sqldf(): แนะนำ function ในการทำงานกับ data frame ด้วย SQL ในภาษา R — ตัวอย่างจาก Cars93

# Install sqldf
install.packages("sqldf")

# Load sqldf
library(sqldf)

sqldf("SELECT * FROM df")

# Install MASS
install.packages("MASS")

# Load MASS
library(MASS)

# Load the dataset
data(Cars93)

# View the dataset structure
str(Cars93)

'data.frame':	93 obs. of  27 variables:
 $ Manufacturer      : Factor w/ 32 levels "Acura","Audi",..: 1 1 2 2 3 4 4 4 4 5 ...
 $ Model             : Factor w/ 93 levels "100","190E","240",..: 49 56 9 1 6 24 54 74 73 35 ...
 $ Type              : Factor w/ 6 levels "Compact","Large",..: 4 3 1 3 3 3 2 2 3 2 ...
 $ Min.Price         : num  12.9 29.2 25.9 30.8 23.7 14.2 19.9 22.6 26.3 33 ...
 $ Price             : num  15.9 33.9 29.1 37.7 30 15.7 20.8 23.7 26.3 34.7 ...
 $ Max.Price         : num  18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ...
 $ MPG.city          : int  25 18 20 19 22 22 19 16 19 16 ...
 $ MPG.highway       : int  31 25 26 26 30 31 28 25 27 25 ...
 $ AirBags           : Factor w/ 3 levels "Driver & Passenger",..: 3 1 2 1 2 2 2 2 2 2 ...
 $ DriveTrain        : Factor w/ 3 levels "4WD","Front",..: 2 2 2 2 3 2 2 3 2 2 ...
 $ Cylinders         : Factor w/ 6 levels "3","4","5","6",..: 2 4 4 4 2 2 4 4 4 5 ...
 $ EngineSize        : num  1.8 3.2 2.8 2.8 3.5 2.2 3.8 5.7 3.8 4.9 ...
 $ Horsepower        : int  140 200 172 172 208 110 170 180 170 200 ...
 $ RPM               : int  6300 5500 5500 5500 5700 5200 4800 4000 4800 4100 ...
 $ Rev.per.mile      : int  2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ...
 $ Man.trans.avail   : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ...
 $ Fuel.tank.capacity: num  13.2 18 16.9 21.1 21.1 16.4 18 23 18.8 18 ...
 $ Passengers        : int  5 5 5 6 4 6 6 6 5 6 ...
 $ Length            : int  177 195 180 193 186 189 200 216 198 206 ...
 $ Wheelbase         : int  102 115 102 106 109 105 111 116 108 114 ...
 $ Width             : int  68 71 67 70 69 69 74 78 73 73 ...
 $ Turn.circle       : int  37 38 37 37 39 41 42 45 41 43 ...
 $ Rear.seat.room    : num  26.5 30 28 31 27 28 30.5 30.5 26.5 35 ...
 $ Luggage.room      : int  11 15 14 17 13 16 17 21 14 18 ...
 $ Weight            : int  2705 3560 3375 3405 3640 2880 3470 4105 3495 3620 ...
 $ Origin            : Factor w/ 2 levels "USA","non-USA": 2 2 2 2 2 1 1 1 1 1 ...
 $ Make              : Factor w/ 93 levels "Acura Integra",..: 1 2 4 3 5 6 7 9 8 10 ...

# Set query
select_query <- "
SELECT
  Manufacturer,
  Model,
  `Min.Price`,
  `Max.Price`
FROM
  Cars93
"

# Select from df
select_result <- sqldf(select_query)

# View the result
select_result

    Manufacturer          Model Min.Price Max.Price
1          Acura        Integra      12.9      18.8
2          Acura         Legend      29.2      38.7
3           Audi             90      25.9      32.3
4           Audi            100      30.8      44.6
5            BMW           535i      23.7      36.2
6          Buick        Century      14.2      17.3
7          Buick        LeSabre      19.9      21.7
8          Buick     Roadmaster      22.6      24.9
9          Buick        Riviera      26.3      26.3
10      Cadillac        DeVille      33.0      36.3
11      Cadillac        Seville      37.5      42.7
12     Chevrolet       Cavalier       8.5      18.3
13     Chevrolet        Corsica      11.4      11.4
14     Chevrolet         Camaro      13.4      16.8
15     Chevrolet         Lumina      13.4      18.4
16     Chevrolet     Lumina_APV      14.7      18.0
17     Chevrolet          Astro      14.7      18.6
18     Chevrolet        Caprice      18.0      19.6
19     Chevrolet       Corvette      34.6      41.5
20      Chrylser       Concorde      18.4      18.4
21      Chrysler        LeBaron      14.5      17.1
22      Chrysler       Imperial      29.5      29.5
23         Dodge           Colt       7.9      10.6
24         Dodge         Shadow       8.4      14.2
25         Dodge         Spirit      11.9      14.7
26         Dodge        Caravan      13.6      24.4
27         Dodge        Dynasty      14.8      16.4
28         Dodge        Stealth      18.5      33.1
29         Eagle         Summit       7.9      16.5
30         Eagle         Vision      17.5      21.2
31          Ford        Festiva       6.9       7.9
32          Ford         Escort       8.4      11.9
33          Ford          Tempo      10.4      12.2
34          Ford        Mustang      10.8      21.0
35          Ford          Probe      12.8      15.2
36          Ford       Aerostar      14.5      25.3
37          Ford         Taurus      15.6      24.8
38          Ford Crown_Victoria      20.1      21.7
39           Geo          Metro       6.7      10.0
40           Geo          Storm      11.5      13.5
41         Honda        Prelude      17.0      22.7
42         Honda          Civic       8.4      15.8
43         Honda         Accord      13.8      21.2
44       Hyundai          Excel       6.8       9.2
45       Hyundai        Elantra       9.0      11.0
46       Hyundai         Scoupe       9.1      11.0
47       Hyundai         Sonata      12.4      15.3
48      Infiniti            Q45      45.4      50.4
49         Lexus          ES300      27.5      28.4
50         Lexus          SC300      34.7      35.6
51       Lincoln    Continental      33.3      35.3
52       Lincoln       Town_Car      34.4      37.8
53         Mazda            323       7.4       9.1
54         Mazda        Protege      10.9      12.3
55         Mazda            626      14.3      18.7
56         Mazda            MPV      16.6      21.7
57         Mazda           RX-7      32.5      32.5
58 Mercedes-Benz           190E      29.0      34.9
59 Mercedes-Benz           300E      43.8      80.0
60       Mercury          Capri      13.3      15.0
61       Mercury         Cougar      14.9      14.9
62    Mitsubishi         Mirage       7.7      12.9
63    Mitsubishi       Diamante      22.4      29.9
64        Nissan         Sentra       8.7      14.9
65        Nissan         Altima      13.0      18.3
66        Nissan          Quest      16.7      21.5
67        Nissan         Maxima      21.0      22.0
68    Oldsmobile        Achieva      13.0      14.0
69    Oldsmobile  Cutlass_Ciera      14.2      18.4
70    Oldsmobile     Silhouette      19.5      19.5
71    Oldsmobile   Eighty-Eight      19.5      21.9
72      Plymouth          Laser      11.4      17.4
73       Pontiac         LeMans       8.2       9.9
74       Pontiac        Sunbird       9.4      12.8
75       Pontiac       Firebird      14.0      21.4
76       Pontiac     Grand_Prix      15.4      21.6
77       Pontiac     Bonneville      19.4      29.4
78          Saab            900      20.3      37.1
79        Saturn             SL       9.2      12.9
80        Subaru          Justy       7.3       9.5
81        Subaru         Loyale      10.5      11.3
82        Subaru         Legacy      16.3      22.7
83        Suzuki          Swift       7.3      10.0
84        Toyota         Tercel       7.8      11.8
85        Toyota         Celica      14.2      22.6
86        Toyota          Camry      15.2      21.2
87        Toyota         Previa      18.9      26.6
88    Volkswagen            Fox       8.7       9.5
89    Volkswagen        Eurovan      16.6      22.7
90    Volkswagen         Passat      17.6      22.4
91    Volkswagen        Corrado      22.9      23.7
92         Volvo            240      21.8      23.5
93         Volvo            850      24.8      28.5

# Set query
where_result <- "
SELECT
  Manufacturer,
  Model,
  `Min.Price`,
  `Max.Price`
FROM
  Cars93
WHERE
  Manufacturer LIKE 'M%'
"

# Filter df
where_result <- sqldf(where_result)

# View the result
where_result

    Manufacturer    Model Min.Price Max.Price
1          Mazda      323       7.4       9.1
2          Mazda  Protege      10.9      12.3
3          Mazda      626      14.3      18.7
4          Mazda      MPV      16.6      21.7
5          Mazda     RX-7      32.5      32.5
6  Mercedes-Benz     190E      29.0      34.9
7  Mercedes-Benz     300E      43.8      80.0
8        Mercury    Capri      13.3      15.0
9        Mercury   Cougar      14.9      14.9
10    Mitsubishi   Mirage       7.7      12.9
11    Mitsubishi Diamante      22.4      29.9

# Set query
aggregate_query <- "
SELECT
  Manufacturer,
  AVG(Price) AS Avg_Price
FROM
  Cars93
GROUP BY
  Manufacturer
ORDER BY
  Avg_Price DESC
LIMIT
  10;
"

# Aggregate df
aggregate_result <- sqldf(aggregate_query)

# View the result
aggregate_result

    Manufacturer Avg_Price
1       Infiniti      47.9
2  Mercedes-Benz      46.9
3       Cadillac      37.4
4        Lincoln      35.2
5           Audi      33.4
6          Lexus      31.6
7            BMW      30.0
8           Saab      28.7
9          Acura      24.9
10         Volvo      24.7