r语言aggredate_R语言中aggregate函数_r语言aggregate函数_aggregate

前言

这个函数的功能比较强大，它首先将数据进行分组(按行)，然后对每一组数据进行函数统计，最后把结果组合成一个比较nice的表格返回。根据数据对象不同它有三种用法，分别应用于数据框(data.frame)、公式(formula)和时间序列(ts)：

aggregate(x, by, FUN, ..., simplify = TRUE)

aggregate(formula, data, FUN, ..., subset, na.action = na.omit)

aggregate(x, nfrequency = 1, FUN = sum, ndeltat = 1, ts.eps = getOption("ts.eps"), ...)

语法

aggregate(x, ...)

## S3 method for class 'default':

aggregate((x, ...))

## S3 method for class 'data.frame':

aggregate((x, by, FUN, ..., simplify = TRUE))

## S3 method for class 'formula':

aggregate((formula, data, FUN, ...,

subset, na.action = na.omit))

## S3 method for class 'ts':

aggregate((x, nfrequency = 1, FUN = sum, ndeltat = 1,

ts.eps = getOption("ts.eps"), ...))

###细节查看 ?aggregate

Example1

我们通过 mtcars 数据集的操作对这个函数进行简单了解。mtcars 是不同类型汽车道路测试的数据框类型数据：

> 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 ...

先用attach函数把mtcars的列变量名称加入到变量搜索范围内，然后使用aggregate函数按cyl(汽缸数)进行分类计算平均值：

> attach(mtcars)

> aggregate(mtcars, by=list(cyl), FUN=mean)

Group.1 mpg cyl disp hp drat wt qsec vs am gear carb

1 4 26.66364 4 105.1364 82.63636 4.070909 2.285727 19.13727 0.9090909 0.7272727 4.090909 1.545455

2 6 19.74286 6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286 0.4285714 3.857143 3.428571

3 8 15.10000 8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000 0.1428571 3.285714 3.500000

by参数也可以包含多个类型的因子，得到的就是每个不同因子组合的统计结果：

> aggregate(mtcars, by=list(cyl, gear), FUN=mean)

Group.1 Group.2 mpg cyl disp hp drat wt qsec vs am gear carb

1 4 3 21.500 4 120.1000 97.0000 3.700000 2.465000 20.0100 1.0 0.00 3 1.000000

2 6 3 19.750 6 241.5000 107.5000 2.920000 3.337500 19.8300 1.0 0.00 3 1.000000

3 8 3 15.050 8 357.6167 194.1667 3.120833 4.104083 17.1425 0.0 0.00 3 3.083333

4 4 4 26.925 4 102.6250 76.0000 4.110000 2.378125 19.6125 1.0 0.75 4 1.500000

5 6 4 19.750 6 163.8000 116.5000 3.910000 3.093750 17.6700 0.5 0.50 4 4.000000

6 4 5 28.200 4 107.7000 102.0000 4.100000 1.826500 16.8000 0.5 1.00 5 2.000000

7 6 5 19.700 6 145.0000 175.0000 3.620000 2.770000 15.5000 0.0 1.00 5 6.000000

8 8 5 15.400 8 326.0000 299.5000 3.880000 3.370000 14.5500 0.0 1.00 5 6.000000

公式(formula)是一种特殊的R数据对象，在aggregate函数中使用公式参数可以对数据框的部分指标进行统计：

> aggregate(cbind(mpg,hp) ~ cyl+gear, FUN=mean)

cyl gear mpg hp

1 4 3 21.500 97.0000

2 6 3 19.750 107.5000

3 8 3 15.050 194.1667

4 4 4 26.925 76.0000

5 6 4 19.750 116.5000

6 4 5 28.200 102.0000

7 6 5 19.700 175.0000

8 8 5 15.400 299.5000

上面的公式 cbind(mpg,hp) ~ cyl+gear 表示使用 cyl 和 gear 的因子组合对 cbind(mpg,hp) 数据进行操作。aggregate在时间序列数据上的应用请参考R的函数说明文档。

Example2

## Compute the averages for the variables in 'state.x77', grouped

## according to the region (Northeast, South, North Central, West) that

## each state belongs to.

aggregate(state.x77, list(Region = state.region), mean)

## Compute the averages according to region and the occurrence of more

## than 130 days of frost.

aggregate(state.x77,

list(Region = state.region,

Cold = state.x77[,"Frost"] > 130),

mean)

## (Note that no state in 'South' is THAT cold.)

## example with character variables and NAs

testDF

v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99) )

by1

by2

aggregate(x = testDF, by = list(by1, by2), FUN = "mean")

# and if you want to treat NAs as a group

fby1

fby2

aggregate(x = testDF, by = list(fby1, fby2), FUN = "mean")

## Formulas, one ~ one, one ~ many, many ~ one, and many ~ many:

aggregate(weight ~ feed, data = chickwts, mean)

aggregate(breaks ~ wool + tension, data = warpbreaks, mean)

aggregate(cbind(Ozone, Temp) ~ Month, data = airquality, mean)

aggregate(cbind(ncases, ncontrols) ~ alcgp + tobgp, data = esoph, sum)

## Dot notation:

aggregate(. ~ Species, data = iris, mean)

aggregate(len ~ ., data = ToothGrowth, mean)

## Often followed by xtabs():

ag

xtabs(len ~ ., data = ag)

## Compute the average annual approval ratings for American presidents.

aggregate(presidents, nfrequency = 1, FUN = mean)

## Give the summer less weight.

aggregate(presidents, nfrequency = 1,

FUN = weighted.mean, w = c(1, 1, 0.5, 1))

Example3

------------------------------------------------------

#load data

data

head(data)

weight Time Chick Diet

1 42 0 1 1

2 51 2 1 1

3 59 4 1 1

4 64 6 1 1

5 76 8 1 1

6 93 10 1 1

#dimension of the data

dim(data)

[1] 578 4

#how many chickens

unique(data$Chick)

[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

[31] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

50 Levels: 18 < 16 < 15 < 13 < 9 < 20 < 10 < 8 < 17 < 19 < 4 < 6 < 11 < 3 < 1 < 12 < ... < 48

#how many diets

unique(data$Diet)

[1] 1 2 3 4

Levels: 1 2 3 4

#how many time points

unique(data$Time)

[1] 0 2 4 6 8 10 12 14 16 18 20 21

library(ggplot2)

ggplot(data=data, aes(x=Time, y=weight, group=Chick, colour=Chick)) +

geom_line() +

geom_point()

------------------------------------------------------

## S3 method for class 'data.frame'

## aggregate(x, by, FUN, ..., simplify = TRUE)

#find the mean weight depending on diet

aggregate(data$weight, list(diet = data$Diet), mean)

diet x

1 1 102.6455

2 2 122.6167

3 3 142.9500

4 4 135.2627

#aggregate on time

aggregate(data$weight, list(time=data$Time), mean)

time x

1 0 41.06000

2 2 49.22000

3 4 59.95918

4 6 74.30612

5 8 91.24490

6 10 107.83673

7 12 129.24490

8 14 143.81250

9 16 168.08511

10 18 190.19149

11 20 209.71739

12 21 218.68889

#use a different function

aggregate(data$weight, list(time=data$Time), sd)

time x

1 0 1.132272

2 2 3.688316

3 4 4.495179

4 6 9.012038

5 8 16.239780

6 10 23.987277

7 12 34.119600

8 14 38.300412

9 16 46.904079

10 18 57.394757

11 20 66.511708

12 21 71.510273

#we could also aggregate on time and diet

head(aggregate(data$weight,

list(time = data$Time, diet = data$Diet),

mean

)

)

time diet x

1 0 1 41.40000

2 2 1 47.25000

3 4 1 56.47368

4 6 1 66.78947

5 8 1 79.68421

6 10 1 93.05263

tail(aggregate(data$weight,

list(time = data$Time, diet = data$Diet),

mean

)

)

time diet x

43 12 4 151.4000

44 14 4 161.8000

45 16 4 182.0000

46 18 4 202.9000

47 20 4 233.8889

48 21 4 238.5556

#to see the weights over time across different diets

ggplot(data) + geom_line(aes(x=Time, y=weight, colour=Chick)) +

facet_wrap(~Diet) +

guides(col=guide_legend(ncol=3))

------------------------------------------------------

Example4

The aggregate function is more difficult to use, but it is included in the base R installation and does not require the installation of another package.

# Get a count of number of subjects in each category (sex*condition)

cdata

cdata

#> sex condition subject

#> 1 F aspirin 5

#> 2 M aspirin 9

#> 3 F placebo 12

#> 4 M placebo 4

# Rename "subject" column to "N"

names(cdata)[names(cdata)=="subject"]

cdata

#> sex condition N

#> 1 F aspirin 5

#> 2 M aspirin 9

#> 3 F placebo 12

#> 4 M placebo 4

# Sort by sex first

cdata

cdata

#> sex condition N

#> 1 F aspirin 5

#> 3 F placebo 12

#> 2 M aspirin 9

#> 4 M placebo 4

# We also keep the __before__ and __after__ columns:

# Get the average effect size by sex and condition

cdata.means

by = data[c("sex","condition")], FUN=mean)

cdata.means

#> sex condition before after change

#> 1 F aspirin 11.06000 7.640000 -3.420000

#> 2 M aspirin 11.26667 5.855556 -5.411111

#> 3 F placebo 10.13333 8.075000 -2.058333

#> 4 M placebo 11.47500 10.500000 -0.975000

# Merge the data frames

cdata

cdata

#> sex condition N before after change

#> 1 F aspirin 5 11.06000 7.640000 -3.420000

#> 2 F placebo 12 10.13333 8.075000 -2.058333

#> 3 M aspirin 9 11.26667 5.855556 -5.411111

#> 4 M placebo 4 11.47500 10.500000 -0.975000

# Get the sample (n-1) standard deviation for "change"

cdata.sd

by = data[c("sex","condition")], FUN=sd)

# Rename the column to change.sd

names(cdata.sd)[names(cdata.sd)=="change"]

cdata.sd

#> sex condition change.sd

#> 1 F aspirin 0.8642916

#> 2 M aspirin 1.1307569

#> 3 F placebo 0.5247655

#> 4 M placebo 0.7804913

# Merge

cdata

cdata

#> sex condition N before after change change.sd

#> 1 F aspirin 5 11.06000 7.640000 -3.420000 0.8642916

#> 2 F placebo 12 10.13333 8.075000 -2.058333 0.5247655

#> 3 M aspirin 9 11.26667 5.855556 -5.411111 1.1307569

#> 4 M placebo 4 11.47500 10.500000 -0.975000 0.7804913

# Calculate standard error of the mean

cdata$change.se

cdata

#> sex condition N before after change change.sd change.se

#> 1 F aspirin 5 11.06000 7.640000 -3.420000 0.8642916 0.3865230

#> 2 F placebo 12 10.13333 8.075000 -2.058333 0.5247655 0.1514867

#> 3 M aspirin 9 11.26667 5.855556 -5.411111 1.1307569 0.3769190

#> 4 M placebo 4 11.47500 10.500000 -0.975000 0.7804913 0.3902456

If you have NA’s in your data and wish to skip them, use na.rm=TRUE:

cdata.means

by = data[c("sex","condition")],

FUN=mean, na.rm=TRUE)

cdata.means

#> sex condition before after change

#> 1 F aspirin 11.06000 7.640000 -3.420000

#> 2 M aspirin 11.26667 5.855556 -5.411111

#> 3 F placebo 10.13333 8.075000 -2.058333

#> 4 M placebo 11.47500 10.500000 -0.975000

喜欢 (3)or分享 (0)