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  大数据分析 - SQL 简介

  • 简述

    SQL 代表结构化查询语言。它是传统数据仓库和大数据技术中用于从数据库中提取数据的最广泛使用的语言之一。为了演示 SQL 的基础知识，我们将使用示例。为了专注于语言本身，我们将在 R 中使用 SQL。就编写 SQL 代码而言，这与在数据库中所做的完全一样。
    SQL 的核心是三个语句：SELECT、FROM 和 WHERE。以下示例利用了最常见的 SQL 用例。导航到文件夹bda/part2/SQL_introduction并打开SQL_introduction.Rproj文件。然后打开 01_select.R 脚本。为了在 R 中编写 SQL 代码，我们需要安装sqldf包，如以下代码所示。 
    
# Install the sqldf package
install.packages('sqldf')  
# load the library 
library('sqldf') 
library(nycflights13)  
# We will be working with the fligths dataset in order to introduce SQL  
# Let’s take a look at the table 
str(flights) 
# Classes 'tbl_d', 'tbl' and 'data.frame': 336776 obs. of  16 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 ... 
# $ 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 ... 
# $ arr_delay: num  11 20 33 -18 -25 12 19 -14 -8 8 ...
# $ carrier  : chr  "UA" "UA" "AA" "B6" ... 
# $ tailnum  : chr  "N14228" "N24211" "N619AA" "N804JB" ... 
# $ flight   : int  1545 1714 1141 725 461 1696 507 5708 79 301 ... 
# $ 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 5 5 5 5 5 5 ... 
# $ minute   : num  17 33 42 44 54 54 55 57 57 58 ...
    select 语句用于从表中检索列并对其进行计算。最简单的 SELECT 语句在ej1. 我们还可以创建新变量，如图所示ej2. 
    
### SELECT statement
ej1 = sqldf(" 
   SELECT  
   dep_time 
   ,dep_delay 
   ,arr_time 
   ,carrier 
   ,tailnum 
   FROM 
   flights
")  
head(ej1) 
#    dep_time   dep_delay  arr_time  carrier  tailnum 
# 1      517         2      830      UA       N14228 
# 2      533         4      850      UA       N24211 
# 3      542         2      923      AA       N619AA 
# 4      544        -1     1004      B6       N804JB 
# 5      554        -6      812      DL       N668DN 
# 6      554        -4      740      UA       N39463  
# In R we can use SQL with the sqldf function. It works exactly the same as in 
a database 
# The data.frame (in this case flights) represents the table we are querying 
and goes in the FROM statement  
# We can also compute new variables in the select statement using the syntax: 
# old_variables as new_variable 
ej2 = sqldf(" 
   SELECT 
   arr_delay - dep_delay as gain, 
   carrier 
   FROM 
   flights
")  
ej2[1:5, ] 
#    gain   carrier 
# 1    9      UA 
# 2   16      UA 
# 3   31      AA 
# 4  -17      B6 
# 5  -19      DL
    SQL 最常用的功能之一是 group by 语句。这允许计算另一个变量的不同组的数值。打开脚本 02_group_by.R。 
    
### GROUP BY      
# Computing the average 
ej3 = sqldf(" 
  SELECT 
   avg(arr_delay) as mean_arr_delay, 
   avg(dep_delay) as mean_dep_delay, 
   carrier 
   FROM 
   flights 
   GROUP BY 
   carrier 
")  
#    mean_arr_delay   mean_dep_delay carrier 
# 1       7.3796692      16.725769      9E 
# 2       0.3642909       8.586016      AA 
# 3      -9.9308886       5.804775      AS 
# 4       9.4579733      13.022522      B6 
# 5       1.6443409       9.264505      DL 
# 6      15.7964311      19.955390      EV 
# 7      21.9207048      20.215543      F9 
# 8      20.1159055      18.726075      FL 
# 9      -6.9152047       4.900585      HA 
# 10     10.7747334      10.552041      MQ
# 11     11.9310345      12.586207      OO 
# 12      3.5580111      12.106073      UA 
# 13      2.1295951       3.782418      US 
# 14      1.7644644      12.869421      VX 
# 15      9.6491199      17.711744      WN 
# 16     15.5569853      18.996330      YV  
# Other aggregations 
ej4 = sqldf(" 
   SELECT 
   avg(arr_delay) as mean_arr_delay, 
   min(dep_delay) as min_dep_delay, 
   max(dep_delay) as max_dep_delay, 
   carrier 
   FROM  
   flights 
   GROUP BY 
   carrier 
")  
# We can compute the minimun, mean, and maximum values of a numeric value 
ej4 
#      mean_arr_delay    min_dep_delay   max_dep_delay   carrier 
# 1       7.3796692           -24           747          9E 
# 2       0.3642909           -24          1014          AA 
# 3      -9.9308886           -21           225          AS 
# 4       9.4579733           -43           502          B6
# 5       1.6443409           -33           960         DL 
# 6      15.7964311           -32           548         EV 
# 7      21.9207048           -27           853         F9 
# 8      20.1159055           -22           602         FL 
# 9      -6.9152047           -16          1301         HA 
# 10     10.7747334           -26          1137         MQ 
# 11     11.9310345           -14           154         OO 
# 12      3.5580111           -20           483         UA 
# 13      2.1295951           -19           500         US 
# 14      1.7644644           -20           653         VX 
# 15      9.6491199           -13           471         WN 
# 16     15.5569853           -16           387         YV  
### We could be also interested in knowing how many observations each carrier has  
ej5 = sqldf(" 
   SELECT 
   carrier, count(*) as count 
   FROM  
   flights 
   GROUP BY 
   carrier 
")  
ej5 
#      carrier  count 
# 1       9E    18460
# 2       AA   32729 
# 3       AS   714 
# 4       B6   54635 
# 5       DL   48110 
# 6       EV   54173 
# 7       F9   685 
# 8       FL   3260 
# 9       HA   342 
# 10      MQ   26397 
# 11      OO   32 
# 12      UA   58665 
# 13      US   20536 
# 14      VX   5162 
# 15      WN   12275 
# 16      YV   601
    SQL 最有用的特性是连接。连接意味着我们要使用一列将表 A 和表 B 合并到一个表中，以匹配两个表的值。实际上，有不同类型的连接，开始使用这些将是最有用的：内连接和左外连接。 
    
# Let’s create two tables: A and B to demonstrate joins.
A = data.frame(c1 = 1:4, c2 = letters[1:4]) 
B = data.frame(c1 = c(2,4,5,6), c2 = letters[c(2:5)])  
A 
# c1 c2 
# 1  a 
# 2  b 
# 3  c 
# 4  d  
B 
# c1 c2 
# 2  b 
# 4  c 
# 5  d 
# 6  e  
### INNER JOIN 
# This means to match the observations of the column we would join the tables by.   
inner = sqldf(" 
   SELECT 
   A.c1, B.c2 
   FROM 
   A INNER JOIN B 
   ON A.c1 = B.c1 
")  
# Only the rows that match c1 in both A and B are returned 
inner 
# c1 c2 
#  2  b 
#  4  c  
### LEFT OUTER JOIN
# the left outer join, sometimes just called left join will return the  
# first all the values of the column used from the A table  
left = sqldf(" 
  SELECT 
   A.c1, B.c2 
  FROM 
   A LEFT OUTER JOIN B 
   ON A.c1 = B.c1 
")  
# Only the rows that match c1 in both A and B are returned 
left 
#   c1    c2 
#    1  <NA> 
#    2    b 
#    3  <NA> 
#    4    c
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