R语言 分组计算，不止group_by
最近在研究excel透视图，想到好像自己在R-分组操作并不是很流畅，顺便学习分享一下。R自带数据集比较多，今天就选择一个我想对了解的mtcars数据集带大家学习一下R语言中的分组计算（操作）。
目录
1 dplyr包中的group_by联合summarize
1.1 group_by语法
1.2 summarise语法
1.3 group_by和summarise单变量分组计算
1.4 group_by和summarise多变量分组计算
2 ddply
2.1 ddply语法
2.2 ddply分组计算示例
3 aggregate
3.1 aggregate语法
3.2 aggregate分组计算示例
3.3 aggregate分组计算补充（formula形式）
4 splite
正文
首先给大家看一下mtcars数据集的基本情况，data.frame类型，32个观测对象，11个变量。
> head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
> str(mtcars)
'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...1 dplyr包中的group_by联合summarize
1.1 group_by语法
data为数据集
...为分组变量，可以是一个也可以是多个，多个的话以逗号分割group_by(mtcars, vs, am)1.2 summarise语法
data为数据集，如果data被group_by定义分组，则根据分组变量分组计算
...为计算函数，可以是一个也可以是多个，多个的话以逗号分割summarise(data,disp = mean(disp),hp = mean(hp))
summarise计算函数Useful functions拓展
Center: mean(), median()
Spread: sd(), IQR(), mad()
Range: min(), max(), quantile()
Position: first(), last(), nth(),
Count: n(), n_distinct()
Logical: any(), all()注：计算函数Useful functions拓展中英语不解释了，应该懂得
1.3 group_by和summarise单变量分组计算示例
> library(dplyr) #加载dplyr包
> by_cyl <- group_by(mtcars,cyl) #对mtcars数据集根据cyl变量进行分组注意行5
> by_cyl
# A tibble: 32 x 11
# Groups: cyl [3]
mpg cyl disp hp drat wt qsec vs am gear carb
* <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# ... with 22 more rows
# 对分组数据的相关变量进行函数计算
> summarise(by_cyl,disp = mean(disp),hp = mean(hp))
# A tibble: 3 x 3
cyl disp hp
<dbl> <dbl> <dbl>
1 4 105. 82.6
2 6 183. 122.
3 8 353. 209.
—————分割线：引入%>%管道符号，等价于上方分步骤使用———————————————————————————————————————————————————————————————————————————————————————————————————————————
>library(dplyr) #加载dplyr包
> mtcars %>% group_by(cyl) %>% summarise(disp = mean(disp),hp = mean(hp))
# A tibble: 3 x 3
cyl disp hp
<dbl> <dbl> <dbl>
1 4 105. 82.6
2 6 183. 122.
3 8 353. 209.1.4 group_by和summarise多变量分组计算示例
> mtcars %>% group_by(vs, am) %>% summarise(n = n())
# A tibble: 4 x 3
# Groups: vs [2]
vs am n
<dbl> <dbl> <int>
1 0 0 12
2 0 1 6
3 1 0 7
4 1 1 72 ddply
接触了Hadley Wickham神包tidyverse以后感觉数据操作那么简单，这里介绍一种可以实现分组计算/操作的方法，就是plyr包的split-apply-combine思想
2.1 ddply语法
ddply(.data, .variables, ... )
.data为数据集
.variables分组变量一定要在“点+括号中”，例如".(sex)或.(group, sex)"
...为计算函数，可以是一个也可以是多个，2.2 ddply分组计算示例
> library(plyr); library(dplyr)
> dfx <- data.frame(
+ group = c(rep('A', 8), rep('B', 15), rep('C', 6)),
+ sex = sample(c("M", "F"), size = 29, replace = TRUE),
+ age = runif(n = 29, min = 18, max = 54)
+ )
>
> ddply(dfx, .(group, sex), summarize,
+ mean = round(mean(age), 2),
+ sd = round(sd(age), 2))
group sex mean sd
1 A F 31.46 8.70
2 A M 28.49 2.78
3 B F 28.75 9.19
4 B M 40.90 8.13
5 C F 32.24 7.37
6 C M 40.77 13.22
>
>
> ddply(dfx,.(sex), summarize,
+ mean = round(mean(age), 2),
+ sd = round(sd(age), 2))
sex mean sd
1 F 30.46 8.10
2 M 38.68 9.72注意ddply中分组变量一定要在“点+括号中”，例如".(sex) 或 .(group, sex)"
3 aggregate
3.1 aggregate语法
aggregate(x, by, FUN)x为数据集by为分组变量列表FUN为计算函数3.2 aggregate分组计算示例
> aggregate(state.x77, list(Region = state.region), mean)
Region Population Income Illiteracy Life Exp Murder HS Grad Frost
1 Northeast 5495.111 4570.222 1.000000 71.26444 4.722222 53.96667 132.7778
2 South 4208.125 4011.938 1.737500 69.70625 10.581250 44.34375 64.6250
3 North Central 4803.000 4611.083 0.700000 71.76667 5.275000 54.51667 138.8333
4 West 2915.308 4702.615 1.023077 71.23462 7.215385 62.00000 102.1538
Area
1 18141.00
2 54605.12
3 62652.00
4 134463.00
————————————————————————————————————————————————————————————————————
> aggregate(state.x77,list(
+ Region = state.region,
+ Cold = state.x77[,"Frost"] > 130),
+ mean)
Region Cold Population Income Illiteracy Life Exp Murder HS Grad
1 Northeast FALSE 8802.8000 4780.400 1.1800000 71.12800 5.580000 52.06000
2 South FALSE 4208.1250 4011.938 1.7375000 69.70625 10.581250 44.34375
3 North Central FALSE 7233.8333 4633.333 0.7833333 70.95667 8.283333 53.36667
4 West FALSE 4582.5714 4550.143 1.2571429 71.70000 6.828571 60.11429
5 Northeast TRUE 1360.5000 4307.500 0.7750000 71.43500 3.650000 56.35000
6 North Central TRUE 2372.1667 4588.833 0.6166667 72.57667 2.266667 55.66667
7 West TRUE 970.1667 4880.500 0.7500000 70.69167 7.666667 64.20000
Frost Area
1 110.6000 21838.60
2 64.6250 54605.12
3 120.0000 56736.50
4 51.0000 91863.71
5 160.5000 13519.00
6 157.6667 68567.50
7 161.8333 184162.173.3 aggregate分组计算补充（formula形式）
aggregate(formula, data, FUN)
#Formulas, one ~ one, one ~ many, many ~ one, and many ~ many:
> aggregate(weight ~ feed, data = chickwts, mean)
feed weight
1 casein 323.5833
2 horsebean 160.2000
3 linseed 218.7500
4 meatmeal 276.9091
5 soybean 246.4286
6 sunflower 328.9167
> aggregate(breaks ~ wool + tension, data = warpbreaks, mean)
wool tension breaks
1 A L 44.55556
2 B L 28.22222
3 A M 24.00000
4 B M 28.77778
5 A H 24.55556
6 B H 18.77778
> aggregate(cbind(Ozone, Temp) ~ Month, data = airquality, mean)
Month Ozone Temp
1 5 23.61538 66.73077
2 6 29.44444 78.22222
3 7 59.11538 83.88462
4 8 59.96154 83.96154
5 9 31.44828 76.89655
> aggregate(cbind(ncases, ncontrols) ~ alcgp + tobgp, data = esoph, sum)
alcgp tobgp ncases ncontrols
1 0-39g/day 0-9g/day 9 261
2 40-79 0-9g/day 34 179
3 80-119 0-9g/day 19 61
4 120+ 0-9g/day 16 24
5 0-39g/day 10-19 10 84
6 40-79 10-19 17 85
7 80-119 10-19 19 49
8 120+ 10-19 12 18
9 0-39g/day 20-29 5 42
10 40-79 20-29 15 62
11 80-119 20-29 6 16
12 120+ 20-29 7 12
13 0-39g/day 30+ 5 28
14 40-79 30+ 9 29
15 80-119 30+ 7 12
16 120+ 30+ 10 134 splite
感觉splite没有太多好讲的，直接上例子体会一下吧~
> require(stats); require(graphics)
> n <- 10; nn <- 100
> g <- factor(round(n * runif(n * nn)))
> x <- rnorm(n * nn) + sqrt(as.numeric(g))
>
> xg_group_length <- split(x, g) %>% sapply(length)
> xg_group_length
0 1 2 3 4 5 6 7 8 9 10
42 105 103 93 119 120 80 88 97 101 52
> xg_group_mean <- split(x, g) %>% sapply(mean)
> xg_group_mean
0 1 2 3 4 5 6 7
0.9776091 1.3270451 1.6645178 1.7567653 2.2137027 2.4426637 2.5394288 2.6557613
8 9 10
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原始发表：2019-08-30，如有侵权请联系 cloudcommunity@tencent.com 删除
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