Scraping Data from CoinMarketCap.com in R Introduction CoinMarketCap.com is a popular platform that provides real-time data on cryptocurrency prices, market capitalization, and other relevant metrics. For users interested in analyzing historical performance of various cryptocurrencies, including Bitcoin, scraping data from CoinMarketCap.com can be an effective solution. In this article, we will explore the best package and method to scrape data from CoinMarketCap.com using R. Required Packages Before starting with the data scraping process, you need to install the required packages in R.

Web Scraping: Downloading Text Files from a Website Introduction In today’s digital age, web scraping has become an essential skill for data extraction and manipulation. In this article, we will explore how to download text files (.txt) from a website using the XML::getHTMLLinks function in R. Prerequisites Before diving into the code, make sure you have the following installed: R XML package (install with install.packages("xml")) XML library (load with library(XML)) Understanding Web Scraping Web scraping involves extracting data from websites that are not provided in a structured format.

Handling Empty CSV Files with Pandas and Python When working with CSV files, it’s essential to handle cases where the files are empty. In this article, we’ll explore how to read through a directory of CSV files, plot non-empty ones, and avoid errors that occur when trying to process empty data. Introduction Pandas is an excellent library for data manipulation and analysis in Python. However, it can be finicky when dealing with empty or malformed data.

Changing pandas strings to integer or float using try: except: Introduction When working with pandas dataframes, it’s common to have columns that contain mixed data types, including strings. In some cases, these strings may represent numerical values that can be converted to integers or floats. However, not all strings can be converted to numbers, and attempting to do so can result in a ValueError exception. In this article, we’ll explore how to handle such situations using pandas’ built-in functions and the try: except: block.

lapply with 2 vectors? A Shiny example The question of applying lapply to two vectors arises frequently when working with data frames and lists in R. This article will delve into the intricacies of using lapply with multiple vectors, providing a clear explanation of the concepts involved. Introduction to lapply For those unfamiliar, lapply is a built-in function in R that applies a function to each element of a list or vector.

Athena/Presto SQL Aggregate Information for Each Day on Historical Data In this article, we will explore how to calculate the total number of active IDs for each day in a historical data set stored in Athena. The problem is as follows: We have a table with historical information captured using change data capture (CDC). For an update on any of the columns, a new entry is added to the table. This means there are multiple versions of the same ID existing in the table.

Understanding the STL Decomposition in R: A Case Study on Time Series Data The STL (Seasonal-Trend Decomposition) decomposition is a statistical technique used to decompose time series data into three components: trend, seasonality, and residuals. The technique is particularly useful for analyzing data with strong seasonal patterns, such as temperature readings from sensors. In this article, we will delve into the world of STL decomposition in R and explore how to apply it to time series data with a frequency of 20 minutes.

Finding Entities Where All Attributes Are Within Another Entity’s Attribute Set In this article, we will delve into the world of database relationships and explore how to find entities where all their attribute values are within another entity’s attribute set. We’ll examine a real-world scenario using a table schema and discuss possible approaches to solving this problem. Understanding the Problem Statement The question presents us with a table containing party information, including partyId, PartyName, and AttributeId.

Finding the Cumulative Number of Missing Days for a Datetime Column in Pandas ===================================================== In this article, we will explore how to find the cumulative number of missing days in a datetime column within a pandas DataFrame. We’ll cover both the old and new methods used by users on Stack Overflow to solve this problem. Introduction Missing values or gaps in data can be challenging to identify and analyze, especially when dealing with continuous data like dates.

Understanding RANK() Function in MySQL and How to Find Min and Max of a Team Based on RANK The RANK() function in MySQL is used to rank the rows within each partition of a result set based on the specified column. In this article, we will explore how to use the RANK() function to find the min and max of a team based on its rank. Background: Teams Table Columns and Desired Output The Teams table has several columns that contain information about each team in a particular league: