Working with Bibliography in R Markdown and LaTeX When creating documents in R Markdown, it’s common to include bibliographies to cite sources. However, sometimes you might want to display additional information from the bibliography, such as notes or access dates. In this post, we’ll explore how to force R Markdown/LaTeX to display these “note” fields in the bibliography. Understanding Bibliography and Citation Styles In LaTeX, a citation style is used to format citations and bibliographies.

Rescaling Normalized Values Based on Group in Pandas ===================================================== In this article, we will explore how to rescale a normalized value column based on different groups in pandas. We will use the np.select() function to achieve this. Background and Problem Statement The problem at hand is to rescale a normalized value column in a pandas DataFrame based on specific group values. The normalized value column ranges between 0 and 1 for each group.

Zonal Statistics R: A Deep Dive into Extracting Polygon Information from Rasters Introduction Zonal statistics is a fundamental concept in remote sensing and geographic information systems (GIS) that allows for the calculation of statistics across spatial units such as polygons, shapes, or regions. In this article, we’ll delve into how to perform zonal statistics using the raster package in R and extract polygon-specific information from raster data. Background The raster package provides an interface to work with raster data, which is a fundamental component of remote sensing and GIS applications.

Introduction to \Sexpr Functions in Exams Package The exams package is a popular tool for creating exercises and quizzes in R. One of its key features is the ability to generate LaTeX code using the \Sexpr function. This function allows users to include R expressions directly into their documents, which can then be evaluated by the exams engine to produce accurate results. However, when working with units in R, it can be challenging to manage the conversion between unit systems and LaTeX code.

Strange Difference in Performance of Pandas, Dataframe on Small & Large Scale Introduction As a data analyst or scientist, working with large datasets can be a daunting task. One of the most popular libraries for data manipulation and analysis is the Python library, pandas. In this article, we’ll explore a strange behavior in pandas when working with large datasets. Specifically, we’ll investigate why appending new rows to an existing dataframe on small scales works as expected but performs poorly on larger scales.

Querying XML Tag Attributes in a SQL Server Database Using PowerShell In this article, we will explore the process of querying an XML tag attribute in a SQL Server database using PowerShell. This involves connecting to the database, executing a query that filters on the desired attribute value, and retrieving the result. Background Information PowerShell is a task automation and configuration management framework from Microsoft. It’s designed to be a powerful tool for Windows system administration and automation tasks.

Understanding the Problem and Requirements In this blog post, we will delve into a common problem faced by time series data analysts: filling missing values (NA) in a time series while limiting the number of consecutive NA values filled to a specified threshold. The goal is to find a vectorized approach that achieves this with a reasonable amount of code. Introduction to Time Series Data Time series data is characterized by its temporal nature, where each observation is related to the others in terms of both space (geographical proximity) and time (sequential ordering).

Retrieving Unknown Column Names from DataFrame.apply Introduction In this blog post, we will explore a common problem when working with pandas DataFrames. We have a DataFrame that we want to apply some operations on it using the apply() function. However, in our case, we don’t know the names of the columns beforehand. How can we retrieve the column names from the result of apply() without knowing them in advance? Background The apply() function is used to apply a given function element-wise to the entire DataFrame (or Series).

Introduction to Skewed Normal Distributions and R In statistics, skewed distributions refer to a type of probability distribution that is asymmetric about its mean. This means that the majority of the data points are concentrated on one side of the distribution, while fewer data points are concentrated on the other side. In this blog post, we’ll explore how to generate random numbers with skewed normal distributions in R. What are Skewed Normal Distributions?

Changing the Labels in Multiple ggplot Legends In this article, we will explore how to change the labels in multiple legends of a ggplot graph using the scale_shape_manual function. We will also delve into the concepts of discrete scales and how to handle them when dealing with multiple legends. Understanding Discrete Scales A discrete scale is a type of scale that uses discrete values, such as categorical variables or integers. When working with discrete scales, it’s essential to understand how they interact with aesthetics like shape in ggplot.