R语言统计分析与图形制作入门指南

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"《Using R for Data Analysis and Graphics. Introduction, Code and Commentary》是由J H Maindonald编著的一本书，主要关注如何使用R语言进行数据解析和图形制作。本书适用于个人学习和课堂教学，但禁止任何形式的未经许可的再分发。书中探讨了语言如何塑造我们的思考方式以及决定我们可以思考的内容，强调了R语言在统计分析领域的强大功能。书中包含了一系列关于生物信息学的实际案例，例如对山林刷尾负鼠种群形态变异的研究，展示了R在处理生物学数据和可视化方面的应用。" 本书详细介绍了R语言的基础知识和高级特性，旨在帮助读者掌握R语言进行数据分析的基本技能。R是一种开源的统计计算和图形软件，其丰富的库支持各种复杂的统计方法和可视化技术。作者J H Maindonald是澳大利亚国立大学的生物信息学中心成员，他通过实例代码和解释，引导读者理解如何使用R进行有效的数据探索、建模和绘图。在数据解析部分，书中可能涵盖了数据导入、清洗、转换和预处理等步骤，这些都是数据分析的基础。R提供了一系列强大的工具，如`readr`包用于读取数据，`dplyr`包用于数据操作，`tidyr`包用于数据整理，这些工具使得数据处理更加高效。在统计分析方面，读者将了解到如何使用R进行描述性统计、假设检验、回归分析、时间序列分析等。例如，书中可能包括如何使用`ggplot2`包创建高质量的图表，这个包提供了丰富的自定义选项，可以制作出专业级别的数据可视化作品。书中提到的生物信息学案例——山林刷尾负鼠的形态变异研究，展示了R在处理生物学数据中的应用。这部分可能会涵盖如何处理分类变量、生态学数据的统计分析，以及如何根据研究结果创建图表来展示物种间的差异和变异趋势。此外，书中还可能讨论了R与其他软件的接口，如与数据库的连接，以及如何将R脚本自动化，以便于重复性工作和大规模数据分析。作者还可能强调了R语言的可扩展性，鼓励读者利用R的包管理系统来探索和应用更多统计和计算方法。《Using R for Data Analysis and Graphics》不仅提供了R语言的实用指南，还深入探讨了统计分析背后的理论，使得读者能够在理解和实践中提升数据科学技能。这本书对于希望在统计学、生物信息学或任何需要数据分析的领域深化知识的人来说，是一份宝贵的资源。

2.1.3 R has extensive graphical abilities

The main R graphics function is plot(). In addition to plot() there are functions for adding points and lines

to existing graphs, for placing text at specified positions, for specifying tick marks and tick labels, for labelling

axes, and so on.

There are various other alternative helpful forms of graphical summary. A helpful graphical summary for the

hills data frame is the scatterplot matrix, shown in Figure 6. For this, type:

> pairs(hills)

Figure 6: Scatterplot matrix for the Scottish hill race data

2.1.4 R will handle a variety of specific analyses

The examples that will be given are correlation and regression.

Correlation:

We calculate the correlation matrix for the hills data:

> options(digits=3)

> cor(hills)

distance climb time

distance 1.000 0.652 0.920

climb 0.652 1.000 0.805

time 0.920 0.805 1.000

Suppose we wish to calculate logarithms, and then calculate correlations. We can do all this in one step, thus:

> cor(log(hills))

distance climb time

distance 1.00 0.700 0.890

climb 0.70 1.000 0.724

time 0.89 0.724 1.000

Unfortunately R was not clever enough to relabel distance as log(distance), climb as log(climb), and time as

log(time). Notice that the correlations between time and distance, and between time and climb, have reduced.

Why has this happened?

Straight Line Regression:

Here is a straight line regression calculation. The data are stored in the data frame elasticband that

accompanies these notes. The variable names are the names of columns in that data frame. The formula that is

supplied to the lm() command asks for the regression of distance travelled by the elastic band (distance) on

the amount by which it is stretched (stretch).

> plot(distance ~ stretch,data=elasticband, pch=16)

> elastic.lm <- lm(distance~stretch,data=elasticband)

> lm(distance ~stretch,data=elasticband)

Call:

lm(formula = distance ~ stretch, data = elasticband)

Coefficients:

(Intercept) stretch

-63.571 4.554

More complete information is available by typing

> summary(lm(distance~stretch,data=elasticband))

Try it!

2.1.5 R is an Interactive Programming Language

We calculate the Fahrenheit temperatures that correspond to Celsius temperatures 25, 26, …, 30:

> celsius <- 25:30

> fahrenheit <- 9/5*celsius+32

> conversion <- data.frame(Celsius=celsius, Fahrenheit=fahrenheit)

> print(conversion)

Celsius Fahrenheit

1 25 77.0

2 26 78.8

3 27 80.6

4 28 82.4

5 29 84.2

6 30 86.0

2.2 R Objects

All R entities, including functions and data structures, exist as objects. They can all be operated on as data.

Type in ls() to see the names of all objects in your workspace. An alternative to ls() is objects(). In both

cases there is provision to specify a particular pattern, e.g. starting with the letter `p’

Typing the name of an object causes the printing of its contents. Try typing q, mean, etc.

In a long session, it makes sense to save the contents of the working directory from time to time. It is also

possible to save individual objects, or collections of objects into a named image file. Some possibilities are:

save.image() # Save contents of workspace, into the file .RData

save.image(file="archive.RData") # Save into the file archive.RData

save(celsius, fahrenheit, file="tempscales.RData")

Image files, from the working directory or (with the path specified) from another directory, can be attached, thus

making objects in the file available on request. For example

attach("tempscales.RData")

ls(pos=2) # Check the contents of the file that has been attached

The parameter pos gives the position on the search list. (The search list is discussed later in this chapter, in

Section 2.9.)

Type in help(ls) and help(grep) to get details. The pattern matching conventions are those used for grep(),

which is modelled on the Unix grep command.

Important: On quitting, R offers the option of saving the workspace image, by default in the file .RData in the

working directory. This allows the retention, for use in the next session in the same workspace, any objects that

were created in the current session. Careful housekeeping may be needed to distinguish between objects that are

to be kept and objects that will not be used again. Before typing q() to quit, use rm() to remove objects that

are no longer required. Saving the workspace image will then save everything remains. The workspace image

will be automatically loaded upon starting another session in that directory.

2.3 Looping

A simple example of a for loop is

for (i in 1:10) print(i)

Here is another example of a for loop, to do in a complicated way what we did very simply in section 2.1.5:

> # Celsius to Fahrenheit

> for (celsius in 25:30)

+ print(c(celsius, 9/5*celsius + 32))

[1] 25 77

[1] 26.0 78.8

[1] 27.0 80.6

[1] 28.0 82.4

[1] 29.0 84.2

[1] 30 86

2.3.1 More on looping

Here is a long-winded way to sum the three numbers 31, 51 and 91:

> answer <- 0

> for (j in c(31,51,91)){answer <- j+answer}

> answer

[1] 173

The calculation iteratively builds up the object answer, using the successive values of j listed in the vector

(31,51,91). i.e. Initially, j=31, and answer is assigned the value 31 + 0 = 31. Then j=51, and answer is

assigned the value 51 + 31 = 82. Finally, j=91, and answer is assigned the value 91 + 81 = 173. Then the

procedure ends, and the contents of answer can be examined by typing in answer and pressing the Enter key.

There is a more straightforward way to do this calculation:

> sum(c(31,51,91))

[1] 173

Skilled R users have limited recourse to loops. There are often, as in this and earlier examples, better

alternatives.

2.4 Vectors

Examples of vectors are

c(2,3,5,2,7,1)

3:10 # The numbers 3, 4, .., 10

c(T,F,F,F,T,T,F)

c(”Canberra”,”Sydney”,”Newcastle”,”Darwin”)

Asterisks (*) identify sections that are more technical and might be omitted at a first reading

Other looping constructs are:

repeat <expression> ## break must appear somewhere inside the loop

while (x>0) <expression>

Here <expression> is an R statement, or a sequence of statements that are enclosed within braces

Vectors may have mode logical, numeric or character

. The first two vectors above are numeric, the third is

logical (i.e. a vector with elements of mode logical), and the fourth is a string vector (i.e. a vector with elements

of mode character).

The missing value symbol, which is NA, can be included as an element of a vector.

2.4.1 Joining (concatenating) vectors

The c in c(2, 3, 5, 7, 1) above is an acronym for “concatenate”, i.e. the meaning is: “Join these numbers

together in to a vector. Existing vectors may be included among the elements that are to be concatenated. In the

following we form vectors x and y, which we then concatenate to form a vector z:

> x <- c(2,3,5,2,7,1)

> x

[1] 2 3 5 2 7 1

> y <- c(10,15,12)

> y

[1] 10 15 12

> z <- c(x, y)

> z

[1] 2 3 5 2 7 1 10 15 12

The concatenate function c() may also be used to join lists.

2.4.2 Subsets of Vectors

There are two common ways to extract subsets of vectors

1. Specify the numbers of the elements that are to be extracted, e.g.

> x <- c(3,11,8,15,12) # Assign to x the values 3, 11, 8, 15, 12

> x[c(2,4)] # Extract elements (rows) 2 and 4

[1] 11 15

One can use negative numbers to omit elements:

> x <- c(3,11,8,15,12)

> x[-c(2,3)]

[1] 3 15 12

2. Specify a vector of logical values. The elements that are extracted are those for which the logical value is T.

Thus suppose we want to extract values of x that are greater than 10.

> x>10 # This generates a vector of logical (T or F)

[1] F T F T T

> x[x>10]

[1] 11 15 12

Arithmetic relations that may be used in the extraction of subsets of vectors are <, <=, >, >=, ==, and !=. The

first four compare magnitudes, == tests for equality, and != tests for inequality.

It will, later in these notes, be important to know the “class” of such objects. This determines how the

method used by such generic functions as print(), plot() and summary(). Use the function class()

to determine the class of an object.

A third more subtle method is available when vectors have named elements. One can then use a vector of

names to extract the elements, thus:

> c(Andreas=178, John=185, Jeff=183)[c("John","Jeff")]

John Jeff

185 183

2.4.3 The Use of NA in Vector Subscripts

Note that any arithmetic operation or relation that involves NA generates an NA. Set

y <- c(1, NA, 3, 0, NA)

Be warned that y[y==NA] <- 0 leaves y unchanged. The reason is that all elements of y==NA evaluate to NA.

This does not select an element of y, and there is no assignment.

To replace all NAs by 0, use

y[is.na(y)] <- 0

2.4.4 Factors

A factor is a special type of vector, stored internally as a numeric vector with values 1, 2, 3, k. The value k is

the number of levels. An attributes table gives the ‘level’ for each integer value

. Factors provide a compact

way to store character strings. They are crucial in the representation of categorical effects in model and

graphics formulae. The class attribute of a factor has, not surprisingly, the value “factor”.

Consider a survey that has data on 691 females and 692 males. If the first 691 are females and the next 692

males, we can create a vector of strings that that holds the values thus:

gender <- c(rep(“female”,691), rep(“male”,692))

(The usage is that rep(“female”, 691) creates 691 copies of the character string “female”, and similarly for

the creation of 692 copies of “male”.)

We can change the vector to a factor, by entering:

gender <- factor(gender)

Internally the factor gender is stored as 691 1’s, followed by 692 2’s. It has stored with it the table:

female

male

Once stored as a factor, the space required for storage is reduced.

In most cases where the context seems to demand a character string, the 1 is translated into “female” and the 2

into “male”. The values “female” and “male” are the levels of the factor. By default, the levels are in

alphanumeric order, so that “female” precedes “male”. Hence:

> levels(gender) # Assumes gender is a factor, created as above

[1] "female" "male"

The order of the levels in a factor determines the order in which the levels appear in graphs that use this

information, and in tables. To cause “male” to come before “female”, use

gender <- relevel(gender, ref=“male”)

An alternative is

gender <- factor(gender, levels=c(“male”, “female”))

This last syntax is available both when the factor is first created, or later when one wishes to change the order of

levels in an existing factor. Incorrect spelling of the level names will generate an error message. Try

gender <- factor(c(rep(“female”,691), rep(“male”,692)))

table(gender)

gender <- factor(gender, levels=c(“male”, “female”))

table(gender)

gender <- factor(gender, levels=c(“Male”, “female”))

# Erroneous - "male" rows now hold missing values

table(gender)

rm(gender) # Remove gender

The attributes() function makes it possible to inspect attributes. For example

attributes(factor(1:3))

The function levels() gives a better way to inspect factor levels.

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