本节致力于向用户介绍R编程语言. R可以从 cran网站下载.对于Windows用户，安装rtools 和 rstudio IDE .
R 背后的一般概念是作为与C，C ++和Fortran等编译语言开发的其他软件的接口，为用户提供分析数据的交互式工具.
导航到图书zip文件的文件夹 bda/part2/R_introduction 并打开 R_introduction.Rproj 文件.这将打开一个RStudio会话.然后打开01_vectors.R文件.逐行运行脚本并按照代码中的注释进行操作.要学习的另一个有用的选择是只输入代码，这将帮助您习惯R语法.在R中，注释用＃符号写成.
为了显示书中运行R代码的结果，在评估代码之后，结果R返回被注释.通过这种方式，您可以复制粘贴书中的代码并在R中直接尝试它的部分.
# Create a vector of numbers
numbers = c(1, 2, 3, 4, 5)
print(numbers)
# [1] 1 2 3 4 5
# Create a vector of letters
ltrs = c('a', 'b', 'c', 'd', 'e')
# [1] "a" "b" "c" "d" "e"
# Concatenate both
mixed_vec = c(numbers, ltrs)
print(mixed_vec)
# [1] "1" "2" "3" "4" "5" "a" "b" "c" "d" "e"让我们分析前面代码中发生的事情.我们可以看到可以创建带数字和字母的向量.我们不需要事先告诉R我们想要什么类型的数据类型.最后，我们能够创建一个包含数字和字母的向量.向量mixed_vec已将数字强制转换为字符，我们可以通过查看值如何在引号内打印来看到这一点.
以下代码显示函数返回的不同向量的数据类型类.通常使用类函数"询问"一个对象，询问他的类是什么.
### Evaluate the data types using class ### One dimensional objects # Integer vector num = 1:10 class(num) # [1] "integer" # Numeric vector, it has a float, 10.5 num = c(1:10, 10.5) class(num) # [1] "numeric" # Character vector ltrs = letters[1:10] class(ltrs) # [1] "character" # Factor vector fac = as.factor(ltrs) class(fac) # [1] "factor"
R也支持二维对象.在下面的代码中，有一些R中使用的最流行的数据结构:矩阵和data.frame.
# Matrix M = matrix(1:12, ncol = 4) # [,1] [,2] [,3] [,4] # [1,] 1 4 7 10 # [2,] 2 5 8 11 # [3,] 3 6 9 12 lM = matrix(letters[1:12], ncol = 4) # [,1] [,2] [,3] [,4] # [1,] "a" "d" "g" "j" # [2,] "b" "e" "h" "k" # [3,] "c" "f" "i" "l" # Coerces the numbers to character # cbind concatenates two matrices (or vectors) in one matrix cbind(M, lM) # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] # [1,] "1" "4" "7" "10" "a" "d" "g" "j" # [2,] "2" "5" "8" "11" "b" "e" "h" "k" # [3,] "3" "6" "9" "12" "c" "f" "i" "l" class(M) # [1] "matrix" class(lM) # [1] "matrix" # data.frame # One of the main objects of R, handles different data types in the same object. # It is possible to have numeric, character and factor vectors in the same data.frame df = data.frame(n = 1:5, l = letters[1:5]) df # n l # 1 1 a # 2 2 b # 3 3 c # 4 4 d # 5 5 e
如前例所示，可以在同一对象中使用不同的数据类型.一般来说，这就是数据如何在数据库中呈现，API的一部分数据是文本或字符向量和其他数字. In是分析师的工作，用于确定要分配的统计数据类型，然后为其使用正确的R数据类型.在统计中，我们通常认为变量具有以下类型 :
数字
标称或分类
Ordinal
在R中，向量可以是以下类 :
数字 - 整数
因子
有序因子
R为每个统计类型的变量提供数据类型.然而，有序因子很少使用，但可以通过函数因子创建，或者有序.
以下部分处理索引的概念.这是一个非常常见的操作，它处理选择对象的部分并对它们进行转换的问题.
# Let's create a data.frame
df = data.frame(numbers = 1:26, letters)
head(df)
# numbers letters
# 1 1 a
# 2 2 b
# 3 3 c
# 4 4 d
# 5 5 e
# 6 6 f
# str gives the structure of a data.frame, it’s a good summary to inspect an object
str(df)
# 'data.frame': 26 obs. of 2 variables:
# $ numbers: int 1 2 3 4 5 6 7 8 9 10 ...
# $ letters: Factor w/ 26 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
# The latter shows the letters character vector was coerced as a factor.
# This can be explained by the stringsAsFactors = TRUE argumnet in data.frame
# read ?data.frame for more information
class(df)
# [1] "data.frame"
### Indexing
# Get the first row
df[1, ]
# numbers letters
# 1 1 a
# Used for programming normally - returns the output as a list
df[1, , drop = TRUE]
# $numbers
# [1] 1
#
# $letters
# [1] a
# Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z
# Get several rows of the data.frame
df[5:7, ]
# numbers letters
# 5 5 e
# 6 6 f
# 7 7 g
### Add one column that mixes the numeric column with the factor column
df$mixed = paste(df$numbers, df$letters, sep = ’’)
str(df)
# 'data.frame': 26 obs. of 3 variables:
# $ numbers: int 1 2 3 4 5 6 7 8 9 10 ...
# $ letters: Factor w/ 26 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...
# $ mixed : chr "1a" "2b" "3c" "4d" ...
### Get columns
# Get the first column
df[, 1]
# It returns a one dimensional vector with that column
# Get two columns
df2 = df[, 1:2]
head(df2)
# numbers letters
# 1 1 a
# 2 2 b
# 3 3 c
# 4 4 d
# 5 5 e
# 6 6 f
# Get the first and third columns
df3 = df[, c(1, 3)]
df3[1:3, ]
# numbers mixed
# 1 1 1a
# 2 2 2b
# 3 3 3c
### Index columns from their names
names(df)
# [1] "numbers" "letters" "mixed"
# This is the best practice in programming, as many times indeces change, but
variable names don’t
# We create a variable with the names we want to subset
keep_vars = c("numbers", "mixed")
df4 = df[, keep_vars]
head(df4)
# numbers mixed
# 1 1 1a
# 2 2 2b
# 3 3 3c
# 4 4 4d
# 5 5 5e
# 6 6 6f
### subset rows and columns
# Keep the first five rows
df5 = df[1:5, keep_vars]
df5
# numbers mixed
# 1 1 1a
# 2 2 2b
# 3 3 3c
# 4 4 4d
# 5 5 5e
# subset rows using a logical condition
df6 = df[df$numbers < 10, keep_vars]
df6
# numbers mixed
# 1 1 1a
# 2 2 2b
# 3 3 3c
# 4 4 4d
# 5 5 5e
# 6 6 6f
# 7 7 7g
# 8 8 8h
# 9 9 9i
