### 统计代写|r语言作业代写代做|Converting Daily Prices to Monthly Returns with tibbletime

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## 统计代写|r语言作业代写代考|Converting Daily Prices to Monthly Returns with tibbletime

This is a good time to introduce the relatively new tibbletime package, which is purpose-built for working with time-aware tibbles. In the flow below, we will first convert prices to a tibble with tk_tbl(). Then, we convert to a tibbletime object with a as_tbl_time(index = date) and then convert to monthly prices with as_period(period = “month”, side = “end”). The side argument anchors to the end of the month instead of the beginning. Try changing it to side = “start”.

This flow might not seem efficient – going from xts to tibble to tibbletime – but in future chapters we will see that rolling functions are smoother with rollify () and we can absorb some inefficiency now for future gains. Plus, the package is new and its capabilities are growing fast.
Before we move on, a quick review of our 4 monthly log return objects:

First, look at the date in each object. asset_returns_xts has a date index, not a column. That index does not have a name. It is accessed via index(asset_returns_xts).

The tibbles have a column called “date”, accessed via the \$date convention, e.g. asset_returns_dplyr_byhand\$date. That distinction is not important when we read with our eyes, but it is very important when we pass these objects to functions.

Second, each of these objects is in “wide” format, which in this case means there is a column for each of our assets: SPY has a column, EFA has a column, IJS has a column, EEM has a column, AGG has a column.

This is the format that xts likes and this format is easier for a human to read. However, the tidyverse calls for this data to be in long or tidy format where each variable has its own column. For asset returns to be tidy, we need a column called “date”, a column called “asset” and a column called “returns”.
To see that in action, here is how it looks.

## 统计代写|r语言作业代写代考|Visualizing Asset Returns in the xts world

It might seem odd that visualization is part of the data import and wrangling work flow and it does not have to be: we could jump straight into the process of converting these assets into a portfolio. However, it is a good practice to chart individual returns because once a portfolio is built, we are unlikely to

back track to visualizing on an individual basis. Yet, those individual returns are the building blocks and raw material of our portfolio and visualizing them is a great way to understand them deeply. It also presents an opportunity to look for outliers, or errors, or anything unusual to be corrected before we move too far along in our analysis.

For the purposes of visualizing returns, we will work with two of our monthly log returns objects, asset_returns_xts and asset_returns_long (the tidy, long-formatted tibble).

We start with the highcharter package to visualize the xts formatted returns.
highcharter is an $\mathrm{R}$ package but Highcharts is a JavaScript library. The $\mathrm{R}$ package is a hook into the JavaScript library. Highcharts is fantastic for visualizing time series and it comes with great built-in widgets for viewing different time frames, plus we get to use the power of JavaScript without leaving the world of $R$ code.

Not only are the visualizations nice, but highcharter “just works” with xts objects in the sense that it reads the index as dates without needing to be told. We pass in an xts object and let the package do the rest. I highly recommend it for visualizing financial time series but you do need to buy a license for use in a commercial setting. ${ }^{1}$
Let’s see how it works for charting our asset monthly returns.

## 统计代写|r语言作业代写代考|Visualizing Asset Returns in the tidyverse

ggplot2 is a very widely-used and flexible visualization package, and it is part of the tidyverse. We will use it to build a histogram and have our first look at how tidy data plays nicely with functions in the tidyverse.

In the code chunk below, we start with our tidy object of returns, asset_returns_long, and then pipe to ggplot() with the $\%>\%$ operator. Next, we call ggplot (aes $(x=$ returns, $f i l 1=$ asset $)$ ) to indicate that returns will be on the $\mathrm{x}$-axis and that the bars should be filled with a different color for each asset. If we were to stop here, ggplot() would build an empty chart and that is because we have told it that we want a chart with certain

$\mathrm{x}$-axis values, but we have not told it what kind of chart to build. In ggplot() parlance, we have not yet specified a geom.

We use geom_histogram() to build a histogram and that means we do not specify a $y$-axis value, because the histogram will be based on counts of the returns.

Because the data frame is tidy and grouped by the asset column (recall when it was built we called group_by (asset)), ggplot() knows to chart a separate histogram for each asset. ggplot() will automatically include a legend since we included fill = asset in the aes () call.

## 统计代写|r语言作业代写代考|Visualizing Asset Returns in the xts world

highcharter 是一个R包，但 Highcharts 是一个 JavaScript 库。这Rpackage 是 JavaScript 库的一个钩子。Highcharts 非常适合可视化时间序列，它带有用于查看不同时间范围的强大内置小部件，此外，我们可以在不离开世界的情况下使用 JavaScript 的强大功能R代码。

## 统计代写|r语言作业代写代考|Visualizing Asset Returns in the tidyverse

ggplot2 是一个使用非常广泛且灵活的可视化包，它是 tidyverse 的一部分。我们将使用它来构建直方图，并首先了解 tidyverse 中的数据如何与函数很好地配合使用。

X-axis 值，但我们还没有告诉它要构建什么样的图表。用 ggplot() 的说法，我们还没有指定一个几何图形。

## 广义线性模型代考

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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。