我有一个大型数据集，我想重塑和聚合，并遇到一些困难。
例如，请考虑以下内容，其中引号将数据库中的列分开。因素包括year，habitat，site和replicate。字段species和biomass是数据：
"year" "habitat" "site" "replicate" "species" "biomass"
2010 inner a 1 sp.1 10
2010 inner a 1 sp.3 6
2010 inner a 1 sp.1 5
2010 inner a 1 sp.2 8
2010 inner a 1 sp.4 4
2010 inner a 1 sp.5 7
2010 inner a 2 sp.3 5
2010 inner a 2 sp.2 6
2010 inner a 2 sp.5 2
2010 inner a 2 sp.1 5
2010 inner a 3 sp.4 5
2010 inner a 3 sp.3 4
2010 inner a 3 sp.6 8
2010 inner a 3 sp.2 5
2010 outer b 1 sp.1 6
2010 outer b 1 sp.3 9
2010 outer b 1 sp.3 3
2010 outer b 1 sp.2 6
2010 outer b 2 sp.5 4
2010 outer b 2 sp.1 5
2010 outer b 2 sp.1 7
2010 outer b 2 sp.2 5
2010 outer b 3 sp.4 2
2010 outer b 3 sp.6 5
2010 outer b 3 sp.2 4
2010 outer b 3 sp.1 4
我想要做的是重塑和聚合我的桌子，同时保留所有因素。因此，species乘site矩阵，能够将因子和聚合保持在复制水平。我在下面列出了我想要的示例图片，其中数据使用Excel数据透视表填充了“sum”函数。在使用dplyr和群组的 R 中是否有一个简单的代码？ tapply和aggregate？
year habitat site replicate sp.1 sp.2 sp.3 sp.4 sp.5 sp.6
2010 inner a 1 15 8 6 4 7 0
2010 inner a 3 0 5 4 5 0 8
2010 outer b 1 6 6 12 0 0 0
2010 outer b 2 12 5 0 0 4 0
2010 outer b 3 4 4 0 2 0 5
答案 0 :(得分：1)
首先，如果你有一个大数据集我建议使用数据表。
我经常使用“BreakBuild”功能，但我无法在你的代码上测试它。
library(data.table)
mydata<-data.table(read.table("mydata"))
aggmydata<- mydata[,list(biomass=sum(biomass)),by=list(year,habitat,site,replicate,species)]
BreakBuild <- function (df,BCol,IDCols,ValCols) {
setkeyv(df,BCol)
NewCols <- unique(df[[BCol]]); #Scan the column for unique entries. Each entry gets a data table.
ldt <- list(); #List that will hold a data table for each break.
ColList <-c(IDCols,ValCols) # List of columns, does not include broken collumn.
for (bframe in NewCols) {
ldt[[bframe]] = df[bframe,ColList, with=FALSE] #Create and store a data table with columns from IDCols and VolCols.
if(length(ValCols)>1){setnames(ldt[[bframe]], ValCols, paste(bframe,ValCols,sep="."))} #Prefix the Value columns with the name of the break.
else {setnames(ldt[[bframe]], ValCols, bframe)} #If there is only one Value Column, give it the name of the break.
}
Build<-Reduce(function(...) merge(...,by=IDCols,all=T),ldt)
return(Build)
}
waggmydata<- BreakBuild(aggmydata,BCol="species",
IDCols=c("year","habitat","site","replicate"),
ValCols="biomass")
你也可以使用reshape2和dcast，虽然我并不熟悉dcast的语法。这就像是
library(reshape2)
waggmydata<-dcast.data.table(mydata, year+habitat+site+replicate~ species, fun=sum)
如果您的数据集非常大（数百万行），那么使用data.table可能是值得的，尽管看起来dplyr自上次尝试以来已经取得了重大进展。
以下是三种测试方法的基准测试结果。
require(data.table)
require(reshape2)
require(dplyr)
require(tidyr)
require(microbenchmark)
BreakBuild <- function (df,BCol,IDCols,ValCols) {
setkeyv(df,BCol)
NewCols <- unique(df[[BCol]]); #Scan the column for unique entries. Each entry gets a data table.
ldt <- list(); #List that will hold a data table for each break.
ColList <-c(IDCols,ValCols) # List of columns, does not include broken collumn.
for (bframe in NewCols) {
ldt[[bframe]] = df[bframe,ColList, with=FALSE] #Create and store a data table with columns from IDCols and VolCols.
if(length(ValCols)>1){setnames(ldt[[bframe]], ValCols, paste(bframe,ValCols,sep="."))} #Prefix the Value columns with the name of the break.
else {setnames(ldt[[bframe]], ValCols, bframe)} #If there is only one Value Column, give it the name of the break.
}
Build<-Reduce(function(...) merge(...,by=IDCols,all=T),ldt)
return(Build)
}
mydata<-data.table(year=sample(c("2010","2011","2012"),1e6,replace=T),
habitat=sample(c("inner","outer"),1e6,replace=T),
site=sample(letters[1:15],1e6,replace=T),
replicate=sample(1:5,1e6,replace=T),
species=sample(paste("sp",1:20,sep="."),1e6,replace=T),
biomass=sample(1:30,1e6,replace=T))
dat1<- as.data.frame(mydata)
microbenchmark(
DPLYR= {
dat1%>%
group_by(year, habitat,site,replicate, species)%>%
summarise(biomass=sum(biomass)) %>%
spread(species, biomass,fill=0)
},
DATATABLE = {
aggmydata<- mydata[,list(biomass=sum(biomass)),by=list(year,habitat,site,replicate,species)]
waggmydata<- BreakBuild(aggmydata,BCol="species",
IDCols=c("year","habitat","site","replicate"),
ValCols="biomass")
},
DCAST.DATA.TABLE = {
waggmydata<-dcast.data.table(mydata, year+habitat+site+replicate~ species, fun=sum)
}
)
## expr min lq median uq max neval
## DPLYR 168.26559 170.3902 171.9306 173.9712 189.5266 100
## DATATABLE 97.21738 101.1543 103.5157 108.2527 125.9114 100
## DCAST.DATA.TABLE 184.58250 189.4021 192.0251 195.3731 242.9994 100
一旦你知道了语法，我就说DCAST.DATA.TABLE是这个例子中最容易编码的。
答案 1 :(得分：1)
使用dplyr（如果dat1是数据集）
library(dplyr)
library(tidyr)
dat1%>%
group_by(year, habitat,site,replicate, species)%>%
summarise(biomass=sum(biomass)) %>%
spread(species, biomass,fill=0)
# Source: local data frame [6 x 10]
# year habitat site replicate sp.1 sp.2 sp.3 sp.4 sp.5 sp.6
#1 2010 inner a 1 15 8 6 4 7 0
#2 2010 inner a 2 5 6 5 0 2 0
#3 2010 inner a 3 0 5 4 5 0 8
#4 2010 outer b 1 6 6 12 0 0 0
#5 2010 outer b 2 12 5 0 0 4 0
#6 2010 outer b 3 4 4 0 2 0 5
BTW：replicate 2栖息地inner没有显示在预期的输出中。