## 使用R的帮助
?t.test()
??t.test()在R中向量可以通过多种方式生成，并且向量也可以包含多种模式，如一个向量的每个元素都可以是字符串，向量也可以是一个因子。
## 解决使用R Markdown时因代码中有中文字符导致警告的问题
## Sys.setlocale('LC_ALL','C')
## 向量得生成
## 1:通过":"产生
A <- 1:5
A## [1] 1 2 3 4 5## 2:通过c()函数生产
A <- c(1,3,5,7,9)
A## [1] 1 3 5 7 9## 3:通过seq()函数指定步长生成
B = seq(from=2,to=10,by=2)
B## [1] 2 4 6 8 10## 通过seq()函数指定数目生成
B = seq(from=2,to=10,length.out = 5)
B## [1] 2 4 6 8 10## 4: 通过rep()函数生成具有重复元素得向量
C <- rep(1:2,5)
C## [1] 1 2 1 2 1 2 1 2 1 2## 也可以分别指定每个元素得重复次数
C <- rep(1:2,c(2,3))
C## [1] 1 1 2 2 2向量里面所包含得内容，不止可以为数字，向量中得元素也可以是字符串,也可以是TRUE或者FALISE等，同时向量也可以是一个因子向量
## 字符串向量
v_char <- c("A","B","C","D","E")
class(v_char)## [1] "character"## 逻辑向量
v_log <- rep(c(T,F),c(2,3))
v_log## [1] TRUE TRUE FALSE FALSE FALSEclass(v_log)## [1] "logical"## 因子形式的向量
v_fac <- factor(x=c("A","B","C","A","C"),levels = c("A","B","C"),
labels = c("A","B","C"))
v_fac## [1] A B C A C
## Levels: A B Clevels(v_fac)## [1] "A" "B" "C"## 因子向量重新排序
v_fac <- ordered(v_fac,c("C","B","A"))
v_fac## [1] A B C A C
## Levels: C < B < Alevels(v_fac)## [1] "C" "B" "A"向量的简单计算和获取指定位置的元素
## 向量如果进行四则运算，则会使用整个向量进行运算
vec <- seq(1,7)
## 进行除法运算
vec / 2## [1] 0.5 1.0 1.5 2.0 2.5 3.0 3.5## 如果两个向量长度相等，则对应位置的元素进行运算
vec / (2*vec)## [1] 0.5 0.5 0.5 0.5 0.5 0.5 0.5## 计算向量的累乘
cumprod(1:5)## [1] 1 2 6 24 120## 计算向量的累加
cumsum(vec)## [1] 1 3 6 10 15 21 28## 计算向量的长度
length(vec)## [1] 7## 从向量中获取需要的元素,可以使用在中括号指定位置
vec[c(1,3,5,7,9)]## [1] 1 3 5 7 NA## 从向量中获取需要的元素,可以使用等长的逻辑向量
## 获取vec中能被3整除的元素
vec %% 3 == 0## [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSEvec[vec %% 3 == 0]## [1] 3 6## 在［］中使用－号可以删除指定位置的元素
vec[c(-1:-5)]## [1] 6 7## 给出向量的倒序
rev(vec)## [1] 7 6 5 4 3 2 1## 给出符合条件元素所在的位置
which(vec %% 2 ==1)## [1] 1 3 5 7## 数字向量转化为字符串向量
vec_num <- seq(from=2,to=10,by=2)
str(vec_num)## num [1:5] 2 4 6 8 10vec_char <- as.character(vec_num)
str(vec_char)## chr [1:5] "2" "4" "6" "8" "10"## 字符串向量转化为numeric
vec_num <- as.numeric(vec_char)
is.numeric(vec_num)## [1] TRUE## 因子向量转化为字符串向量
vec_fac <- factor(c("A","B","C","A","C"))
str(vec_fac)## Factor w/ 3 levels "A","B","C": 1 2 3 1 3vec_fac2char <- as.character(vec_fac)
str(vec_fac2char)## chr [1:5] "A" "B" "C" "A" "C"## 查看向量的取值
unique(vec_fac2char)## [1] "A" "B" "C"## 查看每种取值的个数
table(vec_fac2char)## vec_fac2char
## A B C
## 2 1 2## 计算两个向量的并集
union(c(1:5),seq(2,10,2))## [1] 1 2 3 4 5 6 8 10## 计算两个向量的差集
setdiff(c(1:5),seq(2,10,2))## [1] 1 3 5## 计算两个向量的交集
intersect(c(1:5),seq(2,10,2))## [1] 2 4## 序列1是否是序列2中的元素
is.element(c(1:5),seq(2,10,2))## [1] FALSE TRUE FALSE TRUE FALSE## 也可以使用 %in%
c(1:5) %in% seq(2,10,2)## [1] FALSE TRUE FALSE TRUE FALSE向量属于一维数组，在R中矩阵二维数组可以使用matrix()函数生成
## 1 矩阵的生成
## 使用向量生成矩阵
vec <- seq(1,12)
mat <- matrix(vec,nrow = 2)
mat## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 3 5 7 9 11
## [2,] 2 4 6 8 10 12mat <- matrix(vec,ncol = 4)
mat## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## 生成矩阵时优先排列行
mat <- matrix(vec,nrow = 2,ncol = 4,byrow = TRUE)
mat## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8## 2 使用cbind()按照列连接多个向量
mat <- cbind(c(1,3,5,7),c(2,4,6,8),c(1:4))
mat## [,1] [,2] [,3]
## [1,] 1 2 1
## [2,] 3 4 2
## [3,] 5 6 3
## [4,] 7 8 4## 使用rbind()按照行连接多个向量
mat <- rbind(c(1,3,5,7),c(2,4,6,8),c(1:4))
mat## [,1] [,2] [,3] [,4]
## [1,] 1 3 5 7
## [2,] 2 4 6 8
## [3,] 1 2 3 4## 使用diag生成单位矩阵
diag(4)## [,1] [,2] [,3] [,4]
## [1,] 1 0 0 0
## [2,] 0 1 0 0
## [3,] 0 0 1 0
## [4,] 0 0 0 1## 也可以指定对角元素的内容
diag(c(1:4))## [,1] [,2] [,3] [,4]
## [1,] 1 0 0 0
## [2,] 0 2 0 0
## [3,] 0 0 3 0
## [4,] 0 0 0 4## 为矩阵添加列名和行名
colnames(mat) <- c("A","B","C","D")
rownames(mat) <- c("a","b","c")
mat## A B C D
## a 1 3 5 7
## b 2 4 6 8
## c 1 2 3 4## 查看矩阵的维度
dim(mat)## [1] 3 4## 计算矩阵有多少行
nrow(mat)## [1] 3## 计算矩阵有多少列
ncol(mat)## [1] 4## 计算矩阵的长度，即所有元素的个数
length(mat)## [1] 12获取矩阵中的元素
## 可以使用［行，列］来获取元素
mat <- rbind(c(1,3,5,7),c(2,4,6,8),c(1:4))
colnames(mat) <- c("A","B","C","D")
rownames(mat) <- c("a","b","c")
mat## A B C D
## a 1 3 5 7
## b 2 4 6 8
## c 1 2 3 4## 获取矩阵第2行第3列位置的元素
mat[2,3]## [1] 6## 获取矩阵第2列的元素
mat[,2]## a b c
## 3 4 2## 获取矩阵第1行的元素
mat[1,]## A B C D
## 1 3 5 7## 获取矩阵第"A","C"列的元素
mat[,c("A","C")]## A C
## a 1 5
## b 2 6
## c 1 3## 通过逻辑值获取需要的元素,获取矩阵中的偶数
mat %% 2 == 0## A B C D
## a FALSE FALSE FALSE FALSE
## b TRUE TRUE TRUE TRUE
## c FALSE TRUE FALSE TRUEmat[mat %% 2 == 0]## [1] 2 4 2 6 8 4矩阵的运算
mat <- matrix(c(1:12),nrow = 3)
mat## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12## 矩阵的转置
t(mat)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
## [4,] 10 11 12## 矩阵的行和
rowSums(mat)## [1] 22 26 30apply(mat, 1, sum)## [1] 22 26 30## 矩阵的列和
colSums(mat)## [1] 6 15 24 33apply(mat,2,sum)## [1] 6 15 24 33## 矩阵的行均值
rowMeans(mat)## [1] 5.5 6.5 7.5## 矩阵的列均值
colMeans(mat)## [1] 2 5 8 11## 矩阵与矩阵相乘
## 1 : 对应位置相乘
mat * mat## [,1] [,2] [,3] [,4]
## [1,] 1 16 49 100
## [2,] 4 25 64 121
## [3,] 9 36 81 144## 2 : 矩阵乘法
mat %*% t(mat)## [,1] [,2] [,3]
## [1,] 166 188 210
## [2,] 188 214 240
## [3,] 210 240 270mat2 <- mat %*% t(mat)
mat2## [,1] [,2] [,3]
## [1,] 166 188 210
## [2,] 188 214 240
## [3,] 210 240 270## 得到上三角矩阵
mat2[lower.tri(mat2)] <- 0
mat2## [,1] [,2] [,3]
## [1,] 166 188 210
## [2,] 0 214 240
## [3,] 0 0 270## 计算矩阵的行列式
mat3 <- cbind(1, 2:4, c(2,4,1))
mat3## [,1] [,2] [,3]
## [1,] 1 2 2
## [2,] 1 3 4
## [3,] 1 4 1det(mat3)## [1] -5## 计算矩阵的对角线元素
diag(mat3)## [1] 1 3 1## 矩阵的逆矩阵,求解ax=b,默认b=I(单位矩阵)
set.seed(123)
solve(matrix(runif(16),4,4))## [,1] [,2] [,3] [,4]
## [1,] -2.4929039 -0.7028084 0.7092411 2.243294
## [2,] -0.7010475 -1.8601293 0.5082957 1.653505
## [3,] 1.1783881 1.1148475 0.6243494 -1.668214
## [4,] 2.5479423 1.9728375 -1.5146544 -1.889510## 使用array生成3维数组
arr <- array(1:24,dim = c(3,4,2))
arr## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 13 16 19 22
## [2,] 14 17 20 23
## [3,] 15 18 21 24## 获取数组中的元素
## 第2层数据中的第二行的内容
arr[2,,2]## [1] 14 17 20 23arr[which(arr %% 5 == 0)]## [1] 5 10 15 20dim(arr)## [1] 3 4 2## 对数据的每层计算均值
apply(arr,3,mean)## [1] 6.5 18.5## 对数据的第二维度，列数据求和
apply(arr, 2,sum)## [1] 48 66 84 102## 生成数据框
df <- data.frame(id = c("A","B","C","D"),
age = c(10,15,9,12),
sex = c("F","M","M","F"),
score = c(17:20),
stringsAsFactors = FALSE)
head(df)## id age sex score
## 1 A 10 F 17
## 2 B 15 M 18
## 3 C 9 M 19
## 4 D 12 F 20## 查看数据的汇总
summary(df)## id age sex score
## Length:4 Min. : 9.00 Length:4 Min. :17.00
## Class :character 1st Qu.: 9.75 Class :character 1st Qu.:17.75
## Mode :character Median :11.00 Mode :character Median :18.50
## Mean :11.50 Mean :18.50
## 3rd Qu.:12.75 3rd Qu.:19.25
## Max. :15.00 Max. :20.00## 将sex转化为因子
df$sex <- factor(df$sex)
## 查看数据的汇总
str(df)## 'data.frame': 4 obs. of 4 variables:
## $ id : chr "A" "B" "C" "D"
## $ age : num 10 15 9 12
## $ sex : Factor w/ 2 levels "F","M": 1 2 2 1
## $ score: int 17 18 19 20## 通过矩阵生成数据框
mat <- rbind(c(1,3,5,7),c(2,4,6,8),c(1:4))
mat2df <- as.data.frame(mat)
colnames(mat2df) <- c("A","B","C","D")
mat2df## A B C D
## 1 1 3 5 7
## 2 2 4 6 8
## 3 1 2 3 4选取数据框中的元素
## 通过［］选择
df[,2]## [1] 10 15 9 12## 通过$选择
df$id## [1] "A" "B" "C" "D"## 获取id下得第3个元素
df$id[3]## [1] "C"## 通过变量的名称选择
df[c("id","age")]## id age
## 1 A 10
## 2 B 15
## 3 C 9
## 4 D 12## 通过行索引来选择指定的行
df[df$age > 10,]## id age sex score
## 2 B 15 M 18
## 4 D 12 F 20## 可以通过with函数取消$的使用
with(df,age > 10)## [1] FALSE TRUE FALSE TRUE## 使用逻辑值进行索引
df[df$id %in% c("B","D","F"),1:3]## id age sex
## 2 B 15 M
## 4 D 12 F## 为数据框添加新的变量
df$newvar <- df$score * 2列表可以容纳任何类型和结构的数据。
## 生成list
A <- factor(c("A","B","C","C","B"))
B <- matrix(seq(1:8),nrow = 2)
C <- "Type"
D <- data.frame(id = c("A","B","C","D"),
age = c(10,15,9,12))
## 使用A,B,C,D生成一个列表
mylist <- list(A,B,C,D)
mylist## [[1]]
## [1] A B C C B
## Levels: A B C
##
## [[2]]
## [,1] [,2] [,3] [,4]
## [1,] 1 3 5 7
## [2,] 2 4 6 8
##
## [[3]]
## [1] "Type"
##
## [[4]]
## id age
## 1 A 10
## 2 B 15
## 3 C 9
## 4 D 12str(mylist)## List of 4
## $ : Factor w/ 3 levels "A","B","C": 1 2 3 3 2
## $ : int [1:2, 1:4] 1 2 3 4 5 6 7 8
## $ : chr "Type"
## $ :'data.frame': 4 obs. of 2 variables:
## ..$ id : Factor w/ 4 levels "A","B","C","D": 1 2 3 4
## ..$ age: num [1:4] 10 15 9 12## 获取列表中的内容
## 1 使用［］
mylist[1]## [[1]]
## [1] A B C C B
## Levels: A B Cmylist[[1]]## [1] A B C C B
## Levels: A B Cmylist[[2]][2,1:3]## [1] 2 4 6mylist[[4]]$age[1:3]## [1] 10 15 9## 给列表中的内容添加名字
names(mylist) <- c("one","two","three","four")
names(mylist) ## [1] "one" "two" "three" "four"## 通过$来提取数据
mylist$one## [1] A B C C B
## Levels: A B C## 判断数值能否被3整除
num <- 9
if(num %% 3 == 0) print("数值可以被3整除") else print("数值不能被3整除")## [1] "数值可以被3整除"## 使用 ifelse(test, yes, no)
num <- 10
ifelse(num %% 3 == 0,num,NA)## [1] NA## switch 精确匹配
id = c("A","B","C","D")
switch(id[2],
A = 10,
B = 15,
C = 9,
D = 12)## [1] 15for 循环和while循环
## 找出向量中的偶数和奇数
vec <- seq(1:20)
result1 <- result2 <- vector()
for (ii in 1:length(vec)) {
## 偶数
if(vec[ii] %% 2 == 0){
result1 <- c(result1,vec[ii])
}else{
result2 <- c(result2,vec[ii])
}
}
result1## [1] 2 4 6 8 10 12 14 16 18 20result2## [1] 1 3 5 7 9 11 13 15 17 19## 通过break来跳出循环
## 从向量中找出5个偶数
set.seed(12)
vec <- sample(seq(1:100),40)
ii <- 1
result1 <- vector()
while(ii){
## 保存偶数
if(vec[ii] %% 2 == 0) result1 <- c(result1,vec[ii])
## 满足条件，跳出循环
if (length(result1) == 5){
break
}
ii <- ii + 1
}
result1## [1] 66 90 80 46 92R中的函数使用function来定义
## 1 二分法求方程根
## 编写所要求解单变量非线性方程的函数
solvefunction <- function(x){
x^3-2*x^2-1
}
# 编写二分法求解方程
twosol <- function(a,b,ee=10^(-5)){
#a：左边界，b：右边界，ee=10^(-5)：精度
if (solvefunction(a)*solvefunction(b) > 0 | a > b)
print("请更改边界")
else
while(abs(a-b)>=ee) {
c <- (a+b)/2
if (solvefunction(c) == 0)
return(c)
if (solvefunction(a)*solvefunction(c)<0)
b <- c
if (solvefunction(c)*solvefunction(b)<0)
a <- c
}
return(c)
}
## 求解方程的根
answ <- twosol(0,3,ee=10^(-5))
answ## [1] 2.205568# install.packages("dplyr")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
library(readxl)
library(ggplot2)
library(VIM)## Loading required package: colorspace## Loading required package: grid## Loading required package: data.table##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleeplibrary(tidyr)
library(d3heatmap)
library(treemap)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2##
## Attaching package: 'GGally'## The following object is masked from 'package:dplyr':
##
## nasalibrary(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(gganimate)
library(stringr)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout