Category: Programming

Introducing the GAUSS Data Management Guide

If you’ve worked with real-world data, you know that data cleaning and management can eat up your time. Efficiently tackling tedious data cleaning, organization, and management tasks can have a huge impact on productivity.
We created the **GAUSS Data Management Guide** with that exact goal in mind. It’s aimed to help you save time and make the most of your data.
Today’s blog looks at what the GAUSS Data Management Guide offers and how to best use the guide.

Transforming Panel Data to Long Form in GAUSS

Anyone who works with panel data knows that pivoting between long and wide form, though commonly necessary, can still be painstakingly tedious, at best. It can lead to frustrating errors, unexpected results, and lengthy troubleshooting, at worst.
The new dfLonger and dfWider procedures introduced in GAUSS 24 make great strides towards fixing that. Extensive planning has gone into each procedure, resulting in comprehensive but intuitive functions.
In today’s blog, we will walk through all you need to know about the dfLonger procedure to tackle even the most complex cases of transforming wide form panel data to long form.

Managing String Data with GAUSS Dataframes

Working with strings hasn’t always been easy in GAUSS. In the past, the only option in GAUSS was to store strings separately from numeric data. It made it difficult to work with datasets that contained mixed types. With the introduction of GAUSS dataframes in GAUSS 21 and the enhanced string capabilities of GAUSS 23, that has all changed! I would argue that GAUSS now offers one of the best environments for managing and cleaning mixed-type data. I recently used GAUSS to perform the very practical task of creating an email list from a string-heavy dataset – something I never would have chosen GAUSS for in the past. In this blog, we walk through this data cleaning task, highlighting several key features for handling strings.

Importing FRED Data to GAUSS

The GAUSS FRED database integration, introduced in GAUSS 23, is a time-saving feature that allows you to import FRED data directly into GAUSS. This means you have thousands of datasets at your fingertips without ever leaving GAUSS. These tools also ensure that FRED data is imported directly into a GAUSS dataframe format, which can eliminate hours of data cleaning and the headaches that come with it. In today’s blog, we will learn how to use the FRED import tools to:
  • Search for a FRED data series.
  • Import FRED data to GAUSS, including merging multiple series.
  • Use advanced import tools to perform data transformations.

Introduction to Efficient Creation of Detailed Plots

A few weeks ago, we showed you how to create a detailed plot from a recent article in the American Economic Review. That article contained several plots that contain quite a bit of similar and stylized formatting. Today we will show you how to efficiently create two of these graphs. Our main goals are to get you thinking about code reuse and how it can help you:
  • Get more results from your limited research time.
  • Avoid the frustration that comes from growing mountains of spaghetti code.

Advanced Formatting Techniques for Creating AER Quality Plots

This blog will show you how to reproduce one of the graphs from a paper in the June 2022 issue of the American Economic Review. You will learn how to:
  1. Add and style text boxes with LaTeX.
  2. Set the anchor point of text boxes.
  3. Add and style vertical lines.
  4. Automatically set legend text to use your dataframe’s variable names.
  5. Set the font for all or a subset of the graph text elements.
  6. Set the size of your graph.

Installing the GAUSS Package Manager [Video]

GAUSS packages provide access to powerful tools for performing data analysis. Learn how to install the GAUSS Package Manager, and get the quickest access to the full suite of GAUSS packages, in this short video. Additional Resources GAUSS Package Manager Using GAUSS Packages a Complete Guide

How to Load Excel Data into GAUSS

Loading data is often the first step to your data analysis in GAUSS. In this video, you’ll learn how to save time and avoid data loading errors when working with Excel files. Our video demonstration shows just how quick and easy it can be to load time series, categorical and numeric variables from Excel files into GAUSS. You’ll learn how to:
  • Interactively load Excel data files.
  • Perform advanced loading steps, Such as loading specific sheets, or specifying values as missing values.
  • Use autogenerated code in a program file.
  • Change variable names
  • Set up categoical labels and and base cases.

Getting to Know Your Data With GAUSS 22

There is no getting around the fact that data wrangling, cleaning, and exploring plays an important role in any empirical research. Data management can be time-consuming, error-prone, and can make or break results. GAUSS 22 is built to take the pain out of dealing with your data and to let you move seamlessly towards tackling your important research questions. In today’s blog, we walk through how to efficiently prepare and explore real-world data before modeling or estimation. We’ll look at:
  • Loading and merging data.
  • Cleaning data to eliminate misentries, missing values, and more.
  • Exploring data.

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