ddply和aggregate是两个用来整合数据的功能强大的函数。
aggregate(x, ...)
关于aggregate()函数的使用在《R语言实战》中P105有简单描述，这里重新说一下。此函数主要有一下几种用法：
## Default S3 method:
aggregate(x, ...)
## S3 method for class 'data.frame'
aggregate(x, by, FUN, ..., simplify = TRUE, drop = 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"), ...)
例：
attach(mtcars) aggdata <-aggregate(mtcars, by=list(cyl,gear), FUN=mean, na.rm=TRUE) aggdata 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
得到数据框aggdata，其中的Group.1和Group.2的列名可以指定，只需第二行写成：
aggdata <-aggregate(mtcars, by=list(Group.cyl=cyl, Group.gears=gear),FUN=mean, na.rm=TRUE)
即可。
注意：在使用aggregate()函数的时候， by中的变量必须在一个列表中（即使只有一个变量） 。 指定的函数FUN可为任意的内建或自编函数 。
其他的一些例子：
## 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 <- data.frame(v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9),
v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99) )
by1 <- c("red", "blue", 1, 2, NA, "big", 1, 2, "red", 1, NA, 12)
by2 <- c("wet", "dry", 99, 95, NA, "damp", 95, 99, "red", 99, NA, NA)
aggregate(x = testDF, by = list(by1, by2), FUN = "mean")
# and if you want to treat NAs as a group
fby1 <- factor(by1, exclude = "")
fby2 <- factor(by2, exclude = "")
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 <- aggregate(len ~ ., data = ToothGrowth, mean)
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))
ddply
下面是ddply函数的一般用法：
ddply(.data, .variables, .fun = NULL, ..., .progress = "none",.inform = FALSE, .drop = TRUE, .parallel = FALSE, .paropts = NULL)
例：
# Summarize a dataset by two variables
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)
)
head(dfx)
group sex age
1 A M 22.44750
2 A M 52.92616
3 A F 30.00443
4 A M 39.56907
5 A M 18.89180
6 A F 50.81139
#Note the use of the '.' function to allow
# group and sex to be used without quoting
ddply(dfx, .(group, sex), summarize,mean = round(mean(age), 2),sd = round(sd(age), 2))
group sex mean sd
1 A F 40.41 14.71
2 A M 30.35 13.17
3 B F 34.81 12.76
4 B M 34.04 13.36
5 C F 35.09 13.39
6 C M 28.53 4.57
# An example using a formula for .variables
ddply(baseball[1:100,], ~ year, nrow)
year V1
1 1871 7
2 1872 13
3 1873 13
4 1874 15
5 1875 17
6 1876 15
7 1877 17
8 1878 3
# Applying two functions; nrow and ncol
ddply(baseball, .(lg), c("nrow", "ncol"))
lg nrow ncol
1 65 22
2 AA 171 22
3 AL 10007 22
4 FL 37 22
5 NL 11378 22
6 PL 32 22
7 UA 9 22
# Calculate mean runs batted in for each year
rbi <- ddply(baseball, .(year), summarise,mean_rbi = mean(rbi, na.rm = TRUE))
head(rbi)
year mean_rbi
1 1871 22.28571
2 1872 20.53846
3 1873 30.92308
4 1874 29.00000
5 1875 31.58824
6 1876 30.13333
# Plot a line chart of the result
plot(mean_rbi ~ year, type = "l", data = rbi)
# make new variable career_year based on the
# start year for each player (id)
base2 <- ddply(baseball, .(id), mutate,career_year = year - min(year) + 1)
head(base2)id year stint team lg g ab r h X2b X3b hr rbi sb cs bb so ibb hbp sh sf gidp career_year 1 aaronha01 1954 1 ML1 NL 122 468 58 131 27 6 13 69 2 2 28 39 NA 3 6 4 13 1 2 aaronha01 1955 1 ML1 NL 153 602 105 189 37 9 27 106 3 1 49 61 5 3 7 4 20 2 3 aaronha01 1956 1 ML1 NL 153 609 106 200 34 14 26 92 2 4 37 54 6 2 5 7 21 3 4 aaronha01 1957 1 ML1 NL 151 615 118 198 27 6 44 132 1 1 57 58 15 0 0 3 13 4 5 aaronha01 1958 1 ML1 NL 153 601 109 196 34 4 30 95 4 1 59 49 16 1 0 3 21 5 6 aaronha01 1959 1 ML1 NL 154 629 116 223 46 7 39 123 8 0 51 54 17 4 0 9 19 6