dplyr的使用

做数据预处理一直用Hardly Wickham的plyr软件包，数据量稍微大点，基本就用data.table软件包。Hardly WickHam的dplyr软件包出来有一段时间了，在性能上又有了更大的提高。为了以后使用，做些笔记。

These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (arrange()), pick observations and variables of interest (filter() and select()), add new variables that are functions of existing variables (mutate()) or collapse many values to a summary (summarise()). The remainder of the language comes from applying the five functions to different types of data, like to grouped data, as described next.

例子1：plyr::ddply和dplyr::group_by的比较

 1 system.time({
 2 plans <- group_by(flights, tailnum)
 3 delay <- summarise(plans,
 4 count = n(),
 5 dist = mean(distance, na.rm=T),
 6 delay = mean(arr_delay,na.rm = T)
 7 )
 8 })
 9
10 user system elapsed
11 0.092 0.003 0.097
12
13 system.time({
14 ddply(flights, ‘tailnum‘, function(x) data.frame(count=nrow(x), dist=mean(x$distance,na.rm=T), delay=mean(x$arr_delay,na.rm=T)))
15 })
16
17 user system elapsed
18 2.467 0.016 2.500