To split one dataframe into list of dataframes based on the pattern, use the split function. result <- split(D_MtC, sub('\\d+', '', D_MtC$MS)) This will create a list where each element is a dataframe that corresponds to a unique value in the $MS column. The values are matched based on the pattern specified by the regular expression \\d+, which matches one or more digits. Note: To print the result, use the following code:

Introduction As we continue to rely on our smartphones for both personal and professional purposes, the need to transfer data between devices has become increasingly important. Whether it’s capturing screenshots, sending files, or even just keeping an eye on what’s happening on your device from afar, being able to share data with your computer is a vital feature. In this post, we’ll explore some of the fastest ways to transfer data between an iPhone and a computer (Mac or PC), including the pros and cons of using TCP sockets, Bonjour, and other techniques.

SQL “Delete all records, then add them again” Instantly Bad Practice? Introduction As software developers, we often find ourselves dealing with complex data relationships and constraints. One such issue arises when deciding how to handle data updates, particularly in scenarios where data is constantly being added, updated, or deleted. The question of whether it’s bad practice to “delete all records, then add them again” has sparked debate among developers. In this article, we’ll delve into the world of SQL and explore why this approach can lead to issues, as well as alternative solutions that prioritize data integrity.

Retrieving Data with Special Characters using Oracle and Hive When working with data that contains special characters, it can be challenging to retrieve specific records. In this article, we’ll explore how to use Oracle and Hive to retrieve data that meets certain conditions. Introduction to Special Characters in Oracle and Hive Special characters are non-alphanumeric characters used in text data, such as hyphens (-), dollar signs ($), asterisks (*), question marks (?

Splitting Records into Chunks with Upper and Lower Limit? Introduction When dealing with large datasets, it’s often necessary to process data in chunks. This can be useful for a variety of reasons, such as reducing memory usage or improving performance when working with very large datasets. In this article, we’ll explore how to split records into chunks using the row_number() function and other database-specific functions. Understanding Row Numbers The row_number() function is an analytic function that assigns a unique number to each row within a partition of a result set.

Understanding Window Functions in SQL As data analysis and querying become increasingly complex, the need for advanced techniques like window functions has grown. In this article, we’ll delve into the world of window functions, exploring their benefits, syntax, and application. What are Window Functions? Window functions allow you to perform calculations across rows that are related to the current row, without the need for self-joins or correlated subqueries. They provide a way to analyze data in groups or partitions of rows, making it easier to answer questions like “What is the maximum value in each group?

Reading and Writing Excel Files with Python: A Step-by-Step Guide Reading and writing Excel files is a common task in data analysis and science. In this article, we will explore how to read a portion of an existing Excel sheet, filter the data, and write a single value from the filtered dataframe to a specific cell in the same sheet using Python. Prerequisites Before we begin, make sure you have the necessary libraries installed:

Understanding the Unexpected ‘=’ Error in R for API Connection =========================================================== In this article, we will delve into the unexpected ‘=’ error encountered when trying to access an API using R and explore the correct syntax for making API connections. Introduction to API Connections with R API (Application Programming Interface) connections are essential for accessing external services, such as data repositories or third-party APIs. R is a popular programming language used extensively in data science and statistical analysis.

Understanding Address Book APIs for iOS Development ===================================================== Table of Contents Introduction to Address Book API Address Book Framework Overview Getting Started with the Address Book API Accessing and Copying Contact Data ABAddressBookGetPersonCount ABAddressBookCopyArrayOfAllPeople ABPersonCopyImageData ABPersonCopyImageDataWithFormat Displaying Contact Images in a Table View UITableView, UITableViewCell, UITableViewDelegate, and UITableViewDataSource Common Pitfalls and Considerations Introduction to Address Book API The Address Book API is a powerful tool for accessing and manipulating contact data on iOS devices.

Generating All Combinations of Columns in a Data Frame In this article, we’ll explore how to obtain all combinations of the columns of a data frame taken by 2 without repetition, and avoiding any column with itself. We’ll use R as our programming language for this example. Background and Prerequisites Before diving into the solution, let’s briefly cover some background information and prerequisites: Data Frames in R: A data frame is a two-dimensional data structure in R that consists of rows and columns.