2017-06-22 20:40:53

目录

项目背景

需求

  • 假设现在是2013-02-01，你是纽约空管部门的分析师
  • 每天系统自动导出当日离开纽约的所有航班，存为.csv，文件名格式为yyyy-mm-dd
  • 你需要开发一个自动化脚本，每天例行生成分析报告
    • 描述数据大致分布，如分时段流量描述等
    • 准点率统计分析，包括按机场、航空公司分层
    • 准点与否的相关性分析/预测

变量说明

参考nycflights13::flights

变量 说明
year, month, day Date of departure
dep_time, arr_time Actual departure and arrival times, local tz.
sched_dep_time, sched_arr_time Scheduled departure and arrival times, local tz.
dep_delay, arr_delay Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.
hour, minute Time of scheduled departure broken into hour and minutes.
carrier Two letter carrier abbreviation. See airlines to get name
变量 说明
tailnum Plane tail number
flight Flight number
origin, dest Origin and destination. See airports for additional metadata.
air_time Amount of time spent in the air, in minutes
distance Distance between airports, in miles
time_hour Scheduled date and hour of the flight as a POSIXct date. Along with origin, can be used to join flights data to weather data. 

'data.frame':   842 obs. of  19 variables:
 $ year          : int  2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
 $ month         : int  1 1 1 1 1 1 1 1 1 1 ...
 $ day           : int  1 1 1 1 1 1 1 1 1 1 ...
 $ dep_time      : int  517 533 542 544 554 554 555 557 557 558 ...
 $ sched_dep_time: int  515 529 540 545 600 558 600 600 600 600 ...
 $ dep_delay     : num  2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
 $ arr_time      : int  830 850 923 1004 812 740 913 709 838 753 ...
 $ sched_arr_time: int  819 830 850 1022 837 728 854 723 846 745 ...
 $ arr_delay     : num  11 20 33 -18 -25 12 19 -14 -8 8 ...
 $ carrier       : chr  "UA" "UA" "AA" "B6" ...
 $ flight        : int  1545 1714 1141 725 461 1696 507 5708 79 301 ...
 $ tailnum       : chr  "N14228" "N24211" "N619AA" "N804JB" ...
 $ origin        : chr  "EWR" "LGA" "JFK" "JFK" ...
 $ dest          : chr  "IAH" "IAH" "MIA" "BQN" ...
 $ air_time      : num  227 227 160 183 116 150 158 53 140 138 ...
 $ distance      : num  1400 1416 1089 1576 762 ...
 $ hour          : num  5 5 5 5 6 5 6 6 6 6 ...
 $ minute        : num  15 29 40 45 0 58 0 0 0 0 ...
 $ time_hour     : POSIXct, format: "2013-01-01 05:00:00" "2013-01-01 05:00:00"

工程设计

文件结构

参考A05_01的建议结构

        - nycflights/
           |-- README
           |-- data
           |   |-- raw
           |   |-- processed
           |-- code
           |   |-- raw
           |   |-- final
           |   |-- scripts
           |-- doc
           |   |-- documents
           |   |-- references
           |-- assets
           |   |-- libs
           |   |-- figure
           |   |-- css
           |-- tmp
           |   |-- doc
           |   |-- figure
           |-- misc

工程思路

  1. 从flights.zip提取当日.csv，读入
  2. 建立.nb.rmd文档，逐步探索
    1. 清理
      1. 根据dep_delay、arr_delay定义误点与否(0, 1)
      2. 只保留complete.cases
    2. 描述
      1. 各机场当日不同时间段的流量
      2. 不同目的地的流量
      3. 各机场当天及前溯20天的准点率
    3. 准点率分层分析 (机场、时段、里程)
    4. 预测
  3. 在doc文件夹下生成.html文档
  4. 利用.bat批处理脚本将分析管道自动化

Show Me The Code

YAML头

  • params定义当前日期
  • output定义主题等参数
params:
  day: '01'
  month: '02'
  year: '2013'
output:
  html_document:
    number_sections: yes
    theme: cosmo
    toc: yes
    toc_float: yes
  html_notebook:
    fig_caption: yes
    number_sections: yes
    theme: cosmo
    toc: yes

命令行生成报告

  • 将当前日期改为2013-1-31，通过params参数传入render函数
  • 由此即可轻松实现例行报告一键生成
scr <- "A05_08_caseStudy_files/files/nycflights/code/scripts/analysis.Rmd"
library(rmarkdown)
render(scr, "html_document", "report.html", 
    params=list(day='31', month='01', year='2013'),
    encoding="UTF-8")