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 0.09712 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

dplyr的使用