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

system.time({
plans <- group_by(flights, tailnum)
delay <- summarise(plans, 
count = n(),
dist = mean(distance, na.rm=T),
delay = mean(arr_delay,na.rm = T)
) 
})

user system elapsed 
0.092 0.003 0.097

system.time({
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)))
})

user system elapsed 
2.467 0.016 2.500