如何按行分割数据框，然后处理块？ [英] How to split a data frame by rows, and then process the blocks?

问题描述

我有一个数据框有几个列，其中一个是一个称为站点的因素。如何将数据帧分割成具有唯一值site的行的行，然后使用函数处理每个块？数据如下所示：

 网站年度峰值
 ALBEN 5 101529.6 
 ALBEN 10 117483.4 
 ALBEN 20 132960.9 
 ALBEN 50 153251.2 
 ALBEN 100 168647.8 
 ALBEN 200 184153.6 
 ALBEN 500 204866.5 
 ALDER 5 6561.3 
 ALDER 10 7897.1 
 ALDER 20 9208.1 
 ALDER 50 10949.3 
 ALDER 100 12287.6 
 ALDER 200 13650.2 
 ALDER 500 15493.6 
 AMERI 5 43656.5 
 AMERI 10 51475.3 
 AMERI 20 58854.4 
 AMERI 50 68233.3 
 AMERI 100 75135.9 
 AMERI 200 81908.3 
  

，我想为每个网站创建一个年 vs peak 的图。

解决方案

另一个选择是使用 ggplot2 ddply 函数c $ c>库。但是你提到你主要想要做一个高峰与一年的情节，所以你也可以使用 qplot ：

  A<  -  read.table（example.txt，header = TRUE）
库（ggplot2）
 qplot（peak，year，data = A ，color = site，geom =line，group = site）
 ggsave（peak-year-comparison.png）
  

alt text http://i32.tinypic.com/16nuza.png

另一方面，我喜欢David Smith的解决方案，允许将应用程序应用于多个处理器。

I have a data frame with several columns, one of which is a factor called "site". How can I split the data frame into blocks of rows each with a unique value of "site", and then process each block with a function? The data look like this:

site year peak
ALBEN 5 101529.6
ALBEN 10 117483.4
ALBEN 20 132960.9
ALBEN 50 153251.2
ALBEN 100 168647.8
ALBEN 200 184153.6
ALBEN 500 204866.5
ALDER 5 6561.3
ALDER 10 7897.1
ALDER 20 9208.1
ALDER 50 10949.3
ALDER 100 12287.6
ALDER 200 13650.2
ALDER 500 15493.6
AMERI 5 43656.5
AMERI 10 51475.3
AMERI 20 58854.4
AMERI 50 68233.3
AMERI 100 75135.9
AMERI 200 81908.3

and I want to create a plot of year vs peak for each site.

解决方案

Another choice is use the ddply function from the ggplot2 library. But you mention you mostly want to do a plot of peak vs. year, so you could also just use qplot:

A <- read.table("example.txt",header=TRUE)
library(ggplot2)
qplot(peak,year,data=A,colour=site,geom="line",group=site)
ggsave("peak-year-comparison.png")

alt text http://i32.tinypic.com/16nuza.png

On the other hand, I do like David Smith's solution that allows the applying of the function to be run across several processors.

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