Arrays array matrix list data frame Dataframe

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2. <span style= "FONT-SIZE:14PX;" > #R语言备忘录三 #
4. #数组array和矩阵matrix, list, data frame Dataframe
6. #数组
8. #数组的重要属性就是dim, Number of dimensions
10. Matrix of #得到4
12. Z <-1:12
14. Dim (z) <-C (3,4)
16. Z
18. #构建数组
20. X <-Array (1:20, Dim = C (4,5))
22. #三维
24. Y <-Array (1:18, Dim = C (2,3,3))
26. #数组下标
28. Y[1, 2, 3]
30. #数组的广义转置, dimensions change, turn 2 dimensions into 1 dimensions, turn 3 dimensions into 2 dimensions, 1 dimensions into 3 dimensions, i.e. d[i,j,k] = C[j,k,i]
32. C <-Array (1:24, Dim = C (2,3,4))
34. D <-Aperm (A, C (2,3,1))
36. #apply用于数组固定某一维度不变, perform calculations
38. Apply (A, 1, sum)
40. #矩阵
42. #产生矩阵
44. A <-Matrix (1:15, nrow=3, ncol=5, Byrow=true)
46. B <-Matrix (3:17, nrow=5, ncol=3, Byrow=true)
48. #求方阵行列式的值
50. Det (Matrix (1:4, Ncol = 2))
52. #内积
54. #矩阵的内积
56. A%*% B
58. #也可以使用crossprod函数
60. Crossprod (A, B)
62. Crossprod (A)
64. #向量的外积, also known as cross-product
66. X <-1:5
68. Y <-2*1:5
70. X%o% y
72. #也可以使用tcrossprod函数, outer
74. Outer (x, y)
76. Tcrossprod (x)
78. #产生对角阵
80. #如果变量是一个向量, a diagonal array with a vector as a diagonal element
82. V <-C (1, 3, 5)
84. Diag (v)
86. #如果变量是一个矩阵, we take the diagonal elements of the matrix as diagonal elements
88. Diag (A)
90. #解线性方程Ax =b,
92. b <-Matrix (C (1,1,1), nrow = 3, Byrow = TRUE)
94. B <-Matrix (1:9, nrow = 3, Byrow = TRUE)
96. Solve (B,B)
98. #求矩阵的逆
100. Solve (B)
102. #ev $values is a characteristic root, and ev$vectors is a matrix of eigenvectors.
104. SM <-Crossprod (A, a)
106. EV <-Eigen (SM)
108. #奇异解, SVD$D returns the singular value of matrix A, Svd$u is the orthogonal array u,svd$v corresponding to the orthogonal array V, a = UDVT
110. Svda <-SVD (A)
112. #把矩阵拉成向量
114. As.vector (A)
116. #数组或矩阵的维的名字
118. X <-Matrix (1:6, Ncol = 2,
120. Dimnames = List (c ("One", "B", "three"), C ("First", "Second")),
122. Byrow = T)
124. #亦或是
126. Dimnames (A) <-list (C ("One", "B", "three"), C ("First", "Second"))
128. Colnames (A) <-C ("First", "Second")
130. Rownames (A) <-C ("One", "B", "three")
132. #列表
134. #构建列表
136. Lst <-list (name= "Fred", wife= "Mary", No.children=3, Child.ages=c (4,7,9))
138. #列表元素, list name [[Subscript]]
140. LST[[2]]
142. LST[[4]][2]
144. #也可以使用名字代替下标
146. lst[["Name"]
148. Lst$name
150. #修改列表
152. Lst$name <-C ("John", "Tom")
154. #删除列表某项
156. Lst$name <-NULL
158. #连接几个列表
160. List. ABC <-C (list. A, List. B, List. C
162. #数据框
164. #data. Frame, usage and list are the same, if the list of ingredients satisfies the data frame, you can also use the As.data.frame conversion
166. Df<-data.frame (
168. Name=c ("Alice", "Becka", "James", "Jeffrey", "John"), Sex=c ("F", "F", "M", "M", "M"),
170. Age=c (13, 13, 12, 13, 12),
172. Height=c (56.5, 65.3, 57.3, 62.5, 59.0)
174. Weight=c (84.0, 98.0, 83.0, 84.0, 99.5)
176. )
178. Df
180. #矩阵可以通过data. FRAME (X) conversion
182. #取数据框的一部分
184. Df[1:2, 3:5]
186. df[["Height"]
188. Df$weight
190. #命名
192. Names (DF)
194. Rownames (DF)
196. The #attch () function calls the variables in the data frame into memory to facilitate the invocation of data
198. Attach (DF)
200. #取消连接
202. Detach (DF)
204. #调用edit进行编辑数据
206. Edit (DF)
208. #因子factor
210. Sex <-C ("M", "M", "F", "F")
212. SEXF <-factor (Sex)
214. #因子水平
216. Sex_level <-levels (SEXF)
218. #用table统计各类数据的频数
220. Sex_tab <-table (SEXF)
222. #用gl () Production factor
224. #gl (n, k, length = n * k, labels = 1:n, ordered = FALSE)
226. </span>

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2. #R语言学习备忘录一 #
4. #向量运算 #
8. x <-C (1,2,3,6)
10. #不小于x的最小整数
12. Ceiling (x)
14. #不大于x的最大整数
16. Floor (x)
18. #向0方向截取的x中的整数
20. Trunc (x)
22. #将x舍入为指定位的小数
24. Round (x,digits=2)
26. #将x舍入指定的有效数字位数
28. Signif (x,digits=2)
30. #三角函数
32. COS (x)
34. Sin (x)
36. Tan (x)
38. ACOs (x)
40. ASIN (x)
42. #分位数, such as 25% and 50% of the number of digits
44. Quantile (X,c (. 25,.5))
46. #求值域
48. Range (x)
50. #求乘积函数
52. Prod (x)
54. #滞后差分
56. diff (X)
58. #求最大值和最小值对应的位置
60. Which.min (x)
62. Which.max (x)
64. #数据标准化
66. X<-c (123,232,212,232,120,273)
68. Mydata<-scale (x)
70. MyData
74. #正则表达式匹配
76. Str<-c ("A", "a", "B", "C")
78. grep ("A", Str,fixed=true)
82. #字符
84. #分割符strsplit
86. Strsplit ("ABCDE", "" ")
88. #连接字符paste
90. Paste ("x", 1:10,sep= "")
92. Paste ("x", 1:5,sep= "T")
94. Paste ("Today is", date ())
96. #大写转换toupper
98. ToUpper ("Abcef")
100. #小写转换tolower
102. ToLower ("ABC")
104. #计算字符数量 nchar
106. X<-c ("AB", "Deew", "James")
108. NCHAR (x)
110. NCHAR (x[2])
112. #提取或替换一个数值, and the Excel mid function is almost substr
114. x<-"ABCDEFG"
116. substr (x,2,5) #2到5个元素
118. substr (x,2,5) <-"1111111"
120. X
124. #注意两种等差数列的差别
126. 1:n-1
128. 1: (n-1)
130. #重复函数rep
132. Y<-rep (1:5,2)
134. Y
136. #等间隔函数
138. Seq ( -5, 5, by=.2)
140. Seq (length=51, from=-5, by=.2)
142. #上下两种方式效果一样
144. #求行均值
146. Apply (Mydata,1,mean)
148. #求列均值
150. Apply (Mydata,2,mean)
154. #逻辑变量
156. #判断一个逻辑向量是否都为真的函数是all
158. All (c (1,2,3,4,5,6) >3)
160. #判断一个逻辑向量是否有为真的函数any
162. Any (c (1,2,3,4,5,6) >3)
166. #缺失数据
168. #NA表示数据缺省或缺失
170. Z <-C (1:3, NA)
172. Z
174. A <-is.na (z)
176. #修改缺失数据
178. Z[is.na (z)] <-0
180. #is. Nan () to determine whether the data is accurate, the INF is also a non-accurate
182. x <-C (0/1, 0/0, 1/0, NA)
184. Is.nan (x)
186. Is.finite (x)
188. Is.infinite (x)
190. Is.na (x)
194. #复数向量
196. #复数z =x+isin (x)
198. x <-seq (-pi, pi, by = PI/10)
200. Y <-sin (x)
202. Z <-Complex (re = x, Im = y)
204. Plot (z)
206. Lins (z)
210. #向量的下标运算
212. x <-C (1,4,7)
214. X[c (2,3)]
216. #修改元素值
218. X[c (1,3)] <-C (22, 33)
220. #逻辑向量
222. x <-C (1,4,7)
224. X < 5
226. X[X<5]
228. #分段函数
230. Y <-Numeric (length (x))
232. Y[X<0] <-1-x[x<0]
234. Y[x>=0] <-1-x[x>=0]
236. #即y =1-x,x<0; Y=1+x,x>=0
238. #下标的负整数运算表示的是删除
240. V <-10:20
242. v[-(1:5)]
244. #取字符串为下标
246. Ages <-C (li=33, zhang=29, liu=18)
248. ages["Zhang"]
250. #给向量赋予名字
252. Fruit <-x (5, 10, 1, 29)
254. Names (fruit) <-C ("Orange", "banana", "apple", "peach")

Arrays array matrix list data frame Dataframe