### 统计代写|R语言代写R language代考|SOW-BS086

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

statistics-lab™ 为您的留学生涯保驾护航 在代写R语言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R语言代写方面经验极为丰富，各种代写R语言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

Thus far, we’ve only been entering data directly into the interactive R console. For any data set of non-trivial size this is, obviously, an intractable solution. Fortunately for us, $\mathrm{R}$ has a robust suite of functions for reading data directly from external files.
Go ahead, and create a file on your hard disk called favorites . txt that looks like this:
flavor, number
pistachio, 6
mint chocolate chip, 7
vanilla,5
chocolate, 10
strawberry, 2
neopolitan, 4
This data represents the number of students in a class that prefer a particular flavor of soy ice cream. We can read the file into a variable called favs as follows:

If you get an error that there is no such file or directory, give $\mathrm{R}$ the full path name to your data set or, alternatively, run the following command:
The preceding command brings up an open file dialog for letting you navigate to the file you’ve just created.
The argument sep $=$ “, ” tells $\mathrm{R}$ that each data element in a row is separated by a comma. Other common data formats have values separated by tabs and pipes (“|”). The value of sep should then be ” $\backslash t “$ and ” $\mid$ “, respectively.

The argument header=TRUE tells $\mathrm{R}$ that the first row of the file should be interpreted as the names of the columns. Remember, you can enter ?read. table at the console to learn more about these options.

Reading from files in this comma-separated-values format (usually with the .csv file extension) is so common that $\mathrm{R}$ has a more specific function just for it. The preceding data import expression can be best written simply as: Now, we have all the data in the file held in a variable of class data. frame. A data frame can be thought of as a rectangular array of data that you might see in a spreadsheet application. In this way, a data frame can also be thought of as a matrix; indeed, we can use matrix-style indexing to extract elements from it. A data frame differs from a matrix, though, in that a data frame may have columns of differing types. For example, whereas a matrix would only allow one of these types, the data set we just loaded contains character data in its first column, and numeric data in its second column.

## 统计代写|R语言代写R language代考|Working with packages

Robust, performant, and numerous though base R’s functions are, we are by no means limited to them! Additional functionality is available in the form of packages. In fact, what makes $\mathrm{R}$ such a formidable statistics platform is the astonishing wealth of packages available (well over 7,000 at the time of writing). R’s ecosystem is second to none!
Most of these myriad packages exist on the Comprehensive R Archive Network (CRAN). CRAN is the primary repository for user-created packages.

One package that we are going to start using right away is the ggplot2 package. ggplot2 is a plotting system for $R$. Base $R$ has sophisticated and advanced mechanisms to plot data, but many find ggplot2 more consistent and easier to use. Further, the plots are often more aesthetically pleasing by default.
Let’s install it!

install.packages (“ggplot2”)
Now that we have the package downloaded, let’s load it into the R session, and test it out by plotting our data from the last section:

You’re all wrong, Mint Chocolate Chip is way better!
Don’t worry about the syntax of the ggplot function, yet. We’ll get to it in good time.
You will be installing some more packages as you work through this text. In the meantime, if you want to play around with a few more packages, you can install the gdata and foreign packages that allow you to directly import Excel spreadsheets and SPSS data files respectively directly into $R$.

## R语言代写

neopolitan, 4

favs <- read.table (“favorites.txt”, sep $=$ “,”, header=TRUE)

(), sep $=$ “,”, header=TRUE)

## 统计代写|R语言代写R language代考|Working with packages

#从 CRAN 下载和安装
install.packages (“ggplot2”)

## 广义线性模型代考

statistics-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。