Here is the complete code that uses your data to calculate and plot the absorption ratio over time: # Load necessary libraries library(ggplot2) # Create data in the right shape data <- read.csv("your_data.csv") Ret <- data[,-1] # lookback period in number of days (rolling window) lb.period <- 500 nRow <- nrow(Ret) nCol <- ncol(Ret) n <- nRow-lb.period ar <- rep(0,n) # reserve space for daily absorption ratio for(i in 1:n) { start <- i end <- i+lb.

Data Manipulation with R: Updating Flags for Matching IDs ============================================================= In this article, we will explore how to update flags in a data frame based on matching IDs using the dplyr library in R. Specifically, we will focus on updating the flag for all rows that share the same ID when there exists at least one row with a flag value of “Y”. Introduction Data manipulation is an essential part of working with data in R.

Non-Random Sampling in dplyr: A Practical Guide Introduction The dplyr package is a powerful tool for data manipulation and analysis in R. One of its key features is the ability to non-randomly sample rows from a dataset, which can be particularly useful when working with large datasets or requiring specific patterns of sampling. In this article, we will explore how to achieve non-random sampling every n rows using dplyr. Background In dplyr, the sample_n() function is used to select a random sample of rows from a dataset.

R Language Aware Code Reformatting/Refactoring Tools? In recent days, I’ve found myself working with R code that is all over the map in terms of coding style - multiple authors and individual authors who aren’t rigorous about sticking to a single structure. There are certain tasks that I’d like to automate better than I currently do. What Are We Looking For? I’m looking for a tool (or tools) that can manage the following tasks:

Understanding the R snaive() Function and Its Error The R snaive() function is used for time series forecasting. It takes a time series object as input along with other parameters like h (hence of window) and level for smoothing. The function attempts to predict future values in the time series by replacing past data points with a specified number of new ones, assuming that the time series has a fixed length.

Understanding Memory Leaks in iOS Memory leaks are a common issue in mobile app development that can lead to performance issues and crashes. In this article, we will explore memory leaks specifically related to UIImage objects in iOS. Introduction to Memory Management in iOS Before diving into the specifics of UIImage memory management, it’s essential to understand how memory management works in iOS. Apple uses a manual reference counting system, where each object has a reference count that increments or decrements based on how many times it is retained or released.

Installing R on Ubuntu: A Step-by-Step Guide Installing R on Ubuntu can be a bit tricky, but with this guide, you’ll be able to get started with the popular statistical programming language in no time. Prerequisites Before we dive into the installation process, make sure you have the following: Ubuntu 18.04 or later A terminal emulator (e.g., Terminal, Konsole) Basic knowledge of Linux commands and file management Understanding the Package URL When installing R on Ubuntu, you’ll need to specify a package URL that points to the correct repository for your version of Ubuntu.

Understanding Xcode Linking Behavior in Unity Applications =========================================================== As a developer working with the Unity 3D engine, building iPhone applications can sometimes be a daunting task. One common issue that developers face is trying to understand why certain libraries are being linked during the compilation process in Xcode. In this article, we will delve into the world of Xcode linking behavior and explore ways to identify which functions or classes from external assemblies are being referenced.

Reading a File with Custom Column Names in R: A Deep Dive into CSV and header Row Handling When working with data files, especially those from various sources or created using different tools, it’s not uncommon to encounter issues with column names. In this article, we’ll explore the world of reading CSV files in R and delve into how to handle custom column names, specifically when dealing with header rows.

Understanding the pandas GroupBy Function: A Deep Dive The groupby function in pandas is a powerful tool used for grouping data by one or more columns and performing various operations on the resulting groups. However, when using this function, many developers encounter unexpected results or errors. In this article, we will explore why the groupby method may not work as expected and provide a deeper understanding of its underlying mechanics. We will also examine the common pitfalls that can lead to incorrect results and discuss ways to troubleshoot these issues.