WITH /* enumerate pairs */ cte1 AS ( SELECT ID, EventTime, ReaderNo, COUNT(CASE WHEN ReaderNo = 'In' THEN 1 END) OVER (PARTITION BY ID ORDER BY EventTime) pair FROM test ), /* divide by pairs */ cte2 AS ( SELECT ID, MIN(EventTime) starttime, MAX(EventTime) endtime FROM cte1 GROUP BY ID, pair ), /* get dates range */ cte3 AS ( SELECT CAST(MIN(EventTime) AS DATE) minDate, CAST(MAX(EventTime) AS DATE) maxDate FROM test), /* generate dates list */ cte4 AS ( SELECT minDate theDate FROM cte3 UNION ALL SELECT DATEADD(dd, 1, theDate) FROM cte3, cte4 WHERE theDate < maxDate ), /* add overlapped dates to pairs */ cte5 AS ( SELECT ID, starttime, endtime, theDate FROM cte2, cte4 WHERE theDate BETWEEN CAST(starttime AS DATE) AND CAST(endtime AS DATE) ), /* adjust borders */ cte6 AS ( SELECT ID, CASE WHEN starttime < theDate THEN theDate ELSE starttime END starttime, CASE WHEN CAST(endtime AS DATE) > theDate THEN DATEADD(dd, 1, theDate) ELSE endtime END endtime, theDate FROM cte5 ) /* calculate total minutes per date */ SELECT ID, theDate, SUM(DATEDIFF(mi, starttime, endtime)) workingminutes FROM cte6 GROUP BY ID, theDate ORDER BY 1,2;

Introduction In this article, we will explore the concept of embedding mcmapply in clusterApply and discuss its feasibility, advantages, and potential drawbacks. We will also delve into alternative approaches to achieving similar results and consider the role of Apache Spark in this context. Background mcmapply is a parallel computing function in R that allows for the parallelization of complex computations using multiple cores or even distributed computing frameworks like clusterApply. ClusterApply is another R package that provides an interface to cluster-based parallel computing, allowing users to take advantage of multiple machines and cores for computationally intensive tasks.

Understanding Pandas and DataFrames in Python Overview of Pandas and its Role in Handling DataFrames Pandas is a powerful open-source library used extensively in data science, scientific computing, and data analysis tasks. It provides data structures and functions designed to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables. A DataFrame is the core data structure of Pandas, which is similar to an Excel spreadsheet or a table in a relational database.

Checking the Presence of a Specific Time Dimension in a DateTime Column using Pandas Introduction Pandas is a powerful library for data manipulation and analysis, particularly when dealing with structured data. One common use case involves working with datetime columns, where you may need to check if a specific time dimension (e.g., year, day, hour) is present in the column. In this article, we will explore how to achieve this using Pandas.

Understanding Database Operations in Django ===================================================== Introduction In this article, we will delve into the world of database operations in Django, specifically focusing on how to assign a user to a group in a specific database. We’ll explore the inner workings of Django’s ORM (Object-Relational Mapping) system and provide practical examples to help you better understand the process. Overview of Django’s ORM Django’s ORM is an abstraction layer that allows you to interact with your database using Python code instead of writing raw SQL queries.

Understanding awakeFromNib() and Simulated Metrics in iOS Development Table of Contents Introduction What is awakeFromNib()? Simulated Metrics in iOS Development [Why AwakefromStoryboard() Should Not Be Used](#why-a wakefromstoryboard-should-not-be-used) Alternatives to AwakefromStoryboard(): viewdidload and viewDidLoad Example Use Cases for viewdidload and viewDidLoad Introduction In iOS development, it is common to encounter scenarios where we need to set up our user interface (UI) programmatically. While XIB files are widely used in iOS development, there are situations where we might want to perform UI-related tasks programmatically, such as setting constraints or adjusting layout properties.

Understanding Segmented Controls and Background Colors Introduction to Segmented Controls Segmented controls are a common UI element used in iOS applications for providing users with multiple options or choices. They typically consist of a series of segments, each representing an option, which can be selected by the user. The segmented control is implemented using a UISegmentedControl class, which provides a range of properties and methods for customizing its appearance and behavior.

Aggregating Big Data in R: Efficient Methods for Removing Teams with Variance R is a popular programming language and environment for statistical computing and graphics. It provides an extensive range of libraries and packages for data analysis, machine learning, and visualization. In this article, we will explore an efficient method to aggregate big data in R, specifically focusing on removing teams that have variance in their performance metrics. Introduction Big data refers to the vast amounts of structured or unstructured data that organizations generate and process every day.

Handling Missing Values in Pandas DataFrames: Complementing Daily Time Series with NaN Values until the End of the Year In this article, we will explore a common operation in data analysis: handling missing values in Pandas DataFrames. Specifically, we will focus on complementing daily time series with NaN (Not a Number) values until the end of the year. Introduction Pandas is a powerful library for data manipulation and analysis in Python.

Introduction to SQL Grouping with WHERE When working with databases, one of the most common tasks is data analysis. One of the fundamental concepts in SQL (Structured Query Language), which is used for managing relational databases, is grouping. In this article, we will explore how to use SQL grouping along with the WHERE clause to analyze and summarize data. Understanding SQL Grouping SQL grouping allows us to group rows that share a common characteristic together, known as the grouping column.