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。

assignmentutor-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

统计代写|R语言代写R language代考|Data frames

Data frames are a special type of list, in which each element is a vector or a factor of the same length. They are created with function data. frame $\mathrm{O}$ with a syntax similar to that used for lists-in object-oriented programming we say that data frames are derived from class list. As the expectation is equal length, if vectors of different lengths are supplied as arguments, the shorter vector(s) is/are recycled, possibly several times, until the required full length is reached.

Indexing of data frames is similar to that of the underlying list, but not exactly equivalent. We can index with operator $[[]]$ to extract individual variables, thought of being the columns in a matrix-like list or “worksheet.”

With function class() we can query the class of an R object (see section $2.8$ on page 41). As we saw in the two previous chunks, 1ist and data. frame objects belong to two different classes. However, their relationship is based on a hierarchy of classes. We say that class data. frame is derived from class list. Consequently, data frames inherit the methods and characteristics of lists, as long as they have not been hidden by new ones defined for data frames.

In the same way as with vectors, we can add members to lists and data frames.We have added two columns to the data frame, and in the case of column $\times 3$ recycling took place. This is where lists and data frames differ substantially in their behavior. In a data frame, although class and mode can be different for different variables (columns), they are required to be vectors or factors of the same length. In the case of lists, there is no such requirement, and recycling never takes place when adding a node. Compare the values returned below for a.1s, to those in the example above for a.df.

统计代写|R语言代写R language代考|Data sets in R and packages

To be able to present more meaningful examples, we need some real data. Here we use cars, one of the many data sets included in base R. Function data() is used to load data objects that are included in $\mathrm{R}$ or contained in packages. It is also possible to import data saved in files with foreign formats, defined by other software or commonly used for data exchange. Package ‘foreign’, included in the R distribution, as well as contributed packages make available functions capable of reading and decoding various foreign formats. How to read or import “foreign” data is discussed in R documentation in R Data Import/Export, and in this book, in chapter 8 starting on page 293. It is also good to keep in mind that in R, URLs (Uniform Resource Locators) are accepted as arguments to the file or path parameter of many functions (see section $8.12$ starting on page 322).

In the next example we load data included in $\mathrm{R}$ as $\mathrm{R}$ objects by calling function data(). The loaded $\mathrm{R}$ object cars is a data frame.
$\operatorname{data}($ cars)
Once we have a data set available, the first step is usually to explore it, and we will do this with cars in section $2.17$ on page 81 .

R语言代写

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。

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