统计代写|应用统计代写applied statistics代考|Histograms and density plots

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Histograms and density plots

By default, if you plot just a single continuous variable using $q$ plot( $), R$ will plot a histogram (Figure 4.5, left). Histograms are extremely useful for seeing the distribution of your data. Do they look normally distributed? Are they skewed to one side or the other? There is technically no need to specify a “geom” here, but I think it is a good idea to be clear in your code, so I would recommend it.
Make a basic histogram
ac-qplot (data=Rxp. clean
$\mathrm{x}=$ Mass . final,
geom $=$ “histogram”)
The same principle of using the color or fill arguments as a way to view your data apply to histograms, but with one caveat. If you add a fill or color argument to a histogram in qplot ()$, \mathrm{R}$ will make a stacked histogram (Figure 4.5, middle). It can be more useful to see the data distributions overlayed on one another. This is best achieved with a density plot, which is similar to a histogram but instead plots a smoothed line that shows the shape of the data (Figure 4.5, right). Note that you should use the “col” argument in the density plot instead of the “fill” argument. What happens if you do not?
Hake a stacked histogram
be-qplot (data=RxP. clean,
$x=$ Mass.final,
geom=”histogram”,

Make a stacked histogram,

be-qplot (data=RxP.clean,
x=Mass. final,
geom= “histogram”,
fill=Pred)

Make overlayed density plots

ce-qplot (data=RxP. clean,
x=Mass.final,
geom=”density”,
fill=Pred)
#Make overlayed density plots
ce-qplot (data=RxP. clean,
$x=$ Mass. final,
geoms “density”,

统计代写|应用统计代写applied statistics代考|Scatterplots

The same principle works for continuous response variables. Previously, we defined our $\mathrm{x}$-axis as a categorical variable, but if we instead use a continuous variable $\mathrm{R}$ will plot a scatterplot. We can still use facets or colors to visualize the variation in our data, which is extremely useful. For example, in the following code I’ve filled the points based on the resource treatment, and faceted the data based on the predator treatment. Imagine the possibilities (Figure 4.6)!
Hake a serles of scatter plots
qplot (data=RxP. clean,
$x=\log$ (SVL. final),

Make a series of scatter

qplot (data=RxP. clean,
$\mathrm{x}=\log ($ SvL. final),
$\mathrm{y}=\log$ (Mass. final),
col=Res,
facets=. – Pred)
$y=\log ($ Mass. final),
col=Res,
facets=.-Pred)

Note that when we make a plot like this, many of the points end up on top of one another, making it difficult to see all the data. We can add an argument to set the alpha level, or the degree of translucency of the points to alleviate this issue. This is also particularly useful with density plots. For example, we can remake the density plots from above, but this time we will fill them instead of color them and set the alpha to be $0.5$ (Figure $4.7$ ).
qplot (data=RxP. clean,
$x=$ Mass . final,
geom=” density”,
fill=pred,
alpha $=0.5$ )
Note that setting the alpha level in qplot() makes the alpha level $50 \%$ transparent, no matter what value you enter. I will show you how to set it to whatever you want later.

In addition to visualizing our data by setting the fill or color to one of our variables, we can also change the shape of the points based on a variable in our data frame with the “shape $=$ ” argument (Figure 4.8).
Hake a series of scatter plots
qplot (data =kxp. clean,

Mke a series of scatter plots

qplot (data=RxP. clean,
x=log(SVL. final).
$x=\log$ (SVL. final),

统计代写|应用统计代写applied statistics代考|PLOTTING YOUR DATA

In Chapter 3, we saw how to use functions from the dplyr package to summarize our data and we produced a tibble called RP.means that contained the means and standard errors for SVL.initial for each combination of resource and predator treatments. Now, we will see how to take those summarized data and turn them into a nice looking figure. In particular, we are going to make a bar graph. Why a bar graph you ask? There are several reasons really. First, despite their ubiquity in publications, the R gurus do not like bar graphs (or barplots, as we will call them) and making one is kind of a pain in $\mathrm{R}$. This is because bar graphs have an ability to hide a lot about your data (they just show the mean and whatever your error bar of choice is). But the fact that they are difficult to create makes them an excellent tool for teaching many of the ways you can, and probably should, customize your figures. That said, box plots are much more informative and are finally becoming increasingly used in published science. The second reason is that despite their downsides many people still like bar graphs and want to make them, so it is useful to know how to make one.

The most basic function to make a bar graph is barplot(). There are many, many arguments that can be passed to barplot(), which can be viewed in the help file (remember how to get to the help: ?barplot). A slightly improved version is the function barplot2(), which makes plotting error bars much simpler. barplot2() is found in the gplots package. You can also make a barplot in ggplot2. We will go through both examples. I find it useful and instructive to demonstrate the older technique using base graphics first, as it demonstrates how you can modify every little thing in a figure in R. Much of the coding techniques are also useful in ggplot2, and we will use ggplot2 for most everything in this book. If you feel confident

you will never, ever, ever use base graphics, feel free to skip ahead a few pages to the section on ggplot2. However, if you want to learn a little more about $\mathrm{R}$ and customizing figures, I encourage you to follow along with the next few pages of commands.
4.3.1 Making a barplot in base graphics
Let’s start by simply plotting the bars in their most basic form. Note that I am assuming you still have the RPmeans object we created in Chapter $3 .$

统计代写|应用统计代写applied statistics代考|Histograms and density plots

ac-qplot (data=Rxp.clean
X=大量的 。最后，

be-qplot (data=RxP.clean,
X=Mass.final,
geom=”histogram”,

制作堆叠直方图，

be-qplot（数据=RxP.clean，
x=Mass.final，
geom=“直方图”，

制作叠加密度图

ce-qplot (data=RxP. clean,
x=Mass.final,
geom=”density”,
fill=Pred) #制作

ce-qplot (data=RxP. clean,
X=质量最终，

统计代写|应用统计代写applied statistics代考|Scatterplots

qplot (data=RxP.clean,
X=日志（SVL。最终），

制作一系列散点图

qplot（数据=RxP。干净，
X=日志⁡(SVL。最终的），

col=Res，
facets=。– 预测）

col=Res,
facets=.-Pred)

qplot（数据=RxP。干净，
X=大量的 。最终，
geom=“密度”，

alpha=0.5)

Hake 一系列散点图
qplot (data =kxp.clean,

制作一系列散点图

qplot（数据=RxP.clean，
x=log（SVL.final）。
X=日志（SVL。最终），

统计代写|应用统计代写applied statistics代考|PLOTTING YOUR DATA

4.3.1 在基本图形中制作条形图

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。