R语言数据清洗入门指南

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“Introduction to data cleaning with R.pdf 是一份关于使用R语言进行数据清洗的讨论论文，由Edwin de Jonge和Mark vander Loo撰写。本文档主要探讨了数据清洗在统计分析中的重要性，以及如何利用R语言的工具和技术进行有效数据预处理。” 在统计分析中，数据清洗是不可或缺的一个环节，它甚至可能比实际的统计分析本身更加耗时。"Introduction to data cleaning with R" 提供了一系列技术，这些技术在R统计环境中得以实现，帮助读者构建针对各种数据问题的数据清洗脚本。文章可能会涵盖以下关键知识点： 1. **数据清洗的重要性**：数据清洗是确保分析结果准确性和可靠性的基础，因为它可以去除或修正数据集中的错误、不完整、不一致和无关的数据。 2. **R语言基础**：R语言是数据科学领域广泛使用的工具，尤其适合数据分析和数据清洗。了解R的基本语法、数据结构（如向量、列表、数据框）以及函数的使用是开始数据清洗的前提。 3. **数据质量检查**：包括识别缺失值（NA）、重复值、异常值和不一致值的方法，例如使用`dplyr`包的`distinct()`、`filter()`等函数进行数据筛选。 4. **处理缺失值**：可能介绍如何使用`complete.cases()`、`na.omit()`、`is.na()`等函数处理缺失值，以及插补方法，如平均值、中位数、众数填充或使用模型预测。 5. **数据类型转换**：将数据转换为正确的类型，如日期、数字或因子，可能涉及`as.Date()`、`as.numeric()`、`factor()`等函数。 6. **字符串操作**：使用`stringr`包进行字符串处理，如查找、替换、分割和合并字符串，以清理文本数据。 7. **数据整合**：如果数据分布在多个数据源，可能需要使用`merge()`或`join`操作将它们合并。 8. **数据规范化和标准化**：通过`scale()`和`normalize()`函数对数值数据进行预处理，确保不同变量在同一尺度上。 9. **数据清洗流程**：从数据导入、初步探索（如使用`ggplot2`绘图）到编写可重复使用的清洗代码，形成一套系统化的过程。 10. **错误检测与处理**：如何使用条件语句和循环来处理数据清洗过程中可能出现的问题。 11. **质量控制**：创建数据质量报告，以可视化方式展示数据清洗的结果，确保所有更改都有迹可循。这份文档对于想要提升R语言数据清洗技能的读者来说是一份宝贵的资源，它不仅介绍了理论概念，还提供了实用的R代码示例，有助于读者将所学应用到实际项目中。

# vectors have variables of _one_ type

c(1, 2, "three")

# shorter arguments are recycled

(1:3) * 2

(1:4) * c(1, 2)

# warning! (why?)

(1:4) * (1:3)

Each element of a vector can be given a name. This can be done by passing named arguments to

the c() function or later with the names function. Such names can be helpful giving meaning to

your variables. For example compare the vector

x <- c("red", "green", "blue")

with the one below.

capColor = c(huey = "red", duey = "blue", louie = "green")

Obviously the second version is much more suggestive of its meaning. The names of a vector

need not be unique, but in most applications you'll want unique names (if any).

Elements of a vector can be selected or replaced using the square bracket operator [ ]. The

square brackets accept either a vector of names, index numbers, or a logical. In the case of a

logical, the index is recycled if it is shorter than the indexed vector. In the case of numerical

indices, negative indices omit, in stead of select elements. Negative and positive indices are not

allowed in the same index vector. You can repeat a name or an index number, which results in

multiple instances of the same value. You may check the above by predicting and then verifying

the result of the following statements.

capColor["louie"]

names(capColor)[capColor == "blue"]

x <- c(4, 7, 6, 5, 2, 8)

I <- x < 6

J <- x > 7

x[I | J]

x[c(TRUE, FALSE)]

x[c(-1, -2)]

Replacing values in vectors can be done in the same way. For example, you may check that in

the following assignment

x <- 1:10

x[c(TRUE, FALSE)] <- 1

every other value of x is replaced with 1.

A list is a generalization of a vector in that it can contain objects of diﬀerent types, including

other lists. There are two ways to index a list. The single bracket operator always returns a

sub-list of the indexed list. That is, the resulting type is again a list. The double bracket

operator ([[ ]]) may only result in a single item, and it returns the object in the list itself.

Besides indexing, the dollar operator $ can be used to retrieve a single element. To understand

the above, check the results of the following statements.

L <- list(x = c(1:5), y = c("a", "b", "c"), z = capColor)

L[[2]]

L$y

L[c(1, 3)]

L[c("x", "y")]

L[["z"]]

An introduction to data cleaning with R 9

Especially, use the class function to determine the type of the result of each statement.

A data.frame is not much more than a list of vectors, possibly of diﬀerent types, but with

every vector (now columns) of the same length. Since data.frames are a type of list, indexing

them with a single index returns a sub-data.frame; that is, a data.frame with less columns.

Likewise, the dollar operator returns a vector, not a sub-data.frame. Rows can be indexed

using two indices in the bracket operator, separated by a comma. The ﬁrst index indicates rows,

the second indicates columns. If one of the indices is left out, no selection is made (so

everything is returned). It is important to realize that the result of a two-index selection is

simpliﬁed by R as much as possible. Hence, selecting a single column using a two-index results

in a vector. This behaviour may be switched oﬀ using drop=FALSE as an extra parameter. Here

are some short examples demonstrating the above.

d <- data.frame(x = 1:10, y = letters[1:10], z = LETTERS[1:10])

d[1]

d[, 1]

d[, "x", drop = FALSE]

d[c("x", "z")]

d[d$x > 3, "y", drop = FALSE]

d[2, ]

1.2.2 Special values

Like most programming languages, R has a number of Special values that are exceptions to the

normal values of a type. These are NA, NULL, ±Inf and NaN. Below, we quickly illustrate the

meaning and diﬀerences between them.

NA Stands for not available. NA is a placeholder for a missing value. All basic operations in R

handle NA without crashing and mostly return NA as an answer whenever one of the input

arguments is NA. If you understand NA, you should be able to predict the result of the

following R statements.

NA + 1

sum(c(NA, 1, 2))

median(c(NA, 1, 2, 3), na.rm = TRUE)

length(c(NA, 2, 3, 4))

3 == NA

NA == NA

TRUE | NA

The function is.na can be used to detect NA's.

NULL You may think of NULL as the empty set from mathematics. NULL is special since it has no

class (its class is NULL) and has length 0so it does not take up any space in a vector. In

particular, if you understand NULL, the result of the following statements should be clear to

you without starting R.

length(c(1, 2, NULL, 4))

sum(c(1, 2, NULL, 4))

x <- NULL

c(x, 2)

The function is.null can be used to detect NULL variables.

Inf Stands for inﬁnity and only applies to vectors of class numeric. A vector of class integer can

never be Inf. This is because the Inf in R is directly derived from the international standard

for ﬂoating point arithmetic

. Technically, Inf is a valid numeric that results from

calculations like division of a number by zero. Since Inf is a numeric, operations between Inf

and a ﬁnite numeric are well-deﬁned and comparison operators work as expected. If you

understand Inf, the result of the following statements should be clear to you.

An introduction to data cleaning with R 10

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