R Language Programming Art Learning (1)

# # #R语言编程艺术 =====

# # # #chr1. Quick Start ======

#交互模式

Mean (ABS (Rnorm (100)))

Rnorm (10)

Source ("Z.R")

#批处理模式

SETWD ("/USERS/TANG/DOWNLOADS/R Language Programming Art/")

#shell命令运行脚本

#R CMD BATCH Z.R

Data () # #查看内置数据集

Mean (Nile)

SD (Nile)

hist (Nile)

hist (Nile, breaks = 12)

Q () #linux ctrl-d mac:cmd-d

#赋值运算符: <-,= (failure in special cases)

#超赋值运算符: <<-(Assigning a value to a global variable inside a function)

X <-C (5, 12, 13)

Mode (x)

typeof (X)

Y <-"abc"

Mode (y)

typeof (Y)

U <-paste ("abc", "De", "f", Sep = ":")

V <-unlist (strsplit (u, split = ":"))

M <-Rbind (C (1, 4), C (2, 2))

M

M%*% C (##%*%) matrix multiplication

x <-List (U = 2, v = "abc")

X

#列表的常用方法: Function multiple return values package return

hist (Nile)

HN <-hist (Nile)

Hn

STR (HN)

#数据框本质上是列表 (equal number of elements per component)

D <-data.frame (list (kids = C ("Jack", "Jill"), ages = C (12, 10)))

D$ages

#类

Print (HN)

Summary (HN)

# # #chr2. Vector ======

#主题: cyclic completion \ filtering \ vectorization

#变量类型称为模式 (mode), view available typeof (X) in the program

x <-C (1, 2, 4)

Length (x)

#矩阵和数组本质上也是向量 (The difference is that it has extra attributes: dimension)

M <-Rbind (C (1, 2), C (3, 4))

M + 10:13

M

Y <-vector (mode = "character", length = 2)

Y[1] <-3

Y[2] <-4

typeof (Y)

Y

C (1, 2, 4) + C (6, 0, 9, 20, 22)

#运算符也是一种函数

2 + 3

"+" (2, 3)

x <-C (1, 2, 4)

X + C (5, 0,-1)

X * C (5, 0,-1)

X%*% C (5, 0,-1)

Y <-C (1.2, 3.9, 0.4, 0.12)

Y[c (1, 3)]

Y[2:3]

V <-3:4

Y[V]

#元素重复是允许的

x<-C (4, 2, 17, 5)

Y <-x[c (1, 1, 3)]

Y

#负数下标代表想把相应元素删除

Z <-C (5, 12, 13)

Z[-1]

Z[-1:-2]

Z[1: (Length (z)-1)]

Z[-length (z)]

# ":" Attention to operator precedence issues when using

I <-2

1:i-1#: Priority higher than-

1: (i-1) # () Priority above-

# ? Syntax a detailed description of getting the priority level

Seq (from = a, to = +, by = 3)

SEQ (from = 1.1, to = 2, by = 0.1)

#循环时避免使用length, use SEQ instead

#rep (x, Times) \rep (x, each)

#all \any: Report whether the parameter has at least one or all true

X <-1:10

All (x > 8)

Any (x > 88)

All (x > 88)

All (x > 0)

X > 8

Any (x > 8)

# #扩展案例: Look for a run that appears 1 consecutive runs

Findruns <-function (x, k) {

n <-Length (x)

Runs <-NULL

For (I in 1: (n-k+1)) {

if (All (x[i: (i+k-1)] = = 1)) {

Runs <-c (runs, i)

}

}

Return (runs)

}

Y <-C (1, 0, 0, 1, 1, 1, 0, 1, 1)

Findruns (Y, 3)

Findruns (Y, 2)

Findruns (Y, 6)

#每次调用c (runs, i) re-allocates memory, slowing code speed

Findruns1 <-function (x, k) {

n <-Length (x)

Runs <-vector (length = n)

Count <-0

For (I in 1: (n-k+1)) {

if (All (x[i: (i+k-1)] = = 1)) {

Count <-Count + 1

Runs[count] <-I

}

}

if (Count > 0) {

Runs <-Runs[1:count]

}else runs <-NULL

Return (runs)

}

Y <-C (1, 0, 0, 1, 1, 1, 0, 1, 1)

Findruns1 (Y, 3)

Findruns1 (Y, 2)

Findruns1 (Y, 6)

#预测离散值时间序列

Preda <-function (x, k) {

n <-Length (x)

K2 <-K/2

pred <-vector (length = n-k)

For (I in 1: (N-k)) {

if (SUM (x[i: (i+k-1)]) >= K2) Pred[i] <-0 Else Pred[i] <-0

}

Return (Mean (ABS (pred-x[(k+1): N)))

}

#predb

Predb <-function (x, k) {

n <-Length (x)

K2 <-K/2

Pred <-vector (length = n-k)

SM <-sum (x[1:k])

if (SM >= K2) pred[1] <-1 else pred[1] <-0

if (N-k > 0) {

For (I in 2: (N-k)) {

SM <-SM + x[i+k-1]-x[i-1]

if (SM >= K2) Pred[i] <-1 else pred[i] <-0

}

}

Return (Mean (ABS (pred-x[(k+1): N)))

}

#predc

Predc <-function (x, k) {

n <-Length (x)

K2 <-K/2

pred <-vector (length = n-k)

CSX <-C (0, Cumsum (x))

For (I in 1: (N-k)) {

if (Csx[i+k]-csx[i] >= K2) Pred[i] <-1 else pred[i] <-0

}

Return (Mean (ABS (pred-x[(k+1): N)))

}

#向量化运算符

#向量输入, vector output

U <-C (5, 2, 8)

V <-C (1, 3, 9)

U > V

W <-function (x) return (x + 1)

W (u)

sqrt (1:9)

Y <-C (1.2, 3.9, 0.4)

Z <-round (y)

Z

Round (1.2)

Y <-C (12, 5, 13)

Y + 4

"+" (Y, 4)

F <-function (x, C) return ((x + C) ^2)

F (1:3, 0)

F (1:3, 1:3)

F <-function (x, c) {

if (length (c) > 1) Stop ("Vector c is not allowed")

Return ((x+c) ^2)

}

F (1:3, 1:3)

F (1:3, 0)

#向量输入, matrix output

Z12 <-function (z) return (c (z,z^2))

X <-1:8

Z12 (x)

Matrix (Z12 (x), Ncol = 2)

Sapply (1:8, Z12)

#NA与NULL

#NA的使用

x <-C (D, NA, 12, 168, 13)

X

Mean (x)

Mean (x, na.rm = TRUE)

X <-C (+, NULL, 12, 168, 13)

Mean (x)

X[2]

#NA值模式

X <-C (5, NA, 12)

Mode (x[1])

Mode (x[2])

Y <-C ("abc", "Def", NA)

Mode (y[2])

Mode (Y[3])

#NULL的使用

# (1) for creating vectors in loops

Z <-NULL

For (I-in 1:10) if (i-percent 2 = = 0) z <-c (z, i)

Z

Seq (2, 10, 2)

2 * 1:5

Z <-NA

For (I-in 1:10) if (i%%2 = = 0) z <-c (z, i)

Z

U <-NULL

Length (U)

V <-NA

Length (v)

#2.8 filtering

Z <-C (5, 2,-3, 8)

W <-z[z * z > 8]

W

Z

Z*z > 8

Y <-C (1, 2, 30, 5)

Y[z^2 > 8]

x <-C (1, 3, 8, 2, 20)

X[x > 3] <-0

X

X <-C (6, 1:3, NA, 12)

X

X[x > 5]

Subset (x, x > 5)

Which (Z^2 > 8)

Fisrst1 <-function (x) {

For (I in 1:length (x)) {

if (x[i] = = 1) break

}

return (i)

}

FIRST1A <-function (x) return (which (x = = 1) [1])

#向量化ifelse函数

#ifelse (b, U, v)

X <-1:10

Y <-ifelse (x%%2 = = 0, 5, 12)

Y

X <-C (5, 2, 9, 12)

IfElse (x > 6, 2*x, 3*x)

R Language Programming Art Learning (1)