Robert Schnitman

Statistical Consultant and Tutor

XLops & LBops: Utility Functions for Excel/VBA and LibreOffice Basic/Calc

While I primarily have been focusing on my current employment and freelance projects, I also have made incremental changes to existing packages/repos on my GitHub. In particular, I have renamed VBAops to XLops, which serves as the primary repo where I will write and maintain utility functions for Excel and VBA. Similarly, LBops contains similar modules for LibreOffice Basic/Calc. Modules FunText: Functions for parsing text. FunDates: Functions for parsing dates.

New Package: stringops, String-Processing Tools and Synonyms for R

Introduction When creating syntax, one has to ask themselves about the naming scheme: should I make the functions short for typing efficiency, or long for increased readability? Ruby has the former benefit, but sometimes the methods can be difficult to remember (e.g. is it len or length? Is it swapcase or swap_case?), as there isn’t a consistent naming scheme–however, some functions have synonyms to help those from other programming languages learn Ruby faster (e.

Vectorized String Functions in AutoIt

1. Introduction In my current workplace at Leverage Retirement, Inc., I use Autoit to automate file management operations, such as copying files to archive directories and checking whether files exist for the morning processes. These operations involve the application of regular expressions to find out whether specific files match a given pattern (e.g. Deposit \d{4}-\d{2}-\d{2}, where \d{x} represents a number of x digits). While there are many string functions in AutoIt, most of them apply only to constants and not arrays.

Vectorized String Methods in Ruby

1. Introduction I have been dabbling with the Ruby programming language for work purposes lately. Overall, I enjoy its concise, fun syntax and string methods such as .chomp to remove new-line characters–and I especially like the compactness of map. However, I believe that I have been too accustomed to R’s default property of functions being vectorized, as I initially had some trouble with handling arrays and employing string methods.

New Package and Book: dm, Statistical Data Management Tools for R

Introduction A couple years ago, I started a writing a package on Github that was inspired by the data managment functionalities in other statistical software such as Stata and SPSS. I got distracted by life, especially with work, and I practically stopped developing the package in 2019. This year, however, I finally sat down and finished developing this package, R documentation and all: the end-result was dm. You can read the documentation on this package as a Gitbook online.

New Book: A Short Introduction to Applied Statistical Programming in R

I have a new book in progress called A Short Introduction to Applied Statistical Programming in R, which can be viewed online as a Gitbook or as a PDF. [EDIT 2020-04-01: I will primarily focus on the Gitbook version, as I am running into some typesetting issues with the PDF at the moment.] [EDIT 2020-04-02: The Gitbook version is fairly complete and I do not foresee many major updates to it unless they are requested or if I think of anything else significant to add.

Handbook for the diagnoser R package

The book for my package diagnoser has just been published online! You may view it as a Gitbook (https://rs-diagnoser.netlify.com/) or PDF (https://github.com/robertschnitman/diagnoser/blob/master/docs/diagnoser_handbook.pdf). I recommend reading it if you are interested in a new perspective on diagnosing your model residuals! Installing diagnoser The library diagnoser currently is only installable via GitHub and is contingent on R versions at or above 3.4.2. To install the package, first install devtools so that you may make use of the function install_github, referencing diagnoser by the package creator’s username (“robertschnitman”) followed by “/diagnoser” as shown in the code below:

ABSTRACT: The Narrator and the Noise

Preface This blog post is simply the summary of The Narrator and the Noise. Please read the full version at either of the following locations: Gitbook version: https://rs-ddlc.netlify.com/ PDF version: https://github.com/robertschnitman/RS_Reports/blob/master/DDLC/DDLC.pdf Abstract The focus of this study is to determine the existence and extent of statistical bias towards any of the Doki Doki Literature Club characters with respect to the points distribution of the Poem Minigame.

Web Mining bankrate.com

Introduction The purpose of this blog post is to demonstrate how to web mine the bankrate.com, primarily focusing on extracting and graphing with the R programming language the APY and minimum deposits for 1-year1, 3-year2, and 5-year3 CD Rates. Setup Before the analysis, some necessary libraries will be loaded. First, tidyverse4 and magrittr5 for their data management functions; second, flextable6 for table formatting; third, rvest7 for web mining; and fourth, plotly8 to display interactive graphs.

Data Science and Beyblades

Introduction Beyblade has proven itself to be a strong-running franchise, spanning several TV series and toys. In the shows and on the boxes of said toys, there is an emphasis on the attributes of the beyblades: their Attack, Defense, and Stamina for each component that make up the beyblade. While the validity of these statistics can be questioned, one cannot help but wonder about the relationship between these three traits among the beyblades.