business 2

profilezeel906
FoodieTrip.twbx

3 - Mapping Tutorial - Foodie Trip - TfT (updated).twb

0 ['Tourist Spots$'] Count true 0 "A1:E6:no:A1:E6:0" true 6 Name 130 [Name] ['Tourist Spots$'] Name 0 string Count true "WSTR" [Migrated Data] Comment 130 [Comment] ['Tourist Spots$'] Comment 1 string Count true "WSTR" [Migrated Data] URL 130 [URL] ['Tourist Spots$'] URL 2 string Count true "WSTR" [Migrated Data] x 20 [x] ['Tourist Spots$'] x 3 integer Sum true "I8" [Migrated Data] y 20 [y] ['Tourist Spots$'] y 4 integer Sum true "I8" [Migrated Data] 0 [Restaurants$] Count true 0 "A1:N71:no:A1:N71:0" true 6 Trip Name 130 [Trip Name] [Restaurants$] Trip Name 0 string Count true "WSTR" [Migrated Data] Order of Visit 20 [Order of Visit] [Restaurants$] Order of Visit 1 integer Sum true "I8" [Migrated Data] Restaurant Name 130 [Restaurant Name] [Restaurants$] Restaurant Name 2 string Count true "WSTR" [Migrated Data] Chef 130 [Chef] [Restaurants$] Chef 3 string Count true "WSTR" [Migrated Data] City 130 [City] [Restaurants$] City 4 string Count true "WSTR" [Migrated Data] State 130 [State] [Restaurants$] State 5 string Count true "WSTR" [Migrated Data] Country 130 [Country] [Restaurants$] Country 6 string Count true "WSTR" [Migrated Data] Region 130 [Region] [Restaurants$] Region 7 string Count true "WSTR" [Migrated Data] LatLong 130 [LatLong] [Restaurants$] LatLong 8 string Count true "WSTR" [Migrated Data] Latitude 5 [Latitude] [Restaurants$] Latitude 9 real Sum 15 true "R8" [Migrated Data] Longitude 5 [Longitude] [Restaurants$] Longitude 10 real Sum 15 true "R8" [Migrated Data] Has Michelin 130 [Has Michelin] [Restaurants$] Has Michelin 11 string Count true "WSTR" [Migrated Data] Michelin Stars 20 [Michelin Stars] [Restaurants$] Michelin Stars 12 integer Sum true "I8" [Migrated Data] Cuisine 130 [Cuisine] [Restaurants$] Cuisine 13 string Count true "WSTR" [Migrated Data] Where does Harry want to go? Your best friend Harry is a foodie, and he has been collecting information about restaurants he wants to visit. He has been planning several foodie trips throughout the world, and wants you to help him decide which trip to go on first by showing him the geographic distribution of his desired restaurants. 1. Æ Double click on Country dimension. This will place Latitude (generated) in the Rows shelf and Longitude (generated) and the Columns shelf 2. Drag the Number of Records measure to color 3. Edit the Color legend by clicking on the top right side of the legend border Choose the Red Blue Diverging color palette Click OK when done. 4. Click on Map menu > Background Maps > WMS Servers ... In the WMS Server Connections, click on Add. Type the following in the URL: http://www2.demis.nl/wms/wms.asp?wms=WorldMap& Click OK, and then Close, when done 5. Click on the Map menu again, and this time choose "World Map" instead of the default "Online" 6. Click on the Map menu again, and select Map Options... This should display Map Options window on the left hand side. Check the following: - Bathymetry, Countries, Topography, Hillshading, Coastlines 7. Close Map Options by clicking on that window's right border X when done What is the distribution of Michelin star restaurants? Harry wants to see what proportion of Michelin star restaurants are in each country he is planning to visit. 1. Æ Double click on Country dimension. This will place Latitude (generated) in the Rows shelf and Longitude (generated) and the Columns shelf 2. Drag the Number of Records measure to Color 3. To keep the color scheme consistent with the first workbook, edit the color legend by clicking on the top right side of the legend border Choose the Red Blue Diverging color palette Click OK when done. 4. Æ Ctrl + Drag the Latitude (generated) pill from the Rows shelf to the Rows shelf. This creates a second copy of the Latitude (generated) pill 5. Æ Select the second Latitude (generated) card that was created under the Marks card. This card corresponds to the pill you just created in the previous step 6. Æ On the second Latitude (generated) card, drag the Has Michelin dimension to Color 7. Æ On the second Latitude (generated) card, right click drag the Has Michelin dimension to Angle. Choose CNT(Has Michelin) when prompted 8. Æ Click on the right edge of the second Latitude (generated) pill in the Rows shelf to show the dropdown, and select Dual Axis 9. Æ Drag the Region dimension to the Pages shelf. Now you can click on the < or the > to go display different regions [excel.41504.449629155089].[Multiple Values] [excel.41504.449629155089].[none:Name:nk] What is Japan Trip itinerary like? Harry already planned his trips and already marked the restaurants he wants to visit in sequence. He asks if you can visualize his first trip (Japan Trip) for him using Tableau. Note: for this exercise, we will be using the Latitude and Longitude measures that are not pregenerated in Tableau. These measurements will allow us to pinpoint the precise location of the restaurants. Using the pregenerated Latitude and Longitude measures would only allow us to go as deep as the most granular dimension - in this case, City . 1. Æ Drag Latitude measure to Rows shelf 2. Æ Drag Longitude measure to Columns shelf 3. Æ Drag the Trip Name to Filter This opens a Filter dialog box Choose Japan Trip, and click OK when done 4. Æ Drag Restaurant Name dimension to Label. This shows us each restaurant's location plotted by latitude and longitude 5. Æ Because the restaurants are so close together, our zoom level is too deep to be able to pull a map of it. Zoom out using the controls in the upper left of the map 5. Æ Change the Marks type from Automatic to Line 6. Æ Drag the Order of Visit measure to Path 7. Æ Drag the Order of Visit measure to Label 8. Æ Click on Map -> Map Options 9. Æ Turn on "Streets and Highways" [excel.41496.965312303240].[none:Trip Name:nk] [excel.41496.965312303240].[none:Cuisine:nk] [excel.41496.965312303240].[none:Chef:nk] Areas to visit near Shibuya Harry has a friend who lives near the Shibuya station who he wants to visit. He also wants to visit nearby areas. He asked you to help visualize nearby tourist areas he can visit. He also want to know where these areas are relative to the subway stations. 1. Use the data called Tourist Spots (Foodie Trip.xlsx) for this worksheet by selecting it from the data window 2. Æ Download the Tokyo Subway map from http://bit.ly/japansubway . Save this on your local drive 3. Click on the Map menu > Background Images > Tourist Spots (Foodie Trip.xlsx)... 4. Click on Add Image... Name: Tokyo Subway File or URL: navigate to where you saved the subway map X Field: x Left: 0 Right: 100 Y Field: y Bottom: 0 Top: 60 Washout: slide this to about 50% Click OK when done 5. Drag x measure to Columns and y to Rows 6. Right click on the x pill and y pill, and change the Measure from Sum to Average for both pills 7. Drag Name dimension to Label 8. Drag Comment to Tooltip 9. Edit the tooltip by clicking the Tooltip shelf and remove the x and y values so they don't show up when you hover over that area 10. Click on the Worksheet menu, and select Actions > Add Action > URL and use the following values: For anything in angled bracket, use the "Insert" dropdown menu to select the actual fields. Name: Check < Name> Area in Google Maps Run Action on: Menu URL: https://maps.google.com/maps?q= < Name>,+Tokyo,+Japan&hl=en Click OK, then another OK, when done. 11. Test your custom map by hovering over the marked areas Visualizing Foodie Fantasy This workbook is intended to introduce you to mapping in Tableau using a fictional foodie's fantasy restaurant list. A "foodie" is a colloquial term used to describe a person that spends a keen amount of attention and energy on knowing the ingredients of food, the proper preparation of food, and finds great enjoyment in top-notch ingredients and exemplary preparation ( http://www.urbandictionary.com/ ) This data set was generated using publicly available information about different restaurants around the world. This data set is not guaranteed to be accurate, and was created specifically for purposes of visualizing using Tableau. Notes - You must be online when you're working with (online) maps in Tableau, otherwise the map tiles will not be rendered - It is important to know how to pan and zoom in a map. You can view the icons and pointers here: http://onlinehelp.tableausoftware.com/v8.0/server/en-us/help.htm#zoom.htm Æ Directions Directions for each exercise are contained in a caption. If the caption is long, you can click on the caption area and scroll down to see the rest of the instructions. You can also hide the caption to increase your workspace by clicking on the Caption title dropdown and selecting Hide Card. If you wish to show the caption again, you can bring it back by clicking on the the Worksheet menu > Show Caption. [excel.41496.965312303240].[none:Country:nk] [excel.41504.449629155089].[:Measure Names] [excel.41504.449629155089].[none:Comment:nk] [excel.41504.449629155089].[none:Name:nk] [excel.41496.965312303240].[none:Country:nk] [excel.41496.965312303240].[none:Cuisine:nk] [excel.41496.965312303240].[none:Michelin Stars:ok] [excel.41496.965312303240].[none:Region:nk] [excel.41496.965312303240].[sum:Calculation_2480820131752436:qk] [excel.41496.965312303240].[sum:Michelin Stars:qk] [excel.41504.449629155089].[none:Name:nk] [excel.41496.965312303240].[none:Chef:nk] [excel.41496.965312303240].[none:City:nk] [excel.41496.965312303240].[none:Country:nk] [excel.41496.965312303240].[none:Cuisine:nk] [excel.41496.965312303240].[none:Order of Visit:nk] [excel.41496.965312303240].[none:Region:nk] [excel.41496.965312303240].[none:Restaurant Name:nk] [excel.41496.965312303240].[none:State:nk] [excel.41496.965312303240].[none:Trip Name:nk] [excel.41496.965312303240].[none:Trip Name:ok] 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Data/03 - Mapping/Foodie Trip.xlsx

Restaurants

Trip Name Order of Visit Restaurant Name Chef City State Country Region LatLong Latitude Longitude Has Michelin Michelin Stars Cuisine
Friends the Restaurant Phnom Penh Cambodia Asia 11.567489,104.929212 11.567489 104.929212 No Cambodian
Amber Richard Ekkebus Hong Kong China Asia 22.280936,114.157709 22.280936 114.157709 Yes 2 French
Lung King Heen Chan Yan-tak Hong Kong China Asia 22.287786,114.15674 22.287786 114.15674 Yes 3 Chinese
Tim Ho Wan Hong Kong China Asia 22.285205,114.158203 22.285205 114.158203 Yes 1 Chinese
Indigo Vineet Bhatia. Mumbai India Asia 18.923094,72.832439 18.923094 72.832439 Yes 1 Indian
Baan Aarya Bintan Indonesia Asia 1.184722,104.311699 1.184722 104.311699 No Asian
Garuda Padang Cuisine Jakarta Indonesia Asia 1.266028,103.821441 1.266028 103.821441 No Thai
Japan Trip 8 Ginza Kojyu Tokyo Japan Asia 35.671731,139.762956 35.671731 139.762956 Yes 3 Japanese
Japan Trip 2 L'ATELIER de Joel Robuchon Joël Robuchon Tokyo Japan Asia 35.663015,139.729072 35.663015 139.729072 Yes 3 French
Japan Trip 1 Nihonryori RyuGin Tokyo Japan Asia 35.666362,139.729072 35.666362 139.729072 Yes 3 Japanese
Japan Trip 7 Nodaiwa Tokyo Japan Asia 35.65883,139.743212 35.65883 139.743212 No Japanese
Japan Trip 5 Quintessence Shuzo Kishida Tokyo Japan Asia 35.642545,139.721883 35.642545 139.721883 Yes 3 French
Japan Trip 6 Azabu Yukimura Azabu Yukimura Tokyo Japan Asia 35.657802,139.732915 35.657802 139.732915 Yes 3 Japanese
Japan Trip 4 Esaki Shintaro Esaki Tokyo Japan Asia 35.672358,139.712999 35.672358 139.712999 Yes 3 Japanese
Japan Trip 3 Sukiyabashi Jiro Jiro Ono Tokyo Japan Asia 35.66148,139.729244 35.66148 139.729244 Yes 3 Japanese
Fat Siu Lau Rua da Felicidade Macau Asia 22.195213,113.537539 22.195213 113.537539 No Chinese
Flutes at the Fort Singapore Asia 1.293487,103.848389 1.293487 103.848389 No Western
Restaurant Andre Singapore Asia 1.287009,103.837913 1.287009 103.837913 No Western
Waku Ghin Tetsuya Wakuda Singapore Asia 1.292844,103.858856 1.292844 103.858856 No Japanese
Iggy's Singapore Asia 1.31052,103.829529 1.31052 103.829529 No Western
Byeokje Galbi Seoul South Korea Asia 37.488395,127.052075 37.488395 127.052075 No Korean
Din Tai Fung Taipei Taiwan Asia 35.672614,139.764037 35.672614 139.764037 No Chinese
Bo.lan Bangkok Thailand Asia 13.725044,100.569209 13.725044 100.569209 No Thai
Nahm Bangkok Thailand Asia 13.727212,100.529167 13.727212 100.529167 Yes 1 Thai
Gaggan Gaggan Bangkok Thailand Asia 13.738676,100.542491 13.738676 13.738676 No Indian
Lemongrass Ho Chi Minh City Vietnam Asia 25.033575,121.530074 25.033575 121.530074 No Vietnamese
Steirebeck Vienna Austria Europe 48.205141,16.381166 48.205141 16.381166 Yes 2 Austrian
Hof Van Cleve Peter Goossens Kruishoutem Belgium Europe 50.90444,3.509696 50.90444 3.509696 Yes 3 Belgian
Hertog Jan Gert De Mangeleer  Bruges Belgium Europe 51.201721,3.188567 51.201721 3.188567 Yes 3 Belgian
In De Wulf Dranouter Belgium Europe 50.748975,2.792538 50.748975 2.792538 Yes 1 Western
Noma René Redzepi  Copenhagen Denmark Europe 55.678577,12.596205 55.678577 12.596205 Yes 2 Scandinavian
Chez Dominique Helsinki Finland Europe 60.166579,24.946511 60.166579 24.946511 Yes 2 French
L'arpege Paris France Europe 48.856499,2.316907 48.856499 2.316907 Yes 3 French
Le Chateaurbriand Paris France Europe 48.872167,2.371647 48.872167 2.371647 No French
Guy Savoy Guy Savoy Paris France Europe 48.87705,2.295156 48.87705 2.295156 Yes 3 French
Vendome Bergisch Gladbach Germany Europe 50.966806,7.160633 50.966806 7.160633 Yes 3 Western
Spondi Athens Greece Europe 37.965617,23.743168 37.965617 37.965617 Yes 2 Greek
Osteria Francescana Modena Italy Europe 44.645544,10.921734 44.645544 10.921734 Yes 3 Italian
Piazza Duomo Enrico Crippa Pistoia Italy Europe 43.934711,10.91732 43.934711 10.91732 Yes 2 Italian
Le Louis SV Alain Ducasse Monte Carlo Monaco Europe 43.739445,7.427205 43.739445 7.427205 Yes 3 French
Oud Sluis Sergio Herman Sluis Netherlands Europe 51.307867,3.387602 51.307867 3.387602 Yes 3 Dutch
Vila Joya Albufeira Portugal Europe 37.083804,-8.313072 37.083804 -8.313072 Yes 2 Portuguese
Varvary Moscow Russia Europe 55.767014,37.610443 55.767014 37.610443 Yes 1 Russian
Tickets Albert Adria Barcelona Spain Europe 41.376132,2.15679 41.376132 2.15679 No Spanish
El Celler de Can Roca Joan and Josep Roca Girona Spain Europe 41.993851,2.808116 41.993851 2.808116 Yes 3 Spanish
Arzack Juan Mari Arzak San Sebastian Spain Europe 43.322243,-1.94935 43.322243 -1.94935 Yes 3 Spanish
Restaurant Frantzen Bjorn Frantzén Stockholm Sweden Europe 59.328111,18.067993 59.328111 18.067993 Yes 2 Scandinavian
Schloss Schauenstein Andreas Caminada Furstenau Switzerland Europe 46.7248,9.445812 46.7248 9.445812 Yes 3 Swiss
Hotel de Ville Benoit Violier Crissier Switzerland Europe 46.554419,6.576709 46.554419 6.576709 Yes 3 French
Dinner by Heston Blumenthal Heston Blumenthal London UK Europe 51.502759,-0.159909 51.502759 -0.159909 Yes 1 Western
The Fat Duck Heston Blumenthal Bray UK Europe 51.508742,-0.701887 51.508742 -0.701887 Yes 3 Western
Gordon Ramsay Gordon Ramsay Chelsea UK Europe 51.485846,-0.162138 51.485846 -0.162138 Yes 3 Western
Le Petite Maison Dubai United Arab Emirates Middle East 25.223267,55.283149 25.223267 55.283149 Yes 1 French
Pujol Enrique Olvera Mexico City Mexico North America 19.437781,-99.186142 19.437781 -99.186142 Yes 3 Mexican
The French Laundry Thomas Keller Yountville California USA North America 38.404808,-122.365039 38.404808 -122.365039 Yes 3 French
Eleven Madison Park Daniel Humm New York New York USA North America 40.741908,-73.987409 40.741908 -73.987409 Yes 3 French
Alinea Grant Achatz Chicago Illinois USA North America 41.913759,-87.648371 41.913759 -87.648371 Yes 3 Western
Le Bernardin Eric Ripert New York New York USA North America 40.762081,-73.981726 40.762081 -73.981726 Yes 3 French
Momofuku Ko David Chang New York New York USA North America 40.729763,-73.984344 40.729763 -73.984344 Yes 2 Western
Blue Hill at Stone Barns Westchester New York USA North America 41.107683,-73.829697 41.107683 -73.829697 Yes 1 Western
Picasso Julian Serrano Las Vegas Nevada USA North America 36.120405,-115.173851 36.120405 -115.173851 Yes 2 French
Bibou Philadelphia Pennsylvania USA North America 39.93804,-75.156454 39.93804 -75.156454 No French
Per Se Eli Kaimeh New York New York USA North America 40.790939,-73.826065 40.790939 -73.826065 Yes 3 French
The Tasting Room At Le Quartier Francais Margot Janse Franschhoek South Africa South Africa -33.90832,19.120304 -33.90832 19.120304 No French
D.O.M. Restaurante Alex Atala  Sao Paulo Brazil South America -23.565207,-46.667654 -23.565207 -46.667654 No Western
Roberta Sudbrack Rio de Janeiro Brazil South America -22.961396,-43.217323 -22.961396 -43.217323 No Western
Central Restaurante Virgilio Martinez Lima Peru South America -12.128453,-77.027405 -12.128453 -77.027405 No Western
Attica Ben Shewry Melbourne Australia Australia -37.876564,144.997319 -37.876564 144.997319 No Western
Quay Peter Gilmore  Sidney Australia Australia -33.85748,151.209934 -33.85748 151.209934 No Western
Momofuku Seiobo Ben Greeno Sydney Australia Australia -33.86728,151.194485 -33.86728 151.194485 No Western
http://en.wikipedia.org/wiki/Sukiyabashi_Jiro http://en.wikipedia.org/wiki/Guy_Savoy http://elevenmadisonpark.com/ http://en.wikipedia.org/wiki/Grant_Achatz

Tourist Spots

Name Comment URL x y
Shinjuku Major commercial and administrative centre, housing the busiest train station in the world (Shinjuku Station) and the Tokyo Metropolitan Government (wikipedia.com) http://en.wikipedia.org/wiki/Shinjuku 25 40
Harajuku Where to shop trendy street fashion clothes. Featured in Gwen Stefani's song Harajuku Girls (wikipedia.com) http://en.wikipedia.org/wiki/Harajuku 25 32
Shibuya Shopping and entertainment district in Tokyo (wikipedia.com) http://en.wikipedia.org/wiki/Shibuya 25 28
Roppongi home to the rich Roppongi Hills area and an active night club scene (wikipedia.com) http://en.wikipedia.org/wiki/Roppongi 34 22
Ginza pscale area of Tokyo with numerous department stores, boutiques, restaurants and coffeehouses. Ginza is recognized as one of the most luxurious shopping districts in the world http://en.wikipedia.org/wiki/Ginza 56 23
http://en.wikipedia.org/wiki/Shinjuku http://en.wikipedia.org/wiki/Harajuku http://en.wikipedia.org/wiki/Shibuya http://en.wikipedia.org/wiki/Roppongi http://en.wikipedia.org/wiki/Ginza

Seating

Name Favorite Sushi x y
Harry Toro 5 19
Hermione Otoro 10 13
Ron Ikura 16 4
Ginny Uni 23 28
Luna Aji 30 24
Neville Saba 37 20

Sheet1

Name Comment x y
Asakusa Asakusa 79 70
Ginza Ginza 56 38
Akihabara Akihabara 69 66
Shinjuku Shinjuku 24 66
Ikebukuro Ikebukuro 39 82
Ueno Ueno 69 71
Omotesando Omotesando 32 49
Ichigaya Ichigaya 42 60
Harajuku Harajuku 15 48
Shibuya Shibuya 15 40
Roppongi Roppongi 20 32
Ginza Ginza 34 33
Hibiya Hibiya 31 39
Name Comment x y
Table 100 60

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