### 统计代写|数据可视化代写Data visualization代考|INFS6023

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|Using Ggplot

Recall that long is a dataset with ten participants, two groups (control and target), three tests, and test scores. A natural question to ask is whether the scores in both groups are different. ${ }^{21}$ For that, we could create a bar plot with scores on the $\gamma$-axis and the two groups on the $x$-axis. We want bars (which represent the mean for each group) as well as error bars (for standard errors)-see $₫ 1.3 .4$. An example is shown in Fig. 2.3.

You should look at any plot in $R$ as a collection of layers that are “stitched” together with a “+” sign. Each subsequent layer is automatically indented by RStudio and can add more information to a figure. The very first thing we need to do when using ggplot2 is to tell the package what data you need to plot. You can do that with the function ggplot(). Inside the function, we will also tell gaplot2 what we want to have on our axes. Let’s carefully go over the code that generates Fig. 2.3, shown in code block 9 .

In line 1 , we source our dataPrep. $R$ script (which itself will source other scripts). A month from now, we would simply open our R Project, click on our eda. $R$ script and, by running line 1 in code block 9 , all the tasks discussed earlier would be performed in the background. $R$ would import your data, load the necessary packages, and prepare the data, and we’d be ready to go. This automates the whole process of analyzing our data by splitting the task into separate scripts/components (which we created earlier). Chances are we won’t even remember what the previous tasks are a month from now, but we can always reopen those scripts and check them out.

As with anything we do in $\mathrm{R}$, there are different ways to save your plot. However, before saving it, we should create a folder for it in our current directory (basics) -let’s call it figures. ${ }^{22}$ One way to save plots created with ggplot2 is to use the function ggsave() right after you run the code that generates your plot. Inside ggsave(), we specify the file name (and extension) that we wish to use (file), and we can also specify the scale of the figure as well as the DPI (dots per inch) for our figure (dpi). Thus, if you wanted to save the plot generated in code block 9 to the figures folder, you’d add a line of code after line 12 : ggsave (file = “figures/plot.jpg”, scale $=0.7, \mathrm{dpi}=$ “retina”). In this case, scale $=$ $0.7$ will generate a figure whose dimensions are $70 \%$ of what you can currently see in RStudio. Alternatively, you can manually specify the width and height of the figure by using the width and height arguments. To generate a plot with the exact same size as Fig. 2.3, use ggsave(file $=$ “figures/plot.jpg”, width $=4$, height $=2.5, \mathrm{dpi}=1000)^{23}$ If you realize the font size is too small in the figure, you can either change the dimensions in ggsave() (e.g., $3.5 \times 2$ instead of $4 \times 2.5$ will make the font look larger), or you can specify the

text size within ggplot()_an option we will explore later in the book (in chapter 5). In later chapters, code blocks that generate plots will have a ggsave() line, so you can easily save the plot.

As mentioned earlier, you can run the ggsave() line after running the lines that generate the actual plot (you may have already noticed that by pressing Cmd + Enter, RStudio will take you to the next line of code, so you can press Cmd + Enter again). Alternatively, you can select all the lines that generate the plot plus the line containing ggsave() and run all of them together. Either way, you will now have a file named plot.jpg in the figures directory (folder) of your R Project. ${ }^{24}$

## 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。