data %>% mutate_if(is.logical, as.character) %>% mutate_at(paste0('var'), recode, '1'='0', '2'='1', '3'='2', '4'='3') %>% mutate_at(paste0('var', c(65,73,99)), recode, '1'='0', '2'='0', '3'='0', '4'='1')

Understanding the Error: ’numpy.ndarray’ Object Has No Attribute ‘columns’ Introduction In this article, we will delve into a common error encountered when working with the numpy library in Python. Specifically, we will explore why the 'numpy.ndarray' object has no attribute ‘columns’. We will also discuss how to access columns in a numpy array and apply this knowledge to solve a real-world problem involving feature importance in Random Forest Classification. Background The numpy library is a powerful tool for numerical computations in Python.

Understanding the Issue When creating a new UIWindow to manage the area of the status bar, it’s essential to understand why setting the root view controller in the viewDidAppear method results in a warning, while doing so in the viewDidLoad method is acceptable. To begin with, let’s define what a root view controller is. In iOS development, the root view controller is the topmost view controller that manages the hierarchy of views within an app window.

Here is the reformatted code: # Data to be used are samples <- read.table(text = "Grp ID Result DateTime grp1 1 218.7 7/14/2009 grp1 2 1119.9 7/20/2009 grp1 3 128.1 7/27/2009 grp1 4 192.4 8/5/2009 grp1 5 524.7 8/18/2009 grp1 6 325.5 9/2/2009 grp2 7 19.2 7/13/2009 grp2 8 15.26 7/16/2009 grp2 9 14.58 8/13/2009 grp2 10 13.06 8/13/2009 grp2 11 12.56 10/12/2009", header = T, stringsAsFactors = F) samples$DateTime <- as.

SQL Query: Filtering and Sorting In this article, we’ll delve into the world of SQL queries, focusing on filtering and sorting data. We’ll explore how to write an effective SQL query to display specific information from a database table, while also understanding common pitfalls and best practices. Understanding SQL Basics Before diving into filtering and sorting, it’s essential to grasp the basics of SQL. SQL (Structured Query Language) is a programming language designed for managing and manipulating data in relational database management systems (RDBMS).

Reassigning pandas column in place from a slice of another dataframe Introduction Pandas, a powerful library for data manipulation and analysis in Python, provides an extensive set of features for handling various types of data. One of the key operations in pandas is assigning new values to existing columns or rows. This can be achieved using various methods such as label-based assignment (df['column_name'] = new_values), positional indexing (df.loc[row_index, column_name] = new_value), and vectorized operations.

Understanding OpenGL ES 2.0 Display Issues on iPhone Devices Introduction OpenGL ES (Embedded System) is a family of APIs for rendering graphics on various mobile devices, including iPhones and iPads. In this article, we will delve into the world of OpenGL ES 2.0, exploring why an application built with this API displays fine in the iPhone Simulator but not on the actual device. Background OpenGL ES is designed to be a lightweight, low-power alternative to traditional graphics APIs like DirectX or Vulkan.

Understanding iOS App Icons and Gloss Effects Background When developing iOS applications, one of the first things users notice is the application’s icon on the home screen. The appearance and behavior of these icons are governed by Apple’s Human Interface Guidelines (HIG) and various settings in the app’s project. In this article, we will explore how to configure your application icon so that it doesn’t appear as a standard iPhone button.

Understanding the Problem and Background The problem at hand involves visualizing a dataset using ggalluvial, a package for creating alluvial diagrams in R. The user wants to color each axis according to specific criteria. To tackle this problem, we need to understand what an alluvial diagram is and how it’s used to visualize data. An alluvial diagram is a type of visualization that shows the flow of elements between different categories or bins.

Understanding Matrix Operations in R When working with matrices and vectors in R, it’s essential to understand the underlying concepts and operations. In this article, we’ll explore matrix operations, specifically how to create a matrix by replacing its values one column at a time using vectors. Introduction to Matrices and Vectors In R, matrices are two-dimensional arrays of numbers, while vectors are one-dimensional arrays. Matrices can be used to represent systems of equations, linear transformations, and other mathematical concepts.