Customizing Code Highlighting with R Exams and PDF Generation =========================================================== When working with R/exams to generate exercises in PDF format, it’s essential to consider the rendering of code snippets. In this article, we’ll delve into how to highlight code using R exams and customize the PDF generation process. Understanding the Problem The issue at hand is that when using the exams2pdf function, which embeds exercises into a master LaTeX template, it defaults to the plain.

Cumulative Sum with Refreshing at Intervals using Python and Pandas Cumulative sums are a fundamental concept in data analysis, where the sum of values over a certain interval is calculated. In this article, we’ll explore how to create an expanding cumulative sum that refreshes at intervals using Python and the pandas library. Introduction to Cumulative Sums A cumulative sum is the total value of all previous sums. For example, if we have the following values:

Understanding Objective-C Inter-Object Communication ===================================================== In Objective-C, it’s not uncommon to have classes and controllers that need to communicate with each other. This can be achieved through various means, such as using delegate protocols, notifications, or even property-based communication. In this article, we’ll explore one way to accomplish inter-object communication: calling a function in a controller from a class. Understanding the Objective-C Class-Controller Relationship In Objective-C, a class and its corresponding controller form a crucial relationship.

Sequence Extrapolation in R: A Step-by-Step Guide Introduction When working with data, it’s not uncommon to encounter missing values (NA). In such cases, you might want to extrapolate a sequence of numbers to fill these gaps. This process can be achieved using various methods and techniques in R programming language. In this article, we’ll explore how to use the tidyverse library to fill NA values with a sequence that starts after the maximum non-NA value.

Understanding Icon Requirements for iOS Applications A Comprehensive Guide to Symbols and Assets Creating an iOS application requires a thorough understanding of the various icons, symbols, and assets required for different screen sizes, orientations, and display types. In this article, we will delve into the world of iOS icon requirements, exploring the different types of icons needed, their dimensions, and how to access them. Introduction to iOS Icon Requirements The iPhone and iPad are ubiquitous devices used by millions worldwide, making it essential for developers to understand the various icon requirements for each device.

Converting Pandas DataFrame Column Value from NumPy.ndarray to List Introduction In this article, we will explore how to convert the values in a specific column of a Pandas DataFrame from NumPy.ndarray to list. This conversion is necessary when performing certain operations that require lists instead of arrays. Background The Pandas library is widely used for data manipulation and analysis in Python. It provides data structures like Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).

Vectorizing Step-wise Function for Column in Pandas DataFrame Introduction In this article, we will explore how to vectorize a step-wise function that assigns a quality level to given data based on pre-defined borders and relative borders. We will discuss the limitations of using pandas.apply for large datasets and introduce an alternative approach using np.select. Background The problem statement involves assigning a quality level to each row in a pandas DataFrame based on the difference between two values: measured_value and real_value.

Data Aggregation with Grouping and Conditional Outputs When working with large datasets, it’s often necessary to perform aggregations based on specific criteria. In the case of a dataset with thousands of IDs and corresponding attributes, we want to add a new column that outputs the percentage of “yes” attributes per ID, as well as an indicator for whether there was only one “no” attribute. Problem Statement Given a dataframe df with columns ID and attr, where attr is a categorical variable representing either “yes” or “no”, we want to create a new column result that outputs the following values:

Understanding the Basics of R Packages and getSymbols Function The quantmod package is a popular R package used for financial data analysis. It provides an interface to financial databases and allows users to download historical stock prices, exchange rates, and other market data. In this blog post, we’ll explore how to use the getSymbols function from the quantmod package and return generic xts variable. The getSymbols Function The getSymbols function is used to retrieve financial data from various sources, including Yahoo Finance, Quandl, and Google Finance.

ggplot2 Plotting Data Based on Conditions When working with data visualization using ggplot2, it’s common to have datasets where you want to filter or transform the data based on certain conditions. In this article, we’ll explore how to create a plot that meets specific criteria for each column in your dataset. Understanding the Problem The question presents a scenario where the user has a dataset with 8 columns and wants to create a plot that shows values greater than or less than a particular threshold.