Uji data Menggunakan library(dplyr) Week 9
Dimas Prasetyo_556271
2026-04-29
# Syntax Data 1 - Data Frame
university_ranking <- data.frame(
world_rank = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50),
institution = c("Harvard University","Massachusetts Institute of Technology","Stanford University","University of Cambridge","California Institute of Technology","Princeton University","University of Oxford","Yale University","Columbia University","University of California, Berkeley","University of Chicago","Cornell University","University of Pennsylvania","University of Tokyo","Johns Hopkins University","Swiss Federal Institute of Technology in Zurich","Kyoto University","Weizmann Institute of Science","University of California, Los Angeles","University of California, San Diego","Rockefeller University","Hebrew University of Jerusalem","New York University","University of California, San Francisco","University of Wisconsin–Madison","University of Illinois at Urbana–Champaign","Duke University","Imperial College London","University of Texas Southwestern Medical Center","University of Texas at Austin","University College London","Osaka University","Northwestern University","University of Michigan, Ann Arbor","University of Toronto","University of North Carolina at Chapel Hill","Washington University in St. Louis","University of Utah","University of Washington - Seattle","University of California, Santa Barbara","McGill University","Purdue University, West Lafayette","Carnegie Mellon University","University of Southern California","University of California, Davis","University of Colorado Boulder","University of California, Irvine","University of Paris-Sud","University of Minnesota, Twin Cities","University of Arizona"),
country = c("USA","USA","USA","United Kingdom","USA","USA","United Kingdom","USA","USA","USA","USA","USA","USA","Japan","USA","Switzerland","Japan","Israel","USA","USA","USA","Israel","USA","USA","USA","USA","USA","United Kingdom","USA","USA","United Kingdom","Japan","USA","USA","Canada","USA","USA","USA","USA","USA","Canada","USA","USA","USA","USA","USA","USA","France","USA","USA"),
national_rank = c(1,2,3,1,4,5,2,6,7,8,9,10,11,1,12,1,2,1,13,14,15,2,16,17,18,19,20,3,21,22,4,3,23,24,1,25,26,27,28,29,2,30,31,32,33,34,35,1,36,37),
quality_of_education = c(7,9,17,10,2,8,13,14,23,16,15,21,31,32,34,26,42,4,62,61,1,24,89,101,64,82,65,84,19,101,35,77,101,68,101,101,74,92,101,101,70,95,30,101,79,83,101,48,101,101),
alumni_employment = c(9,17,11,24,29,14,28,31,21,52,26,42,16,19,77,66,38,101,59,101,101,93,75,101,63,101,43,73,101,78,101,101,32,60,101,86,62,101,101,101,91,70,81,101,101,88,101,101,101,101),
quality_of_faculty = c(1,3,5,4,7,2,9,12,10,6,8,14,24,31,20,11,19,22,23,15,16,13,17,21,33,18,55,35,32,27,45,44,101,101,34,56,101,41,40,28,54,49,26,63,92,48,38,25,85,88),
publications = c(1,12,4,16,37,53,15,14,13,6,34,22,9,8,11,40,25,101,3,10,101,101,42,19,17,35,20,26,101,41,27,39,24,2,7,31,32,74,5,68,33,61,101,46,23,71,59,73,18,45),
influence = c(1,4,2,16,22,33,13,6,12,5,20,21,10,19,9,51,36,67,11,8,28,91,24,3,30,71,15,26,43,47,23,44,25,17,14,29,18,52,7,72,39,101,101,48,40,54,57,96,31,42),
citations = c(1,4,2,11,22,26,19,15,14,3,28,16,8,23,9,44,43,101,6,10,96,101,34,13,21,39,12,29,84,40,33,51,20,7,18,31,30,67,5,36,47,58,61,32,25,56,52,101,17,45),
broad_impact = c(5,1,15,50,18,101,26,66,5,16,101,10,9,3,7,34,23,29,13,22,101,28,62,33,21,44,20,41,101,57,86,11,35,8,101,29,14,12,101,101,101,19,101,23,32,101,65,101,84,27),
score = c(100,91.67,89.5,86.17,85.21,82.5,82.34,79.14,78.86,78.55,73.82,73.69,73.64,69.49,66.94,66.69,65.76,65.09,64.05,63.11,61.74,60.76,60.55,59.7,59.66,59,58.37,57.53,56.43,56.18,55.21,54.43,54.4,53.72,53.43,53.09,52.9,52.64,52.25,52.15,51.72,51.66,51.6,51.38,51.06,50.68,50.64,50.44,50.3,50.29),
year = c(2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012,2012)
)
print(university_ranking)## world_rank institution country
## 1 1 Harvard University USA
## 2 2 Massachusetts Institute of Technology USA
## 3 3 Stanford University USA
## 4 4 University of Cambridge United Kingdom
## 5 5 California Institute of Technology USA
## 6 6 Princeton University USA
## 7 7 University of Oxford United Kingdom
## 8 8 Yale University USA
## 9 9 Columbia University USA
## 10 10 University of California, Berkeley USA
## 11 11 University of Chicago USA
## 12 12 Cornell University USA
## 13 13 University of Pennsylvania USA
## 14 14 University of Tokyo Japan
## 15 15 Johns Hopkins University USA
## 16 16 Swiss Federal Institute of Technology in Zurich Switzerland
## 17 17 Kyoto University Japan
## 18 18 Weizmann Institute of Science Israel
## 19 19 University of California, Los Angeles USA
## 20 20 University of California, San Diego USA
## 21 21 Rockefeller University USA
## 22 22 Hebrew University of Jerusalem Israel
## 23 23 New York University USA
## 24 24 University of California, San Francisco USA
## 25 25 University of Wisconsin–Madison USA
## 26 26 University of Illinois at Urbana–Champaign USA
## 27 27 Duke University USA
## 28 28 Imperial College London United Kingdom
## 29 29 University of Texas Southwestern Medical Center USA
## 30 30 University of Texas at Austin USA
## 31 31 University College London United Kingdom
## 32 32 Osaka University Japan
## 33 33 Northwestern University USA
## 34 34 University of Michigan, Ann Arbor USA
## 35 35 University of Toronto Canada
## 36 36 University of North Carolina at Chapel Hill USA
## 37 37 Washington University in St. Louis USA
## 38 38 University of Utah USA
## 39 39 University of Washington - Seattle USA
## 40 40 University of California, Santa Barbara USA
## 41 41 McGill University Canada
## 42 42 Purdue University, West Lafayette USA
## 43 43 Carnegie Mellon University USA
## 44 44 University of Southern California USA
## 45 45 University of California, Davis USA
## 46 46 University of Colorado Boulder USA
## 47 47 University of California, Irvine USA
## 48 48 University of Paris-Sud France
## 49 49 University of Minnesota, Twin Cities USA
## 50 50 University of Arizona USA
## national_rank quality_of_education alumni_employment quality_of_faculty
## 1 1 7 9 1
## 2 2 9 17 3
## 3 3 17 11 5
## 4 1 10 24 4
## 5 4 2 29 7
## 6 5 8 14 2
## 7 2 13 28 9
## 8 6 14 31 12
## 9 7 23 21 10
## 10 8 16 52 6
## 11 9 15 26 8
## 12 10 21 42 14
## 13 11 31 16 24
## 14 1 32 19 31
## 15 12 34 77 20
## 16 1 26 66 11
## 17 2 42 38 19
## 18 1 4 101 22
## 19 13 62 59 23
## 20 14 61 101 15
## 21 15 1 101 16
## 22 2 24 93 13
## 23 16 89 75 17
## 24 17 101 101 21
## 25 18 64 63 33
## 26 19 82 101 18
## 27 20 65 43 55
## 28 3 84 73 35
## 29 21 19 101 32
## 30 22 101 78 27
## 31 4 35 101 45
## 32 3 77 101 44
## 33 23 101 32 101
## 34 24 68 60 101
## 35 1 101 101 34
## 36 25 101 86 56
## 37 26 74 62 101
## 38 27 92 101 41
## 39 28 101 101 40
## 40 29 101 101 28
## 41 2 70 91 54
## 42 30 95 70 49
## 43 31 30 81 26
## 44 32 101 101 63
## 45 33 79 101 92
## 46 34 83 88 48
## 47 35 101 101 38
## 48 1 48 101 25
## 49 36 101 101 85
## 50 37 101 101 88
## publications influence citations broad_impact score year
## 1 1 1 1 5 100.00 2012
## 2 12 4 4 1 91.67 2012
## 3 4 2 2 15 89.50 2012
## 4 16 16 11 50 86.17 2012
## 5 37 22 22 18 85.21 2012
## 6 53 33 26 101 82.50 2012
## 7 15 13 19 26 82.34 2012
## 8 14 6 15 66 79.14 2012
## 9 13 12 14 5 78.86 2012
## 10 6 5 3 16 78.55 2012
## 11 34 20 28 101 73.82 2012
## 12 22 21 16 10 73.69 2012
## 13 9 10 8 9 73.64 2012
## 14 8 19 23 3 69.49 2012
## 15 11 9 9 7 66.94 2012
## 16 40 51 44 34 66.69 2012
## 17 25 36 43 23 65.76 2012
## 18 101 67 101 29 65.09 2012
## 19 3 11 6 13 64.05 2012
## 20 10 8 10 22 63.11 2012
## 21 101 28 96 101 61.74 2012
## 22 101 91 101 28 60.76 2012
## 23 42 24 34 62 60.55 2012
## 24 19 3 13 33 59.70 2012
## 25 17 30 21 21 59.66 2012
## 26 35 71 39 44 59.00 2012
## 27 20 15 12 20 58.37 2012
## 28 26 26 29 41 57.53 2012
## 29 101 43 84 101 56.43 2012
## 30 41 47 40 57 56.18 2012
## 31 27 23 33 86 55.21 2012
## 32 39 44 51 11 54.43 2012
## 33 24 25 20 35 54.40 2012
## 34 2 17 7 8 53.72 2012
## 35 7 14 18 101 53.43 2012
## 36 31 29 31 29 53.09 2012
## 37 32 18 30 14 52.90 2012
## 38 74 52 67 12 52.64 2012
## 39 5 7 5 101 52.25 2012
## 40 68 72 36 101 52.15 2012
## 41 33 39 47 101 51.72 2012
## 42 61 101 58 19 51.66 2012
## 43 101 101 61 101 51.60 2012
## 44 46 48 32 23 51.38 2012
## 45 23 40 25 32 51.06 2012
## 46 71 54 56 101 50.68 2012
## 47 59 57 52 65 50.64 2012
## 48 73 96 101 101 50.44 2012
## 49 18 31 17 84 50.30 2012
## 50 45 42 45 27 50.29 2012#Jumlah Universias di setiap negara yang masuk top 50 university rangking (Dimas Prasetyo_556271)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.5.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionuniversity_ranking %>%
group_by(country) %>%
summarise(Jumlah = n())## # A tibble: 7 × 2
## country Jumlah
## <chr> <int>
## 1 Canada 2
## 2 France 1
## 3 Israel 2
## 4 Japan 3
## 5 Switzerland 1
## 6 USA 37
## 7 United Kingdom 4#Mengitung skor rata-rata Universitas suatu negara dalam peringkat top 50 universitas (Dimas Prasetyo_556271)
university_ranking %>%
group_by(country) %>%
summarise(across(score, mean))## # A tibble: 7 × 2
## country score
## <chr> <dbl>
## 1 Canada 52.6
## 2 France 50.4
## 3 Israel 62.9
## 4 Japan 63.2
## 5 Switzerland 66.7
## 6 USA 63.5
## 7 United Kingdom 70.3"\n\n"## [1] "\n\n"#Syntax Data 2, Data Frame
order_details <- data.frame(
order_id = c("AG-2011-2040", "IN-2011-47883", "HU-2011-1220", "IT-2011-3647632",
"IN-2011-47883", "IN-2011-47883", "CA-2011-1510", "IN-2011-79397", "ID-2011-80230",
"IZ-2011-4680", "IN-2011-65159", "IN-2011-65159", "ES-2011-4869686", "IN-2011-33652",
"ID-2011-80230", "MX-2011-160234", "IR-2011-770", "ID-2011-80230", "ID-2011-80230",
"ID-2011-12596", "IN-2011-79397", "IR-2011-7690", "IR-2011-770", "TZ-2011-7370",
"IZ-2011-4680", "IN-2011-65159", "IR-2011-770", "MX-2011-111255", "MX-2011-140641",
"MX-2011-158771", "ES-2011-4939443", "MX-2011-111255", "MX-2011-140641",
"US-2011-136007", "MX-2011-159373", "MX-2011-159373", "MX-2011-159373",
"IT-2011-2942451", "CA-2011-103800", "IN-2011-33036", "IT-2011-2942451", "SU-2011-5190",
"SU-2011-5190", "MX-2011-109267", "ES-2011-3848439", "ES-2011-3848439", "CA-2011-112326",
"IN-2011-27681", "CA-2011-112326"),
order_date = as.Date(c("1/1/2011", "1/1/2011", "1/1/2011", "1/1/2011", "1/1/2011", "1/1/2011",
"2/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011",
"3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011",
"3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "3/1/2011", "4/1/2011", "4/1/2011", "4/1/2011",
"4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011",
"4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "4/1/2011", "5/1/2011", "5/1/2011", "5/1/2011",
"5/1/2011", "5/1/2011", "5/1/2011"), format = "%m/%d/%Y"),
ship_date = as.Date(c("6/1/2011", "8/1/2011", "5/1/2011", "5/1/2011", "8/1/2011", "8/1/2011",
"6/1/2011", "3/1/2011", "9/1/2011", "7/1/2011", "7/1/2011", "7/1/2011", "7/1/2011", "9/1/2011",
"9/1/2011", "7/1/2011", "7/1/2011", "9/1/2011", "9/1/2011", "8/1/2011", "3/1/2011", "8/1/2011",
"7/1/2011", "8/1/2011", "7/1/2011", "7/1/2011", "7/1/2011", "9/1/2011", "9/1/2011", "11/1/2011",
"8/1/2011", "9/1/2011", "9/1/2011", "11/1/2011", "8/1/2011", "8/1/2011", "8/1/2011", "9/1/2011",
"8/1/2011", "8/1/2011", "9/1/2011", "8/1/2011", "8/1/2011", "9/1/2011", "7/1/2011", "7/1/2011",
"9/1/2011", "11/1/2011", "9/1/2011"), format = "%m/%d/%Y"),
ship_mode = c("Standard Class", "Standard Class", "Second Class", "Second Class", "Standard
Class", "Standard Class", "Standard Class", "Same Day", "Standard Class", "Standard Class",
"Second Class", "Second Class", "Standard Class", "Standard Class", "Standard Class", "Standard
Class", "Standard Class", "Standard Class", "Standard Class", "Standard Class", "Same Day",
"Second Class", "Standard Class", "Standard Class", "Standard Class", "Second Class", "Standard
Class", "Second Class", "Standard Class", "Standard Class", "Standard Class", "Second Class",
"Standard Class", "Standard Class", "Standard Class", "Standard Class", "Standard Class",
"Standard Class", "Standard Class", "Standard Class", "Standard Class", "Standard Class",
"Standard Class", "Standard Class", "First Class", "First Class", "Standard Class", "Standard
Class", "Standard Class"),
customer_name = c("Toby Braunhardt", "Joseph Holt", "Annie Thurman", "Eugene Moren",
"Joseph Holt", "Joseph Holt", "Magdelene Morse", "Kean Nguyen", "Ken Lonsdale", "Lindsay
Williams", "Larry Blacks", "Larry Blacks", "Dorothy Dickinson", "Dennis Pardue", "Ken Lonsdale",
"Stewart Visinsky", "Jas O'Carroll", "Ken Lonsdale", "Ken Lonsdale", "Chris McAfee", "Kean
Nguyen", "Nat Gilpin", "Jas O'Carroll", "Jack Garza", "Lindsay Williams", "Larry Blacks", "Jas
O'Carroll", "Russell Applegate", "Maya Herman", "Beth Thompson", "Arthur Prichep", "Russell
Applegate", "Maya Herman", "Beth Thompson", "Arthur Wiediger", "Arthur Wiediger", "Arthur
Wiediger", "Grant Thornton", "Darren Powers", "Bradley Drucker", "Grant Thornton", "Jasper
Cacioppo", "Jasper Cacioppo", "Jennifer Halladay", "Michael Granlund", "Michael Granlund",
"Phillina Ober", "Shaun Weien", "Phillina Ober"),
segment = c("Consumer", "Consumer", "Consumer", "Home Office", "Consumer", "Consumer",
"Consumer", "Corporate", "Consumer", "Corporate", "Consumer", "Consumer", "Consumer", "Home
Office", "Consumer", "Consumer", "Consumer", "Consumer", "Consumer", "Consumer",
"Corporate", "Corporate", "Consumer", "Consumer", "Corporate", "Consumer", "Consumer",
"Consumer", "Corporate", "Home Office", "Consumer", "Consumer", "Corporate", "Home Office",
"Home Office", "Home Office", "Home Office", "Corporate", "Consumer", "Consumer", "Corporate",
"Consumer", "Consumer", "Consumer", "Home Office", "Home Office", "Home Office", "Consumer",
"Home Office"),
country = c("Algeria", "Australia", "Hungary", "Sweden", "Australia", "Australia", "Canada",
"Australia", "New Zealand", "Iraq", "Philippines", "Philippines", "United Kingdom", "Malaysia", "New
Zealand", "Guatemala", "Iran", "New Zealand", "New Zealand", "Thailand", "Australia", "Iran", "Iran",
"Tanzania", "Iraq", "Philippines", "Iran", "Brazil", "Mexico", "Cuba", "France", "Brazil", "Mexico",
"Brazil", "Cuba", "Cuba", "Cuba", "United Kingdom", "United States", "Japan", "United Kingdom",
"Sudan", "Sudan", "Mexico", "France", "France", "United States", "Taiwan", "United States"),
product_name = c("Tenex Lockers, Blue", "Acme Trimmer, High Speed", "Tenex Box, Single
Width", "Enermax Note Cards, Premium", "Eldon Light Bulb, Duo Pack", "Eaton Computer Printout
Paper, 8.5 x 11", "Okidata Inkjet, Wireless", "Hoover Microwave, White", "Hewlett Wireless Fax,
Laser", "Novimex Swivel Stool, Set of Two", "Tenex Lockers, Industrial", "Chromcraft Round Table,
Adjustable Height", "Dania Corner Shelving, Traditional", "Hewlett Fax and Copier, Laser", "Hon
Rocking Chair, Set of Two", "Nokia Headset, VoIP", "Breville Coffee Grinder, Black", "Belkin
Numeric Keypad, Bluetooth", "SAFCO Chairmat, Black", "Smead File Cart, Blue", "Avery Color
Coded Labels, Laser Printer Compatible", "BIC Sketch Pad, Water Color", "Rogers Folders,
Industrial", "Stiletto Scissors, Serrated", "Cameo Interoffice Envelope, Set of 50", "Stockwell
Staples, Metal", "Advantus Rubber Bands, Metal", "Dania Classic Bookcase, Pine", "Enermax
Keyboard, Bluetooth", "Jiffy Interoffice Envelope, Set of 50", "Binney & Smith Sketch Pad,
Easy-Erase", "Fiskars Letter Opener, Easy Grip", "Sharp Ink, Laser", "Jiffy Interoffice Envelope, Set
of 50", "SAFCO Chairmat, Black", "Memorex Mouse, USB", "Kraft Peel and Seal, Recycled",
"Boston Markers, Easy-Erase", "Message Book, Wirebound, Four 5 1/2\" X 4\" Forms/Pg., 200
Dupl. Sets/Book", "Harbour Creations File Folder Labels, 5000 Label Set", "Eldon Folders, Single
Width", "Boston Pens, Fluorescent", "Avery Hole Reinforcements, Durable", "Hoover Stove, Black",
"Sanford Canvas, Fluorescent", "Binney & Smith Pencil Sharpener, Water Color", "SAFCO Boltless
Steel Shelving", "Rubbermaid Photo Frame, Durable", "Avery 508"),
sales = c(408, 120, 66, 45, 114, 55, 314, 276, 912, 667, 338, 211, 854, 193, 159, 195, 123, 69, 69,
135, 36, 52, 62, 81, 47, 6, 17, 1648, 223, 186, 140, 149, 166, 74, 38, 38, 39, 27, 16, 27, 17, 15, 6,
3029, 207, 90, 273, 49, 12),
quantity = c(2, 3, 4, 3, 5, 2, 1, 1, 4, 4, 3, 1, 7, 1, 2, 4, 2, 2, 2, 2, 3, 1, 2, 4, 1, 1, 1, 6, 4, 6, 3, 8, 2, 6,
1, 2, 3, 2, 2, 3, 2, 1, 1, 8, 4, 3, 3, 1, 3),
discount = c(0, 0.1, 0, 0.5, 0.1, 0.1, 0, 0.1, 0.4, 0, 0.45, 0.55, 0, 0, 0.4, 0, 0, 0.4, 0.4, 0.47, 0.1, 0,
0, 0, 0, 0.45, 0, 0, 0, 0, 0, 0, 0.002, 0.6, 0, 0, 0, 0.5, 0.2, 0, 0.5, 0, 0, 0, 0, 0, 0.2, 0, 0.2),
profit = c(106.14, 36.036, 29.64, -26.055, 37.77, 15.342, 3.12, 110.412, -319.464, 253.32,
-122.8005, -70.3995, 290.43, 50.13, -95.676, 44.88, 42.9, 3.42, -26.412, -45.9018, 4.743, 7.77, 8.7,
26.76, 17.07, 0.546, 4.17, 609.84, 13.28, 3.6, 20.88, 28.16, 49.42824, -107.856, 6.88, 2.24, 7.68,
-21.9, 5.5512, 13.68, -1.05, 2.61, 2.1, 999.36, 76.56, 20.52, -64.7748, 22.92, 4.2717),
shipping_cost = c(35.46, 9.72, 8.17, 4.82, 4.7, 1.8, 24.1, 125.32, 107.1, 81.26, 33.75, 21.32,
12.56, 10.4, 10.07, 8.43, 8.41, 8.34, 8.17, 7.74, 7.46, 5.91, 5.16, 5.11, 3.57, 0.8, 0.54, 109.13,
42.28, 16.39, 10.78, 10.38, 9.54, 7.04, 4.25, 3.94, 3.51, 2.11, 1.82, 1.54, 0.9, 0.82, 0.51, 191.2,
20.64, 15.27, 13.59, 5.82, 0.99)
)
print(order_details)## order_id order_date ship_date ship_mode customer_name
## 1 AG-2011-2040 2011-01-01 2011-06-01 Standard Class Toby Braunhardt
## 2 IN-2011-47883 2011-01-01 2011-08-01 Standard Class Joseph Holt
## 3 HU-2011-1220 2011-01-01 2011-05-01 Second Class Annie Thurman
## 4 IT-2011-3647632 2011-01-01 2011-05-01 Second Class Eugene Moren
## 5 IN-2011-47883 2011-01-01 2011-08-01 Standard\nClass Joseph Holt
## 6 IN-2011-47883 2011-01-01 2011-08-01 Standard Class Joseph Holt
## 7 CA-2011-1510 2011-02-01 2011-06-01 Standard Class Magdelene Morse
## 8 IN-2011-79397 2011-03-01 2011-03-01 Same Day Kean Nguyen
## 9 ID-2011-80230 2011-03-01 2011-09-01 Standard Class Ken Lonsdale
## 10 IZ-2011-4680 2011-03-01 2011-07-01 Standard Class Lindsay\nWilliams
## 11 IN-2011-65159 2011-03-01 2011-07-01 Second Class Larry Blacks
## 12 IN-2011-65159 2011-03-01 2011-07-01 Second Class Larry Blacks
## 13 ES-2011-4869686 2011-03-01 2011-07-01 Standard Class Dorothy Dickinson
## 14 IN-2011-33652 2011-03-01 2011-09-01 Standard Class Dennis Pardue
## 15 ID-2011-80230 2011-03-01 2011-09-01 Standard Class Ken Lonsdale
## 16 MX-2011-160234 2011-03-01 2011-07-01 Standard\nClass Stewart Visinsky
## 17 IR-2011-770 2011-03-01 2011-07-01 Standard Class Jas O'Carroll
## 18 ID-2011-80230 2011-03-01 2011-09-01 Standard Class Ken Lonsdale
## 19 ID-2011-80230 2011-03-01 2011-09-01 Standard Class Ken Lonsdale
## 20 ID-2011-12596 2011-03-01 2011-08-01 Standard Class Chris McAfee
## 21 IN-2011-79397 2011-03-01 2011-03-01 Same Day Kean\nNguyen
## 22 IR-2011-7690 2011-03-01 2011-08-01 Second Class Nat Gilpin
## 23 IR-2011-770 2011-03-01 2011-07-01 Standard Class Jas O'Carroll
## 24 TZ-2011-7370 2011-03-01 2011-08-01 Standard Class Jack Garza
## 25 IZ-2011-4680 2011-03-01 2011-07-01 Standard Class Lindsay Williams
## 26 IN-2011-65159 2011-03-01 2011-07-01 Second Class Larry Blacks
## 27 IR-2011-770 2011-03-01 2011-07-01 Standard\nClass Jas\nO'Carroll
## 28 MX-2011-111255 2011-04-01 2011-09-01 Second Class Russell Applegate
## 29 MX-2011-140641 2011-04-01 2011-09-01 Standard Class Maya Herman
## 30 MX-2011-158771 2011-04-01 2011-11-01 Standard Class Beth Thompson
## 31 ES-2011-4939443 2011-04-01 2011-08-01 Standard Class Arthur Prichep
## 32 MX-2011-111255 2011-04-01 2011-09-01 Second Class Russell\nApplegate
## 33 MX-2011-140641 2011-04-01 2011-09-01 Standard Class Maya Herman
## 34 US-2011-136007 2011-04-01 2011-11-01 Standard Class Beth Thompson
## 35 MX-2011-159373 2011-04-01 2011-08-01 Standard Class Arthur Wiediger
## 36 MX-2011-159373 2011-04-01 2011-08-01 Standard Class Arthur Wiediger
## 37 MX-2011-159373 2011-04-01 2011-08-01 Standard Class Arthur\nWiediger
## 38 IT-2011-2942451 2011-04-01 2011-09-01 Standard Class Grant Thornton
## 39 CA-2011-103800 2011-04-01 2011-08-01 Standard Class Darren Powers
## 40 IN-2011-33036 2011-04-01 2011-08-01 Standard Class Bradley Drucker
## 41 IT-2011-2942451 2011-04-01 2011-09-01 Standard Class Grant Thornton
## 42 SU-2011-5190 2011-04-01 2011-08-01 Standard Class Jasper\nCacioppo
## 43 SU-2011-5190 2011-04-01 2011-08-01 Standard Class Jasper Cacioppo
## 44 MX-2011-109267 2011-05-01 2011-09-01 Standard Class Jennifer Halladay
## 45 ES-2011-3848439 2011-05-01 2011-07-01 First Class Michael Granlund
## 46 ES-2011-3848439 2011-05-01 2011-07-01 First Class Michael Granlund
## 47 CA-2011-112326 2011-05-01 2011-09-01 Standard Class Phillina Ober
## 48 IN-2011-27681 2011-05-01 2011-11-01 Standard\nClass Shaun Weien
## 49 CA-2011-112326 2011-05-01 2011-09-01 Standard Class Phillina Ober
## segment country
## 1 Consumer Algeria
## 2 Consumer Australia
## 3 Consumer Hungary
## 4 Home Office Sweden
## 5 Consumer Australia
## 6 Consumer Australia
## 7 Consumer Canada
## 8 Corporate Australia
## 9 Consumer New Zealand
## 10 Corporate Iraq
## 11 Consumer Philippines
## 12 Consumer Philippines
## 13 Consumer United Kingdom
## 14 Home\nOffice Malaysia
## 15 Consumer New\nZealand
## 16 Consumer Guatemala
## 17 Consumer Iran
## 18 Consumer New Zealand
## 19 Consumer New Zealand
## 20 Consumer Thailand
## 21 Corporate Australia
## 22 Corporate Iran
## 23 Consumer Iran
## 24 Consumer Tanzania
## 25 Corporate Iraq
## 26 Consumer Philippines
## 27 Consumer Iran
## 28 Consumer Brazil
## 29 Corporate Mexico
## 30 Home Office Cuba
## 31 Consumer France
## 32 Consumer Brazil
## 33 Corporate Mexico
## 34 Home Office Brazil
## 35 Home Office Cuba
## 36 Home Office Cuba
## 37 Home Office Cuba
## 38 Corporate United Kingdom
## 39 Consumer United States
## 40 Consumer Japan
## 41 Corporate United Kingdom
## 42 Consumer Sudan
## 43 Consumer Sudan
## 44 Consumer Mexico
## 45 Home Office France
## 46 Home Office France
## 47 Home Office United States
## 48 Consumer Taiwan
## 49 Home Office United States
## product_name
## 1 Tenex Lockers, Blue
## 2 Acme Trimmer, High Speed
## 3 Tenex Box, Single\nWidth
## 4 Enermax Note Cards, Premium
## 5 Eldon Light Bulb, Duo Pack
## 6 Eaton Computer Printout\nPaper, 8.5 x 11
## 7 Okidata Inkjet, Wireless
## 8 Hoover Microwave, White
## 9 Hewlett Wireless Fax,\nLaser
## 10 Novimex Swivel Stool, Set of Two
## 11 Tenex Lockers, Industrial
## 12 Chromcraft Round Table,\nAdjustable Height
## 13 Dania Corner Shelving, Traditional
## 14 Hewlett Fax and Copier, Laser
## 15 Hon\nRocking Chair, Set of Two
## 16 Nokia Headset, VoIP
## 17 Breville Coffee Grinder, Black
## 18 Belkin\nNumeric Keypad, Bluetooth
## 19 SAFCO Chairmat, Black
## 20 Smead File Cart, Blue
## 21 Avery Color\nCoded Labels, Laser Printer Compatible
## 22 BIC Sketch Pad, Water Color
## 23 Rogers Folders,\nIndustrial
## 24 Stiletto Scissors, Serrated
## 25 Cameo Interoffice Envelope, Set of 50
## 26 Stockwell\nStaples, Metal
## 27 Advantus Rubber Bands, Metal
## 28 Dania Classic Bookcase, Pine
## 29 Enermax\nKeyboard, Bluetooth
## 30 Jiffy Interoffice Envelope, Set of 50
## 31 Binney & Smith Sketch Pad,\nEasy-Erase
## 32 Fiskars Letter Opener, Easy Grip
## 33 Sharp Ink, Laser
## 34 Jiffy Interoffice Envelope, Set\nof 50
## 35 SAFCO Chairmat, Black
## 36 Memorex Mouse, USB
## 37 Kraft Peel and Seal, Recycled
## 38 Boston Markers, Easy-Erase
## 39 Message Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200\nDupl. Sets/Book
## 40 Harbour Creations File Folder Labels, 5000 Label Set
## 41 Eldon Folders, Single\nWidth
## 42 Boston Pens, Fluorescent
## 43 Avery Hole Reinforcements, Durable
## 44 Hoover Stove, Black
## 45 Sanford Canvas, Fluorescent
## 46 Binney & Smith Pencil Sharpener, Water Color
## 47 SAFCO Boltless\nSteel Shelving
## 48 Rubbermaid Photo Frame, Durable
## 49 Avery 508
## sales quantity discount profit shipping_cost
## 1 408 2 0.000 106.14000 35.46
## 2 120 3 0.100 36.03600 9.72
## 3 66 4 0.000 29.64000 8.17
## 4 45 3 0.500 -26.05500 4.82
## 5 114 5 0.100 37.77000 4.70
## 6 55 2 0.100 15.34200 1.80
## 7 314 1 0.000 3.12000 24.10
## 8 276 1 0.100 110.41200 125.32
## 9 912 4 0.400 -319.46400 107.10
## 10 667 4 0.000 253.32000 81.26
## 11 338 3 0.450 -122.80050 33.75
## 12 211 1 0.550 -70.39950 21.32
## 13 854 7 0.000 290.43000 12.56
## 14 193 1 0.000 50.13000 10.40
## 15 159 2 0.400 -95.67600 10.07
## 16 195 4 0.000 44.88000 8.43
## 17 123 2 0.000 42.90000 8.41
## 18 69 2 0.400 3.42000 8.34
## 19 69 2 0.400 -26.41200 8.17
## 20 135 2 0.470 -45.90180 7.74
## 21 36 3 0.100 4.74300 7.46
## 22 52 1 0.000 7.77000 5.91
## 23 62 2 0.000 8.70000 5.16
## 24 81 4 0.000 26.76000 5.11
## 25 47 1 0.000 17.07000 3.57
## 26 6 1 0.450 0.54600 0.80
## 27 17 1 0.000 4.17000 0.54
## 28 1648 6 0.000 609.84000 109.13
## 29 223 4 0.000 13.28000 42.28
## 30 186 6 0.000 3.60000 16.39
## 31 140 3 0.000 20.88000 10.78
## 32 149 8 0.000 28.16000 10.38
## 33 166 2 0.002 49.42824 9.54
## 34 74 6 0.600 -107.85600 7.04
## 35 38 1 0.000 6.88000 4.25
## 36 38 2 0.000 2.24000 3.94
## 37 39 3 0.000 7.68000 3.51
## 38 27 2 0.500 -21.90000 2.11
## 39 16 2 0.200 5.55120 1.82
## 40 27 3 0.000 13.68000 1.54
## 41 17 2 0.500 -1.05000 0.90
## 42 15 1 0.000 2.61000 0.82
## 43 6 1 0.000 2.10000 0.51
## 44 3029 8 0.000 999.36000 191.20
## 45 207 4 0.000 76.56000 20.64
## 46 90 3 0.000 20.52000 15.27
## 47 273 3 0.200 -64.77480 13.59
## 48 49 1 0.000 22.92000 5.82
## 49 12 3 0.200 4.27170 0.99#Menghitung segmen terbanyak dalam data order tersebut (Dimas Prasetyo_556271)
library(dplyr)
order_details %>%
group_by(segment) %>%
summarise(Jumlah = n())## # A tibble: 4 × 2
## segment Jumlah
## <chr> <int>
## 1 "Consumer" 29
## 2 "Corporate" 9
## 3 "Home\nOffice" 1
## 4 "Home Office" 10#Melihat rata-rata penjualan dan kuantitas di setiap segmen (Dimas Prasetyo_556271)
order_details %>%
group_by(segment) %>%
summarise(across(sales:quantity, mean))## # A tibble: 4 × 3
## segment sales quantity
## <chr> <dbl> <dbl>
## 1 "Consumer" 324. 3
## 2 "Corporate" 168. 2.22
## 3 "Home\nOffice" 193 1
## 4 "Home Office" 100. 3.4R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.