### 统计代写|应用统计代写applied statistics代考|Hex colors

statistics-lab™ 为您的留学生涯保驾护航 在代写应用统计applied statistics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。我们的专家在代写应用统计applied statistics代写方面经验极为丰富，各种代写应用统计applied statistics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|应用统计代写applied statistics代考|Hex colors

Technically, R actually stores color in a system called hexadecimal, which defines each color (red, green, or blue) in terms of two values that range from 0-9, then from A-F, giving a total of 16 different values for each character. Thus, since each color is defined in terms of two number/letter combos, each color has 256 different possible values (the same as we see with RGB!!). Although the code for hexadecimal may not be intuitive, it is easy to look up online exactly what the code is for any color you want to use (just do an internet search for something like “RGB to Hex color”). The main advantage of the Hex system over RGB is that it is very compact to specify whatever color you want. In R, the hex color code goes in quotes and is preceded by a #. An additional two characters can be added to define a degree of translucency (aka, the alpha level). For example, the translucent red color defined previously would be as follows.

## 统计代写|应用统计代写applied statistics代考|DATA EXPLORATION USING ggplot2

In the last chapter, you learned a little bit about how to make fairly simple figures using base graphics, i.e., the graphics functions that are built into the version of R you downloaded from CRAN. One problem with base graphics is that the figures produced are relatively utilitarian and ugly (at least in many people’s view). There is a whole universe of functions and arguments you can use to make them look better, but in their basic version they are kind of boring and ugly. The relatively recently designed package ggplot2 makes it very easy to make nice looking figures. However, the syntax for coding in ggplot2 is a little different than base graphics. There are two workhorse functions in ggplot 2: the first is qplot() (which stands for “quick plot”) and the other is ggplot(). We will cover $g g p l o t()$ at a later date. For now, let’s explore qplot().

## 统计代写|应用统计代写applied statistics代考|Boxplots

Earlier, you made a boxplot using base graphics. The syntax for this was conveniently the same that we will use to define statistical models (“response $\sim$ predictor”). ggplot2 does things differently. Instead, you explicitly specify what variable you want on the $x$ or $y$ axes, and you specify the type of plot you want using the geom argument, which is short for geometric object. Don’t forget that in order to use the $q$ plot() function you first need to load the ggplot2 library. Also remember that you have to do this each time you restart $R$. In the following code I am using the number sign (#) to annotate the code. When you come back to your analyses in a week, a month, or a year, you need to have notes to remind yourself of what you were doing. Always leave notes in your script file for your future self.

Even in the most basic form, the figure made with ggplot2 (Figure $4.1$ ) is nicer looking (to many people), but this is not why ggplot2 is so useful. Where ggplot2 really shines is in its ability to add colors and to plot data across many different variables at once, as well as to easily take the same type of data and plot it in different ways within a single coding framework. For example, if you want to add colors to your figures, you can use either the “col” or “fill” arguments. In the case of a boxplot, “col” will change the color of the outline of the boxes whereas “fill” changes the color inside each one. The effects of “col” or “fill” will differ based on the particular type of plot you are making. One thing that is really cool about plotting with ggplot2 is that we define the colors as one of the variables in our dataset. R will be able to look at our data frame and know how many categories we have, and therefore how many colors to plot, and will even add a legend for us. So handy！

## 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。