小编gri*_*fin的帖子

我有时间序列数据(我在这里将其作为data.frame发布):

x <- structure(list(date = structure(c(1264572000, 1266202800, 1277362800, 
1277456400, 1277859600, 1278032400, 1260370800, 1260892800, 1262624400, 
1262707200), class = c("POSIXt", "POSIXct"), tzone = ""), data = c(-0.00183760994446658, 
0.00089738603087497, 0.000423513598318936, 0, -0.00216496690393131, 
-0.00434836817931339, -0.0224199153445617, 0.000583823085470003, 
0.000353088613905206, 0.000470295331234771)), .Names = c("date", 
"data"), row.names = c("1", "2", "3", 
"4", "5", "6", "7", "8", "9", "10"
), class = "data.frame")

将这个绘制为ggplot中的条形图的最佳方法是什么,它将显示每月的总价值(月份名称为文本)？

我可以通过添加月份字段手动执行此操作:

x$month <- format(x$date, format="%B")
ddply(x, .(month), function(x) sum(x[, "data"]))

然后独立绘制这个,但是使用这种方法没有正确地订购月份(假设我需要创建一个有序因子？); 我也假设ggplot有一种"更简单"的方式.

我有一个类型的数据库字段time with time zone.如何使用输出格式hh:mm:ss在EST5EDT时区中查询此字段？

我能找到的就是使用多个来电EXTRACT:

SELECT EXTRACT(HOUR FROM TIME WITH TIME ZONE '20:38:40-07' AT TIME ZONE 'EST5EDT');

[编辑:]

要清楚,这个查询:

SELECT TIME WITH TIME ZONE '20:38:40.001-07' AT TIME ZONE 'EST5EDT';

返回"22:38:40.001",但我只想要"22:38:40"作为输出.

我正在尝试在Ubuntu 10.04上安装Rcpp并收到此错误(这意味着它不存在):

> install.packages("Rcpp")
Warning in install.packages("Rcpp") :
  argument 'lib' is missing: using '/home/vadmin/R/i486-pc-linux-gnu-library/2.10'
Warning message:
In getDependencies(pkgs, dependencies, available, lib) :
  package ‘Rcpp’ is not available

我有一个工作的R实例,并安装了许多其他包.

编辑

现在我在R CMD build Rcppsvn上运行源代码时遇到此错误:

**构建包索引...错误:在小插图上运行Stangle'/home/vadmin/src/R/rcpp/pkg/Rcpp/inst/doc/Rcpp-modules.Rnw'失败并显示消息:'lang'的值无效:cpp错误:安装包索引失败*删除'/ tmp/Rinst3041838816/Rcpp'ERROR安装失败.

我有一个KNN模型,我正在使用该contour函数进行绘图.这是我正在做的事情的简单例子(基于此Purdue考试):

library(class)
library(nnet)

TrainC<-read.table("http://miner.chem.purdue.edu/Exam1/TrainC.dat")
names(TrainC)<-c("x1","x2","y")

K=15

p <- as.matrix(TrainC[, -3])
xp <- seq(min(TrainC$x1), max(TrainC$x1), length = 50); np <- length(xp)
yp <- seq(min(TrainC$x2), max(TrainC$x2), length = 50)
tp<-TrainC$y

yhat <- knn(p, p, tp, k = K)

plot(TrainC[, 1], TrainC[, 2], xlab = "x1", ylab = "x2", col=as.numeric(TrainC$y)+1)
pt <- expand.grid(x1 = xp, x2 = yp)
Z <- knn(p, pt, tp, k = K)
zp<-class.ind(Z)[,1] - class.ind(Z)[,2]

contour(xp, yp, matrix(zp, np), add = T, levels = 0, …