title: R语言的基础学习
date: 2018-11-22 22:45:19
tags:
- R语言
又来翻之前的笔记啦，这里是一些R语言的基础知识，将会分为几篇，接下来再慢慢更新吧
发现之前的笔记还是简单了一些，这里也做了一些补充~ 通俗易懂，赶紧学起来吧~
如果感觉对你有帮助，可以关注：专栏-生物信息学-小白成长记
R语言学习系列
R语言-0基础学习1-数据结构
R语言-0基础学习2-构建子集
R语言-0基础学习3-循环排序信息处理函数
R语言-0基础学习4-实战1-常见操作
R语言数据结构
1. 五种基本类型
字符
x <- "233"
数值
x <- 3.14
x <- 1
整数
x <- 3L
复数
x <- 1+2i
逻辑
x <- TRUE
2. 向量
# Vector
# 三种表达方式
x <- vector("character", length=10)
x1 <- 1:4
x2 <- c(1,2,3,4)
# x
# [1] "" "" "" "" "" "" "" "" "" ""
# x1
# [1] 1 2 3 4
# x2
# [1] 1 2 3 4
x3 <- c(TRUE,10,"a")
x4 <- c("a","b","c")
x5 <- c(TRUE,FALSE)
# x3
# [1] "TRUE" "10" "a"
# x4
# [1] "a" "b" "c"
# x5
# [1] TRUE FALSE
# 转换
as.numeric(x2)
# [1] 1 2 3 4
as.logical(x5)
# [1] TRUE FALSE
as.character(x4)
# [1] "a" "b" "c"
# 查看类型
class(x1)
# [1] "integer"
# 重命名
names(x2) <- c("a","b","c","d")
x2
# a b c d
# 1 2 3 4
3. 矩阵
矩阵=向量+纬度
# matrix
# 创建方法1
x <- matrix(1:6, nrow = 3, ncol = 2)
# [,1] [,2]
# [1,] 1 4
# [2,] 2 5
# [3,] 3 6
# 创建方法2
y <- 1:6
dim(y) <- c(2,3)
y
# [,1] [,2] [,3]
# [1,] 1 3 5
# [2,] 2 4 6
# 属性与纬度的查看
dim(x)
# [1] 3 2
attributes(x)
# $dim
# [1] 3 2
# 矩阵扩展
y2 <- matrix(1:6, nrow = 2, ncol = 3)
rbind(y,y2)
# [,1] [,2] [,3]
# [1,] 1 3 5
# [2,] 2 4 6
# [3,] 1 3 5
# [4,] 2 4 6
cbind(y,y2)
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1 3 5 1 3 5
# [2,] 2 4 6 2 4 6
4.数组
数组 = 矩阵+n纬度
# 数组
x <- array(1:24, dim = c(4,6))
x
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] 1 5 9 13 17 21
# [2,] 2 6 10 14 18 22
# [3,] 3 7 11 15 19 23
# [4,] 4 8 12 16 20 24
x2 <- array(1:24, dim = c(2,3,4))
x2
# , , 1
# [,1] [,2] [,3]
# [1,] 1 3 5
# [2,] 2 4 6
# , , 2
# [,1] [,2] [,3]
# [1,] 7 9 11
# [2,] 8 10 12
# , , 3
# [,1] [,2] [,3]
# [1,] 13 15 17
# [2,] 14 16 18
# , , 4
# [,1] [,2] [,3]
# [1,] 19 21 23
# [2,] 20 22 24
5. 列表
# 列表
# 可以添加不同类型的变量
listvalue <- list("a", 2, 10L, 1+2i, TRUE)
listvalue
# [[1]]
# [1] "a"
# [[2]]
# [1] 2
# [[3]]
# [1] 10
# [[4]]
# [1] 1+2i
# [[5]]
# [1] TRUE
listvalue2 <- list(c(1,2,3), c("a","b","c"))
listvalue2
# [[1]]
# [1] 1 2 3
# [[2]]
# [1] "a" "b" "c"
# 给矩阵命名
x <- matrix(1:6, nrow = 2, ncol = 3)
dimnames(x) <- list(c("a","b"), c("c","d","e"))
x
# c d e
# a 1 3 5
# b 2 4 6
6. 因子
# 因子
# 整数向量+标签
x <- factor(c("female","female","male","female","male"))
x
# [1] female female male female male
# Levels: female male
y <- factor(c("female","female","male","female","male"),levels = c("male", 'female'))
y
# [1] female female male female male
# Levels: male female
table(x)
# x
# female male
# 3 2
table(y)
# y
# male female
# 2 3
unclass(x)
# [1] 1 1 2 1 2
# attr(,"levels")
# [1] "female" "male"
# 查看类型为factor
class(x)
# [1] "factor"
class(unclass(x))
# [1] "integer"
7. 缺失值
NA
NaN
# 缺失值 NA与NAN，类似""与NULL(皮一下)
x <- c(1, NA, 2, NA, 3)
is.na(x)
# [1] FALSE TRUE FALSE TRUE FALSE
is.nan(x)
# [1] FALSE FALSE FALSE FALSE FALSE
y <- c(1, NaN, 2, NaN, 3)
is.na(y)
is.nan(y)
8. 数据框
存储表格数据，视为各元素长度相同的列表
# 数据框
df <- data.frame(id = c(1,2,3,4), name = c("a","b","c","d"), gender=c(TRUE, TRUE, FALSE, FALSE))
df
# id name gender
# 1 1 a TRUE
# 2 2 b TRUE
# 3 3 c FALSE
# 4 4 d FALSE
nrow(df)
# [1] 4
ncol(df)
# [1] 3
# 数据框转换为矩阵
data.matrix(df)
# id name gender
# [1,] 1 1 1
# [2,] 2 2 1
# [3,] 3 3 0
# [4,] 4 4 0
9. 日期与时间
# 日期与时间
# character
x <- date()
# [1] "Fri Apr 17 07:57:43 2020"
x
class(x)
# Date
x2 <- Sys.Date()
x2
# [1] "2020-04-17"
class(x2)
# [1] "Date"
x3 <- as.Date("2018-11-23")
x3
# [1] "2018-11-23"
class(x3)
# [1] "Date"
weekdays(x3)
# [1] "星期五"
months(x3)
# [1] "十一月"
quarters(x3)
# [1] "Q4"
julian(x3)
# [1] 17858
# attr(,"origin")
# [1] "1970-01-01"
# 算时差
x4 <- as.Date("2018-04-25")
x3-x4
# Time difference of 212 days
as.numeric(x3-x4)
# [1] 212
# -----------------------
x <- Sys.time()
x
# [1] "2020-04-17 08:01:36 CST"
class(x)
# [1] "POSIXct" "POSIXt"
p <- as.POSIXlt(x)
p
# [1] "2020-04-17 08:01:36 CST"
class(p)
# [1] "POSIXlt" "POSIXt"
# 获取p下的属性名称
names(unclass(p))
# [1] "sec" "min" "hour" "mday" "mon" "year" "wday" "yday" "isdst" "zone" "gmtoff"
# 获取p下对应属性的值
p$sec
# [1] 36.0718
10. 数据结构-小结
数据结构-小结
如果感觉对你有帮助，可以关注：专栏-生物信息学-小白成长记
