descriptive statistics better than proc freq and proc means.”
- Taha Azizi, Senior Business Insight Analyst, TD