### 统计代写|数据可视化代写Data visualization代考|ETX2250

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
• (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|Data Frames

Data frames resemble matrices (another data structure in $\mathrm{R}$; run ?matrix() ${ }^{10}$ to learn about it in your Help tab). But data frames have a very important characteristic that sets them apart-each column in a data frame can have its own class of objects. A data frame is roughly equivalent to an Excel spreadsheet. Unlike vectors, which have one dimension, data frames have two dimensions: rows and columns. Crucially, each column in a data frame is a vector.

Data frames are definitely not the fastest type of data structure; data tables, for example, are considerably faster (Dowle and Srinivasan 2019). However, they are the most popular, and unless you have a huge amount of data to analyze, data frames will be more than enough to get the job done.

You will probably not create a data frame in $R$. Instead, what typically happens is that you have some data in a file, for example, and you want to import it into $\mathrm{R}$. That being said, data frames can also be useful when we want to create new data to explore the predictions of a statistical model-we will do this later on in Part III in this book. Let’s take a look at a simple example that builds on the vectors we have already created. Here, we will make a data frame from myList2.

To create a data frame in $\mathrm{R}$, we use the data.frame() command, as shown in code block $3^{11}$-remember to add this code block to rBasics. R. We then add column names and contents (every column in a data frame must have the same number of rows) – you can choose any name you want, but they must not start with special symbols, and they should not have spaces in them. Alternatively, because we want to have a data frame that has the exact content of myList2, we can use the as.data.frame() function (line 7 in code block 3)-but first we need to give our list entries names (line 6). We have already defined $\mathrm{~ m y N u m b e r s ~ i u d ~ m y W o r d s ~ a n ~ b l e ~ s u u s ~ s c r e p ~ ( c l e c k ~ o s ~ s e s ~ w l u e l l e r ~}$ two objects/variables are in your Environment pane in RStudio. If you call (i.e., run) a variable, say, $A B C$, which no longer exists, you will get an error along the lines of Error: object ‘ABC’ not found. To avoid that, make sure you are still using the same script (rBasics. R) and that you have not closed RStudio in the meantime. If you have closed it, then rerun the lines of code where the variables are assigned and everything should work.

In theory, an Excel file should contain only your data. Hcre’s what that means: you have multiple columns, one observation per row, and all your columns have the same length (i.e., the same number of rows). The name of your columns should not contain spaces or special symbols. It is not a problem if you have empty cells, of course, but your file should not contain comments and notes besides the data, for example. Some people tend to write comments and notes in different cells in the same spreadsheet that they have their dataset. Other people also like to add formulæ to some cells, say, to calculate the mean of a given column. If that’s your case, first copy just your data onto a new spreadsheet so that you have an Excel file that only contains your data and nothing else.

Once you have a file that only has data in it, you are ready to start. Even though $R$ can certainly read Excel files $(. x \mid s)$, it is always a better idea to work with other file formats-.xls files will store not only your data but also charts and formulæ used in your spreadsheet, which are useless if we’re using $\mathrm{R}$ for our data visualization and statistical analyses. In this book, we will use .csv files, which are plain text files where columns are separated by commas-hence the name comma-separated values. These files are lighter than .xIs files and can be opened in any text editor. If your data is currently an Excel spreadsheet, simply save it as a .csv file.

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

Whether you use SPSS or $\mathrm{R}$, every research project that we develop has a number of files. Examples include folders for papers, reading materials,abstracts, and data files. Hopefully, all these folders are located in a single folder that gathers all the files that are related to a given research project. File organization is a good habit to cultivate, and RStudio offers us an incredibly handy tool for that: a file extension called .Rproj.

To understand what $\mathrm{R}$ Projects are, follow these steps. In RStudio, go to File $\succ$ New Project…. You will then have some options, two of which are New Directory and Existing Directory. As the names suggest, you should pick the former if you don’t have a folder for a project yet and the latter in case you already have a folder where you want to place your data analysis files. We already created a directory earlier called basics, and that’s where we will save our R Project. Therefore, choose Existing Directory and click on browse to locate the basics folder. Finally, click on Create Project. Your project will inherit the same name as the directory in which you create it, so it will be called basics.RProj. We will use this $R$ Project for all the coding in the remainder of this chapter.

Once you have created your $\mathrm{R}$ Project, you will notice that RStudio will reappear on your screen. Only three panes will be visible (no script is open), so you can see your console, your environment, and pane D (from Fig. 2.1), where your Files tab is located. In that tab, you can see the contents of your newly created directory, where your $\mathrm{R}$ Project is located-you should be able to see only one file in the directory: basics. Rproj. You can confirm that this is the only file in the folder if you open that folder on your computer.
An Rproj file has no content in and of itself. It only exists to “anchor” your project to a given directory. Therefore, you could have multiple $\mathrm{R}$ Projects open at the same time, each of which would be self-contained in a separate RStudio session, so you would end up with multiple RStudios open on your computer. Each project would know exactly what directory to point to-that is another advantage of working with projects as opposed to single scripts. You do not necessarily need to use $\mathrm{R}$ Projects, but they can certainly help you manage all the files in your project. This book will use $\mathrm{R}$ Projects several tines, and you’re encouraged to do the same (your luture sell will thank you). I return to this point in chapter 3 (e.g., Fig. 3.1). Finally, you can place your rBasics. R file (created earlier for code blocks 1,2 , and 3 ) in the same directory as basics.Rproj, so there will be two files in the directory-you can delete df.csv, created in code block 3 , since we won’t use that file anymore.

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

Rproj 文件本身没有内容。它只存在于将您的项目“锚定”到给定目录。因此，您可以拥有多个R项目同时打开，每个项目都将在单独的 RStudio 会话中自包含，因此您最终会在计算机上打开多个 RStudio。每个项目都将确切地知道指向哪个目录——这是使用项目而不是单个脚本的另一个优势。您不一定需要使用R项目，但它们当然可以帮助您管理项目中的所有文件。本书将使用R投射多个尖齿，并鼓励您这样做（您的诱饵销售会感谢您）。我将在第 3 章回到这一点（例如，图 3.1）。最后，您可以放置​​您的 rBasics。R 文件（之前为代码块 1,2 和 3 创建）与 basics.Rproj 在同一目录中，因此目录中将有两个文件 – 您可以删除在代码块 3 中创建的 df.csv，因为我们赢了’不要再使用那个文件了。

## 有限元方法代写

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## 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。