Praktikum 3 Sains Data
Shaly Wanda Hamzah
9/9/2021
Northwind
Northwind<-DBI::dbConnect(RSQLite::SQLite(), "C:/Users/hp/Desktop/Sains Data/2/praktik pak bagus/Northwind_large.sqlite")
RSQLite::dbListTables(Northwind)## [1] "Category" "Customer" "CustomerCustomerDemo"
## [4] "CustomerDemographic" "Employee" "EmployeeTerritory"
## [7] "Order" "OrderDetail" "Product"
## [10] "ProductDetails_V" "Region" "Shipper"
## [13] "Supplier" "Territory"shipper<-dplyr::tbl(Northwind,"shipper"); shipper## # Source: table<shipper> [?? x 3]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id CompanyName Phone
## <int> <chr> <chr>
## 1 1 Speedy Express (503) 555-9831
## 2 2 United Package (503) 555-3199
## 3 3 Federal Shipping (503) 555-9931category<-dplyr::tbl(Northwind,"category"); category## # Source: table<category> [?? x 3]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id CategoryName Description
## <int> <chr> <chr>
## 1 1 Beverages Soft drinks, coffees, teas, beers, and ales
## 2 2 Condiments Sweet and savory sauces, relishes, spreads, and seasonin~
## 3 3 Confections Desserts, candies, and sweet breads
## 4 4 Dairy Products Cheeses
## 5 5 Grains/Cereals Breads, crackers, pasta, and cereal
## 6 6 Meat/Poultry Prepared meats
## 7 7 Produce Dried fruit and bean curd
## 8 8 Seafood Seaweed and fishcustomer<-dplyr::tbl(Northwind,"customer"); customer## # Source: table<customer> [?? x 11]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id CompanyName ContactName ContactTitle Address City Region PostalCode
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 ALFKI Alfreds Fut~ Maria Anders Sales Repres~ Obere ~ Berl~ Weste~ 12209
## 2 ANATR Ana Trujill~ Ana Trujillo Owner Avda. ~ Méxi~ Centr~ 05021
## 3 ANTON Antonio Mor~ Antonio Mor~ Owner Matade~ Méxi~ Centr~ 05023
## 4 AROUT Around the ~ Thomas Hardy Sales Repres~ 120 Ha~ Lond~ Briti~ WA1 1DP
## 5 BERGS Berglunds s~ Christina B~ Order Admini~ Berguv~ Luleå North~ S-958 22
## 6 BLAUS Blauer See ~ Hanna Moos Sales Repres~ Forste~ Mann~ Weste~ 68306
## 7 BLONP Blondesddsl~ Frédérique ~ Marketing Ma~ 24, pl~ Stra~ Weste~ 67000
## 8 BOLID Bólido Comi~ MartÃn Somm~ Owner C/ Ara~ Madr~ South~ 28023
## 9 BONAP Bon app Laurence Le~ Owner 12, ru~ Mars~ Weste~ 13008
## 10 BOTTM Bottom-Doll~ Elizabeth L~ Accounting M~ 23 Tsa~ Tsaw~ North~ T2F 8M4
## # ... with more rows, and 3 more variables: Country <chr>, Phone <chr>,
## # Fax <chr>employee<-dplyr::tbl(Northwind,"employee"); employee## # Source: table<employee> [?? x 18]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id LastName FirstName Title TitleOfCourtesy BirthDate HireDate Address
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 Davolio Nancy Sales ~ Ms. 1980-12-~ 2024-05~ 507 - 20~
## 2 2 Fuller Andrew Vice P~ Dr. 1984-02-~ 2024-08~ 908 W. C~
## 3 3 Leverling Janet Sales ~ Ms. 1995-08-~ 2024-04~ 722 Moss~
## 4 4 Peacock Margaret Sales ~ Mrs. 1969-09-~ 2025-05~ 4110 Old~
## 5 5 Buchanan Steven Sales ~ Mr. 1987-03-~ 2025-10~ 14 Garre~
## 6 6 Suyama Michael Sales ~ Mr. 1995-07-~ 2025-10~ Coventry~
## 7 7 King Robert Sales ~ Mr. 1992-05-~ 2026-01~ Edgeham ~
## 8 8 Callahan Laura Inside~ Ms. 1990-01-~ 2026-03~ 4726 - 1~
## 9 9 Dodsworth Anne Sales ~ Ms. 1998-01-~ 2026-11~ 7 Hounds~
## # ... with 10 more variables: City <chr>, Region <chr>, PostalCode <chr>,
## # Country <chr>, HomePhone <chr>, Extension <chr>, Photo <blob>, Notes <chr>,
## # ReportsTo <int>, PhotoPath <chr>order<-dplyr::tbl(Northwind,"order"); order## # Source: table<order> [?? x 14]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id CustomerId EmployeeId OrderDate RequiredDate ShippedDate ShipVia
## <int> <chr> <int> <chr> <chr> <chr> <int>
## 1 10248 VINET 5 2012-07-04 2012-08-01 2012-07-16 3
## 2 10249 TOMSP 6 2012-07-05 2012-08-16 2012-07-10 1
## 3 10250 HANAR 4 2012-07-08 2012-08-05 2012-07-12 2
## 4 10251 VICTE 3 2012-07-08 2012-08-05 2012-07-15 1
## 5 10252 SUPRD 4 2012-07-09 2012-08-06 2012-07-11 2
## 6 10253 HANAR 3 2012-07-10 2012-07-24 2012-07-16 2
## 7 10254 CHOPS 5 2012-07-11 2012-08-08 2012-07-23 2
## 8 10255 RICSU 9 2012-07-12 2012-08-09 2012-07-15 3
## 9 10256 WELLI 3 2012-07-15 2012-08-12 2012-07-17 2
## 10 10257 HILAA 4 2012-07-16 2012-08-13 2012-07-22 3
## # ... with more rows, and 7 more variables: Freight <dbl>, ShipName <chr>,
## # ShipAddress <chr>, ShipCity <chr>, ShipRegion <chr>, ShipPostalCode <chr>,
## # ShipCountry <chr>supplier<-dplyr::tbl(Northwind,"supplier"); supplier## # Source: table<supplier> [?? x 12]
## # Database: sqlite 3.36.0 [C:\Users\hp\Desktop\Sains Data\2\praktik pak
## # bagus\Northwind_large.sqlite]
## Id CompanyName ContactName ContactTitle Address City Region PostalCode
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 Exotic Liqu~ Charlotte C~ Purchasing M~ 49 Gil~ Lond~ Briti~ EC1 4SD
## 2 2 New Orleans~ Shelley Bur~ Order Admini~ P.O. B~ New ~ North~ 70117
## 3 3 Grandma Kel~ Regina Murp~ Sales Repres~ 707 Ox~ Ann ~ North~ 48104
## 4 4 Tokyo Trade~ Yoshi Nagase Marketing Ma~ 9-8 Se~ Tokyo Easte~ 100
## 5 5 Cooperativa~ Antonio del~ Export Admin~ Calle ~ Ovie~ South~ 33007
## 6 6 Mayumi's Mayumi Ohno Marketing Re~ 92 Set~ Osaka Easte~ 545
## 7 7 Pavlova, Lt~ Ian Devling Marketing Ma~ 74 Ros~ Melb~ Victo~ 3058
## 8 8 Specialty B~ Peter Wilson Sales Repres~ 29 Kin~ Manc~ Briti~ M14 GSD
## 9 9 PB Knäckebr~ Lars Peters~ Sales Agent Kaload~ Göte~ North~ S-345 67
## 10 10 Refrescos A~ Carlos Diaz Marketing Ma~ Av. da~ Sao ~ South~ 5442
## # ... with more rows, and 4 more variables: Country <chr>, Phone <chr>,
## # Fax <chr>, HomePage <chr>Tabel yang akan digunakan adalah tabel Supplier dan Employee. Pada tabel Supplier hanya akan digunakan variabel CompanyName, dan City. Sementara, pada tabel Employee hanya akan digunakan variabel LastName Title dan City.
(d1 <- tibble (data.frame (supplier %>% select(CompanyName, City))))## # A tibble: 29 x 2
## CompanyName City
## <chr> <chr>
## 1 Exotic Liquids London
## 2 New Orleans Cajun Delights New Orleans
## 3 Grandma Kelly's Homestead Ann Arbor
## 4 Tokyo Traders Tokyo
## 5 Cooperativa de Quesos 'Las Cabras' Oviedo
## 6 Mayumi's Osaka
## 7 Pavlova, Ltd. Melbourne
## 8 Specialty Biscuits, Ltd. Manchester
## 9 PB Knäckebröd AB Göteborg
## 10 Refrescos Americanas LTDA Sao Paulo
## # ... with 19 more rows(d2<- tibble (data.frame(employee %>% select(LastName, Title, City))))## # A tibble: 9 x 3
## LastName Title City
## <chr> <chr> <chr>
## 1 Davolio Sales Representative Seattle
## 2 Fuller Vice President, Sales Tacoma
## 3 Leverling Sales Representative Kirkland
## 4 Peacock Sales Representative Redmond
## 5 Buchanan Sales Manager London
## 6 Suyama Sales Representative London
## 7 King Sales Representative London
## 8 Callahan Inside Sales Coordinator Seattle
## 9 Dodsworth Sales Representative Londoninner_join()
inner_join(d1,d2, by="City")## # A tibble: 4 x 4
## CompanyName City LastName Title
## <chr> <chr> <chr> <chr>
## 1 Exotic Liquids London Buchanan Sales Manager
## 2 Exotic Liquids London Suyama Sales Representative
## 3 Exotic Liquids London King Sales Representative
## 4 Exotic Liquids London Dodsworth Sales Representativeleft_join()
left_join(d1, d2, by="City")## # A tibble: 32 x 4
## CompanyName City LastName Title
## <chr> <chr> <chr> <chr>
## 1 Exotic Liquids London Buchanan Sales Manager
## 2 Exotic Liquids London Suyama Sales Representative
## 3 Exotic Liquids London King Sales Representative
## 4 Exotic Liquids London Dodsworth Sales Representative
## 5 New Orleans Cajun Delights New Orleans <NA> <NA>
## 6 Grandma Kelly's Homestead Ann Arbor <NA> <NA>
## 7 Tokyo Traders Tokyo <NA> <NA>
## 8 Cooperativa de Quesos 'Las Cabras' Oviedo <NA> <NA>
## 9 Mayumi's Osaka <NA> <NA>
## 10 Pavlova, Ltd. Melbourne <NA> <NA>
## # ... with 22 more rowsright_join()
right_join(d1,d2, by="City")## # A tibble: 9 x 4
## CompanyName City LastName Title
## <chr> <chr> <chr> <chr>
## 1 Exotic Liquids London Buchanan Sales Manager
## 2 Exotic Liquids London Suyama Sales Representative
## 3 Exotic Liquids London King Sales Representative
## 4 Exotic Liquids London Dodsworth Sales Representative
## 5 <NA> Seattle Davolio Sales Representative
## 6 <NA> Tacoma Fuller Vice President, Sales
## 7 <NA> Kirkland Leverling Sales Representative
## 8 <NA> Redmond Peacock Sales Representative
## 9 <NA> Seattle Callahan Inside Sales Coordinatorfull_join()
full_join(d1,d2, by="City")## # A tibble: 37 x 4
## CompanyName City LastName Title
## <chr> <chr> <chr> <chr>
## 1 Exotic Liquids London Buchanan Sales Manager
## 2 Exotic Liquids London Suyama Sales Representative
## 3 Exotic Liquids London King Sales Representative
## 4 Exotic Liquids London Dodsworth Sales Representative
## 5 New Orleans Cajun Delights New Orleans <NA> <NA>
## 6 Grandma Kelly's Homestead Ann Arbor <NA> <NA>
## 7 Tokyo Traders Tokyo <NA> <NA>
## 8 Cooperativa de Quesos 'Las Cabras' Oviedo <NA> <NA>
## 9 Mayumi's Osaka <NA> <NA>
## 10 Pavlova, Ltd. Melbourne <NA> <NA>
## # ... with 27 more rowssemi_join()
semi_join(d1,d2, by="City")## # A tibble: 1 x 2
## CompanyName City
## <chr> <chr>
## 1 Exotic Liquids Londonanti_join()
anti_join(d1,d2, by="City")## # A tibble: 28 x 2
## CompanyName City
## <chr> <chr>
## 1 New Orleans Cajun Delights New Orleans
## 2 Grandma Kelly's Homestead Ann Arbor
## 3 Tokyo Traders Tokyo
## 4 Cooperativa de Quesos 'Las Cabras' Oviedo
## 5 Mayumi's Osaka
## 6 Pavlova, Ltd. Melbourne
## 7 Specialty Biscuits, Ltd. Manchester
## 8 PB Knäckebröd AB Göteborg
## 9 Refrescos Americanas LTDA Sao Paulo
## 10 Heli Süßwaren GmbH & Co. KG Berlin
## # ... with 18 more rowsSpatial Data
mengimpor data dari direktori
selanjutnya akan diimpor data peta Jawa yang dapat didownload pada link berikut: http://bit.ly/ShapeFile_Jawa
## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS
## = dumpSRS, : Discarded datum D_unknown in Proj4 definition: +proj=longlat
## +ellps=GRS80 +no_defs## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hp\Desktop\Spasial\Map of Jawa (original)", layer: "jawa"
## with 116 features
## It has 5 fields## MISKIN KODE_KAB NAMA_KAB KODE_PROP NAMA_PROP
## 0 0 3501 Pacitan 35 Jawa Timur
## 1 0 3502 Ponorogo 35 Jawa Timur
## 2 0 3503 Trenggalek 35 Jawa Timur
## 3 0 3504 Tulungagung 35 Jawa Timur
## 4 0 3508 Lumajang 35 Jawa Timur
## 5 0 3511 Bondowoso 35 Jawa Timur
## 6 0 3514 Pasuruan 35 Jawa Timur
## 7 0 3517 Jombang 35 Jawa Timur
## 8 0 3518 Nganjuk 35 Jawa Timur
## 9 0 3519 Madiun 35 Jawa Timur
## 10 0 3520 Magetan 35 Jawa Timur
## 11 0 3521 Ngawi 35 Jawa Timur
## 12 0 3522 Bojonegoro 35 Jawa Timur
## 13 0 3523 Tuban 35 Jawa Timur
## 14 0 3524 Lamongan 35 Jawa Timur
## 15 0 3526 Bangkalan 35 Jawa Timur
## 16 0 3528 Pamekasan 35 Jawa Timur
## 17 0 3571 Kediri (City) 35 Jawa Timur
## 18 0 3572 Blitar (City) 35 Jawa Timur
## 19 0 3573 Malang (City) 35 Jawa Timur
## 20 0 3574 Probolinggo (City) 35 Jawa Timur
## 21 0 3575 Pasuruan (City) 35 Jawa Timur
## 22 0 3576 Mojokerto (City) 35 Jawa Timur
## 23 0 3577 Madiun (City) 35 Jawa Timur
## 24 0 3578 Surabaya (City) 35 Jawa Timur
## 25 0 3579 Batu (City) 35 Jawa Timur
## 26 0 3505 Blitar 35 Jawa Timur
## 27 0 3506 Kediri 35 Jawa Timur
## 28 0 3516 Mojokerto 35 Jawa Timur
## 29 0 3510 Banyuwangi 35 Jawa Timur
## 30 0 3525 Gresik 35 Jawa Timur
## 31 0 3509 Jember 35 Jawa Timur
## 32 0 3507 Malang 35 Jawa Timur
## 33 0 3513 Probolinggo 35 Jawa Timur
## 34 0 3527 Sampang 35 Jawa Timur
## 35 0 3515 Sidoarjo 35 Jawa Timur
## 36 0 3512 Situbondo 35 Jawa Timur
## 37 0 3529 Sumenep 35 Jawa Timur
## 38 0 3401 Kulon Progo 34 Yogyakarta
## 39 0 3402 Bantul 34 Yogyakarta
## 40 0 3403 Gunung Kidul 34 Yogyakarta
## 41 0 3404 Sleman 34 Yogyakarta
## 42 0 3471 Yogyakarta (City) 34 Yogyakarta
## 43 0 3301 Cilacap 33 Jawa Tengah
## 44 0 3302 Banyumas 33 Jawa Tengah
## 45 0 3303 Purbalingga 33 Jawa Tengah
## 46 0 3304 Banjarnegara 33 Jawa Tengah
## 47 0 3305 Kebumen 33 Jawa Tengah
## 48 0 3306 Purworejo 33 Jawa Tengah
## 49 0 3307 Wonosobo 33 Jawa Tengah
## 50 0 3308 Magelang 33 Jawa Tengah
## 51 0 3309 Boyolali 33 Jawa Tengah
## 52 0 3310 Klaten 33 Jawa Tengah
## 53 0 3311 Sukoharjo 33 Jawa Tengah
## 54 0 3312 Wonogiri 33 Jawa Tengah
## 55 0 3313 Karanganyar 33 Jawa Tengah
## 56 0 3314 Sragen 33 Jawa Tengah
## 57 0 3315 Grobogan 33 Jawa Tengah
## 58 0 3316 Blora 33 Jawa Tengah
## 59 0 3317 Rembang 33 Jawa Tengah
## 60 0 3318 Pati 33 Jawa Tengah
## 61 0 3319 Kudus 33 Jawa Tengah
## 62 0 3320 Jepara 33 Jawa Tengah
## 63 0 3321 Demak 33 Jawa Tengah
## 64 0 3322 Semarang 33 Jawa Tengah
## 65 0 3323 Temanggung 33 Jawa Tengah
## 66 0 3324 Kendal 33 Jawa Tengah
## 67 0 3325 Batang 33 Jawa Tengah
## 68 0 3326 Pekalongan 33 Jawa Tengah
## 69 0 3327 Pemalang 33 Jawa Tengah
## 70 0 3328 Tegal 33 Jawa Tengah
## 71 0 3329 Brebes 33 Jawa Tengah
## 72 0 3371 Magelang (City) 33 Jawa Tengah
## 73 0 3372 Surakarta (City) 33 Jawa Tengah
## 74 0 3373 Salatiga (City) 33 Jawa Tengah
## 75 0 3374 Semarang (City) 33 Jawa Tengah
## 76 0 3375 Pekalongan (City) 33 Jawa Tengah
## 77 0 3376 Tegal (City) 33 Jawa Tengah
## 78 0 3201 Bogor 32 Jawa Barat
## 79 0 3202 Sukabumi 32 Jawa Barat
## 80 0 3203 Cianjur 32 Jawa Barat
## 81 0 3204 Bandung 32 Jawa Barat
## 82 0 3205 Garut 32 Jawa Barat
## 83 0 3206 Tasikmalaya 32 Jawa Barat
## 84 0 3207 Ciamis 32 Jawa Barat
## 85 0 3208 Kuningan 32 Jawa Barat
## 86 0 3209 Cirebon 32 Jawa Barat
## 87 0 3210 Majalengka 32 Jawa Barat
## 88 0 3211 Sumedang 32 Jawa Barat
## 89 0 3212 Indramayu 32 Jawa Barat
## 90 0 3213 Subang 32 Jawa Barat
## 91 0 3214 Purwakarta 32 Jawa Barat
## 92 0 3215 Karawang 32 Jawa Barat
## 93 0 3216 Bekasi 32 Jawa Barat
## 94 0 3271 Bogor (City) 32 Jawa Barat
## 95 0 3272 Sukabumi (City) 32 Jawa Barat
## 96 0 3273 Bandung (City) 32 Jawa Barat
## 97 0 3274 Cirebon (City) 32 Jawa Barat
## 98 0 3275 Bekasi (City) 32 Jawa Barat
## 99 0 3276 Depok (City) 32 Jawa Barat
## 100 0 3277 Cimahi (City) 32 Jawa Barat
## 101 0 3278 Tasikmalaya (City) 32 Jawa Barat
## 102 0 3279 Banjar (City) 32 Jawa Barat
## 103 0 3101 Kepulauan Seribu 31 DKI Jakarta
## 104 0 3171 Jakarta Selatan (City) 31 DKI Jakarta
## 105 0 3173 Jakarta Pusat (City) 31 DKI Jakarta
## 106 0 3172 Jakarta Timur (City) 31 DKI Jakarta
## 107 0 3174 Jakarta Barat (City) 31 DKI Jakarta
## 108 0 3175 Jakarta Utara (City) 31 DKI Jakarta
## 109 0 3602 Lebak 36 Banten
## 110 0 3603 Tangerang 36 Banten
## 111 0 3671 Tangerang (City) 36 Banten
## 112 0 3601 Pandeglang 36 Banten
## 113 0 3604 Serang 36 Banten
## 114 0 3672 Cilegon (City) 36 Banten
## 115 0 3217 Bandung Barat 32 Jawa Baratmenampilkan peta Pula Jawa
selanjutnya akan ditampilan peta pulau Jawa sebagai berikut:
plot(jawa)
## memberi warna yang berbeda setiap provinsi selanjutnya kita juga dapat memberikan warna berbeda untuk setiap provinsinya dengan beberapa pilihan warna sebagai berikut:
plot(jawa,col=jawa$KODE_PROP-30)
palette(rainbow(6));plot(jawa, col=jawa$KODE_PROP-30)
palette(terrain.colors(10));plot(jawa, col=jawa$KODE_PROP-30)