Optimized Code The optimized code is as follows: pip <- function(ps, interp = NULL, breakpoints = NULL) { if (missing(interp)) { interp <- approx(x = c(ps[1,"x"], ps[nrow(ps),"x"]), y = c(ps[1,"y"],ps[nrow(ps),"y"]), n = nrow(ps)) interp <- do.call(cbind, interp) breakpoints <- c(1, nrow(ps)) } else { ds <- sqrt(rowSums((ps - interp)^2)) # close by euclidean distance ind <- which.max(ds) ends <- c(min(ind-breakpoints[breakpoints<ind]), min(breakpoints[breakpoints>ind]-ind)) leg1 <- approx(x = c(ps[ind-ends[1],"x"], ps[ind,"x"]), y = c(ps[ind-ends[1],"y"], ps[ind,"y"]), n = ends[1]+1) leg2 <- approx(x = c(ps[ind,"x"], ps[ind+ends[2],"x"]), y = c(ps[ind,"y"], ps[ind+ends[2],"y"]), n = ends[2]) interp[(ind-ends[1]):ind, "y"] <- leg1$y interp[(ind+1):(ind+ends[2]), "y"] <- leg2$y breakpoints <- c(breakpoints, ind) } list(interp = interp, breakpoints = breakpoints) } constructPIP <- function(ps, times = 10) { res <- pip(ps) for (i in 2:times) { res <- pip(ps, res$interp, res$breakpoints) } res } Explanation

Comparing Substrings in SQL: A Deep Dive into Updating Column Values Based on Another Table’s Substring As a technical blogger, I’ve encountered numerous questions and challenges related to updating column values based on substring comparisons between different tables. In this article, we’ll explore the concepts behind substring comparison in SQL, delve into various approaches for achieving this goal, and provide concrete examples using real-world scenarios. Understanding Substring Comparison in SQL Substring comparison is a fundamental concept in SQL that involves comparing two or more substrings of strings.

Loading Nested JSON Data into MS SQL (Returning NULLs) In this article, we’ll explore how to load nested JSON data into a Microsoft SQL Server database. We’ll dive into the details of using OPENJSON and OPENROWSET to parse the JSON data, including how to access nested elements. Understanding JSON in MS SQL Before we begin, let’s quickly review how JSON is stored and accessed in MS SQL Server. When you store a JSON value as a blob column in a table, it’s essentially just a string that contains the JSON data.

Extracting Outputs from For Loops with Dplyr Pipes into Dataframe in R ===================================================== In this post, we will explore how to use dplyr pipes and data manipulation in R to extract outputs from for loops. We’ll discuss the importance of using dplyr pipes to avoid errors and improve readability. Introduction to Dplyr Pipes The tidyverse package in R provides a consistent and efficient way to manipulate data. One of its powerful tools is the pipe operator, %>%, which allows us to chain together multiple operations on a dataset.

Creating a Custom UIResponder Subclass for OpenGL Game Engines In this article, we will explore the possibilities and challenges of creating a custom UIResponder subclass for an OpenGL game engine. We’ll delve into the world of Objective-C programming, event handling, and hit testing to understand how you can create your own responder pattern for touch events in a 3D graphics context. Introduction The question at hand is whether it’s possible to create a custom UIResponder subclass similar to Sprite Kit’s SKNode class, which inherits from UIResponder to handle touch events.

Dynamic Variable Assignment in Python Loops In this article, we will explore the concept of dynamic variable assignment in Python loops. Specifically, we’ll examine how to assign variables based on elements in a loop, and provide examples and explanations to illustrate the process. Introduction Python’s syntax allows for flexible and dynamic programming, enabling developers to write efficient and readable code. One common technique used in Python is the use of loops to iterate over data structures such as lists or dictionaries.

Truncating Timestamps in SQL Server: A Step-by-Step Guide Overview of Timestamp Truncation Timestamp truncation is a common requirement in various applications, where the goal is to convert input timestamps into their corresponding top or bottom hour. For instance, taking a timestamp like 2020-02-12 06:56:00 and converting it to 2020-02-12 06:00:00, or taking another timestamp like 2020-02-12 07:14:00 and converting it to 2020-02-12 08:00:00. This process can be achieved using SQL Server’s built-in date functions.

Creating Dataframe-Specific Lists in a Function As data analysts, we often work with multiple datasets, each containing different information. Creating lists or arrays to store this information can be tedious and time-consuming, especially when working with large datasets. In this article, we’ll explore how to create dataframe-specific lists in a function, making it easier to manage and manipulate our data. Understanding Dataframes Before diving into creating lists from dataframes, let’s quickly review what dataframes are.

Understanding the Problem with Concatenating Truncated Degree Titles As a data analyst or scientist, you’ve probably encountered situations where data is incomplete or truncated, requiring you to clean and transform it for analysis. In this article, we’ll explore how to use the popular Pandas library in Python to concatenate rows that have truncated degree titles. Background on DataFrames and Concatenation A DataFrame is a two-dimensional table of data with columns of potentially different types.

Adding Multiple New Columns with the Same Logic in R When working with dataframes in R, it’s common to need to create new columns based on existing ones. In this article, we’ll explore how to add multiple new columns with the same logic using different approaches and libraries. Understanding the Problem The problem presented is a classic example of needing to create new columns based on the values of existing columns in R.