来自单个数据帧的多个或多个时间序列图输出

本文介绍了来自单个数据帧的多个或多个时间序列图输出的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我已经为这个问题苦苦挣扎了一段时间，如果有人能帮助我，我将不胜感激.

I have been struggling with this problem for a while now and anyone who can help me out I would greatly appreciate it.

首先，我正在处理包含多个时间序列的单个数据框中的时间序列数据.太多，无法单独输出到图表中.我已经尝试通过 ddply() 传递 qplot() 但是 r 告诉我它 qplot 不是一个函数，因此它不会工作.

First off, I am working with time series data in a single data frame containing multiple time series. Too many to output individually into graphs. I have tried passing qplot() through ddply() however r tells me it qplot is not a function and therefore it will not work.

我的数据结构是这样的……

the structure of my data is like this...

goodlocs <- Loc Year dir Artesia 1983 1490 Artesia 1984 1575 Artesia 1986 1567 Artesia 1987 1630 Artesia 1990 1680 Bogota 1983 1525 Bogota 1984 1610 Bogota 1985 1602 Bogota 1986 1665 Bogota 1990 1715 Carlsbad 1983 1560 Carlsbad 1985 1645 Carlsbad 1986 1637 Carlsbad 1987 1700 Carlsbad 1990 1750 Carlsbad 1992 1595 Datil 1987 1680 Datil 1990 1672 Datil 1991 1735 Datil 1992 1785

我有大约 250 个位置 (Locs)，希望能够在如下图所示的图表上查看每个站点的数据，这样我就可以直观地检查我的所有数据.

I have about 250 Locations(Locs) and would like to be able to go over each stations data on a graph like the following one so I can inspect all of my data visually.

Artesia <- goodlocs[goodlocs$Loc == "Artesia",] qplot(YEAR, dir, data = Artesia, geom = c("point", "line"), xlab = "Year", ylab = "DIR", main = "Artesia DIR Over Record Period") + geom_smooth(method=lm)

我知道 Par() 应该有助于做到这一点，但我一生都无法弄清楚.非常感谢任何帮助.

I understand that Par() is supposed to help do this but I can not figure it out for the life of me. Any help is greatly appreciated.

谢谢，

-齐亚

编辑-

正如 Arun 指出的那样，我正在尝试保存一个 .pdf，其中包含我的 goodlocs df 的 250 个不同图表，按Loc"分割，并带有用于数据审查的点和线几何......

as Arun pointed out, I am trying to save a .pdf of 250 different graphs of my goodlocs df split by "Loc", with point and line geometry for data review....

我还尝试通过 qplot 将我的 df 的 ddply 作为数据传递，但它也不起作用，我并没有真正期待它，但我不得不尝试.

I also tried passing a ddply of my df through qplot as the data but it did not work either, I was not really expecting it to but i had to try.

推荐答案

这个怎么样?

require(ggplot2) require(plyr) require(gridExtra) pl <- dlply(df, .(Loc), function(dat) { ggplot(data = dat, aes(x = Year, y = dir)) + geom_line() + geom_point() + xlab("x-label") + ylab("y-label") + geom_smooth(method = "lm") }) ml <- do.call(marrangeGrob, c(pl, list(nrow = 2, ncol = 2))) ggsave("my_plots.pdf", ml, height = 7, width = 13, units = "in")

思路:首先按Loc分割数据，并为每个子集创建图.拆分部分是使用 plyr 函数 dlply 完成的，该函数基本上以 data.frame 作为输入并提供 list作为输出.绘图元素存储在与子集对应的列表的每个元素中.然后，我们使用 gridExtra 包的 marrangeGrob 函数来排列多个绘图(其中还有非常有用的 nrow 和 ncol参数来设置参数).然后，您可以使用 ggplot2 中的 ggsave 保存它.

The idea: First split the data by Loc and create the plot for each subset. The splitting part is done using plyr function dlply that basically takes a data.frame as input and provides a list as output. The plot element is stored in each element of the list corresponding to the subset. Then, we use gridExtra package's marrangeGrob function to arrange multiple plots (which also has the very useful nrow and ncol arguments to set the argument). Then, you can save it using ggsave from ggplot2.

我会留给您可能需要的任何其他调整.

I'll leave you to any additional tweaks you may require.