R语言初学者指南：英文PDF教程

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"R for Beginners" 是一本针对R语言初学者的入门教程，由Emmanuel Paradis编写，来自Institut des Sciences de l'Evolution de Montpellier II。该书以PDF格式提供，旨在帮助读者理解和掌握R语言的基础知识。作者感谢了多位同行和R社区成员对其文档的早期版本提供的评论和建议，以及R Development Core Team在开发R语言和维护讨论列表'rhelp'上所做的努力。本书内容包括但不限于以下几个方面： 1. **前言（Preamble）**：这部分通常会介绍作者写书的动机、目标读者以及书籍结构，可能还会提供一些关于如何最好地利用这本书的指导。 2. **开始前的一些概念（Afewconceptsbeforestarting）**：这部分会讲解R语言的基本概念，如R的工作环境、数据类型（如数值、字符、逻辑等）、变量的声明和赋值，以及基本的运算操作。 3. **R工作流程（HowRworks）**：这一章节可能会详细解释R的交互式环境，如何启动R，以及如何在R中执行命令和脚本。此外，还可能涵盖R的加载和保存数据、控制流（如条件语句和循环）以及函数的使用。 4. **数据结构（Datastructures）**：R支持向量、矩阵、数组、列表和数据框等多种数据结构，这部分会详细介绍这些结构的创建、操作和管理。 5. **图形系统（Graphics）**：R拥有强大的图形功能，初学者将学习如何创建基本的图表，如散点图、线图、直方图等，以及如何自定义图形的各个元素。 6. **统计分析（Statisticalanalysis）**：作为一款统计软件，R包含丰富的统计方法，如描述性统计、假设检验、回归分析、聚类等。这部分会介绍如何使用R进行基本的统计计算和分析。 7. **编程和调试（Programminganddebugging）**：这部分可能涵盖R的编程特性，如函数定义、错误处理、调试技巧等，帮助读者提升R语言的编程能力。 8. **R包和扩展（Packagesandextensions）**：R有一个庞大的生态系统，包含了众多用于不同领域的包。这部分会介绍如何安装和使用这些包，以及如何探索R的CRAN仓库。 9. **附录和参考资源（Appendices andreferenceressources）**：可能包括R的基本语法参考、常用函数列表、R语言的帮助系统，以及推荐的进一步学习资料。这本书对于R新手来说是一份宝贵的资源，它不仅覆盖了R语言的基础，还可能深入到一些高级话题，让读者能够逐步熟练使用R进行数据分析和编程。虽然为英文版，但对于有英语基础的读者来说，是学习R语言的良好起点。

3 Data with R

3.1 Objects

We have seen that R works with objects which are, of course, characterized by

their names and their content, but also by attributes which specify the kind of

data represented by an object. In order to understand the usefulness of these

attributes, consider a variable that takes the value 1, 2, or 3: such a variable

could be an integer variable (for instance, the number of eggs in a nest), or

the coding of a categorical variable (for instance, sex in some populations of

crustaceans: male, female, or hermaphrodite).

It is clear that the statistical analysis of this variable will not be the same in

both cases: with R, the attributes of the object give the necessary information.

More technically, and more generally, the action of a function on an object

depends on the attributes of the latter.

All objects have two intrinsic attributes: mode and length. The mode

is the basic type of the elements of the object; there are four main modes:

numeric, character, complex

, and logical (FALSE or TRUE). Other modes exist

but they do not represent data, for instance function or expression. The length

is the number of elements of the object. To display the mode and the length

of an object, one can use the functions mode and length, respectively:

> x <- 1

> mode(x)

[1] "numeric"

> length(x)

[1] 1

> A <- "Gomphotherium"; compar <- TRUE; z <- 1i

> mode(A); mode(compar); mode(z)

[1] "character"

[1] "logical"

[1] "complex"

Whatever the mode, missing data are represented by NA (not available).

A very large numeric value can be speciﬁed with an exponential notation:

> N <- 2.1e23

> N

[1] 2.1e+23

R correctly represents non-ﬁnite numeric values, such as ±∞ with Inf and

-Inf, or values which are not numbers with NaN (not a number ).

The mode complex will not be discussed in this document.

A vector is a variable in the commonly admitted meaning. A factor is a

categorical variable. An array is a table with k dimensions, a matrix being

a particular case of array with k = 2. Note that the elements of an array

or of a matrix are all of the same mode. A data frame is a table composed

with one or several vectors and/or factors all of the same length but possibly

of diﬀerent modes. A ‘ts’ is a time series data set and so contains additional

attributes such as frequency and dates. Finally, a list can contain any type of

object, included lists!

For a vector, its mode and length are suﬃcient to describe the data. For

other objects, other information is necessary and it is given by non-intrinsic

attributes. Among these attributes, we can cite dim which corresponds to the

dimensions of an object. For example, a matrix with 2 lines and 2 columns

has for dim the pair of values [2, 2], but its length is 4.

3.2 Reading data in a ﬁle

For reading and writing in ﬁles, R uses the working directory. To ﬁnd this

directory, the command getwd() (get working directory) can be used, and the

working directory can be changed with setwd("C:/data") or setwd("/home/-

paradis/R"). It is necessary to give the path to a ﬁle if it is not in the working

directory.

R can read data stored in text (ASCII) ﬁles with the following functions:

read.table (which has several variants, see below), scan and read.fwf. R

can also read ﬁles in other formats (Excel, SAS, SPSS, . . . ), and access SQL-

type databases, but the functions needed for this are not in the package base.

These functionalities are very useful for a more advanced use of R, but we will

restrict here to reading ﬁles in ASCII format.

The function read.table has for eﬀect to create a data frame, and so is

the main way to read data in tabular form. For instance, if one has a ﬁle

named data.dat, the command:

> mydata <- read.table("data.dat")

will create a data frame named mydata, and each variable will be named, by de-

fault, V1, V2, . . . and can be accessed individually by mydata$V1, mydata$V2,

. . . , or by mydata["V1"], mydata["V2"], . . . , or, still another solution, by

mydata[, 1], mydata[,2 ], . . .

There are several options whose default

values (i.e. those used by R if they are omitted by the user) are detailed in

the following table:

read.table(file, header = FALSE, sep = "", quote = "\"’", dec = ".",

Under Windows, it is useful to create a short-cut of Rgui.exe then edit its properties

and change the directory in the ﬁeld “Start in:” under the tab “Short-cut”: this directory

will then be the working directory if R is started from this short-cut.

There is a diﬀerence: mydata$V1 and mydata[, 1] are vectors whereas mydata["V1"] is

a data frame. We will see later (p. 18) some details on manipulating objects.

row.names, col.names, as.is = FALSE, na.strings = "NA",

colClasses = NA, nrows = -1,

skip = 0, check.names = TRUE, fill = !blank.lines.skip,

strip.white = FALSE, blank.lines.skip = TRUE,

comment.char = "#")

file the name of the ﬁle (within "" or a variable of mode character),

possibly with its path (the symbol \ is not allowed and must be

replaced by /, even under Windows), or a remote access to a ﬁle of

type URL (http://...)

header a logical (FALSE or TRUE) indicating if the ﬁle contains the names of

the variables on its ﬁrst line

sep the ﬁeld separator used in the ﬁle, for instance sep="\t" if it is a

tabulation

quote the characters used to cite the variables of mode character

dec the character used for the decimal point

row.names a vector with the names of the lines which can be either a vector of

mode character, or the number (or the name) of a variable of the

ﬁle (by default: 1, 2, 3, . . . )

col.names a vector with the names of the variables (by default: V1, V2, V3,

. . . )

as.is controls the conversion of character variables as factors (if FALSE)

or keeps them as characters (TRUE); as.is can be a logical, numeric

or character vector specifying the variables to be kept as character

na.strings the value given to missing data (converted as NA)

colClasses a vector of mode character giving the classes to attribute to the

columns

nrows the maximum number of lines to read (negative values are ignored)

skip the number of lines to be skipped before reading the data

check.names if TRUE, checks that the variable names are valid for R

fill if TRUE and all lines do not have the same number of variables,

“blanks” are added

strip.white (conditional to sep) if TRUE, deletes extra spaces before and after

the character variables

blank.lines.skip if TRUE, ignores “blank” lines

comment.char a character deﬁning comments in the data ﬁle, the rest of the

line after this character is ignored (to disable this argument, use

comment.char = "")

The variants of read.table are useful since they have diﬀerent default

values:

read.csv(file, header = TRUE, sep = ",", quote="\"", dec=".",

fill = TRUE, ...)

read.csv2(file, header = TRUE, sep = ";", quote="\"", dec=",",

fill = TRUE, ...)

read.delim(file, header = TRUE, sep = "\t", quote="\"", dec=".",

fill = TRUE, ...)

read.delim2(file, header = TRUE, sep = "\t", quote="\"", dec=",",

fill = TRUE, ...)

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