向量
赋值
> a <- c(2, 5, 8)
> a
[1] 2 5 8
筛选
> a[1:2]
[1] 2 5
> a[a>4]
[1] 5 8
> a>4
[1] FALSE TRUE TRUE
合并向量
> c(a[1], 3, a[2:3], 1)
[1] 2 3 5 8 1
循环补齐
> a <- c(3, 4)
> b <- c(1, 2, 5, 6)
> a+b
[1] 4 6 8 10
关于向量的几个函数
> length(b)
[1] 4
> which.max(b)
[1] 4
> which(b>3)
[1] 3 4
矩阵
本质上来说就是多维向量
创建
> a <- matrix(c(1, 2, 3, 4), nrow=2)
> a
[,1] [,2]
[1,] 1 3
[2,] 2 4
> a <- matrix(c(1, 2, 3, 4), nrow=2, byrow=TRUE)
> a
[,1] [,2]
[1,] 1 2
[2,] 3 4
筛选矩阵
> a[1:2, 2]
[1] 2 4
线性代数
> a * a
[,1] [,2]
[1,] 1 4
[2,] 9 16
> a %*% a
[,1] [,2]
[1,] 7 10
[2,] 15 22
矩阵相关函数
> t(a)
[,1] [,2]
[1,] 1 3
[2,] 2 4
> solve(a)
[,1] [,2]
[1,] -2.0 1.0
[2,] 1.5 -0.5
数据框
可以有不同的数据类型
> data("iris")
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
> summary(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
> names(iris)
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
summary() 对于数值变量，我们可以看到最小值，中位数等等统计信息。而对于分类变量，我们看到的是计数信息。
列表
一种递归式的向量,我们可以用列表来存储不同类型的数据
> l <- list(name="jiawen", pigu_num=2, is_handsome=TRUE)
> l
$name
[1] "jiawen"
$pigu_num
[1] 2
$is_handsome
[1] TRUE
列表的多种索引方式
> l$name
[1] "jiawen"
> l[[2]]
[1] 2
> l[['is_handsome']]
[1] TRUE
网络资源
https://www.datacamp.com/
http://cos.name/
http://xccds1977.blogspot.com/
http://adv-r.had.co.nz/
tidyverse 生态链
readr：读取数据
tidyr：整理数据
dplyr：数据转换
ggplot：可视化
purrr：函数式编程
> library(tidyverse)
> mpg
# A tibble: 234 x 11
manufacturer model displ year cyl trans drv cty hwy fl class
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
4 audi a4 2 2008 4 auto(av) f 21 30 p compact
5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compact
7 audi a4 3.1 2008 6 auto(av) f 18 27 p compact
8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26 p compact
9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25 p compact
10 audi a4 quattro 2 2008 4 manual(m6) 4 20 28 p compact
# ... with 224 more rows
manufacture: 制造商
model: 车型
displ： 汽车排放量
year: 制造年度
cyl: 排气管数量
trans: 排放类型
drv: 驱动方式
cty: 每公里耗油量（城市道路）
hwy: 每公里耗油量（高速路）
fl: 油的种类
class: 车的类型
> ggplot(data=mpg) + geom_point(mapping=aes(x=displ, y=hwy))
image.png
> ggplot(data=mpg) + geom_point(mapping=aes(x=displ, y=hwy, color=class))
image.png
> ggplot(data=mpg) + geom_point(mapping=aes(x=displ, y=hwy)) + facet_wrap(~class)
image.png
> ggplot(data=mpg) + geom_point(mapping=aes(x=displ, y=hwy)) + geom_smooth(mapping=aes(x=displ, y=hwy))
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
image.png
> ggplot(mpg, aes(x=displ, y=hwy)) + geom_point() + geom_smooth(method="lm")
`geom_smooth()` using formula 'y ~ x'
image.png
chrome-extension://cdonnmffkdaoajfknoeeecmchibpmkmg/assets/pdf/web/viewer.html?file=https%3A%2F%2Frstudio.com%2Fwp-content%2Fuploads%2F2015%2F03%2Fggplot2-cheatsheet.pdf
filter() 过滤函数
> mpg %>% filter(displ>=5, hwy<20)
# A tibble: 29 x 11
manufacturer model displ year cyl trans drv cty hwy fl
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr>
1 chevrolet c150~ 5.3 2008 8 auto~ r 11 15 e
2 chevrolet c150~ 5.7 1999 8 auto~ r 13 17 r
3 chevrolet c150~ 6 2008 8 auto~ r 12 17 r
4 chevrolet k150~ 5.3 2008 8 auto~ 4 14 19 r
5 chevrolet k150~ 5.3 2008 8 auto~ 4 11 14 e
6 chevrolet k150~ 5.7 1999 8 auto~ 4 11 15 r
7 chevrolet k150~ 6.5 1999 8 auto~ 4 14 17 d
8 dodge dako~ 5.2 1999 8 manu~ 4 11 17 r
9 dodge dako~ 5.2 1999 8 auto~ 4 11 15 r
10 dodge dura~ 5.2 1999 8 auto~ 4 11 16 r
# ... with 19 more rows, and 1 more variable: class <chr>
arrange() 排序函数
> mpg %>% filter(displ>=5, hwy<20) %>% arrange(desc(year), hwy)
# A tibble: 29 x 11
manufacturer model displ year cyl trans drv cty hwy fl
<chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr>
1 chevrolet k150~ 5.3 2008 8 auto~ 4 11 14 e
2 jeep gran~ 6.1 2008 8 auto~ 4 11 14 p
3 chevrolet c150~ 5.3 2008 8 auto~ r 11 15 e
4 chevrolet c150~ 6 2008 8 auto~ r 12 17 r
5 dodge ram ~ 5.7 2008 8 auto~ 4 13 17 r
6 ford f150~ 5.4 2008 8 auto~ 4 13 17 r
7 dodge dura~ 5.7 2008 8 auto~ 4 13 18 r
8 ford expe~ 5.4 2008 8 auto~ r 12 18 r
9 jeep gran~ 5.7 2008 8 auto~ 4 13 18 r
10 lincoln navi~ 5.4 2008 8 auto~ r 12 18 r
# ... with 19 more rows, and 1 more variable: class <chr>
select() 提取函数
> mpg %>% filter(displ>=5, hwy<20) %>% arrange(desc(year), hwy) %>% select(model)
# A tibble: 29 x 1
model
<chr>
1 k1500 tahoe 4wd
2 grand cherokee 4wd
3 c1500 suburban 2wd
4 c1500 suburban 2wd
5 ram 1500 pickup 4wd
6 f150 pickup 4wd
7 durango 4wd
8 expedition 2wd
9 grand cherokee 4wd
10 navigator 2wd
# ... with 19 more rows
mutate() 添加新列
> mpg %>% mutate(ave_displ=displ/cyl) %>% select(ave_displ)
# A tibble: 234 x 1
ave_displ
<dbl>
1 0.45
2 0.45
3 0.5
4 0.5
5 0.467
6 0.467
7 0.517
8 0.45
9 0.45
10 0.5
# ... with 224 more rows
group_by() 条件分组函数
> mpg %>% group_by(class) %>% summarise(mean(displ), mean(hwy))
# A tibble: 7 x 3
class `mean(displ)` `mean(hwy)`
<chr> <dbl> <dbl>
1 2seater 6.16 24.8
2 compact 2.33 28.3
3 midsize 2.92 27.3
4 minivan 3.39 22.4
5 pickup 4.42 16.9
6 subcompact 2.66 28.1
7 suv 4.46 18.1
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