快速掌握R语言数据科学基础

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"R Data Science Essentials 是一本由 Raja B. Koushik 和 Sharan Kumar Ravindran 合著的书籍，旨在教授读者快速掌握数据科学和可视化的基本知识，主要针对R语言。这本书由Packt Publishing出版，于2016年1月首次发布，ISBN号为978-1-78528-654-4。" 在本书的第一章“Getting Started with R”中，作者介绍了R语言的基础知识和操作，包括： 1. 从不同来源读取数据：R语言可以处理各种格式的数据，如CSV、Excel或数据库。这一部分会教你如何使用readr等包来导入数据。 2. 从数据库读取数据：R语言与多种数据库（如MySQL、PostgreSQL）兼容，可以使用DBI和RMySQL等包进行连接和查询。 3. 数据类型：R中的数据类型包括数值型（integer, double）、字符型（character）、逻辑型（logical）以及向量、列表、数据框和矩阵等复杂类型。 4. 变量数据类型：理解不同数据类型是处理数据的关键，例如整数、浮点数、字符串等。 5. 数据预处理技术：包括数据清洗、缺失值处理、异常值检测和转换。 6. 数据操作：R提供了丰富的函数来执行各种数据操作，如加减乘除、字符串拼接、聚合函数（如mean、median、sum等）。 7. 控制结构：学习如何使用if/else、for、while、repeat/break和next/return来控制程序流程。 8. 将数据转化为可用格式：如将数据转换为数据框、矩阵或其他结构，便于分析。 9. 总结：本章最后，作者会总结所学内容，帮助读者巩固基础知识。第二章“Exploratory Data Analysis”（探索性数据分析）则会深入到实际数据的探索，使用R工具如ggplot2进行数据可视化，以及如何使用统计方法来理解数据的分布、关联和趋势，以 Titanic 数据集为例，展示了如何对实际问题进行初步的数据分析。这部分可能会涵盖描述性统计、相关性分析、生存分析和数据可视化等方面。通过这些章节的学习，读者能够迅速上手R语言并进行基础的数据分析工作。

Chapter 1

[ 7 ]

Having explored the variable data types, now we will move up the hierarchy and

explore these data types: vector, matrix, list, and dataframe.

A vector is a sequence of elements of a basic data type. It could be a sequence of

numeric or logical characters. A vector can't have a sequence of elements with

different data types. The following are the examples for the numeric, character,

and logical vectors:

v1 <- c(12, 34, -21, 34.5, 100) # numeric vector

class(v1)

[1] "numeric"

v2 <- c("sam", "paul", "steve", "mark") # character vector

class(v2)

[1] "character"

v3 <- c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE) #logical vector

class(v3)

[1] "logical"

Now, let's consider the v1 numeric vector and the v2 character vector, combine these

two, and see the resulting vector:

newV <- c(v1,v2)

class(newV)

[1] "character"

We can see that the resultant vector is a character vector; we will see what happened

to the numeric elements of the rst vector. From the following output, we can see

that the numeric elements are now converted into character vectors represented in

double quotes, whereas the numeric vector will be represented without any quotes:

newV

[1] "12" "34" "-21" "34.5" "100" "sam" "paul" "steve"

"mark"

A matrix is a collection of elements that has a two-dimensional representation, that

is, columns and rows. A matrix can contain elements of the same data type only. We

can create a matrix using the following code. First, we pass the intended row names

and column names to the rnames and cnames variables, then using the matrix

function, we will create the matrix. We specify the row names and column names

using the dimnames parameter:

rnames <- c("R1", "R2", "R3", "R4", "R5")

cnames <- c("C1", "C2", "C3", "C4", "C5")

Chapter 1

[ 9 ]

A dataframe is similar to the matrix, but in a data frame, the columns can hold data

elements of different types. The data frame will be the most commonly used data

type for most of the analysis. As any dataset would have multiple data points, each

could be of a different type. R comes with a good number of built-in datasets such as

mtcars. When we use sample datasets to cover the various examples in the coming

chapters, you will get a better understanding about the data types discussed so far.

Data preprocessing techniques

The rst step after loading the data to R would be to check for possible issues such as

missing data, outliers, and so on, and, depending on the analysis, the preprocessing

operation will be decided. Usually, in any dataset, the missing values have to be

dealt with either by not considering them for the analysis or replacing them with a

suitable value.

To make this clearer, let's use a sample dataset to perform the various operations.

We will use a dataset about India named

IndiaData. You can nd the dataset at

https://github.com/rsharankumar/R_Data_Science_Essentials. We will

perform preprocessing on the dataset:

data <- read.csv("IndiaData.csv", header = TRUE)

#1. To check the number of Null

sum(is.na(data))

[1] 6831

After reading the dataset, we use the is.na function to identify the presence of NA

in the dataset, and then using sum, we get the total number of NAs present in the

dataset. In our case, we can see that a large number of rows has NA in it. We can

replace the NA with the mean value or we can remove these NA rows.

The following function can be used to replace the NA with the column mean for

all the numeric columns. The numeric columns are identied by the

sapply(data,

is.numeric)

function. We will check for the cells that have the NA value, then we

identify the mean of these columns using the mean function with the na.rm=TRUE

parameter, where the NA values are excluded while computing the mean function:

for (i in which(sapply(data, is.numeric))) {

data[is.na(data[, i]), i] <- mean(data[, i], na.rm = TRUE)

}

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