R聚合使用来自相同数据的子集给出不同结构的结果

Question

我根据几年小时数据的数据框(球)进行昼夜风速循环.我想按季节绘制它们,所以我将我需要的日期分类并按照以下方式加入它们:

b8 = subset(ball, as.Date(date)>="2008-09-01 00:00:00, GMT" & as.Date(date)<= "2008-11-30 23:00:00, GMT"  )
b9  = subset(ball, as.Date(date)>="2009-09-01 00:00:00, GMT" & as.Date(date)<= "2009-11-30 23:00:00, GMT"  )
b10 = subset(ball,  as.Date(date)>="2010-09-01 00:00:00, GMT" & as.Date(date)<= "2010-11-30 23:00:00, GMT")
ballspr = rbind(b8,b9,b10)

然后我用这个得到一个昼夜循环:

sprwsdiurnal <- aggregate(ballspr["ws"], format(ballspr["date"],"%H"),summary, na.rm=T)

对于四个季节中的三个季节,这使得具有这种结构的对象:

   date                                               ws
1    00  0.200, 1.000, 1.600, 2.021, 2.500, 8.000, 5.000
2    01  0.100, 1.000, 1.600, 1.988, 2.500, 8.600, 1.000
3    02  0.100, 1.000, 1.700, 1.982, 2.600, 8.900, 1.000

......到24小时......

23   22  0.100, 1.200, 1.800, 2.222, 2.950, 9.100, 1.000
24   23  0.100, 1.000, 1.600, 2.072, 2.700, 8.800, 1.000

这就是我想要的,因为boxplot将用于此:

par(  mar = c(5, 5, 2, 2))
boxplot(sprwsdiurnal$ws, col="dodger blue",pch=16,font.lab=2,cex.lab=1.5,cex.axis=2,xlab="Hour",range=0, ylab=quote(Windspeed ~ "(" * m ~ s ^-1 * ")"),xaxt="n",main="Spring")
axis(1, at=seq(1,24, by=1),labels=seq(1,24, by=1),cex.axis=1.5, cex.lab=1.5, font.lab=2)

问题是一个季节就像这样:

      date ws.Min. ws.1st Qu. ws.Median ws.Mean ws.3rd Qu. ws.Max. ws.NA's
1    00   0.000      1.300     2.100   2.539      3.200  10.500   2.000
2    01   0.100      1.275     2.100   2.499      3.200   9.800   2.000
3    02   0.200      1.200     2.000   2.514      3.400   9.000   2.000

......到24小时......

23   22   0.100      1.200     1.950   2.582      3.325  11.900   2.000
24   23   0.100      1.300     2.000   2.585      3.400  11.200   2.000

Boxplot不适用于此格式.我无法解释为什么会发生这种情况,因为每个季节的所有代码都是相同的,并且它们是从同一数据帧中进行子集化的.为什么一个出来的不同？任何想法都赞赏.

编辑:这是数据.我已经检查了这两个季节,他们仍然给出了上面显示的两种不同的格式.

https://www.dropbox.com/s/v5kss0bgjyhrtw1/ball.csv

ball=read.csv("ball.csv", header=T)
ball$date = as.POSIXct(strptime(ball$date, format = "%Y-%m-%d %H:%M:%S", "GMT"))

win9  = subset(ball, as.Date(date)>="2009-06-01 00:00:00, GMT" & as.Date(date)<= "2009-08-31 23:00:00, GMT"  )
aut9  = subset(ball, as.Date(date)>="2009-03-01 00:00:00, GMT" & as.Date(date)<= "2009-05-31 23:00:00, GMT"  )
spr9  = subset(ball, as.Date(date)>="2009-09-01 00:00:00, GMT" & as.Date(date)<= "2009-11-30 23:00:00, GMT"  )
sum9  = subset(ball, as.Date(date)>="2008-12-01 00:00:00, GMT" & as.Date(date)<= "2009-02-28 23:00:00, GMT"  )


sprdiurnal <- aggregate(spr9["ws"], format(spr9["date"],"%H"),summary, na.rm=T)
par(  mar = c(5, 5, 4, 2))
 boxplot(sprdiurnal$ws, col=colours()[109],pch=16,cex.lab=1.5,cex.axis=1.5,xlab="Hour",range=0, ylab=quote(Wind ~ speed ~ "(" * m * "s" ^-1 * ")"),xaxt="n",main="")
axis(1, at=seq(1,24, by=1),labels=seq(1,24, by=1),cex.axis=1.5, cex.lab=1.5) 

windiurnal <- aggregate(win9["ws"], format(win9["date"],"%H"),summary, na.rm=T)
par(  mar = c(5, 5, 4, 2))
boxplot(windiurnal$ws, col=colours()[109],pch=16,cex.lab=1.5,cex.axis=1.5,xlab="Hour",range=0, ylab=quote(Wind ~ speed ~ "(" * m * "s" ^-1 * ")"),xaxt="n",main="")
axis(1, at=seq(1,24, by=1),labels=seq(1,24, by=1),cex.axis=1.5, cex.lab=1.5)

Answer 1

据我所知,"问题"是summary你的aggregate函数" sprdiurnal"的结果导致一个矩形数据集,R存储为a matrix,而对于你的其他子集,因为几个小时包括,NA而其他不包括数据集不是矩形,因此R将摘要存储为list.

我将使用"虹膜"数据集进行演示,但首先,我还将创建一个具有一个NA值的"iris_2"数据集.

iris_2 <- iris
iris_2$Sepal.Length[10] <- NA

让我们比较聚合输出,在这些情况下,它只是第二列.你会看到没有缺失值的"虹膜"数据集返回一个矩形矩阵作为你的第二个"列" data.frame.由于我们的一个NA值,"iris_2"数据集会被存储为a list,这是您想要的特定目的.

(irisagg <- aggregate(iris["Sepal.Length"], iris["Species"], summary))[[2]]
#      Min. 1st Qu. Median  Mean 3rd Qu. Max.
# [1,]  4.3   4.800    5.0 5.006     5.2  5.8
# [2,]  4.9   5.600    5.9 5.936     6.3  7.0
# [3,]  4.9   6.225    6.5 6.588     6.9  7.9
(iris_2agg <- aggregate(iris_2["Sepal.Length"], iris_2["Species"], summary))[[2]]
# $`0`
#     Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#    4.300   4.800   5.000   5.008   5.200   5.800       1 
# 
# $`1`
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   5.600   5.900   5.936   6.300   7.000 
# 
# $`2`
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   6.225   6.500   6.588   6.900   7.900 

以下是我们如何将其重新列入清单.

irisagg$Summary <- unlist(apply(irisagg[[2]], 1, list), recursive = FALSE)
irisagg$Summary
# [[1]]
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.300   4.800   5.000   5.006   5.200   5.800 
# 
# [[2]]
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   5.600   5.900   5.936   6.300   7.000 
# 
# [[3]]
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   6.225   6.500   6.588   6.900   7.900 

当然,一个更直接的方法是利用simplify论证aggregate并做:

(iris_3agg <- aggregate(iris["Sepal.Length"], 
                        iris["Species"], summary, 
                        simplify = FALSE))[[2]]
# $`0`
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.300   4.800   5.000   5.006   5.200   5.800 
# 
# $`1`
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   5.600   5.900   5.936   6.300   7.000 
# 
# $`2`
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   4.900   6.225   6.500   6.588   6.900   7.900 

将它应用于您的示例,"sprdiurnal"是给您带来麻烦的子集.单独查看sprdiurnal$ws并验证它是一个矩阵.我们将其转换为列表.

sprdiurnal$ws2 <- unlist(apply(sprdiurnal$ws, 1, list), recursive=FALSE)

现在你可以继续boxplot像其他季节一样.

boxplot(sprdiurnal$ws2, e..t..c...) 

或者,sprdiurnal使用以下方法重新制作对象:

sprdiurnal <- aggregate(spr9["ws"], 
                        format(spr9["date"],"%H"), 
                        summary, na.rm = TRUE, 
                        simplify = FALSE)

和以前一样继续.