### 统计代写|数据可视化代写Data visualization代考|COMM2501

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化Data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化Data visualization代写方面经验极为丰富，各种代写数据可视化Data visualization相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|数据可视化代写Data visualization代考|Why R

$R(R$ Core Team 2020) is a computer language based on another computer language called S. It was created in New Zealand by Ross Ihaka and Robert Gentlemen in 1993 and is today one of the most (if not the most) powerful tuuls used lor data andysis. I dsume lhat yuu lave never hieard ul ur used 13 and that therefore $R$ is not installed on your computer. I also assume that you have little or no experience with programming languages. In this chapter, we will discuss everything you need to know about $R$ to understand the code used in this book. Additional readings will be suggested, but they are not required for you to understand what is covered in the chapters to come. You may be wondering why the book does not employ IBM’s SPSS, for example, which is perhaps the most popular statistical tool used in second language research. If we use Google Scholar citations as a proxy for popularity, we can clearly see that SPSS was incredibly popular up until 2010 (see report on http://r4stats.com/articles/popularity/). In the past decade, however, its

popularity has seen a steep decline. Among its limitations are a subpar graphics system, slow performance across a wide range of tasks, and its inability to handle large datasets effectively.

There are several reasons that using $\mathrm{R}$ for data analysis is a smart decision. One reason is that $R$ is open-source and has a substantial online community. Being open-source, different users can contribute packages to $\mathrm{R}$, much like different Wikipedia users can contribute new articles to the online encyclopedia. A package is basically a collection of tools (e.g., functions) that we can use to accomplish specific goals. As of October 2020 , $R$ had over 15,000 packages, so chances are that if you need to do something specific in your analysis, there is a package for that-naturally, we only need a fraction of these packages. Having an active online community is also important, as users can easily and quickly find help in forum threads.

Another reason that $\mathrm{R}$ is advantageous is its power. First, because $\mathrm{R}$ is a language, we are not limited by a set of preestablished menu options or buttons. If we wish to accomplish a goal, however specific it may be, we can simply create our own functions. Typical apps such as SPSS have a more user-friendly Graphical User Interface (GUI), but that can certainly constrain what you can do with the app. Second, because $\mathrm{R}$ was designed specifically for data analysis, even the latest statistical techniques will be available in its ecosystem. As a result, no matter what type of model you need to run, $R$ will likely have it in the form of a package.

## 统计代写|数据可视化代写Data visualization代考|Installing R and RStudio

The first thing we need to do is install $\mathrm{R}$, the actual programming language. We will then install RStudio, which is a powerful and user-friendly editor that uses $R$. Throughout this book, we will use $R S t u d i o$, and I will refer to ” $\mathrm{R}$ ” and “RStudio” interchangeably, since we will use $\mathrm{R}$ through $\mathrm{RStudio.}$

1. Go to https://rstudio.com and click on “Download RStudio”
2. Choose the free version and click “Download”
3. Under “Installers”, look for your operating system
You should now have both $R$ and $R$ Studio installed on your computer. If you are a Mac user, you may also want to install XQuartz (https://www.xquartz.org) -you don’t need to do it now, but if you run into problems generating figures or using different graphics packages later on, installing XQuartz is the solution. Because we will use RStudio throughout the book, in the next section, we will explore its interface. Finally, RStudio can also be used online at http:// rstudio.cloud for free (as of August 2020 ), which means you technically don’t need to install anything. That being said, this book (and all its instructions) is based on the desktop version of RStudio, not the cloud version-you can install $\mathrm{R}$ and RStudio and then later use RStudio online as a secondary tool. For reference, the code in this book was last tested using $R$ version 4.0.2 (2020-06-22)_”Taking Off Again” and RStudio Version 1.3.1073 (Mac OS). Therefore, these are the versions on which the coding in this book is based.

## 统计代写|数据可视化代写Data visualization代考|Interface

Once you have installed both $\mathrm{R}$ and RStudio, open RStudio and click on File $\succ$ New File $\succ$ R Script. Alternatively, press Ctrl $+$ Shift $+\mathrm{N}$ (Windows) or $\mathrm{Cm}+\mathrm{Shift}+\mathrm{N}$ (Mac)-keyboard shortcuts in RStudio are provided in Appendix B. You should now have a screen that looks like Fig. 2.1. Before we explore RStudio’s interface, note that the interface is virtually the same for Mac, Linux, and Windows versions, so while all the examples given in this book are based on the Mac version of RStudio, they also apply to any Linux and Windows versions of RStudio. As a result, every time you see a keyboard shortcut containing Cmd, simply replace that with Ctrl if you are using a Linux or Windows version of RStudio.

What you see in Fig. $2.1$ is that RStudio’s interface revolves around different panes (labeled by dashed circles). Panes B, C, and D were visible when you first opened RStudio-note that their exact location may be slightly different on your RStudio and your operating system. Pane A appeared once you created a new $\mathrm{R}$ script (following the earlier steps). If you look carefully, you will note that a tab called Untitled1 is located at the top of pane A-immediately below the tab you see a group of buttons that include the floppy disk icon for saving documents. Much like your web browser, pane A supports multiple tabs, each of which can contain a file (typically an R script). Each script can contain lines of code, which in turn means that each script can contain an analysis, parts of an analysis, or multiple analyses. If you hit $\mathrm{Cm}+\mathrm{Shift}+\mathrm{N}$ to create another $\mathrm{R}$ Script, another tab will be added to pane A. Next, let’s examine each pane in detail.

Pane A is probably the most important pane in RStudio. This is the pane where we will write our analysis and our comments, that is, this is RStudio’s script window. By the end of this book, we will have written and run several lines of code in pane A. For example, click on pane A and write $2+$ 5. This is your first line of code, that’s why you see 1 on the left margin of pane A. Next, before you hit enter to go to the next line, run that line of code by pressing Cmd + Enter. You can also click on the Run button to the left of Source in Fig. 2.1. You should now see the result of your calculation in pane B: [1] $7 .$

Pane B is RStudio’s console, that is, it is where all your results will be printed. This is where $\mathrm{R}$ will communicate with you. Whereas you will write your questions (in the form of code) in pane A, your answers will appear in pane Bwhen you ran line 1 earlier, you were asking a simple math question in pane $A$ and received the calculated answer in pane B. Finally, note that you can run code directly in pane B. You could, for example, type $2+5$ (or $2+5$ without spaces) in pane B and hit Enter, which would produce the same output as before. You could certainly use pane B for quick calculations and simple tasks, but for an actual analysis with several lines of code and comments,you certainly want the flexibility of pane $A$, which allows you to save your script much like you would save a Word document.

## 统计代写|数据可视化代写Data visualization代考|Why R

R(RCore Team 2020）是一种基于另一种称为 S 的计算机语言的计算机语言。它由 Ross Ihaka 和 Robert Gentlemen 于 1993 年在新西兰创建，是当今使用数据分析的最强大（如果不是最强大）的 tuul 之一。我认为 lhat yuu lave 从来没有听说过你用过 13，因此R未安装在您的计算机上。我还假设您对编程语言几乎没有经验。在本章中，我们将讨论您需要了解的所有内容R理解本书中使用的代码。将建议您阅读其他阅读材料，但您不需要它们来理解接下来的章节中所涵盖的内容。您可能想知道为什么本书没有使用 IBM 的 SPSS，例如，它可能是第二语言研究中最流行的统计工具。如果我们使用谷歌学术引用作为流行度的代表，我们可以清楚地看到 SPSS 在 2010 年之前非常流行（参见 http://r4stats.com/articles/popularity/ 上的报告）。然而，在过去的十年中，其

## 统计代写|数据可视化代写Data visualization代考|Installing R and RStudio

1. 转到 https://rstudio.com 并单击“下载 RStudio”
2. 选择免费版本并点击“下载”
3. 在“安装程序”下，查找您的操作系统
您现在应该同时拥有R和RStudio 安装在您的计算机上。如果您是 Mac 用户，您可能还想安装 XQuartz (https://www.xquartz.org) – 您现在不需要这样做，但如果您以后在生成图形或使用不同的图形包时遇到问题上，安装 XQuartz 是解决方案。因为我们将在整本书中使用 RStudio，所以在下一节中，我们将探索它的界面。最后，还可以在 http://rstudio.cloud 上免费在线使用 RStudio（截至 2020 年 8 月），这意味着您在技术上不需要安装任何东西。话虽如此，这本书（及其所有说明）是基于 RStudio 的桌面版本，而不是云版本——你可以安装R和 RStudio，然后在线使用 RStudio 作为辅助工具。作为参考，本书中的代码最后使用R版本 4.0.2 (2020-06-22)_“再次起飞”和 RStudio 版本 1.3.1073 (Mac OS)。因此，这些是本书编码所基于的版本。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。