Using rvest to Extract Both Hypertext and Hyperlink from a Column in a Table In this article, we’ll explore how to use the popular R package rvest to extract both hypertext and hyperlinks from a column in a table. We’ll go through the process of scraping a webpage using rvest, extracting the desired data, and then cleaning and processing it for further analysis. Introduction The European Medicines Agency (EMA) is an agency of the European Union responsible for evaluating the safety and efficacy of medicines.

Correlation between Columns Where They Share Name Introduction In this article, we’ll explore how to calculate the correlation between columns in a Pandas DataFrame where those columns share the same name. This problem is particularly relevant when working with datasets that contain multiple observations or measurements for the same variable. The Problem Consider a large DataFrame df containing information about which site the data comes from, a name, a code, and empty rows followed by data.

Restoring the Original Order of a Vector in R When working with vectors in R, it’s not uncommon to need to manipulate their order. This can be done using various functions and techniques, but sometimes you may want to switch back to the original order after performing certain operations on the vector. In this article, we’ll explore how to achieve this using the order() function. Understanding Vectors and Indexing in R Before diving into the solution, let’s take a brief look at vectors and indexing in R.

Developing Shiny Apps on Linux and Deploying Them as Desktop Apps on Windows Introduction In today’s data-driven world, interactive visualizations are becoming increasingly popular for data analysis and presentation. RStudio’s Shiny app framework is a powerful tool for creating web-based interactive dashboards. However, when it comes to sharing these apps with colleagues who use different operating systems, deployment can be a challenge. In this article, we will explore the process of developing shiny apps on Linux, deploying them as desktop applications on Windows.

Working with DataFrames in Pandas: Unpacking and Extracting Values from Column Data =========================================================================== In this article, we’ll delve into the world of Pandas, a powerful Python library for data manipulation and analysis. We’ll explore how to extract values from column data in a DataFrame, specifically focusing on unpacking and extracting specific columns or values. Introduction to DataFrames A DataFrame is a two-dimensional table of data with rows and columns. It’s a fundamental data structure in Pandas, allowing for efficient storage and manipulation of data.

Using Purrr or Furrr to Filter, Map and Pass Character Vectors into Additional Functions ===================================================== In this article, we will explore how the popular R package purrr (or its sister package furrr) can be used to simplify and speed up data manipulation tasks. Specifically, we will focus on using purrr::map to filter datasets, pass filtered datasets into additional functions, and then use Reduce to combine the results. Introduction The R community has long been aware of the importance of efficient data manipulation when working with large datasets.

Converting Rows of One Table to JSON and Adding it to Another Table in PostgreSQL =========================================================== In this article, we will explore how to convert rows from one table to JSON format and then add the resulting JSON to another table in a PostgreSQL database. Background Information PostgreSQL is a powerful object-relational database system known for its robust features and flexibility. One of its key strengths is its support for JSON data type, which allows us to store and manipulate structured data in a more human-readable format.

Pandas Dataframe: Replace Character Conditionally In this article, we will explore how to replace character values in a pandas dataframe conditionally. We’ll delve into the world of string manipulation and data cleaning using pandas’ powerful features. Introduction The pandas library is one of the most widely used libraries for data analysis in Python. It provides an efficient way to handle structured data, including tabular data such as spreadsheets and SQL tables.

Creating Multiple PySpark Dataframes from a Single DataFrame Introduction When working with large datasets in PySpark, it’s common to need to create multiple dataframes based on different criteria. In this article, we’ll explore how to create multiple PySpark dataframes from a single dataframe using Python. Limitations of Dynamic Variable Names One of the challenges when creating multiple dataframes is assigning dynamic variable names. Unfortunately, in Python, it’s not possible to dynamically assign variable names or access them at runtime.

Understanding RAY Workers Being Killed by OOM Pressure ===================================================== In this article, we’ll delve into the issue of RAY workers being killed due to out-of-memory (OOM) pressure when working with large datasets. We’ll explore the underlying causes, discuss potential workarounds and optimizations, and provide guidance on how to tackle this challenge efficiently. Background: Understanding RAY and Modin RAY is a high-performance computing framework that provides a scalable and fault-tolerant way to parallelize compute tasks.