Tips for Charting in Veera

Being able to collect and identify valuable data is important for making sound decisions to meet long-term goals, but collection and identification are only part of the process. Just as important is the ability to provide visual representations to transform your raw data into actionable information. This guide will help to create basic graphs in Veera and to improve them by making them more clear and eye-catching.

First and foremost, before you get started, you’ll need to identify what data you’d like to represent in your chart. You may need to whittle down your data so that it isn’t overwhelming but still fairly represents your population. For this purpose, you might consider using a Filter node to filter down to just the entries you’re truly interested in, or using a Cleanse node to create an “other” category to concatenate some of the smaller categories.

Once you have the dataset you’d like to use and have opened the chart node, your first step will be to select the chart type (pie chart, bar chart, etc.). Once you’ve done that, you’ll want to fill in all necessary fields on the right side of the Chart Data window, such as deciding which variables should be your x and y axes. Once everything has been labeled, you can edit the look of your chart by clicking on the colored icon in the top right of the window. 

As you begin to navigate through the Chart Style Editor window, here are some things to keep in mind:

1. The chart type does not automatically identify the chart type that was selected in the previous window – you’ll want to update this and select the appropriate chart type.

2. The sample chart you see on the right will update to reflect any changes you make in the chart editor. Use it to evaluate your aesthetic decisions as you go.

3. Below the sample chart you can choose whether or not to include data labels and adjust the font, size, style, color, and chart background.

4. When charting binary or categorical variables, you may not see a need to include a legend. If you select the ‘Legend’ tab and uncheck the ‘Display Legend’ box, you can remove the legend. If you do remove the legend, be sure the chart is labeled accurately in the first window.

5. If you’d like to make any of your charts 3D, you can do so by going to the ‘3D’ tab and checking the ‘Display in 3D’ box. There you can also change the inclination, depth, rotation, etc.

6. Once you’ve edited the background color and text color of your chart in (below the sample chart), you can choose a color palette for the rest of the chart by going to the ‘Color Palette’ tab. There you can select a pre-programmed palette or create your own custom palette.

7. One of the more subtle functions in the chart node is the ability to shade ranges within a chart – found in the ‘Axis’ tab. Here you can choose to assign colors to ranges within a chart to better visualize certain areas of interest in your data. This is especially nice when looking at retention rates or variables represented as percentages because the ranges are easy to define & interpret.

8. Another use of the ‘Axis’ tab is to help label your data. Too many times I’ve gone to chart my data but found that half my data points are unlabeled in my graph. Here, choose the axis that should contain those missing labels and unselect the ‘Auto Label’ box. Now under the ‘Major Tick Marks’ heading, unselect the ‘Auto Interval’ box and set the interval to be 1 to insert all missing data point labels. It should look something like this (note that I’ve chosen the y-axis):

9. Now that you know how to edit each chart individually, you might find that you like a particular style and want to save it to use later. You can do this within the Chart Node by clicking on the ‘…’ button next to the ‘Chart Style’ drop-down. Here, click the green plus button to add a new chart style – give it a name and you’ll find yourself in the ‘Chart Style Editor’ window. Once you’ve finished editing, you’ll be able to access your saved chart style within the Chart Node from the Chart Style drop-down menu.

10. Once you’ve created a chart type that you love, you can also export it to the Collaborative Cloud so that others can use it – if you have a minute, check out the cloud and search for chart styles for the opportunity to download styles that other users have created.

Do you have any tips for charting, or questions about how to use the charting node? Leave them in the comments below :)

Happy charting!

-by Jon MacMillan, Data Analyst at Rapid Insight

Guide to Rapid Insight Resources

Networking:
We have created several opportunities for networking with other Rapid Insight users, including a Rapid Insight LinkedIn customers-only group, and several more subject-specific subgroups.

Rapid Insight LinkedIn Page

• Strategic Enrollment Management Network
• Fundraising and Advancement Network
• Institutional Research Network

Webinars:
Check out our list of upcoming webinars here. These are a few you might want to check out:

Predictive Modeling (PM) for Higher Ed ** PM for Fundraising

Dashboards and Reporting for Higher Ed ** PM for Healthcare 

Training Resources:

• To set up a training session with one of our analysts, please email support@rapidinsightinc.com
• Check out our training videos
• Browse the Veera and Analytics user manual downloads
• Got questions? Post or browse for answers in our forum
• For more resources, see the Training page of our website

Blog:
Here are links to a couple of recently featured series:

• Customer Tips: tips from customers on ways to make your life easier.
• Creating Variables: on how any why to augment your dataset by creating additional variables using Veera.
• The Forgotten Tabs: on the benefits of utilizing some of Analytics’ lesser talked about tabs.

Conference:
Rapid Insight is proud to host an annual User Conference each summer. Information about the conference will be available on our User Conference page as the conference draws near. 

If you have any additional questions about Rapid Insight resources or products, please feel free to contact me directly at caitlin.garrett@rapidinsightinc.com. 

-Caitlin Garrett, Statistical Analyst

How to Score a Dataset Using Veera

After you’ve ‘memorized’ the predictive model you’d like to use, you’re ready to start the scoring process. There are actually two ways to score a dataset using the Rapid Insight software suite. In this post, we’ll talk about how to import your scoring model into Veera and quickly score your dataset.

We’ll start at the point where you save your scoring model within Analytics. After memorizing your model in the Model tab, you’ll want to move down to the Compare Models tab. This tab allows you to compare any two models side-by-side. Once you’ve decided which model you like better, you’re ready to save it by selecting the model and clicking the “Save Scoring Model” as button, as shown below.

Analytics will prompt you to navigate to where you’d like the file to be saved, and will save it with a .rism (Rapid Insight Scoring Model) extension. Once you’ve saved the file, you’re ready to move into Veera to score your dataset.

In Veera, you’ll want to create a new job for scoring. In that job, bring in your input file (the file you’d like to score), and connect it to an output files. When configuring the output file, you can choose to write your scores to a file, spreadsheet, or back to a database table. Once the input and output files are connected, you’ll be importing the scoring model between them. To do so, right-click on the line connecting the two files and select Wizard -> Import Scoring Model, as shown below:

You will need to navigate to where you saved your scoring (.rism) file and select it to finish the import. Once you’ve done so, you’ll see four or five new nodes populate on the line between your input and output file. These nodes, shown below, are Analytics’ way of communicating the scoring process to Veera.

One very important thing about this scoring process is that your model is not a black-box model – you can explore each step to see how your data is scored. Feel free to open each of the nodes and see what they are accomplishing. If you open the “Create New Variables” node, you’ll be able to see any of the transformations used in your predictive model; you can also access the model formula itself by opening the “Calculate Probability” node. To get the probability scores, go ahead and run your job. The scores will be outputted as a new column called “Probability” in your output file or database. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Fall Pricing Promotion

For both new and existing customers - now until November 16th!

Not a customer yet? Rapid Insight is offering 10% off your software purchase through November 16th, 2012. 

Already feeling the love? We're offering existing customers the "add a license" promotion: add a license of either Rapid Insight Analytics or Veera for only $3,000 each. 

We hope this is what you've been waiting for!



For more information, please contact Sheryl Kovalik at 
sheryl.kovalik@rapidinsightinc.com or (603) 447-0240 ext. 7568

Customer Tips From... Scott Alessandro (MIT - Sloan School of Management)

The third edition of our customer tips series is brought to you by Scott Alessandro, Associate Director of Sloan Educational Services at MIT's Sloan School of Management. Scott is a long-time and avid Veera user who has been very creative with his applications of the software. Here are his tips: 

1. You are able to remap your data files by clicking on the connection icon either under the connections menu or in the job on the actual file. When I first started using Veera, I was afraid to move files around as it would break connections. Now I know better.  

2. Use the Find DeDup or Remove Dup node when you are working with a data file for the first time. You will be surprised how often duplicate records exist in data files (well, really we should not be surprised, but sometimes are).

3. With the output node, you can check or uncheck the columns you want to include. This is especially useful when you are using transform or merge nodes. I like to keep all of the columns throughout my job and then only select out the relevant ones in the output node. Helps you to keep track of what you are doing within a job (especially also if you create a ‘test’ output you move around the job).

4. Use ‘Set Run Order’ when you have multiple outputs in a job or a job that relies on one output to run another output. Akin to that, in the Merge node, you can also change the order that files are merged together by right clicking on the file number. Since I like to merge a lot of different files together, it is useful to be able to change the merge order especially if you add files later. 

5. Right click on a job to make a copy and then paste it onto the workspace.

6. In Cleanse, can multi-select columns and run the same type of cleanse, rather than selecting each column individually. 

7. Use the Rename node to re-order columns.

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

Customer Tips From... Dr. Loralyn Taylor (Paul Smith's College)

Our new customer tips series will feature tips from customers on using either of our software applications. Each entry will focus on one customer’s ideas to make your lives easier.

We’re kicking things off with Dr. Loralyn Taylor, from Paul Smith’s College. Dr. Taylor is a one-woman IR office and Registrar and is constantly looking for ways to save time when creating reports and executing jobs. Here are her five tips:

1.  Take the time to rename your nodes so that you can easily follow your line of thought as you move through the job.
2. Remember that there are multiple ways of doing things. The shortest is not always the best – to me it is often more important to be able to easily follow my thought on how I am working through the problem than to do it elegantly in the fewest number of nodes.
3. Common problems to check for: data format incompatibility (just use a convert node), and sometimes a null is not actually a null (just because something looks blank doesn’t mean that it is).
4. Remember that creating a job is like solving a puzzle; you have to think about it and play with it.
5. I often have to run jobs many times to get them right. Helpful tip: Set up a test data output that you can move around to different parts of the job to see how your data is coming through at different points when you are troubleshooting. 

...Have tips of your own? Email them to caitlin.garrett@rapidinsightinc.com!

Veera's Been Updated!

If you haven't already, be sure to download the newest version of Veera, which was released this week. Updates include:

1. The ability to change the order of the jobs and other tabs

To switch the order of jobs or other tabs you have open in Veera, simply drag and drop the tabs at the top of the screen until they match the desired order. 

2. Easier way to view jobs and other tabs in separate windows with the introduction of a "tear off" feature

To view jobs or other tabs in a separate, un-docked window, simply drag the tab you'd like to view outside of the Veera window. 

3. Addition of editable job descriptions to the Workspace area

By double-clicking in the area highlighted in orange above, you can now add job descriptions to label your jobs. Here you can include information like how and why the jobs were created, which data files were used, and which outputs come from each job. This feature is also helpful when sharing jobs as a place to get a quick idea of what the job should accomplish. 

4. New ways to format data in the Convert node, including SSN and multiple phone number formats

To utilize the new formats in the Convert Column Data Type node, change the data type to 'Text', and select the appropriate new format from the drop-down menu. Included are four ways of expressing phone number, and an SSN format. See below. 

5. Can now add new categories to "Transpose By Values" list in Transpose node

You can now add new categories (ones that currently don't exist in your dataset) as "Transpose By Values" in anticipation of them being included in your dataset. To do so, select your "Transpose By" variable in the Transpose node, and click the Get Values button as you normally would. 

Next, click the button highlighted in orange above, which will open a new screen that allows you to add additional categories. 

Once in the new screen (shown above), click the green plus sign to add additional values to your "Transpose By" list. 

We hope you like the new features. My personal favorite so far is the tear-off feature to easily check out tabs in a new window (which is also great for side-by-side comparisons). If there is a new feature that you'd like to see in the next build of Veera, please email me and I'll make sure that your request finds its way to the right person. In the meantime, have fun exploring!

"Job Security"

I think it’s a universally acknowledged fact that most people don’t back up their files as often as they should. That said, I recently learned how to back up my files in Veera, and it was painless. In fact, the whole process took less than a minute. If the thought of losing your jobs or having to reset all of your connections frightens you as much as it does me, you should follow these easy steps to back up all of your Veera jobs and connections:

1. Open Veera

2. Go to File -> Database -> Backup.

3. In the open window, navigate to the place where you’d like to save your Veera Backup File.

4. Celebrate! You’re all backed up.

If, in the event of a data emergency, you ever need to access these backed-up files, you can do so by going to File -> Database -> Restore. This will restore both your connections and your jobs. Choosing the Recover Lost Jobs option will allow you to recover any jobs that may have been corrupted along the way without affecting your connections. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Introducing the Rapid Insight Collaborative Cloud

With the new version 4.1 of Veera comes the Rapid Insight Collaborative Cloud. The Collaborative Cloud allows Veera users to share and collaborate on any analytic processes that they develop; these processes can be utilized, discussed, and enhanced by anyone in the RI user community. We hope that the cloud will increase efficiency by allowing users to share resources and ideas in real time and draw upon the knowledge of their peers whenever they begin a new analytic challenge.

Accessing the Cloud
To access the Collaborative Cloud, click on ‘RI CC’ on the title bar and select “Browse RI Collaborative Cloud” as shown: 

Downloading from the Cloud

Once in the cloud, you have the ability to search for Veera jobs, transform operations, and chart styles to download.  You can choose to search these by contributor. You also have the ability to order search results by contribution date or type, number of downloads, number of comments, name, date modified, or contributor.

To get details on an item or select an item to download, simply double-click on its title. To view or leave comments, double-click on the “# Comments’ field.

If you choose to download a transform operation or chart style, Veera will direct you to save to a folder outside of the Veera program, similar to the way you would save any outputs from Veera jobs. If you choose to download a job from the Collaborative Cloud, Veera will place that job in an automatically created folder titled ‘From RI Collaborative Cloud’, which you’ll see along with your other folders in the Workspace tab:

You can access any of the jobs you download from the cloud by navigating to this folder. 

Uploading to the Cloud
Right-click on the item to be uploaded (job, transform, chart style, or add-in) and select “Contribute [item type] to RI Collaborative Cloud”. Once selected, the Contribute to Rapid Insight Collaborative Cloud window will open with the original name of the item entered already at the top. 

Before submitting by clicking “Contribute”, the user must enter a unique name, a description of the item, and check the Terms & Conditions box before the item can be contributed. Additional files (like related sample data) can also be contributed as part of the submission.

Other Ways to Download
You can quickly download jobs from the Collaborative Cloud by right-clicking in the “Jobs” area of the Workspace tab and selecting “Get Job from RI Collaborative Cloud”:

You can also import transforms from within a Transform node. To do so, right-click on the white area in the “Transform Operations” window at the bottom of the screen and select ‘Get transform from RI Collaborative Cloud”.

To import a chart style from within the Chart Data node, click on the ellipsis to the right of the “Chart Style” drop-down menu. In that window, right-click and select “Get style from RI Collaborative Cloud” as shown below. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Updated Features in Veera 4.1

The new and improved version 4.1 of Veera has quite a few updates. Here are 12 of the biggest changes you'll see when you update your version:

1. Transforms can now be saved for future use. If you have a useful formula or transform that you’d like to save to apply to multiple datasets, this means you can save the whole transform and export from or import into other jobs as needed.

2. Cache node now has a ‘view data’ option. So, once a cache is full, you can see what the data looks like at that point in a job, rather than having to check it out an output.

3. When using a Combine Input node, you can now create a “file date created” column and a “file modified column” to better track the dates associated with each of the files you’re importing.

4. Job and data view windows can now be un-docked and float outside of the main Veera window.

5. Sampling node now has ‘range’ option. Instead of having to sample records from the top or bottom of a dataset (if you didn’t want to sample randomly), you can now choose which range of records you’d like to sample from.

6. Sampling node is also parameterized. You can set a parameter to prompt you for the number of records to sample in each sample node.

7. When restoring connections, Veera will now Auto-backup in case anything was to go wrong with the restoration process.

8. A “busy” window will now show when Veera Client is connecting with the server.

9. Setting run order can now be done from a list rather than having to click on each output in the order you’d like them to run.

10. The Quantile node now allows you to choose whether to sort values in a descending or ascending order.

11. Fields on an Excel Output file can now be parameterized. So, if you’re running jobs with a particular parameter, this parameter can now be included as part of the outputted field names.

12. The new Variable Reduction node has now been implemented. When available, Variable Reduction will filter down the variables in your dataset so that you’re only looking at variables that are statistically significantly related to your chosen Y-variable from that point forward. 

...As always, we update our packages based on our customers' ideas and needs. If you have any feedback or ideas regarding either of our packages, we encourage you to leave them in the comments or email us directly. We'll make sure your ideas find their way to the right person.  


-Caitlin Garrett, Statistical Analyst

Creating Variables: Retention and Attrition

Now that we’ve highlighted some basic variables in our Creating Variables series, I think we’re ready to move on to a variable that is a little trickier to create: retention. The retention variable we’ll create will represent whether or not a freshman is retained from one fall semester to the next. Because of the number of different factors you can choose to include or exclude, I hope that you’ll use these instructions as a general guide rather than a hard-and-fast manual on how to create a retention variable.

In order to create a fall-to-fall retention variable, we need to be looking at enrollment data from several consecutive fall semesters. In order to focus just on freshmen, we’ll start by placing a filter on our first semester:

Within the filter, you’ll want to focus on the field in your dataset that represents student year and filter down to just those students who are freshmen. If you’d like to include other filters here, such as making sure you’re focusing on first-time freshmen rather than transfer students, or undergraduate rather than graduate students, this is the time to do so.

The next step is to merge this dataset and filter with the next consecutive fall semester:

Inside the merge, you’ll want to connect the datasets on an identifying field, such as student ID. In doing so, you’ll want to take all of the information about each student from only the first column, so your merge should look something like this:

Here’s where the magic happens. After you’ve set up the merge, you need to do one more step before exiting the merge node. See the section titled “Source Table Flag Columns” in the bottom right corner? That’s where we’re headed. This section allows you to create a flag variable that tells you which dataset your information came from. In this case, we’ll create a flag to let us know whenever a student shows up in both datasets and we’ll call this variable “Retained” (double click on the existing name to rename it). It should look something like this:

Now we’re getting close, but we’re not done yet: those of you who have done some predictive modeling probably know that usually when we talk about retention, we’re actually planning to model attrition. (So, instead of modeling each student’s likelihood of leaving, we’d model each student’s likelihood of staying.)Modeling attrition rather than retention is helpful because we’re focusing on a smaller population of students and honing in on the characteristics of students who are likely to leave more directly. To accomplish this, we’ll need to add a transform in order to create a new attrition variable.

In the transform, we’re going to set up the attrition variable to be the opposite of the “retained” variable. To do this, we’ll want to select the “retained” variable to work with and type the following into the Enter a Formula window: IF (A=1, 0, 1). This basically turns zeros to ones and ones to zeros. Set the result type to binary and be sure to name the variable something like “attrition” before creating and exiting… And there you have it, a state-of-the-art attrition variable to add to your collection!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Creating Variables: Out-of-state Flag

Sometimes it’s good to see which of your students or donors are in-state because an in-state population may be more likely to enroll or be retained or give than an out-of-state population. Creating an out-of-state flag from a “state” variable allows you to easily differentiate between your in-state and out-of-state prospects. I should also note that it is just as easy to create an in-state flag if that better suits your data. In any case, here’s how:

The first step is to hook your data source to a transform node:

Because we’ll be creating a binary (“yes or no”) variable, we’ll want to click on the “if” button (at the top of the buttons on the right side), which will automatically generate an equation that we can change to suit our data. 

In the “Enter a Formula” window, we’ll want to edit the auto-generated equation so it reads:

Where ‘[A]’ is the variable in our dataset that represents state, and the term it is set equal to (in this case, ‘NH’) is the term in our dataset that represents our institution’s state. Note that we could have set state equal to ‘New Hampshire’ or a numerical code, as long as it matches the term that represents New Hampshire in our dataset. The equation outputs a variable that is equal to ‘1’ when state is NOT New Hampshire and ‘0’ otherwise, thus flagging records which are out-of-state.

The final step before naming and saving your out-of-state flag is to select “binary” from the “Result Type” list.

And, voila, it’s easy as that! You now have a quick way of identifying in-state vs. out-of-state students in your dataset; let the reporting begin!

PS: If you guys have any specific requests for a variable to be featured in the "Creating Variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Creating Variables: Age

Hi all! Today I’d like to a cover a pretty universally predictive variable: age. Age can be created in relation to the date of a particular event (like an application date or a mailing date), or as a reflection of age today, at this moment. Either way, age is often predictive and easy to add to your dataset by creating it in Veera from a “birth date” field.

The first step in doing so is to hook your data source to a transform node: 

After opening the transform node, we’ll want to click on the function button and select the second “YearsBetween” function.

[Note: Veera is capable of outputting the number of years between two dates in two separate ways. The first function on the list calculates the number of years between two dates, regardless of the actual day and month, while the second function calculates the number of years between two dates taking day and month into account. To illustrate this point, take the dates December 1, 1960, and April 1, 1980. Using the first “YearsBetween” function, the number of years between these dates is 20. Using the second “Years Between” function, the number of years between these dates is 19. See the difference?]

Here, we have two options. We can (a) calculate age today or (b) calculate age at a specific point in time, depending on what we type in the “Enter a Formula” window.

(a) Age today:  

Where ‘[A]’ corresponds to the variable in your dataset that represents birthdate, and “TODAY()” is the Today function from the drop-down menu on the right. 

or

(b) Age at a specific point in time:

Where ‘[A]’ corresponds to the variable in your dataset the represents birthdate, and ‘00/00/0000’ represents the specific date on which you’d like to measure age. 

Be sure to save before exiting the transform node, and there you have it, a brand-new age variable!

PS: If you guys have any specific requests for a variable to be featured in the "creating variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Creating Variables: Days Between Application Date and Term Start

Hello everybody! This post will be a continuation of the Creating Variables series. Today we’ll be discussing how and why to create a “days between application date and term start” variable.

At first glance, this variable seems a little long-winded, but I can assure you, it’s worth its weight in characters. As you all know, for any institution that accepts applications on a non-rolling basis, there exists a window of time during which applications must be filed to be considered for acceptance. The amount of time between when an application is submitted and when the relevant admission term begins can be an indication of a student’s interest in a particular institution. For example, a student may turn in an application to his first-choice college during the first week that applications are accepted, but this same student might wait until the day or week before the deadline to turn in applications to his safety or back-up schools. In this way, the amount of time between the day that a student turns in an application and the term start date can be seen as an indicator of that student’s interest. Let’s go ahead and calculate this:

The first step is to hook applicant data into a transform node:

Next, after opening the transform node, we’ll need to select the “Days Between” formula from the drop-down menu:

In the “Enter a Formula” window, we’ll want to enter:

…Where ‘[A]’ corresponds to the variable in your dataset that represents the date each application was submitted, ‘09/01/2012’ represents the start date for the term you’re admitting for, and “date” is actually the date function from the formula drop-down menu:

Before naming and saving this new variable, be sure to switch the “Result Type” to “integer”:

And, voila! Now you have a “days between application date and term start variable” to add to your predictive variable arsenal.

-Caitlin Garrett, Statistical Analyst at Rapid Insight