📅 最后修改于: 2020-12-02 06:41:13 🧑 作者: Mango
本节专门向用户介绍R编程语言。可以从cran网站下载R。对于Windows用户，安装rtools和rstudio IDE很有用。
R背后的一般概念是用作以编译语言(例如C，C++和Fortran)开发的其他软件的接口，并为用户提供用于分析数据的交互式工具。
导航至书籍zip文件bda / part2 / R_introduction的文件夹，然后打开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，并遵循代码中的注释。学习的另一个有用选项是只键入代码，这将帮助您习惯R语法。在R中，注释用＃符号书写。
为了在书中显示运行R代码的结果，在对代码进行评估之后，对R返回的结果进行注释。这样，您可以将代码复制粘贴到书中，然后直接在R中尝试部分代码。
# 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"
让我们分析一下先前代码中发生的情况。我们可以看到可以创建带有数字和字母的向量。我们不需要事先告诉R我们想要哪种数据类型。最后，我们能够创建一个既包含数字又包含字母的向量。向量blend_vec已将数字强制转换为字符，我们可以通过可视化值在引号内的打印方式来看到这一点。
以下代码显示了由函数类返回的不同向量的数据类型。通常使用类函数“询问”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。在以下代码中，有R中使用的两种最流行的数据结构的示例：matrix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
如前面的示例所示，可以在同一对象中使用不同的数据类型。通常，这就是数据在数据库中的呈现方式，API的一部分是文本或字符向量以及其他数字。分析员的工作是确定要分配哪种统计数据类型，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-
在R中，向量可以属于以下类别-
R为变量的每种统计类型提供一种数据类型。但是，排序因数很少使用，但可以由函数因数创建或排序。
下一节讨论索引的概念。这是一个非常常见的操作，处理选择对象的各个部分并对其进行转换的问题。
# 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