R数据导入导出手册：权限与格式详解

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R语言的数据导入/导出是R生态系统中的一项关键功能，它允许用户在R环境中处理和操作来自不同源的数据。这个版本（2.14.1，发布于2011年12月22日）由R开发核心团队提供，其文档明确了许可条件，确保了对原版手册的复制、修改和翻译都遵循相应的版权规定。 1. **数据导入** (Imports) - RDataImport模块提供了丰富的功能，包括读取各种格式的数据，如文本文件（支持多种编码），这对于数据科学家和分析师来说非常重要。例如，`read.table`函数是基础工具，可以处理CSV、Excel等常见格式的数据。通过了解和调整参数，用户可以根据不同的数据特点进行定制。 2. **编码管理** (Encodings) 在数据导入过程中，编码选择至关重要，因为不同的文件可能使用不同的字符集。R允许用户指定输入文件的编码，例如UTF-8、ASCII等，以正确解析非标准字符。理解并适当地设置编码能够避免数据解析时可能出现的乱码问题。 3. **文本文件导出** (Export to text files) R同样提供了写入文本文件的能力，这使得将处理后的数据保存为易于分享或进一步分析的格式变得简单。`write.table`函数是常用的导出工具，用户可以根据需要选择是否包含列名、行尾分隔符等细节。 4. **XML支持** (XML) R与XML数据格式的交互也十分顺畅，对于处理结构化的网络数据尤其有用。通过使用特定的包（如XML或XML2），用户可以读取、创建和操作XML文件，这对于数据清洗、提取和转换过程中的数据整合非常关键。 5. **表格和数据框处理** (Spreadsheet-like data) R通过多种方式处理类似电子表格的数据，比如对`read.table`的变体如`read.csv`、`read.xlsx`等，支持读取不同格式的电子表格数据。此外，固定宽度格式文件和DIF（Data Interchange Format）的处理也是重要的功能，便于处理特定行业或机构的专有格式。 6. **直接扫描文件** (Using `scan` directly) `scan`函数允许用户逐行或逐元素读取文件，适用于处理文本格式的复杂数据输入，例如用户定义的分隔符或模式。 7. **数据重塑** (Re-shaping data) 数据在导入后可能需要进行重新组织，R提供了诸如`reshape2`包等功能，方便数据清洗和整理，以便于后续分析或报告。 8. **扁平化数据** (Flat contingency tables) 对于简单的计数或频率数据，可以直接导出为简单的文本文件或使用`write.table`输出，而对于更复杂的交叉表，可能需要使用特定工具来生成扁平化的表示。 R Data Import/Export功能是R语言生态中不可或缺的一部分，它涵盖了数据处理的各个环节，从多样化的文件导入、数据格式转换到格式化输出，满足了用户在数据分析和挖掘中的多样化需求。

Chapter 1: Introduction 3

In a few cases, data have been stored in a binary form for compactness and speed of

access. One application of this that we have seen several times is imaging data, which

is normally stored as a stream of bytes as represented in memory, possibly preceded by a

header. Such data formats are discussed in Chapter 5 [Binary ﬁles], page 23 and Section 6.5

[Binary connections], page 27.

For much larger databases it is common to handle the data using a database management

system (DBMS). There is once again the option of using the DBMS to extract a plain ﬁle,

but for many such DBMSs the extraction operation can be done directly from an R package:

See Chapter 4 [Relational databases], page 16. Importing data via network connections is

discussed in Chapter 7 [Network interfaces], page 29.

1.1.1 Encodings

Unless the ﬁle to be imported from is entirely in ASCII, it is usually necessary to know how

it was encoded. For text ﬁles, a good way to ﬁnd out something about its structure is the

file command-line tool (for Windows, included in Rtools). This reports something like

text.Rd: UTF-8 Unicode English text

text2.dat: ISO-8859 English text

text3.dat: Little-endian UTF-16 Unicode English character data,

with CRLF line terminators

intro.dat: UTF-8 Unicode text

intro.dat: UTF-8 Unicode (with BOM) text

Modern Unix-alike systems, including Mac OS X, are likely to produce UTF-8 ﬁles. Win-

dows may produce what it calls ‘Unicode’ ﬁles (UCS-2LE or just possibly UTF-16LE

). Oth-

erwise most ﬁles will be in a 8-bit encoding unless from a Chinese/Japanese/Korean locale

(which have a wide range of encodings in common use). It is not possible to automatically

detect with certainty which 8-bit encoding (although guesses may be possible and file may

guess as it did in the example above), so you may simply have to ask the originator for

some clues (e.g. ‘Russian on Windows’).

‘BOMs’ (Byte Order Marks, http://en.wikipedia.org/wiki/Byte_order_mark)

cause problems for Unicode ﬁles. In the Unix world BOMs are rarely used, whereas in the

Windows world they almost always are for UCS-2/UTF-16 ﬁles, and often are for UTF-8

ﬁles. The file utility will not even recognize UCS-2 ﬁles without a BOM, but many other

utilities will refuse to read ﬁles with a BOM and the IANA standards for UTF-16LE and

UTF-16BE prohibit it. We have too often been reduced to looking at the ﬁle with the

command-line utility od or a hex editor to work out its encoding.

1.2 Export to text ﬁles

Exporting results from R is usually a less contentious task, but there are still a number of

pitfalls. There will be a target application in mind, and normally a text ﬁle will be the

most convenient interchange vehicle. (If a binary ﬁle is required, see Chapter 5 [Binary

ﬁles], page 23.)

Function cat underlies the functions for exporting data. It takes a file argument, and

the append argument allows a text ﬁle to be written via successive calls to cat. Better,

the distinction is subtle, http://en.wikipedia.org/wiki/UTF-16/UCS-2, and the use of surrogate pairs

is very rare.

Chapter 1: Introduction 4

especially if this is to be done many times, is to open a file connection for writing or

appending, and cat to that connection, then close it.

The most common task is to write a matrix or data frame to ﬁle as a rectangular grid

of numbers, possibly with row and column labels. This can be done by the functions

write.table and write. Function write just writes out a matrix or vector in a speciﬁed

number of columns (and transposes a matrix). Function write.table is more convenient,

and writes out a data frame (or an object that can be coerced to a data frame) with row

and column labels.

There are a number of issues that need to be considered in writing out a data frame to

a text ﬁle.

1. Precision

Most of the conversions of real/complex numbers done by these functions is to full

precision, but those by write are governed by the current setting of options(digits).

For more control, use format on a data frame, possibly column-by-column.

2. Header line

R prefers the header line to have no entry for the row names, so the ﬁle looks like

dist climb time

Greenmantle 2.5 650 16.083

...

Some other systems require a (possibly empty) entry for the row names, which is what

write.table will provide if argument col.names = NA is speciﬁed. Excel is one such

system.

3. Separator

A common ﬁeld separator to use in the ﬁle is a comma, as that is unlikely to ap-

pear in any of the ﬁelds in English-speaking countries. Such ﬁles are known as CSV

(comma separated values) ﬁles, and wrapper function write.csv provides appropri-

ate defaults. In some locales the comma is used as the decimal point (set this in

write.table by dec = ",") and there CSV ﬁles use the semicolon as the ﬁeld sepa-

rator: use write.csv2 for appropriate defaults. There is an IETF standard for CSV

ﬁles (which mandates commas and CRLF line endings, for which use eol = "\r\n"),

RFC4180 (see http://tools.ietf.org/html/rfc4180), but what is more important

in practice is that the ﬁle is readable by the application it is targeted at.

Using a semicolon or tab (sep = "\t") are probably the safest options.

4. Missing values

By default missing values are output as NA, but this may be changed by argument na.

Note that NaNs are treated as NA by write.table, but not by cat nor write.

5. Quoting strings

By default strings are quoted (including the row and column names). Argument quote

controls if character and factor variables are quoted: some programs, for example

Mondrian, do not accept quoted strings (which are the default).

Some care is needed if the strings contain embedded quotes. Three useful forms are

> df <- data.frame(a = I("a \" quote"))

> write.table(df)

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R数据导入导出手册：权限与格式详解

R Data Import Export

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dataimport

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simulink中怎么从工作区获取参数

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使用PyCharm编程实现SparkCore的WordCount单词统计基础，并保存在HDFS或本地文件中注意：新建文件为words.txt，文件路径在/export/server/data下面，内容如下 Spark Flink flume hadoop Flink spark flume hadoop