【摘要】解锁 SQL Server 2022的时间序列数据功能 SQL Server2022在处理时间序列数据时，SQL Server 提供了一些优化和功能，比如 DATE_BUCKET 函数、窗口函数（如 FIRST_VALUE 和 LAST_VALUE）以及其他时间日期函数，以便更高效地处理时间序列数据阅读全文 ...继续阅读 (38)

[social4i size="small" align="align-left"][This article was first published onR-Blog on Data modelling to develop ..., and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionThis blog is about world happiness ladder using the world happiness report data sets (Helliwell et. al., 2024). The basic objective is to demonstrate the use of panel data which is quite distinct from cross-sectional or time series data.Global happiness ladderCross-sectional happine ...继续阅读 (36)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionIn R, finding patterns in text is a common task, and one of the most powerful functions to do this isgrep(). This function is used to search for patterns in strings, allowing you to locate elements that match a specific pattern. Today, we’ll explore how to use wildcard characters withgrep ...继续阅读 (38)

本文主要介绍了Flask模板的使用，包括jinja2的语法、内置过滤器和自定义过滤器。首先，阐述了模板的概念，即包含占位变量的文件，通过动态赋值后返回给用户。接着，详细介绍了jinja2的三种语法：控制结构、变量取值和注释。然后，列举了Flask内置的过滤器，如绝对值、默认值、格式化字符串等，并提供了示例代码。最后，讲述了如何自定义过滤器，通过使用app.template_filter()装饰器实现字符串反转的示例，使读者更好地理解自定义过滤器的应用。模板的概念模板简单来说就是一个其中包涵占位变量表示动态的部分的文件，模板文件在经过动态赋值后，返回给用户。 jinja2是Flask作者开发的一个模板系统，起初是仿django模板的一个模板引擎，为Flask提供模板支持，由于其灵活，快速和安全等优点被广泛使用。在jinja2中，存在三种语法：控制结构 {% %}变量取值 {{ }}注释 {# #}变量取值 {{ }}在Flask中，render_template中填入参数data，可以将数据传入from flask import Flask, abort, request, make_response, render_template app = Flask(__name__) @app.route('/', methods=['GET' ...继续阅读 (31)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionWelcome back, data enthusiasts! Today, we’re diving into the fascinating world of random walks using the TidyDensity R package. If you’re working with time series data, financial modeling, or stochastic processes, understanding random walks is essential. And with TidyDensity, implementing ...继续阅读 (36)

在开发应用的时候，经常需要一些提示，特别当用户打开应用时，有一些事情需要提醒一下用户，但是不能自动打开这个窗口提示，这样会让用户比较烦。方法1： Badge(value: {count: number, position?在这里显示红点的地方，就是目前介绍的容器Badge实现的功能。从上面来看，有三种不同的形式，一种是在图标上显示一个红点，一种是图标后面显示红色文字，最后一种是显示一个数量。方法2： Badge(value: {value: string, position?根据字符串创建标记组件。 ...继续阅读 (46)

[social4i size="small" align="align-left"][This article was first published onAlbert Rapp, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.Have you ever tried to create a line chart with ggplot only to find that the chart remains blank and a warning showing up that each group consists out of one observation? That’s a common struggle and I used to attempt a lot of trial and error to fix this. In today’s blog post we’re going to take a look what’s going on whe ...继续阅读 (39)

[social4i size="small" align="align-left"][This article was first published onR Archives » Data Science Tutorials, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.The postCalculating Z-Scores in R: A Step-by-Step Guideappeared first onData Science TutorialsUnravel the Future: Dive Deep into the World of Data Science Today!Data Science Tutorials.Calculating Z-Scores in R: A Step-by-Step Guide, Z-scores measure how many standard deviations an individual data v ...继续阅读 (39)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionIn the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance, physics, and biology. Today, we’ll explore these concepts using functions from thehealthyR.tspackage.Random WalksA Random ...继续阅读 (37)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionHello, everyone! Today, we’ll be diving into a practical example of how to run a macro when a cell value changes in VBA. This is particularly useful when you need to trigger certain actions based on user input or dynamic data changes in your Excel sheets. Let’s get started!Setting Up the ...继续阅读 (43)

[social4i size="small" align="align-left"][This article was first published onNumbers around us - Medium, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.#193–194PuzzlesAuthor:ExcelBIAll files (xlsx with puzzle and R with solution) for each and every puzzle are available on myGithub. Enjoy.Puzzle #193Summary again? Yes, but this time not as usual. We have sales and bonus per salesperson per quarter, and as result we also need total per each of those partitio ...继续阅读 (43)

[social4i size="small" align="align-left"][This article was first published onNumbers around us - Medium, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.Puzzles no. 479–483PuzzlesAuthor:ExcelBIAll files (xlsx with puzzle and R with solution) for each and every puzzle are available on myGithub. Enjoy.Puzzle #479Today we have pretty weird sequence to generate — Recaman’s sequence. It doesn’t make any sense in real life, it is just mathematical concept linked ...继续阅读 (43)

[social4i size="small" align="align-left"][This article was first published onDataGeeek, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.TheWarner Music Grouplaunched a new company in April,Warner Music South Asiatargeting region markets that include Bangladesh, Nepal, Pakistan, and Sri Lanka. The company thought that the region was populated and diverse. I will analyze the reasons behind that decision based on some economic data related to South Asia.librar ...继续阅读 (39)

该渲染由 marked 生成，可能存在排版问题，最佳体验请前往：https://xxu.do/posts/structure/Color-the-model-by-sequence-conservation-with-ConSurfConSurf | Evolutionary conservation profiles of proteins, which analyze the sequence conservation of proteins and color the model by the result, it could be helpful ifthe proteins you studied are conserved on structure but vary on the sequence.Analysis conservationAll you need to prepare is aprotein model, you need the sequence at least if you don't have one, the structure can bepredictedfrom it.If you have amultiple sequence alignment(MSA)file, you can upload it, and then the se ...继续阅读 (72)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionToday I am going to go over some quick yet practical examples of ways that you can use thehealthyR.tspackage. This package is designed to help you analyze time series data in a more efficient and effective manner.Let’s just jump right into it!Load the librarieslibrary(healthyR.ts) library ...继续阅读 (47)

前言众所周知，JSON.parse方法用于将一个json字符串转换成由字符串描述的 JavaScript 值或对象，该方法支持传入2个参数，第一个参数就是需要被转换的json字符串，第二个参数则是一个转换器函数(reviver,也叫还原函数)，这个函数会针对每个键/值对都调用一次,这个转换器函数又接受2个参数,第一个参数为转换的每一个属性名，第二个参数则为转换的每一个属性值，并且该函数需要返回一个值，如果返回的是undefined，则结果中就会删除相应的键，如果返回了其他任何值，则该值就会成为相应键的值插入到结果中。对于转换器函数更具体点讲就是：解析值本身以及它所包含的所有属性，会按照一定的顺序（从最最里层的属性开始，一级级往外，最终到达顶层，也就是解析值本身）分别的去调用reviver函数，在调用过程中，当前属性所属的对象会作为this值，当前属性名和属性值会分别作为第一个和第二个参数传入reviver中。如果reviver返回undefined，则当前属性会从所属对象中删除，如果返回了其他值，则返回的值会成为当前属性新的属性值。当遍历到最顶层的值（解析值）时，传入reviver函数的参数会是空字符串""（因为此时已经没有真正的属性）和当前的解析值（有可能已经被修改过了），当前的this值会是{"": 修改过的解析值}，在编写reviver函数时，要注意到这个特例。（这个函数的遍 ...继续阅读 (74)

QuestionDesign a max stack data structure that supports the stack operations and supports finding the stack's maximum element.Implement theMaxStackclass:MaxStack()Initializes the stack object.void push(int x)Pushes elementxonto the stack.int pop()Removes the element on top of the stack and returns it.int top()Gets the element on the top of the stack without removing it.int peekMax()Retrieves the maximum element in the stack without removing it.int popMax()Retrieves the maximum element in the stack and removes it. If there is more than one maximum element, only remove thetop-mostone.You must co ...继续阅读 (18)

需求描述最近产品说，某个el-table要实现按住shift键快速勾选功能大概就是仿windows系统的文件shift按住选中功能反正就是尽可能多的让用户勾选方便用户快速勾选操作github完整代码：https://github.com/shuirongshuifu/vue3-echarts5-exampleWindows系统的功能效果图比如可以向前多选或者向后多选大家可以自己尝试一下自己实现的el-table勾选效果图实现思路页面加载好了以后，绑定监听事件，监听用户键盘按下和抬起事件，看看是不是Shift键页面销毁时候，再卸载一下搞三个变量记录是否按下Shift键、勾选el-table是第几行，和再次勾选el-table是第几行假设第一次勾选的是第四行，第二次勾选的是第七行，只需要把四行和七行中间的五六行控制为勾选即可同理，第一次勾选第七行，第二次勾选第四行也是一样最后shift键抬起的时候，控制把三个变量重置即可less word，more code代码先来个el-table// 数据 import { ref, onMounted, onBeforeUnmount, reactive } from 'vue' const tableData = ref([ { id: 1, date: '2016-05-01', nam ...继续阅读 (51)

Tip #001Rust 不支持静态 vec(static vec),但是最接近的是静态数组。例如,如果你想存储三个字符串的数组,可以尝试这样:static STRINGS : [&str;3] = ["a", "b", "c"]Tip #002什么是可选值(optional)和unwrap()? 可以将可选值想象成一个信封,它可以包含一个值(Some(item))或者什么都没有(None)。对可选值调用unwrap()要么返回包含的值,要么如果可选值是None的话就会使程序panic。Tip #003关于可选值(optional)的安全解包方式:使用match语句明确处理不同情况unwrap_or_default: 要么解包得到值,要么返回默认值unwrap_or_else: 允许你指定一个函数来处理None/Error解包结果Tip #004如果你没有时间完成特定的一段代码,但仍然希望程序可以编译,可以考虑使用todo!()或unimplemented!()宏。你的代码会继续编译通过,但如果程序运行到包含这些宏的代码块中,它将会panic。todo!更适合临时标记,而unimplemented!则更适合长期未实现的情况。Tip #005如果你想测试一个枚举类型的实例是否符合枚举的特定变体,你可以使用matches!宏,例如:let match_res = matches!(my ...继续阅读 (62)

[social4i size="small" align="align-left"][This article was first published onR – TomazTsql, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.Calculating cumulative percentage or percentage per group for each time can sometimes be a task with a slight twist. Let’s check this with ggplot2 and tidyverse.library(ggplot2) library(tidyverse) data <- data.frame( sector = rep(1:20, each = 5), item = rep(1:5, times = 20), value = rpois(100, 10) )Th ...继续阅读 (52)

[social4i size="small" align="align-left"][This article was first published onrstats on Irregularly Scheduled Programming, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.I saw a toot celebrating a short, clean implementation of a square root finding algorithm and wanted to dig a bit deeper into how it works, with a diversion into some APL.Thiswas the toot from Jim GardnerDoubly pleased with myself.Been doing the Tour of Go. Got to the section where you make ...继续阅读 (42)

is是 TypeScript 中用于类型断言的关键字，它用于在运行时对值的类型进行判断。通过使用is，你可以编写自定义的类型保护函数，帮助 TypeScript 编译器理解代码中变量的类型。下面是is的语法和一个简单的使用案例：语法function isType(variable: any): variable is ExpectedType { // 检查 variable 是否为 ExpectedType 类型的值 // 返回值为布尔类型，指示 variable 是否为 ExpectedType 类型 }使用案例假设我们有一个User类型和一个isAdmin函数，用于判断用户是否为管理员。我们可以使用is关键字创建一个类型保护函数来告诉 TypeScript 编译器，当user为管理员时，它是一个AdminUser类型的值。interface User { name: string; isAdmin: boolean; } interface AdminUser extends User { privileges: string[]; } function isAdmin(user: User): user is AdminUser { return user.isAdmin === true; } // 使用 isAdmin 函数进行类型保护 f ...继续阅读 (63)

在 TypeScript 中，as const是一种类型断言的用法，它用于告诉编译器将某个位置的类型视为一个常量（const）类型。这在 TypeScript 的类型守卫和类型细化中非常有用，尤其是在处理字面量类型或确保某个位置的值不会被修改时。基本用法当你使用as const时，你告诉 TypeScript 编译器，某个位置的值是常量，不应该被重新赋值。const config = { apiEndpoint: "https://api.example.com", timeout: 5000, } as const; // 下面的代码将导致 TypeScript 编译错误，因为根据 as const，config 被视为只读的 // config.apiEndpoint = "https://new-api.example.com";在这个例子中，config对象被断言为as const，这意味着它的属性apiEndpoint和timeout被推断为只读属性。在类型守卫中使用as const在类型守卫中特别有用，因为它可以帮助确保某个位置的值是字面量类型。function isStringLiteral(value: any): value is string { return typeof value === "string" && value !== __VUE ...继续阅读 (61)

@Value注解相信很多Spring Boot的开发者都已经有接触了，通过使用该注解，我们可以快速的把配置信息加载到Spring的Bean中。比如下面这样，就可以轻松的把配置文件中key为com.didispace.title配置信息加载到TestService中来使用@Service public class TestService { @Value("${com.didispace.title}") private String title; }这个注解可以说非常的好用！但是为什么不推荐大家使用它呢？核心原因是：当我们使用@Value来直接提取配置信息使用的时候，会产生配置信息加载的碎片化。比如，同一个配置，可能背多个Service或者Controller使用，当我们再要修改它的时候，就会存在一个遗漏的风险。我们无法方便的维护这些配置加载而导致一些问题。那么，如果不使用@Value，我们应该用什么来替代呢？我比较推荐的就是使用@ConfigurationProperties来分类和加载各种配置信息，比如，我要加载关于com.didispace的相关配置时候，就写一个这样的实现：@Configuration @ConfigurationProperties(prefix = "com.didispace") public class Didispac ...继续阅读 (75)

[social4i size="small" align="align-left"][This article was first published onSteve's Data Tips and Tricks, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.IntroductionWorking with vectors is one of the fundamental aspects of R programming. Sometimes, you need to remove specific elements from a vector to clean your data or prepare it for analysis. This post will guide you through several methods to achieve this, using base R,dplyr, anddata.table. We’ll ...继续阅读 (46)

在这里调用函数napi_create_double，它是用来从C++类型double创建一个JS类型对象返回，并且把返回结果保存在变量napiResult，由于napi_value是指针，那么&napiResult;就是指向指针的指针。返回值采用napi_value类型定义，napi_value类型是一个指针类型，并且是一个通用的指针类型，其实它就是相当于void*类型，可以保存任何类型的数值指针。接着定义它返回napi_value类型，这个类型是NAPI的通用类型，可以表示数字、字符串和布尔值等。 ...继续阅读 (57)

[social4i size="small" align="align-left"][This article was first published onNumbers around us - Medium, and kindly contributed toR-bloggers]. (You can report issue about the content on this pagehere)Want to share your content on R-bloggers?click hereif you have a blog, orhereif you don't.#181–182PuzzlesAuthor:ExcelBIAll files (xlsx with puzzle and R with solution) for each and every puzzle are available on myGithub. Enjoy.Puzzle #181This weekend we have very interesting situation, because we are basing on the same data, but different direction of activities. In first one we have stacked and ...继续阅读 (40)

Chapter9 Consistency and ConsensusWe need to build systems that is fault tolerant -> can be with fault but still aliveThe best way of building fault tolerant system is to find some general purpose abstraction with useful guarantees , implement once and let the system rely on those grantees.Consensus : all nodes agree on something.e.g. in single leader replication , all nodes need the consensus that : there should be only one leader in the systemConsistency GuaranteesIn replication, data reach to different node at different time -> you may read different data at a time point from different node ...继续阅读 (21)

Replication of database helps in many aspectsScalability -> spread increasing data loadFault tolerance/high availability -> one down, another take over;Latency/performance-> multiple datacenter across world might help avoid network travel across the earth;Scale to higher load:Shared memory (powerful machine): cost grow fast; limited fault tolerance -> vertical scalingShared disk: several independent CPU and a shared disk(they are connected via network): may suffer from lockShared nothing : horizontal scaling , has many nodes(machines) with their own CPU and disks;Data being distributed across ...继续阅读 (15)