The provided code seems to be a mix of R and Python. To extract the required data from the webpage, we need to use rvest and RSelenium. Here’s an example of how you can modify the code: library(rvest) library(RSelenium) # Launch browser url = 'https://en.trend.az/archive/2021-11-02' driver <- rsDriver(browser = c("firefox")) remDr <- driver["client"] # Navigate to the webpage remDr$navigate(url) # Wait for the page to load Sys.sleep(2) # Click outside in an empty space remDr$findElement(using = "xpath", value = '/html/body/div[1]/div/div[1]/h1')$clickElement() webElem <- remDr$findElement("css", "body") # Scroll to the end of webpage for (i in 1:17) { Sys.

Finding the Value of New Column Based on Values and Conditions in Another Columns In this article, we will explore how to create a new column based on conditions applied to values in another columns. We’ll use a sample dataset with various activities performed by individuals across different age groups. Introduction We often encounter situations where we need to analyze or manipulate data based on certain conditions. In such cases, creating new columns that reflect these conditions can be helpful for further analysis or modeling.

HTTP Authentication Headers for IIS Windows Authentication Introduction When building web applications that interact with servers behind a proxy or firewall, understanding how to handle HTTP authentication headers is crucial. In this article, we will delve into the world of HTTP authentication headers and specifically focus on how they work with IIS (Internet Information Services) and Windows authentication. Windows Authentication Basics Before we dive into HTTP authentication headers, let’s first understand what Windows authentication entails.

Understanding np.select and NaN Values in Pandas DataFrames As a data scientist or engineer working with pandas DataFrames, you’ve likely encountered the np.select function to create new columns based on multiple conditions applied to other columns. However, there’s a common source of frustration when using this function: why does np.select return ’nan’ as a string instead of np.nan when np.nan is set as the default value? In this article, we’ll delve into the world of pandas arrays and missing values to understand why np.

Understanding the Problem and the Current Query The problem at hand involves determining the availability of a specific item, denoted by listing.id = 1, within a given date range specified by the booking table. The current query attempts to achieve this by joining various tables (transaction, booking, transaction_item, and listing) and applying filters based on the date range. Current Query Analysis The provided SQL query contains several sections: Inner Join: It starts with an inner join between transaction and booking based on matching id values in both tables.

Closest Points from Another Dataset within a Certain Direction Introduction In data analysis, it is common to work with multiple datasets that contain points in a coordinate system. When dealing with these datasets, one of the key challenges is finding the closest point between two datasets based on certain criteria. In this article, we will explore how to find the closest points from one dataset within a specific direction to another dataset.

Using Cumulative Functions in Dplyr: A Practical Guide to Repeating Values After Identifying a “1” In this article, we will explore how to use the cummax function from the dplyr package to create a new column in a tibble that repeats values after identifying a specific value. We will provide an example of using cummax to repeat “1” until the end of records for a given ID. Introduction The dplyr package provides a range of functions for data manipulation, including group_by, summarise, and mutate.

Step 1: Understanding the Problem The problem requires finding a way to efficiently retrieve the descendants of a specific category (identified by ID 19) from a database table named “products”. The descendants are represented by IDs that contain the path or hierarchy leading to the original category. Step 2: Considering Alternatives for Handling Hierarchical Data Given the hierarchical nature of the problem, several strategies can be considered: Using recursive SQL queries with the “WITH” clause.

Full Page Width Rendering to PDF in Quarto Documents In this article, we will explore how to render a full page width when rendering a quarto document to PDF without modifying the margins for the entire document or the paper size. This is particularly useful when working with tables and other content that needs to be displayed at its full extent. Background and Context Quarto is an R Markdown document format that provides a flexible and powerful way to create documents.

Handling COALESCE in Dynamic SQL with CONVERT When working with dynamic SQL, it’s common to encounter scenarios where we need to filter data based on user input or default values. In this response, we’ll explore how to handle the COALESCE function in dynamic SQL queries using CONVERT. Understanding COALESCE and CONVERT Before diving into the solution, let’s briefly discuss what COALESCE and CONVERT are: COALESCE: The COALESCE function returns the first non-null value from an argument list.