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

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• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
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• 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),

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），

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

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