R语言入门指南：打造数据分析基石

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"R for Beginners" 是一本经典的R语言教材，由Emmanuel Paradis撰写，旨在帮助初学者掌握这门在现代数据分析领域至关重要的一门语言。该书作者来自于法国蒙彼利埃二大的进化科学研究所，他的工作得到了多位同行如Julien Claude、Christophe Declercq等的评论和建议，以及R开发团队成员的辛勤付出，特别是他们在R软件开发和讨论列表'rhelp'上的活跃贡献。在本书中，作者首先引入了一些基本概念，让读者对R有一个初步的认识。这部分内容可能包括R语言的历史背景、其作为统计计算和图形处理工具的主要特点，以及它在数据科学中的广泛应用。R因其强大的统计分析功能，易用的语法，以及庞大的社区支持而备受推崇。第二部分详细讲解了R编程的基础，如变量类型（如数值型、字符型、逻辑型等）、数据结构（如向量、列表、数据框等）和操作方法。读者将学会如何创建、操作和理解这些核心概念，这是学习R语言的第一步。接下来，可能会涉及R的工作流程，包括读取和清洗数据、编写函数、数据可视化、数据挖掘和机器学习的基本步骤。作者会逐步引导读者熟悉R的工作环境，比如R Studio，以及如何利用R包（如ggplot2、dplyr、tidyverse等）进行高级数据分析和图形制作。此外，书中还可能涵盖编程技巧，如控制流（条件语句、循环）、错误处理以及面向对象编程的概念。这些都是构建复杂数据分析项目的基石。最后，章节可能还会介绍R的社区和支持系统，如Stack Overflow、GitHub和R语言官方论坛，鼓励读者积极参与讨论，解决实际问题，并分享自己的学习经验。 "R for Beginners"是一本全面且实用的指南，适合任何希望入门R语言并对数据分析感兴趣的人阅读。通过这本书，初学者不仅能掌握R的基本技能，还能了解到R语言在当今数据分析领域的核心地位及其生态系统。

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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