├── .gitignore ├── img └── title-slide.png ├── ds-puzzles.Rproj ├── 11_sandwiches ├── 11_soln.R ├── 11_text.Rmd ├── sample-data-walkthrough.Rmd ├── sample-data-walkthrough.md └── 11_data.csv ├── README.Rmd └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | .Rhistory 2 | .RData 3 | .Rproj.user 4 | -------------------------------------------------------------------------------- /img/title-slide.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isteves/ds-puzzles/HEAD/img/title-slide.png -------------------------------------------------------------------------------- /ds-puzzles.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 4 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /11_sandwiches/11_soln.R: -------------------------------------------------------------------------------- 1 | #' --- 2 | #' title: Sandwiches 3 | #' --- 4 | #' 5 | #' Use _Ctrl (Cmd) + Shift + K_ to render this file 6 | #' 7 | #+ r setup, include = FALSE 8 | options(tidyverse.quiet = TRUE) 9 | 10 | #+ r 11 | library(tidyverse) 12 | library(here) 13 | data_path <- here::here('11_sandwiches', '11_data.csv') 14 | 15 | # YOUR SOLUTION CODE HERE 16 | 17 | -------------------------------------------------------------------------------- /11_sandwiches/11_text.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: html_document 3 | --- 4 | 5 | ```{r setup, include = FALSE} 6 | knitr::opts_chunk$set(echo = FALSE, warning = FALSE, error = FALSE) 7 | library(tidyverse) 8 | ``` 9 | 10 | The neighborhood sandwich store makes the _best_ sandwiches! They’ve got everything from classics like BLTs to more unusual options like Fluffernutters. Since many of their specialty ingredients keep going bad, they've decided to cut their selection and only focus on their best-selling sandwich. 11 | 12 | To help with the decision, the storeowners have collected data on their customers’ favorite sandwiches. Most people listed several varieties (in no particular order). Here’s a sample of the data: 13 | 14 | ```{r warning = FALSE, echo = FALSE} 15 | library(tidyverse) 16 | sw <- tibble::tribble( 17 | ~names, ~sandwiches, 18 | "Abby", "Denver; BLT; Torta ahogada; Barbecue", 19 | "Abigail", "BLT; Ftira; Primanti; Ice cream; Choripán", 20 | "Adam", "Corned beef; Montadito; Cheesesteak; Tripleta; Dagwood; Jambon-beurre", 21 | "Alexa", "Dagwood; Mortadella", 22 | "Alexandria", "Slider; Beschuit met muisjes; Chicken salad", 23 | "Ana", "Fried brain; Polish boy; Vegetable; Pudgy Pie; Dagwood" 24 | ) 25 | 26 | sw %>% knitr::kable() 27 | ``` 28 | 29 | In this sample, the Dagwood sandwich is the most popular. 30 | 31 | In the full dataset, **what is the most popular sandwich among the customers?** 32 | 33 | -------------------------------------------------------------------------------- /11_sandwiches/sample-data-walkthrough.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Sample data walk-through" 3 | output: md_document 4 | --- 5 | 6 | ```{r setup, include=FALSE} 7 | knitr::opts_chunk$set(echo = TRUE) 8 | options(tidyverse.quiet = TRUE) 9 | ``` 10 | 11 | ![](https://www.flickr.com/photos/skywhisperer/5724550360/) 12 | 13 | In the puzzle, we're given some sample data to use as a _test case_. That is, if we can determine the most popular sandwich in our sample, we'll be most--if not all--of the way to answering this question for our full dataset. 14 | 15 | ```{r warning = FALSE, echo = FALSE} 16 | library(tidyverse) 17 | sw <- tibble::tribble( 18 | ~names, ~sandwiches, 19 | "Abby", "Denver; BLT; Torta ahogada; Barbecue", 20 | "Abigail", "BLT; Ftira; Primanti; Ice cream; Choripán", 21 | "Adam", "Corned beef; Montadito; Cheesesteak; Tripleta; Dagwood; Jambon-beurre", 22 | "Alexa", "Dagwood; Mortadella", 23 | "Alexandria", "Slider; Beschuit met muisjes; Chicken salad", 24 | "Ana", "Fried brain; Polish boy; Vegetable; Pudgy Pie; Dagwood" 25 | ) 26 | 27 | sw %>% knitr::kable() 28 | ``` 29 | 30 | Let's take a look at the sample data in tibble-mode. Note that there are a few non-English letters that could give you some trouble depending on how you import the data into R. Sometimes the letters get "translated" into a mix of letters and punctuation (i.e. "Choripán" rather than "Choripán"). 31 | 32 | ```{r} 33 | sw 34 | ``` 35 | 36 | As first step, let's separate out the sandwiches into individual observations using the handy `tidyr` function, `separate_rows()`. 37 | 38 | ```{r} 39 | sw %>% 40 | separate_rows(sandwiches, sep = "; ") 41 | ``` 42 | 43 | Keep in mind that omitting the space in the separator may cause some of the results not to match up. For example, "BLT" and " BLT" would require an extra cleaning step, such as `mutate(sandwiches = str_trim(sandwiches))`. 44 | 45 | Next, we can count the sandwiches to determine which type is the most popular. Adding `sort = TRUE` brings the most popular sandwich to the top of the tibble. 46 | 47 | ```{r} 48 | sw %>% 49 | separate_rows(sandwiches, sep = "; ") %>% 50 | count(sandwiches, sort = TRUE) 51 | ``` 52 | 53 | That's it! With this small sample, you've got the basics of a working wrangling script that you can try out on the full data. 54 | -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: md_document 3 | --- 4 | # Teaching data science with puzzles 5 | ### useR! 2019 [slides](https://speakerdeck.com/isteves/teaching-data-science-with-puzzles-fd46c088-e5d5-4297-9629-60e81cc6403c) 6 | ### rstudio::conf 2019, [slides](https://speakerdeck.com/isteves/teaching-data-science-with-puzzles), [video](https://resources.rstudio.com/rstudio-conf-2019/teaching-data-science-with-puzzles) 7 | 8 | Of the many coding puzzles on the web, few focus on the programming skills needed for handling untidy data. During my summer internship at RStudio, I worked with Jenny Bryan to develop a series of data science puzzles known as the "Tidies of March." These puzzles isolate data wrangling tasks into bite-sized pieces to nurture core data science skills such as importing, reshaping, and summarizing data. We also provide access to puzzles and puzzle data directly in R through an accompanying Tidies of March package. I will show how this package models best practices for both data wrangling and project management. 9 | 10 | [![](img/title-slide.png)](https://speakerdeck.com/isteves/teaching-data-science-with-puzzles) 11 | 12 | If you'd like to take a closer look at the sandwiches example from the talk, check out the [sandwiches folder](https://github.com/isteves/ds-puzzles/tree/master/11_sandwiches) in this repo. 13 | 14 | ## Additional resources 15 | 16 | - [How to name files](https://speakerdeck.com/jennybc/how-to-name-files) talk by Jenny Bryan 17 | - [A summer of puzzles at RStudio](https://irene.rbind.io/post/summer-rstudio/) blogpost about my internship experience 18 | - [it’s not the maths, it’s the code - how testing has changed my workflow](http://cantabile.rbind.io/posts/2019-01-05-its-not-not-the-math-its-the-code/) blogpost by Charles T. Gray 19 | 20 | Packages mentioned in my talk: 21 | 22 | - [usethis](https://usethis.r-lib.org/) - a workflow package: it automates repetitive tasks that arise during project setup and development, both for R packages and non-package projects 23 | - [testthat](https://testthat.r-lib.org/) - to make testing fun 24 | - [testrmd](https://github.com/ropenscilabs/testrmd) - test chunks for RMarkdown 25 | - [reprex](https://reprex.tidyverse.org/) - render bits of R code for sharing, e.g., on GitHub or StackOverflow 26 | - [rmarkdown](https://rmarkdown.rstudio.com/) - create reproducible text and analyses 27 | 28 | ## Thank yous 29 | 30 | A big thanks to the Tidyverse team, fellow interns, and RStudio folks for a fun & interesting summer! 31 | 32 | Also thanks to Maria Novosolov, Alex Slavenko, Alex Hayes, Steven Chong, and Julien Brun for their comments and support in early versions of this talk! 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Teaching data science with puzzles 2 | ================================== 3 | 4 | ### useR! 2019 [slides](https://speakerdeck.com/isteves/teaching-data-science-with-puzzles-fd46c088-e5d5-4297-9629-60e81cc6403c) 5 | 6 | ### rstudio::conf 2019, [slides](https://speakerdeck.com/isteves/teaching-data-science-with-puzzles), [video](https://resources.rstudio.com/rstudio-conf-2019/teaching-data-science-with-puzzles) 7 | 8 | Of the many coding puzzles on the web, few focus on the programming 9 | skills needed for handling untidy data. During my summer internship at 10 | RStudio, I worked with Jenny Bryan to develop a series of data science 11 | puzzles known as the "Tidies of March." These puzzles isolate data 12 | wrangling tasks into bite-sized pieces to nurture core data science 13 | skills such as importing, reshaping, and summarizing data. We also 14 | provide access to puzzles and puzzle data directly in R through an 15 | accompanying Tidies of March package. I will show how this package 16 | models best practices for both data wrangling and project management. 17 | 18 | ![](img/title-slide.png) 19 | 20 | If you'd like to take a closer look at the sandwiches example from the 21 | talk, check out the [sandwiches 22 | folder](https://github.com/isteves/ds-puzzles/tree/master/11_sandwiches) 23 | in this repo. 24 | 25 | Additional resources 26 | -------------------- 27 | 28 | - [How to name 29 | files](https://speakerdeck.com/jennybc/how-to-name-files) talk by 30 | Jenny Bryan 31 | - [A summer of puzzles at 32 | RStudio](https://irene.rbind.io/post/summer-rstudio/) blogpost about 33 | my internship experience 34 | - [it’s not the maths, it’s the code - how testing has changed my 35 | workflow](http://cantabile.rbind.io/posts/2019-01-05-its-not-not-the-math-its-the-code/) 36 | blogpost by Charles T. Gray 37 | 38 | Packages mentioned in my talk: 39 | 40 | - [usethis](https://usethis.r-lib.org/) - a workflow package: it 41 | automates repetitive tasks that arise during project setup and 42 | development, both for R packages and non-package projects 43 | - [testthat](https://testthat.r-lib.org/) - to make testing fun 44 | - [testrmd](https://github.com/ropenscilabs/testrmd) - test chunks for 45 | RMarkdown 46 | - [reprex](https://reprex.tidyverse.org/) - render bits of R code for 47 | sharing, e.g., on GitHub or StackOverflow 48 | - [rmarkdown](https://rmarkdown.rstudio.com/) - create reproducible 49 | text and analyses 50 | 51 | Thank yous 52 | ---------- 53 | 54 | A big thanks to the Tidyverse team, fellow interns, and RStudio folks 55 | for a fun & interesting summer! 56 | 57 | Also thanks to Maria Novosolov, Alex Slavenko, Alex Hayes, Steven Chong, 58 | and Julien Brun for their comments and support in early versions of this 59 | talk! 60 | -------------------------------------------------------------------------------- /11_sandwiches/sample-data-walkthrough.md: -------------------------------------------------------------------------------- 1 | ![](https://www.flickr.com/photos/skywhisperer/5724550360/) 2 | 3 | In the puzzle, we’re given some sample data to use as a *test case*. 4 | That is, if we can determine the most popular sandwich in our sample, 5 | we’ll be most–if not all–of the way to answering this question for our 6 | full dataset. 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 |
namessandwiches
AbbyDenver; BLT; Torta ahogada; Barbecue
AbigailBLT; Ftira; Primanti; Ice cream; Choripán
AdamCorned beef; Montadito; Cheesesteak; Tripleta; Dagwood; Jambon-beurre
AlexaDagwood; Mortadella
AlexandriaSlider; Beschuit met muisjes; Chicken salad
AnaFried brain; Polish boy; Vegetable; Pudgy Pie; Dagwood
42 | 43 | Let’s take a look at the sample data in tibble-mode. Note that there are 44 | a few non-English letters that could give you some trouble depending on 45 | how you import the data into R. Sometimes the letters get “translated” 46 | into a mix of letters and punctuation (i.e. “Choripán” rather than 47 | “Choripán”). 48 | 49 | sw 50 | 51 | ## # A tibble: 6 x 2 52 | ## names sandwiches 53 | ## 54 | ## 1 Abby Denver; BLT; Torta ahogada; Barbecue 55 | ## 2 Abigail BLT; Ftira; Primanti; Ice cream; Choripán 56 | ## 3 Adam Corned beef; Montadito; Cheesesteak; Tripleta; Dagwood; Jamb~ 57 | ## 4 Alexa Dagwood; Mortadella 58 | ## 5 Alexandria Slider; Beschuit met muisjes; Chicken salad 59 | ## 6 Ana Fried brain; Polish boy; Vegetable; Pudgy Pie; Dagwood 60 | 61 | As first step, let’s separate out the sandwiches into individual 62 | observations using the handy `tidyr` function, `separate_rows()`. 63 | 64 | sw %>% 65 | separate_rows(sandwiches, sep = "; ") 66 | 67 | ## # A tibble: 25 x 2 68 | ## names sandwiches 69 | ## 70 | ## 1 Abby Denver 71 | ## 2 Abby BLT 72 | ## 3 Abby Torta ahogada 73 | ## 4 Abby Barbecue 74 | ## 5 Abigail BLT 75 | ## 6 Abigail Ftira 76 | ## 7 Abigail Primanti 77 | ## 8 Abigail Ice cream 78 | ## 9 Abigail Choripán 79 | ## 10 Adam Corned beef 80 | ## # ... with 15 more rows 81 | 82 | Keep in mind that omitting the space in the separator may cause some of 83 | the results not to match up. For example, “BLT” and " BLT" would require 84 | an extra cleaning step, such as 85 | `mutate(sandwiches = str_trim(sandwiches))`. 86 | 87 | Next, we can count the sandwiches to determine which type is the most 88 | popular. Adding `sort = TRUE` brings the most popular sandwich to the 89 | top of the tibble. 90 | 91 | sw %>% 92 | separate_rows(sandwiches, sep = "; ") %>% 93 | count(sandwiches, sort = TRUE) 94 | 95 | ## # A tibble: 22 x 2 96 | ## sandwiches n 97 | ## 98 | ## 1 Dagwood 3 99 | ## 2 BLT 2 100 | ## 3 Barbecue 1 101 | ## 4 Beschuit met muisjes 1 102 | ## 5 Cheesesteak 1 103 | ## 6 Chicken salad 1 104 | ## 7 Choripán 1 105 | ## 8 Corned beef 1 106 | ## 9 Denver 1 107 | ## 10 Fried brain 1 108 | ## # ... with 12 more rows 109 | 110 | That’s it! With this small sample, you’ve got the basics of a working 111 | wrangling script that you can try out on the full data. 112 | -------------------------------------------------------------------------------- /11_sandwiches/11_data.csv: -------------------------------------------------------------------------------- 1 | names,sandwiches 2 | Aaliyah,Falafel; Cheese; Donkey burger; Breakfast roll; Bauru; Barros Jarpa 3 | Aaron,Hamdog; Zapiekanka; Polish boy; British Rail 4 | Abby,Tuna; Pambazo; Breakfast roll 5 | Alana,Bun kebab; Steak bomb; Croque-monsieur; Gerber 6 | Amanda,Runza; Martino; Primanti; Barbecue 7 | Amber,Lox; Mettbrötchen 8 | Ana,Mother-in-law; Tuna; Cheese and pickle; Horseshoe 9 | Ashlyn,Leberkäse; Runza; Open-faced sandwich; Cheese and pickle; Pork tenderloin; Bologna sandwich; Naan sandwich 10 | Ashton,Chili burger; Crisp; Yakisoba-pan; Hamdog; Tripleta; Bauru 11 | Autumn,Polish boy; Tramezzino; Chipped beef; Cheese and pickle; Martino 12 | Avery,Elvis sandwich; De miga; Tripleta; Pulled pork sandwich; British Rail 13 | Ayden,Bauru; Marmite; Egg; Polish boy; Steak bomb; Pebete; S'more 14 | Braden,Cucumber; Pork tenderloin; Ham and cheese; Vegemite; Runza; Barros Jarpa; Mitraillette; Falafel; Bun kebab 15 | Breanna,Breakfast; Pljeskavica; Open-faced sandwich; Leberkäse; Pudgy Pie 16 | Brendan,Francesinha poveira; Smörgåstårta; Tofu Sandwich 17 | Brooke,Jucy Lucy; Cuban; Ham and cheese; Toastie 18 | Bryce,Tea; Pljeskavica; Tongue toast; Steak; Dyrlægens natmad; Crisp 19 | Caden,Gatsby 20 | Cameron,Fool's Gold Loaf; Gyro 21 | Cesar,Pudgy Pie; St. Paul; Tofu Sandwich; Francesinha poveira 22 | Charlotte,Mitraillette; Monte Cristo 23 | Chloe,"Pljeskavica; Patty melt; Jucy Lucy; TLT (Tempeh, Lettuce, and Tomato); Bánh mì" 24 | Claire,Jucy Lucy; Cheesesteak; Prosperity Sandwich; Ploughman's lunch; Hot brown; Corned beef; Shawarma; Spiedie; Po' boy 25 | Colby,Katsu sando; Hot dog; Club; Gyro; Pistolette; Bun kebab; Ham and egg bun; Ftira 26 | Dalton,Bun kebab; Cheesesteak; Bosna; Bauru 27 | Daniela,Po' boy 28 | Derek,Lettuce; Barros Luco; Italiano; Kokoretsi; S'more; Chivito; Gyro; Ham and egg bun 29 | Elijah,Toast Hawaii; Sausage; Ham and egg bun 30 | Ella,Italiano; Kokoretsi 31 | Emmanuel,Vada pav; Polish boy; Marmite; Tavern; Bosna 32 | Erick,Ham and egg bun; Denver; Dynamite; Toastie 33 | Fernando,Prawn roll; Pork chop bun; Toast Hawaii; Lox 34 | Gabriel,Tongue toast; Crisp; Wrap; Roti john 35 | Gabriela,"Monte Cristo; Salt beef bagel; Breakfast; Bacon, egg and cheese; Baked bean; Steak bomb" 36 | Giovanni,Cheese; Bun kebab; Cheese and pickle 37 | Giselle,Medianoche; French dip; Shawarma; Guajolota 38 | Hayden,Dyrlægens natmad; Torta; Fairy bread 39 | Henry,Melt; Crisp; Fried brain; Wrap 40 | Hunter,Chip butty; Smörgåstårta; Katsu sando; Tripleta; Dynamite 41 | Isabel,Corned beef; Egg; Ice cream; British Rail; Ham and cheese; Club; Chicken salad 42 | Ivan,Katsu sando; Sloppy joe (New Jersey); Prawn roll; Steak; Doubles 43 | Jada,Cuban; Hamdog; Toast Hawaii; Melt; Pljeskavica 44 | Jaden,Vada pav; Francesinha; Mollete; Zapiekanka; Naan sandwich; Sloppy joe (New Jersey); Denver 45 | Jazmin,Luther burger; Primanti; Fried brain; Barros Jarpa 46 | Jeffrey,Pork chop bun; Torta; Tofu Sandwich 47 | Jenna,French dip; Barbecue; Bosna; Ham; Dagwood; S'more 48 | Jillian,Smörgåstårta; Muffuletta; Crisp 49 | Johnathan,Torta; Leberkäse; Ice cream; Tofu Sandwich; BLT 50 | Jordan,Porilainen; Lox; Panini; Gerber; Kokoretsi; Hot brown; De miga 51 | Jose,Dyrlægens natmad; Torta ahogada; Mollete 52 | Joshua,Roti john; Barros Luco; Tramezzino; Wurstbrot (sausage bread) 53 | Julian,Katsu sando; Deli sandwich; French dip; Chickpea salad; Cheesesteak 54 | Kaden,Mollete; Beef on weck; Wrap; Prosperity Sandwich 55 | Kaitlyn,Po' boy; Hamdog; BLT; Bánh mì; Medianoche; Corned beef; Naan sandwich 56 | Kelly,"Bacon, egg and cheese; Ploughman's lunch; Jam; S'more" 57 | Kimberly,"Ham and cheese; Denver; Bacon, egg and cheese; Ham and egg bun; Montreal-style smoked meat; Guajolota; Sol over Gudhjem" 58 | Kylee,Ice cream; Peanut butter and jelly; Martino; Chicken salad; The Scooch; Cheesesteak 59 | Landon,Leberkäse; Prawn roll 60 | Lauren,Chacarero; French dip; Dyrlægens natmad; Sloppy joe; Meatball; Tramezzino; Dynamite 61 | Lucy,Italian beef; Souvlaki; Tongue toast; Cuban; Marmite; Chow mein; Chickpea salad; Doubles; Pambazo 62 | Luis,Guajolota; Welsh rarebit; Lettuce; Rou jia mo; Naan sandwich; BLT; Club 63 | Lydia,Lobster roll; Monte Cristo; Slider; Guajolota 64 | Mackenzie,Beschuit met muisjes; Sloppy joe; Sloppy joe (New Jersey); Club; Deli sandwich; Meatball; Smörgåstårta 65 | Makayla,Lox; Sol over Gudhjem; Tripleta; Medianoche; Shawarma; Breakfast roll; Leberkäse 66 | Malachi,"Lobster roll; Gatsby; Ham; Bacon, egg and cheese" 67 | Marcus,Tea; Fluffernutter; Cheese and pickle; Chicken salad; Primanti 68 | Marissa,Pudgy Pie; Tuna; Club; Cheesesteak; Bosna 69 | Mckenzie,"Hot dog; Reuben; Fool's Gold Loaf; Chicken salad; Tongue toast; Pistolette; Elvis sandwich; Pljeskavica; Bacon, egg and cheese; Tofu Sandwich" 70 | Mia,Crisp; Jucy Lucy; Falafel; Hamdog; Cudighi; Mettbrötchen; Slider 71 | Miranda,Vegetable; Torta ahogada; Bacon 72 | Naomi,Mollete; Open-faced sandwich; Chicken salad; Ham and cheese; Chow mein 73 | Nevaeh,Breakfast; Martino; Jucy Lucy; Marmite; Luther burger 74 | Nicholas,Doubles; Kottenbutter; Cheesesteak; Chivito; Mother-in-law; Panini; Vegetable 75 | Oliver,Hot chicken; Dagwood; Chow mein; Lobster roll; Bauru 76 | Oscar,Fischbrötchen; Bánh mì; Chacarero 77 | Paige,"Chicken salad; Bacon, egg and cheese; Jibarito; Beef on weck" 78 | Paul,Kokoretsi; Tripleta; Bologna sandwich 79 | Peter,Deli sandwich; Sandwich loaf; Chip butty; Beschuit met muisjes; Vegemite; Barros Luco 80 | Rachel,Bauru; Falafel; Luther burger 81 | Raymond,Fool's Gold Loaf; Melt; Chip butty; Chacarero 82 | Reagan,Gatsby; Melt; Shawarma; Ham; Dagwood; Polish boy 83 | Rebecca,Hamburger; Falafel; Corned beef; Horseshoe; Egg; Toastie; Mother-in-law; Wrap; Shawarma; Bun kebab; Chacarero; Barbecue; Lettuce 84 | Riley,Horseshoe; Chacarero; Toast; Barros Luco; Deli sandwich; Pork chop bun 85 | Ryan,Deli sandwich; Ftira; De miga 86 | Rylee,Smørrebrød; Tavern; Katsu sando; Montadito; Egg 87 | Sebastian,Primanti; Hot turkey; Chili burger 88 | Serenity,"Sandwich loaf; Chipped beef; Lox; Jam; Ham and cheese; Pistolette; TLT (Tempeh, Lettuce, and Tomato)" 89 | Shawn,Tea; Souvlaki; Pork chop bun; Gyro; Fool's Gold Loaf 90 | Shelby,St. Paul; Gyro; Melt 91 | Sierra,Bosna; Fried brain; Tripleta 92 | Sofia,Lox; Butterbrot; Hamdog; Italian beef; Tramezzino; Tuna 93 | Summer,Gyro; French dip; Chivito; Souvlaki 94 | Sydney,Tofu Sandwich; Pambazo; Broodje kroket 95 | Tanner,Baked bean; Ice cream; Smørrebrød; Leberkäse 96 | Taylor,"The Scooch; BLT; Chicken salad; TLT (Tempeh, Lettuce, and Tomato); Beschuit met muisjes; Tuna" 97 | Valerie,Sandwich loaf; Vada pav; Beef on weck; Cucumber; Rou jia mo; Hot chicken; Cemita 98 | Veronica,"Fool's Gold Loaf; Tongue toast; De miga; Smörgåstårta; Ham; Runza; Bacon, egg and cheese; Prawn roll" 99 | Victoria,Toast Hawaii; Pambazo; Pork chop bun 100 | Zachary,Francesinha; Cemita; Cucumber; Sloppy joe (New Jersey); Mollete; Barbecue 101 | Barbie,"Bacon, egg and cheese" 102 | --------------------------------------------------------------------------------