R

• Building an R Package with LLMs: Insights and Observations

  September 15, 2023

  I just watched a YouTube video by Jared P. Lander, focusing on building an R package with the help of a Language Model (LLM). The video speaks to several intriguing aspects of using LLMs for programming tasks, particularly in R. Basic Function Creation The initial task involved creating a function that counts different file types in a specified folder. When the model was instructed with the prompt, “write a function whose input is a path to a folder and the output is a count of each of the different file types in the folder,” it produced a function using base R.

• From Forecast to Fable: Advanced Time Series Analysis in R

  September 15, 2023

  I watched Mitchell O’Hara-Wild’s talk on Fable, which provided a comprehensive examination of the evolution from the forecast package to Fable in R, highlighting the design decisions that were made to improve time series analysis. Quantitative Focus on Design Decisions Package Evolution: Starting with the TS package at the turn of the century, it brought basic functions such as ARIMA and seasonality filters. The forecast package, introduced in 2003 by Rob Hyndman, advanced this by adding the Exponential Smoothing State Space Model (ETS) and other methodologies.

• Fitting Non-linear Functions and Understanding Polynomial Regression in R

  February 23, 2023

  I recently watched a video that delves into fitting non-linear functions in R, focusing on polynomial regression. You can find it here. The speaker provides a systematic breakdown of how to implement this while utilizing R’s markdown functionality, enabling clear documentation of work. Fitting Polynomial Regression The session begins with polynomial regression using the wage dataset. The primary objective is to model wage as a function of age, fitting a fourth-degree polynomial.

• Geospatial Analytics: Insights from Ayanthi Gunawardana on Mapping with R

  February 15, 2023

  In a recent presentation, Ayanthi Gunawardana discussed the intricacies of creating precise and aesthetically-pleasing maps using R. This focused exploration of geospatial data offered valuable insights that go beyond common knowledge in the data science community. For the full presentation, you can watch the video here. Understanding Geospatial Data and Mapping Gunawardana draws attention to various geospatial data types—predominantly vector and raster. Vector data encapsulates geographic information related to distinct points, lines, and polygons.