探索R语言：统计分析利器

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"R Succinctly 是一本由Syncfusion在2014年出版的书籍，旨在介绍R语言的基础知识，特别针对统计分析。这本书不旨在将读者培养成专业统计学家，而是帮助读者掌握在R中进行数据分析的基本方法，并理解统计分析中的选择。书中的观点强调，虽然基本的数据分析工具如电子表格、条形图和透视表是重要的，但当需要更灵活和复杂的解决方案时，R语言成为了理想选择。R的基础功能强大，并且拥有众多可扩展的包（截至写作时有5,423个）。作者Barton Poulson的目标是让读者熟悉R环境，并激发他们探索如何利用R解决特定问题和数据需求。此书适合对R感兴趣并希望提升数据分析能力的读者。" 在《R Succinctly》中，作者Barton Poulson深入浅出地介绍了R语言在统计分析中的应用。首先，他引导读者了解R语言的基础，包括安装和设置R环境，以及如何使用RStudio等开发工具。接着，书中详细讲解了数据导入与导出，这是任何数据分析项目的起点。读者会学习到如何处理各种格式的数据，如CSV、Excel或数据库文件。此外，书中涵盖了R语言的基础语法，包括变量、数据类型、控制结构和函数，这些都是编写R脚本的基础。对于统计分析，Poulson介绍了描述性统计、假设检验、线性回归、分类模型等核心概念，这些都是数据分析师常用的统计方法。他还讨论了可视化工具，如ggplot2包，用于创建各种图表，如散点图、直方图和箱线图，帮助读者更好地理解数据分布和关系。为了应对复杂的数据需求，书中还简要介绍了数据预处理技巧，如缺失值处理、异常值检测和数据转换。此外，Poulson还提到了如何利用R的包来实现更高级的分析，如时间序列分析、机器学习算法等。本书特别强调实践应用，通过实例来演示如何在R中执行统计分析，让读者能够快速上手。同时，书中也鼓励读者探索R社区，利用CRAN上的包来扩展R的功能，以满足特定项目的需求。《R Succinctly》是一本实用的入门指南，它不仅教授R语言的基本知识，还启发读者如何利用R进行高效的数据探索和分析。尽管它不能涵盖R的所有内容，但对于初学者和希望提升数据分析技能的人来说，它提供了一个良好的起点，帮助他们踏入R世界的大门。

RStudio organizes the separate windows of R into a single panel. It also provides links to

functions that can otherwise be difficult to find. RStudio has a few other advantages as well:

 It allows you to divide your work into contexts or “projects.” Each project has its own

working directory, workspace, history, and source documents.

 It has GitHub integration.

 It saves a graphics history.

 It exports graphics in many sizes and formats.

 It can create interactive graphics via the Manipulate package.

 It provides code completion with the tab key.

 It has standardized keyboard shortcuts.

RStudio is a convenient way of working with R, but there are other options. You may want to

spend a little time looking at some of the alternatives so you can find what works best for you

and your projects.

The R console

When you open RStudio, the two windows where you will work the most are on the left by

default. The bottom window on the left is the R console, which has the R command prompt: >

(the “greater than” sign). Two things can happen in the console. First, you can run commands

here by typing at the prompt, although you cannot save your work there. Second, R gives the

output for the commands.

We can try entering a basic command in the console to see how it works. We’ll start with

addition. Enter the following text at the command prompt and press Enter:

> 9 + 11

The first line contains the command you entered; in this case 9 + 11. Note that you do not need

to type an equal sign or any other command terminator, such as a semicolon. Also, although it

is not necessary to put spaces before and after the plus sign, it is good form.

The output looks

like this:

[1] 20

The second line does not have a command prompt because it has the program’s output. The “1”

in square brackets, [1], requires some explanation. R uses vectors to do math and that it how it

returns the responses. The number in brackets is the index number for the first item in the

vector on this line of output. (Many other programs begin with an index number of 0, but R

begins at 1.) After the index number, R prints the output, the sum “20” in this case.

For more information on good form in R, see Google's style guide at http://google-

styleguide.googlecode.com/svn/trunk/Rguide.xml.

The contents of the console will scroll up as new information comes in. You can also clear the

console by selecting Edit > Clear console or pressing ctrl-l (a lower-case L) on a Mac or PC.

Note that this only clears the displayed data, it does not purge the data from the memory or lose

the history of commands.

The Script window

The console is the default window in R, but it is not the best place to do your work. The major

problem is that you cannot save your commands. Another problem is that you can enter only

one command at a time. Given these problems, a much better way to work with R is to use the

Script window. In RStudio, this is the window on the top left, above the console. (In case you

see nothing there, go to File > New File > R Script or press Shift+Command+N to create a new

script document.)

A script in R is a plain text file with the extension “.R.” When you create a new script in R, you

can save that script and you can select and run one or more lines of it at a time. We can

recreate the simple addition problem we did in the console by creating a new script and then

typing the command again. You can also enter more than one command in a script, even if you

only run one at a time. To see how this works, you should type the following three lines.

9 + 11

1:50

print("Hello World")

Note that there is no command prompt > in the script window. Instead, there are just numbered

lines of text. Next, save this script by either selecting File > Save or by pressing Command+S

on the Mac and Ctrl+S on Windows.

If you want to run one command at a time, then place your cursor anywhere on the line of

desired command. Then select Code > Run Line(s) or press Command+Return (Ctrl+Return on

Windows). This will send the selected command down to the console and display the results.

For the first command, 9 + 11, this will produce the same results that we had earlier when we

entered the command at the console.

The next two lines of code illustrate a few other, basic functions. The command 1:50 creates a

list of numbers from 1 to 50. You can also see that the number in square brackets at the

beginning of the line is the index number for the first item on that line.

[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

[24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

[47] 47 48 49 50

If you run the third line of text, print("Hello World!"), you get this output.

[1] "Hello World!"

The output "Hello World!" is a character vector of length 1. This is the same as a string in C

or other languages.

Comments

It is good form to add comments to your code. Comments can help you remember what each

section of your code does. Comments also help make your code reproducible because other

people can follow your logic. This is critical in collaborative projects, as well as projects that you

might revisit later.

To make a comment in R, type # followed by your text. You can also “comment out” a line of

code to disable it while you try alternative lines. To make a multiline comment, you will need to

comment each line, as R has no built-in multiline function. RStudio makes it easy to comment

out lines. Just select the text and go to Code > Comment/Uncomment Lines or press

Shift+Command+C (Shift+Ctrl+C on Windows).

# These lines demonstrate commenting in R.

# First, add an inline comment on a line of code to explain it.

print("Hello World!") # Prints "Hello World" in the console.

# Second, comment out a variation on a line of code.

# print("Hello R!") # This line will not run while commented out.

Data structures

R recognizes four basic structures of data:

1. Vectors. A vector is a one-dimensional array. All of the data must be in the same format,

such as numeric, character, and so on. This is the basic data object in R.

2. Matrices and Arrays. A matrix is similar to a vector in that all of the data must be of the

same format. A matrix, however, has two dimensions; the data is arranged in rows and

columns (and the columns must be the same length), but the columns are not named.

An array is similar to a matrix except that it can have more than two dimensions.

3. Data frames. A data frame is a collection of vectors that are all the same length. The

difference between a data frame and a matrix is that a data frame can have vectors of

different data types, such as a numeric vector and a character vector. The vectors can

also have names. A data frame is similar to a data sheet in SPSS or a worksheet in

Excel (with the difference, again, that the vectors in a data frame must all be the same

length).

4. Lists. A list is the most general data structure in R. A list is an ordered collection of

elements of any class, length, or structure (including other lists). Many statistical

functions, however, cannot be applied to lists.

R also has several built-in functions for converting or coercing data from one structure to

another:

 as.vector() can coerce matrices to one-dimensional vectors, although it may be

necessary to first coerce them to matrices

 as.matrix() can coerce data structures into the matrix structure

 as.data.frame() can coerce data structure into data frames

 as.list() can coerce data structures to lists

Variables

Variables are easy to create in R. Just type the name of the variable, there is no need to assign

the variable type. Next, use the assignment operator, which is <-. You can read this as “gets,"

so that x <- 2 means "x gets 2." It is possible to use the equal sign for assigning values, but

that is bad form in R. In the following two lines, I create a variable x, assign the values 1 to 5,

and then display the contents of x by typing its name.

x <- 1:5 # Put the numbers 1-5 in the variable x

x # Displays the values in x

If you want to specify each value that you assign to a variable, you can use the function c. This

stands for "concatenate," although you can also think of it as "combine" or "collection." This

function will create a single vector with the items you assign to it. As a note, RStudio has a

convenient shortcut for the assignment operator, <-. When you are typing in your code, use the

shortcut Alt+Hyphen and RStudio will insert a leading space, the assignment operator, and a

trailing space. You can then continue with your coding.

Here I assign the values 7, 12, 5, 4, and 9 to the vector y.

y <- c(7, 12, 5, 4, 9)

The assignment operator can also go from left to right or it can include several variables at

once.

15 -> a # Can go left to right, but is confusing.

a <- b <- c <- 30 # Assign the same value to multiple variables.

To remove a variable from R's workspace, use the rm function.

rm(x) # Remove the object x from the workspace.

rm(a, b) # Remove more than one object.

rm(list = ls()) # Clear the entire workspace.

Packages

The default installation of R is impressive in its functionality but it can't do everything. One of the

great strengths of R is that you can add packages. Packages are bundles of code that extend

R's capabilities. In other languages, these bundles are libraries, but in R the library is the place

that stores all the packages. Packages for R can come from two different places.

Some packages ship with R but are not active by default. You can see these in the Packages

tab in RStudio. Other packages are available online at repositories. A list of available packages

can be viewed here. This webpage is part of the Comprehensive R Archive Network (CRAN). It

contains a list of topics or "task views" for packages. If you click on a topic, it will take you to an

annotated list with links to individual packages. You can also search for packages by name

here. Another good option is the website CRANtastic. All the packages at these sites are, like R,

free and open source.

To see which packages are currently installed or loaded, use the following functions:

library() # Brings up editor list of installed packages.

search() # Shows packages that are currently loaded.

library() will bring up a text list of functions. The same information is available in hyperlinked

format under the Packages tab in RStudio. search() will display the names of the active

packages in the console. These are the same packages that have checks in RStudio's Package

tab.

To install new packages, you have several options in RStudio. First, you can use the menus

under Tools > Install Packages. Second, you can click "Install Packages" at the top of the

Packages tab. Third, you can use the function install.packages(). Just put the name of the

desired package in quotes (and remember that, like most programming languages, R is case-

sensitive). The last option is best if you want to save the command as part of a script.

install.packages("ggplot2") # Download and install the ggplot2 package.

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