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D01-R语言基础学习

时间:2019-04-11 01:13:11      阅读:201      评论:0      收藏:0      [点我收藏+]

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R语言基础学习——D01

20190410内容纲要:

  1、R的下载与安装

  2、R包的安装与使用方法

    (1)查看已安装的包

    (2)查看是否安装过包

    (3)安装包

    (4)更新包

  3、结果的重用

  4、R处理大数据集

  5、R的数据结构

    (1)向量

    (2)矩阵

    (3)数组

    (4)数据框

    (5)列表

  6、实例演练

  7、小结

 

 1 R的下载与安装

R是用于统计分析、绘图的语言和操作环境。R是属于GNU系统的一个自由、免费、源代码开放的软件,它是一个用于统计计算和统计制图的优秀工具。

学习它那就先下载它!话不多说看链接:

Windows镜像:  http://mirror.fcaglp.unlp.edu.ar/CRAN/

当然也有Linux和Mac版本。

安装,就不多少,直接下一步,下一步,下一步。别忘了更改安装路径就行!!!

先随便玩点什么?

>demo()
>demo(graphics)
>help.start()
>help("mean")
>?mean
>getwd()
>setwd("path")
>history()

看完这些,觉得R跟linux和Matlab有点像。据说R的前身是S语言。S语言是什么?https://baike.baidu.com/item/S%E8%AF%AD%E8%A8%80

 

2 R包的安装与使用方法

(1)查看已安装的包。

首先,如果照1方法安装完成之后打开软件。在R console中输入library()就能查看当前已经安装的包。

>library()
技术图片
 1 图书馆‘F:/R/R-3.5.3/library’里有个程辑包:
 2 
 3 abind                                                                 Combine Multidimensional Arrays
 4 assertthat                                                            Easy Pre and Post Assertions
 5 base                                                                  The R Base Package
 6 BH                                                                    Boost C++ Header Files
 7 boot                                                                  Bootstrap Functions (Originally by Angelo Canty for S)
 8 car                                                                   Companion to Applied Regression
 9 carData                                                               Companion to Applied Regression Data Sets
10 cellranger                                                            Translate Spreadsheet Cell Ranges to Rows and Columns
11 class                                                                 Functions for Classification
12 cli                                                                   Helpers for Developing Command Line Interfaces
13 clipr                                                                 Read and Write from the System Clipboard
14 cluster                                                               "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.
15 codetools                                                             Code Analysis Tools for R
16 compiler                                                              The R Compiler Package
17 crayon                                                                Colored Terminal Output
18 curl                                                                  A Modern and Flexible Web Client for R
19 data.table                                                            Extension of `data.frame`
20 datasets                                                              The R Datasets Package
21 ellipsis                                                              Tools for Working with ...
22 fansi                                                                 ANSI Control Sequence Aware String Functions
23 forcats                                                               Tools for Working with Categorical Variables (Factors)
24 foreign                                                               Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, Weka, dBase, ...
25 graphics                                                              The R Graphics Package
26 grDevices                                                             The R Graphics Devices and Support for Colours and Fonts
27 grid                                                                  The Grid Graphics Package
28 haven                                                                 Import and Export SPSS, Stata and SAS Files
29 hms                                                                   Pretty Time of Day
30 KernSmooth                                                            Functions for Kernel Smoothing Supporting Wand & Jones (1995)
31 lattice                                                               Trellis Graphics for R
32 lme4                                                                  Linear Mixed-Effects Models using Eigen and S4
33 magrittr                                                              A Forward-Pipe Operator for R
34 maptools                                                              Tools for Handling Spatial Objects
35 MASS                                                                  Support Functions and Datasets for Venables and Ripleys MASS
36 Matrix                                                                Sparse and Dense Matrix Classes and Methods
37 MatrixModels                                                          Modelling with Sparse And Dense Matrices
38 methods                                                               Formal Methods and Classes
39 mgcv                                                                  Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
40 minqa                                                                 Derivative-free optimization algorithms by quadratic approximation
41 nlme                                                                  Linear and Nonlinear Mixed Effects Models
42 nloptr                                                                R Interface to NLopt
43 nnet                                                                  Feed-Forward Neural Networks and Multinomial Log-Linear Models
44 openxlsx                                                              Read, Write and Edit XLSX Files
45 parallel                                                              Support for Parallel computation in R
46 pbkrtest                                                              Parametric Bootstrap and Kenward Roger Based Methods for Mixed Model Comparison
47 pillar                                                                Coloured Formatting for Columns
48 pkgconfig                                                             Private Configuration for R Packages
49 prettyunits                                                           Pretty, Human Readable Formatting of Quantities
50 progress                                                              Terminal Progress Bars
51 quantreg                                                              Quantile Regression
52 R6                                                                    Encapsulated Classes with Reference Semantics
53 Rcpp                                                                  Seamless R and C++ Integration
54 RcppEigen                                                             Rcpp Integration for the Eigen Templated Linear Algebra Library
55 readr                                                                 Read Rectangular Text Data
56 readxl                                                                Read Excel Files
57 rematch                                                               Match Regular Expressions with a Nicer API
58 rio                                                                   A Swiss-Army Knife for Data I/O
59 rlang                                                                 Functions for Base Types and Core R and Tidyverse Features
60 rpart                                                                 Recursive Partitioning and Regression Trees
61 sp                                                                    Classes and Methods for Spatial Data
62 SparseM                                                               Sparse Linear Algebra
63 spatial                                                               Functions for Kriging and Point Pattern Analysis
64 splines                                                               Regression Spline Functions and Classes
65 stats                                                                 The R Stats Package
66 stats4                                                                Statistical Functions using S4 Classes
67 survival                                                              Survival Analysis
68 tcltk                                                                 Tcl/Tk Interface
69 tibble                                                                Simple Data Frames
70 tools                                                                 Tools for Package Development
71 translations                                                          The R Translations Package
72 utf8                                                                  Unicode Text Processing
73 utils                                                                 The R Utils Package
74 zip                                                                   Cross-Platform zip Compression
View Code

(2)查看当前是否安装过包

>help(package="car")        #car就是具体的某个包的名称

如果已经安装过,会自动跳转本机的12569端口查看网页版的详细介绍。如果没有那就装吧~

(3)安装包

安装包的时候会提示选择镜像源,选中国的就行,剩下的就看网络给不给力了~

install.packages("car")

(4)更新包

update.packages()    #不生命的话就默认更新全部

 

3 结果的重用

>head(mtcars)                                      #mtcars是一个数据集  
>lm(mpg~wt, data=mtcars                     #lm是线性拟合的命令
>Result = lm(mpg~wt, data=mtcars)
>summary(Result)
>plot(Result)
>predict(Result, mynewdata)                   #mynewdata是自己要预测的值

有很多东西看不懂没事,后面还会有详细说明。~~

 

 4 R处理大数据集

(1)R有专门用于大数据分析的包。如biglm()能以内存高效的方式实现大型数据的线性模型拟合。

(2)R与大数据平台的结合。如Rhadoop、RHive、RHipe。

R的数据集通常是由数据构成的一个矩形数组,行表示记录,列表示属性(字段)。形式可以使Excel、txt、SAS、Mysql

对数据库有兴趣的话可以看看:2019最受欢迎的数据库是?     https://mp.weixin.qq.com/s/9fhPicVCjMpfMmjbhZUoFA

 

5 R的数据结构

话不多说,还是通过代码比较容易理解。。

(1)向量

向量中的元素可以是数字型、字符型、也可以是布尔型。但是当数组型和字符型混一起时,有没有什么说法自己动手试试吧!!

>a <- c(1,3,5,7,2,-4)
>b <- c("one","two","three")
>c <- c(TRUE,TRUE,FALSE)
>d <- c(1,3,5,"ONE")

此外,关于切片其实跟python有点类似

>d[c(1,3,4)]
>d[3]
>d[1:3]

(2)矩阵  matrix

>?matrix
>y <- matrix(5:24, nrow=4, ncol=5)
>x <- c(2,45,68,94)
>rnames <- c("R1","R2")
>cnames <- c("C1","C2")
>newMatrix <- matrix(x, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames,cnames))
>>newMatrix <- matrix(x, nrow=2, ncol=2,dimnames=list(rnames,cnames))        #默认按列填充
>x[3,]
>x[2,3]
>x[,4]

(3)数组  array

>?array
>dim1 <- c("A1","A2", "A3")
>dim2 <- c("B1", "B2")
>dim3 <- c("C1","C2", "C3")
>d <- array(1:24, c(3,2,4), dimnames=list(dim1,dim2,dim3))
>d[1,2,3]
技术图片
 1 #输出结果
 2 > d
 3 , , C1
 4 
 5    B1 B2
 6 A1  1  4
 7 A2  2  5
 8 A3  3  6
 9 
10 , , C2
11 
12    B1 B2
13 A1  7 10
14 A2  8 11
15 A3  9 12
16 
17 , , C3
18 
19    B1 B2
20 A1 13 16
21 A2 14 17
22 A3 15 18
23 
24 , , C4
25 
26    B1 B2
27 A1 19 22
28 A2 20 23
29 A3 21 24
30 
31 > d[1,2,3]
32 [1] 16
View Code

(4)数据框  data.frame()

技术图片

 

>patientID <- c(1,2,3,4)
>age <- c(25,34,28,52)
>diabetes <- c("Type1", "Type2", "Type3", "Type2")
>status <- c("poor", "Improved, "Excllent", "poor")
>patientData <- data.frame(patientID, age, diabetes, status)
> patientData
  patientID age diabetes   status
1         1  25    Type1     poor
2         2  34    Type2 Improved
3         3  28    Type3 Excllent
4         4  52    Type2     poor
>patientData[1:2]
>patientData[c("diabetes","status")]
>patientData$age  
#虽然age直接输入age也能调出,但是这是因为前面创建数据帧的时候包含age。如果没有呢?
#下面举个例子
>head(mtcars)
>mtcars$mpg
>mpg
#为什么会报错呢,这个时候是因为mpg并没有关联到R中。这个时候可以用attach这个命令进行关联,解除用detach
>attach(mtcars)
>mpg
>detach(mtcars)
>mpg
#因子
> diabetes <- factor(diabetes)
> diabetes
[1] Type1 Type2 Type3 Type2
Levels: Type1 Type2 Type3

(5)列表  list

> g <- "My first list"
> h <- c(12,23,34)
> j <- c("one","two","there")
> k <- matrix(1:10, nrow=2)
> mylist <- list(g,h,j,k
> mylist
[[1]]
[1] "My first list"

[[2]]
[1] 12 23 34

[[3]]
[1] "one"   "two"   "there"

[[4]]
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    3    5    7    9
[2,]    2    4    6    8   10

但是,列表的切片方式略有不同。双中括号!!!

>mylist[[2]]

 

6 实例演练

技术图片

 

>age <- c(1,3,5,2,11,9,3,9,12,3)
>weight <- c(4.4, 5.3, 7.2, 5.2, 8.5, 7.3, 6.0, 10.4, 10.2, 6.1)
>mean(weight)        #求均值
>sd(weight)            #求方差
>cor(age, weight)    #求相关性
>plot(age,weight)

 

7 推荐

推荐1: 数据分析从零开始实战 | 基础篇  https://mp.weixin.qq.com/s/4ESKjlF4B63IveiIlfCdDA

推荐2:给入行数据分析的8个建议    https://mp.weixin.qq.com/s/FYQ192iwstn2J2QejDvNhA

我是尾巴~

数据分析必将大有所为!!!

 

D01-R语言基础学习

标签:bsp   中括号   groups   weka   ffffff   evel   clust   image   parse   

原文地址:https://www.cnblogs.com/zhangkanghui/p/10687195.html

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