Category: Programming

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.

Understanding Errors: G0058 Index out-of-Range

Today we will help you to understand and resolve Error G0058 Index Out-of-Range We will :
  1. Explain the cause of the index out-of-range error in GAUSS.
  2. Explain why performing index assignments past the end of your data can lead to bad outcomes.
  3. Show how to use some functions and operators that can assist with diagnosing and resolving this error.
  4. Work through an example to resolve an indexing problem.
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Introduction to Handling Missing Values

Handling missing values is an important step in data cleaning that can impact model validity and reliability. Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:
  • Types of missing values.
  • Dealing with missing values.
  • Missing values in practice.

Understanding Errors: G0064 Operand Missing

Today we will help you to understand and resolve Error G0064: Operand Missing. We will answer the questions:
  1. What is an operand?
  2. How do common mathematical and non-mathematical operators interact with operands?
  3. What are common causes of operand missing errors?
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Dates and Times Made Easy

Working with dates in data analysis software can be tedious and error-prone. The new GAUSS date type, introduced in GAUSS 21, can save you time and prevent frustration and errors. The date data type is part of the GAUSS dataframe alongside the category, string, and numeric type. In this blog, we will explore the advantages the date type has to offer, including:
  1. Loading and viewing dates side-by-side with other data types.
  2. Viewing and displaying dates in easy-to-read formats.
  3. Easily changing the date format.
  4. Using familiar date formats for filtering data.

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