1.R基本操作

基本操作

1. 安装加载包

install.packages("name")
library ("name", "location")

查看如何使用 
help(plot)

2. 数学操作

1. 赋值 
   num = 2
   num <- 2

2. 运算
   a = 7, a + b, a - b, a * b, a / b, a ** b(power), a ^ b(power), a %% b(remainder), sqrt(a), log2(b), log(b,2), exp(a)

3. 逻辑运算
   ！，&，|，isTRUE(x == x)

4. 取小数位round()
   round(pi,3) ## [1] 3.142

5. 因式分解、最小公分母、最大公约数 
   library(schoolmath) #导入schoolmath包
   prime.factor(8) # 2,2,2 
   scm(2, scm(3, 4)) # 12
   gcd(4,6) # 2

3. 简单数据结构

3.1 向量

1. c()：创造向量
   a <- c(6, -1, 3, -12, 3.4, 7.05)

2. seq(from=, to=, by=)：生成相同间隔的向量
   e = seq(from=0, to=1, by =.01)
   f = seq(1,100,20) 
   g = seq(1,15, length.out=8)

3. rep(x, times=) or rep(x, each=)：生成有重复数据的向量
   rep(c(1,2,3), times=2) #[1] 1 2 3 1 2 3
   rep(c(1,2,3), each=2) #[1] 1 1 2 2 3 3

4. 索引
   注意：从1开始计数，负索引表示去掉这个数，包左也包右 a <- c(6, -1, 3, -12, 3.4, 7.05) 
   a[1] # [1] 6
   a[-1] # [1] -1.00 3.00 -12.00 3.40 7.05
   b[2:4] # [1] "car" "apple" "banana" ( from 2nd to 4th element)

5. 向量的函数
   length(n1)：长度
   mean(n1)： 均值
   median(n1)：中位数 
   min(n1)：最小值
   max(n1)：最大值
   summary(n1)： min, max, mean, med, quartile information

6. 向量间运算
   注意：[]中的运算是选择的意思（只挑选满足条件的元素），向量直接参与运算是每个元素参与运算
   注意：不同的向量间可以共享索引，参与混合运算 
   n1 * n2：每个元素参与运算
   a[a>5] # [1] 6.00 7.05
   o[o%%2==0] # 求o中的even偶数 o[is.even(o)] # 求o中偶数 a>5 # [1] TRUE FALSE FALSE FALSE FALSE TRUE
   y[x==6] #shows elements of x whose condition indexes in x were true

7. schoolmath学习 
   x[is.prim(x)] #求出x中为素数的数
   primes()[1:50] # 求前50个素数

3.2 矩阵

1. matrix():构造矩阵
   matrix_name <- matrix(vector, nrow=r, ncol=c, byrow=FALSE,dimnames=list(char_vector_rownames, char_vector_colnames))
   X2 = rbind(c(1,2,4,5), c(3,4,9,1)) #binds by row
   X3 = cbind(c(1,2,4), c(3,5,1), c(5,6,11)) #binds by column

   values <- c(1,34,5,99)
   rnames <- c("R1", "R2")
   cnames <- c("C1", "C2")
   Y <- matrix(values, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames))

2. 矩阵索引：只取用'c'，连着取用'：'，不想要用负号'-' 
   X[3,] # 3rd row of matrix 
   X[2:3,1:3] # rows 2,3 of columns 1,2,3
   X[, c(2,4)] # 2nd and 4th column
   X[,-1]

3. 矩阵运算 
   注意：[]中的运算是选择的意思（只挑选满足条件的元素），矩阵直接参与运算是每个元素参与运算 A * B # 每个元素相乘 
   A %*% B # 矩阵相乘
   A != B # 每个元素相比

   A[A%%2==0] # [1] 10 2 8
   A%%2==0 [,1] [,2] [,3]
   [1,] FALSE TRUE FALSE
   [2,] FALSE TRUE FALSE
   [3,] TRUE FALSE FALSE

4. 矩阵函数
   mean(A) # calculates mean across all values 
   colMeans(A) # mean by column
   rowMeans(A) # mean by row
   sd(A) # standard deviation across all values
   length(A) #total number of elements
   ncol(C) # number of columns
   nrow(C) # number of rows
   rowSums(C) # adds elements per row
   colSums(C) # adds elements per column

5. 矩阵操作
   diag(A) #矩阵A的对角矩阵
   t(A) #矩阵A的转置
   det(A) #A的行列式
   矩阵的逆:
   g_aux= diag(rep(1,times=3)) #A是3行3列矩阵 
   g=solve(A,g_aux)

6. 练习题补充sum、which、colSums使用
   G=c(0,1,0,1,1,0) 
   sum(G==1) #G中为1的个数

   avgs = c(45.6, 70.8, 74.0, 87.2, 60.8, 61.2) 
   sum(avgs>=55 & avgs<=75)

   which.min(abs(apply(N,2, max)-apply(N,2,min)))
   求矩阵N中的每一列的最大最小值之差的最小值所在的列，结果为3.

   colSums(N>=70)
   注意：如果colSums(A)其中A是boolean矩阵的话，返回的是每一列中为TRUE的个数

   通过向量的索引来选择矩阵
   N = rbind(c(32, 52, 50, 44, 50),
   c(88, 67, 59, 70, 70),
   c(78, 77, 68, 67, 80),
   c(89, 90, 81, 89, 87),
   c(61, 65, 50, 78, 50),
   c(67, 68, 65, 40, 66))
   G=c(0,1,0,1,1,0)
   M=N[G==0,] 
   which.max(colSums(M<50)
   G[which.max(N[,4])

3.3 数组Arrays

注意：数组跟矩阵类似，只是数组可以超过2维

1. array()：创建数组 
   ar1 = array(1:27, dim=c(3,3,3))
   ar2 = array(sample.int(100,27), dim=c(3,3,3)) # sample.int(a,b) generates b integer nums in the range [0,a]

4. 画简单图plot()

a = seq(1,15)
c = 2*a+1
b = a + 2
plot(a, c, type = 'l', col="red", main = "A Title", xlab = "X Label", ylab = "Y Label")
lines(a, b, col='blue')

1. 三角函数
   sin(x)
   cos(x)

2. The cos function between -4π and 4π with red markers every 0.01 values
   x1 = seq(-4pi,4pi,by=0.01)
   y1 = cos(x1)
   plot(x1,y1,col="red",pch="*", main = "Cos(x) between -4Pi and 4Pi")

   

3. Create a 2x2 plot that shows the following functions in each quadrant. Each plot should: a) be shown between the -5 and 5 at intervals of 0.5 and b) Have sensible titles and axes labels:
   a. y = x
   b. y = x
   c. y = x
   d. y = x

   sinx= seq(-5,5, by=0.5)
   siny1 = sinx
   siny2= sinx^2
   siny3= sinx^3
   siny4= sinx^4
   par(mfrow=c(2,2))
   plot(sinx,siny1,main= "y=x", xlab = "x axis", ylab = "y axis")
   plot(sinx,siny2,main= "y=x^2", xlab = "x axis", ylab = "y axis")
   plot(sinx,siny3,main= "y=x^3", xlab = "x axis", ylab = "y axis")
   plot(sinx,siny4,main= "y=x^4", xlab = "x axis", ylab = "y axis")