Milk Tea Exploratory Data Analysis in the San Gabriel Valley
Kim Santos
Table of Contents
- Introduction
- Setup
- Data Wrangling
- Exploratory Data Analysis
- Conclusion
Introduction
An exploratory data analysis is used to observe the experience of five individuals when they acquired milk tea drinks around the San Gabriel Valley in Southern California.
The Milk Tea data set was generated by the group of five, and it was frequently updated as they each provided information about: the shop name visited, which city the shop was located, travel distance from home to destination in miles, what their reviewer name is, price of the drink, the drink name, if they had boba toppings, how they rate their drink, what rank they consider the drink to be, and any comments overall.
This Exploratory Data Analysis will answer the following research questions:
Research Questions
RQ1: Which city is the most to least visited?
RQ2: What is the average Distance Reviewers travel to each City?
RQ3: Is there a correlation between how far Reviewers travel and the cost of milk tea drinks?
RQ4: How are the Ratings in each city?
RQ5: Which City receives the most Boba consumption?
RQ6: Which Reviewers get Boba toppings in their drink?
RQ7: How much do Reviewers pay for their drinks?
Setup
# This cell loads the necessary libraries needed to execute the project
library(readr)
library(ggplot2)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v purrr 0.3.4 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# The working directory is set to where the .csv file is stored in the computer
# The file is loaded and read with the variable name 'boba'.
setwd("~/Excel Documents")
boba <- read_csv("SGV Boba.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## Shop = col_character(),
## City = col_character(),
## Distance = col_double(),
## Reviewer = col_character(),
## Price = col_double(),
## `Drink Name` = col_character(),
## Boba = col_character(),
## Rating = col_double(),
## Rank = col_character(),
## Comments = col_character()
## )Data Wrangling
This section will load and clean the ‘boba’ data set. An overview of the data will be seen to get its general info and identify any values not applicable to their corresponding variables or are irrelevant observations for analysis. This provides the exploration of the data accurate results for the responses to our questions.
Information to clean:
1. Check data types of variables
2. Remove columns: Rank and Comments
#view of the whole data set
as.data.frame(boba)## Shop City Distance Reviewer Price
## 1 85 Degrees Alhambra 7.1 Reviewer C 3.00
## 2 Wushiland San Gabriel 3.8 Reviewer E 4.74
## 3 Wushiland San Gabriel 3.8 Reviewer E 3.99
## 4 Wushiland San Gabriel 3.8 Reviewer E 3.99
## 5 Wushiland San Gabriel 5.7 Reviewer A 5.74
## 6 Wushiland San Gabriel 3.3 Reviewer B 3.99
## 7 OinkMoo Pasadena 7.1 Reviewer B 4.75
## 8 Nobibi Ice cream & Tea Chino Hills 18.0 Reviewer B 5.10
## 9 Vita Cane Rosemead 2.9 Reviewer C 7.50
## 10 Chakaa Arcadia 5.2 Reviewer D 4.25
## 11 Chakaa Arcadia 4.2 Reviewer B 4.75
## 12 Chakaa Arcadia 6.1 Reviewer C 4.75
## 13 Gotcha Rosemead 1.5 Reviewer B 5.25
## 14 Tenju Rowland Heights 13.0 Reviewer A 5.00
## 15 A+ tea house San Gabriel 4.5 Reviewer C 5.00
## 16 Hello Boba El Monte 1.3 Reviewer E 4.75
## 17 4 Season Tea House Rosemead 3.2 Reviewer C 5.00
## 18 Unique Tea Bar Hacienda Heights 8.5 Reviewer D 4.45
## 19 Sunright Tea Studio Diamond Bar 13.0 Reviewer D 4.75
## 20 Sunright Tea Studio Diamond Bar 14.0 Reviewer C 4.75
## 21 Ding Tea El Monte 0.9 Reviewer E 4.00
## 22 Tea Brick Monterey Park 5.2 Reviewer D 5.95
## 23 Chun Feng Sugar Koreatown 13.0 Reviewer B 4.95
## 24 Dragonfly tea bar Rowland Heights 12.0 Reviewer D 6.50
## 25 FTB San Gabriel 5.2 Reviewer C 4.25
## 26 FTB San Gabriel 5.5 Reviewer D 4.25
## 27 FTB San Gabriel 5.5 Reviewer D 4.25
## 28 FTB San Gabriel 5.5 Reviewer D 4.25
## 29 FTB San Gabriel 5.8 Reviewer E 4.25
## 30 FTB San Gabriel 4.2 Reviewer B 5.25
## 31 Wushiland San Gabriel 3.3 Reviewer B 5.49
## 32 Omomo Chino Hills 18.0 Reviewer B 5.00
## 33 Onezo Pasadena 12.0 Reviewer A 4.31
## 34 Onezo Pasadena 5.7 Reviewer E 5.75
## 35 Onezo Monterey Park 5.0 Reviewer B 5.75
## 36 Chi Cha San Gabriel 6.1 Reviewer A 6.25
## 37 Quickly Arcadia 3.3 Reviewer A 4.00
## 38 Yifang Rowland Heights 9.8 Reviewer B 5.75
## 39 Yifang Rowland Heights 11.2 Reviewer E 5.75
## 40 Mom's Bakery El Monte 1.9 Reviewer C 3.75
## 41 7Leaves Alhambra 6.9 Reviewer D 4.40
## 42 7Leaves Alhambra 11.0 Reviewer A 4.40
## 43 7Leaves Alhambra 11.0 Reviewer A 4.40
## 44 7Leaves Alhambra 6.9 Reviewer C 5.00
## 45 Happy Lemon Monterey Park 6.6 Reviewer D 5.50
## 46 Happy Lemon Monterey Park 6.3 Reviewer C 5.50
## 47 Tenju Rowland Heights 12.0 Reviewer E 5.00
## 48 Tenju Rowland Heights 9.6 Reviewer B 5.00
## 49 Hello Boba El Monte 1.6 Reviewer A 4.25
## 50 Happy Lemon Monterey Park 5.9 Reviewer E 5.50
## 51 Chakaa Arcadia 5.7 Reviewer A 5.50
## 52 OinkMoo Pasadena 9.6 Reviewer E 5.75
## 53 Feng Cha Costa Mesa 32.0 Reviewer E 5.00
## 54 Ono Tea House El Monte 0.6 Reviewer E 4.50
## 55 Twinkle Brown Sugar Alhambra 5.4 Reviewer C 5.45
## 56 Milk + T Little Tokyo 11.0 Reviewer B 5.50
## 57 Bubble Crush Monterey Park 4.1 Reviewer C 4.75
## 58 Bubble Crush Monterey Park 4.1 Reviewer C 5.00
## 59 4 Season Tea House Alhambra 3.2 Reviewer C 5.00
## 60 Ten Ren Monterey Park 6.2 Reviewer A 5.50
## 61 TanCha San Gabriel 3.4 Reviewer B 5.75
## 62 Miss Cheese Cafe Pasadena 7.8 Reviewer B 5.75
## 63 Neighbors Rosemead 3.1 Reviewer D 4.00
## 64 Neighbors Rosemead 3.1 Reviewer D 4.25
## 65 Neighbors Rosemead 2.8 Reviewer C 4.25
## 66 Dots Cafe Arcadia 5.8 Reviewer B 4.50
## 67 Lollicup Arcadia 6.7 Reviewer C 4.25
## 68 Yu Cake Rosemead 2.3 Reviewer B 5.25
## 69 Nest Tea House Arcadia 2.9 Reviewer B 4.15
## 70 Why Thirsty Arcadia 5.9 Reviewer C 4.50
## 71 Half & Half Monterey Park 8.0 Reviewer A 4.25
## 72 Tea Maru San Gabriel 6.5 Reviewer A 4.50
## 73 Boba Box Monrovia 8.6 Reviewer E 5.50
## 74 Feng Cha Costa Mesa 32.0 Reviewer E 4.75
## 75 Bobacraft Alhambra 8.6 Reviewer A 3.75
## 76 Bobacraft Alhambra 7.1 Reviewer D 4.50
## 77 Happy Lemon Monterey Park 5.0 Reviewer B 4.75
## 78 Twinkle Brown Sugar Alhambra 5.4 Reviewer C 5.75
## 79 Moo Tea Alhambra 3.8 Reviewer B 6.25
## 80 Tastea Alhambra 6.8 Reviewer D 5.50
## 81 Chami Tea Bar Whittier 7.8 Reviewer A 5.25
## 82 Chami Tea Bar Whittier 5.4 Reviewer D 4.00
## 83 Sunright Tea Studio Diamond Bar 13.0 Reviewer D 5.50
## 84 Sunright Tea Studio Diamond Bar 8.0 Reviewer A 5.50
## 85 Bon Appetea San Gabriel 6.3 Reviewer E 4.25
## 86 Oh My Pan San Gabriel 3.9 Reviewer D 3.05
## 87 Oh My Pan San Gabriel 3.9 Reviewer D 4.00
## 88 Boba Ave Arcadia 6.0 Reviewer D 4.25
## 89 Tancca Arcadia 5.5 Reviewer D 4.25
## 90 Tastea Alhambra 8.6 Reviewer A 5.50
## 91 Tenju Rowland Heights 11.0 Reviewer C 5.50
## 92 Ono Tea House El Monte 0.6 Reviewer C 5.50
## 93 Ono Tea House El Monte 0.5 Reviewer D 5.50
## 94 Uncle Tea West Covina 13.0 Reviewer C 4.25
## 95 Tenju Rowland Heights 12.0 Reviewer D 4.75
## 96 Wushiland San Gabriel 4.1 Reviewer C 4.74
## 97 Cafe Roule Alhambra 3.1 Reviewer B 4.75
## Drink Name Boba Rating Rank
## 1 Sea Salt Mountain Tea No 9.0 A
## 2 Red Bean Milk Tea No 10.0 A
## 3 Assam Black Tea Yes 10.0 A
## 4 Jasmine Green Milk Tea Yes 8.5 A
## 5 Ice Cream Green Milk Tea Yes 8.5 A
## 6 Jasmine Green Milk Tea Yes 9.0 A
## 7 Jasmine Milk Tea Yes 8.0 A
## 8 Ube Cloud Yes 8.0 A
## 9 Sugar Cane Juice No 10.0 A
## 10 Jasmine Milk Tea No 9.0 A
## 11 Jasmine Milk Tea Yes 8.0 A
## 12 White Peach Oolong w/o cheese foam No 9.0 A
## 13 Brown Sugar Fresh Milk Yes 8.5 A
## 14 Honey Green Tea Yes 10.0 A
## 15 Honey green tea No 10.0 A
## 16 Jasmine Milk Tea Yes 9.0 A
## 17 Jasmine Green Milk Tea No 10.0 A
## 18 Jasmine Green Milk Tea No 10.0 A
## 19 Jasmine Green Milk Tea No 9.0 A
## 20 Ceylon Milk Tea No 9.0 A
## 21 Wintermelon MT Yes 10.0 A
## 22 Mango Snow Milk No 10.0 A
## 23 Brown Sugar Fresh Milk Yes 8.0 A
## 24 Rainbow tea Yes 9.0 A
## 25 Rose Green Tea No 7.0 B
## 26 Almond Milk Tea Yes 10.0 B
## 27 Nom Nom Green Tea Yes 10.0 B
## 28 Jasmine Milk Tea No 9.0 B
## 29 Brown Sugar Milk Yes 8.0 B
## 30 Dirty Nom Nom Yes 8.0 B
## 31 Yogurt Green Tea Yes 7.8 B
## 32 Omomo Matcha Yes 8.0 B
## 33 Honey Green Tea Yes 7.0 B
## 34 Brown Sugar Milk @ Pasadena Yes 3.0 B
## 35 Brown Sugar Milk Yes 8.0 B
## 36 Brown Sugar Milk Yes 11.0 B
## 37 Jasmine Milk Tea Yes 7.5 B
## 38 Brown Sugar Milk Yes 8.0 B
## 39 Brown Sugar Milk Yes 8.0 B
## 40 Jasmine Milk Tea No 10.0 B
## 41 Jasmine Milk Tea No 8.0 B
## 42 Mung Bean Drink No 8.0 B
## 43 Hibiscus Tea thing No 8.3 B
## 44 Love Cream No 7.5 B
## 45 Jasmine Milk Tea w/ Cheese Foam? No 8.0 B
## 46 Black Tea w/ Salted Cheese No 9.0 B
## 47 Jasmine Milk Tea Yes 8.0 B
## 48 Jasmine Milk Tea Yes 8.0 B
## 49 Jasmine Milk Tea Yes 7.5 B
## 50 Brown Sugar Milk Yes 7.5 B
## 51 Brown Sugar Milk Yes 7.7 B
## 52 Brown Sugar Milk Yes 8.5 B
## 53 Brown Sugar Milk Yes 9.0 B
## 54 Brown Sugar Milk Yes 6.5 B
## 55 Chrysanthemum Tea No 8.0 B
## 56 Guilt Trip Yes 8.0 B
## 57 White Peach Oolong Tea No 7.5 B
## 58 Ceylon Milk Tea No 7.5 B
## 59 Mango Green Tea No 8.0 B
## 60 Dirty Latte Yes 7.5 B
## 61 Tiramisu Foam Cocao No 7.0 C
## 62 Brown Sugar milk Yes 7.0 C
## 63 Green Tea No 3.5 C
## 64 Almond Milk Tea No 4.0 C
## 65 Jasmine Milk Tea No 6.0 C
## 66 Lychee Black Tea Yes 6.5 C
## 67 Honey Green Tea Yes 3.5 C
## 68 Fresh Fruit tea No 5.0 C
## 69 Mango Green Tea Yes 5.0 C
## 70 Jasmine milk tea Yes 4.0 C
## 71 Ice Milk Drink w/ pudding & honey boba Yes 8.0 C
## 72 Wintermelon milk tea No 7.5 C
## 73 Green Milk Tea Yes 5.5 C
## 74 Jasmine Green Tea No 5.0 C
## 75 Jasmine Green Tea Yes 3.0 C
## 76 Jasmine Milk Tea No 4.0 C
## 77 Yogurt Green Tea Yes 5.5 C
## 78 Jasmine Milk Tea Honey No 5.0 C
## 79 Purple Yam Fresh Milk Yes 6.0 C
## 80 Almond Milk Tea No 6.5 C
## 81 Chami Mixed Fruit Tea No 4.0 C
## 82 Chami Milk Tea No 3.0 C
## 83 Pudding Milk Tea No 6.0 C
## 84 Brown Sugar Milk Yes 6.5 C
## 85 Green Milk Tea No 5.0 C
## 86 Jasmine Milk Tea No 0.0 F
## 87 Lavender Milk Tea No 0.0 F
## 88 Jasmine Milk Tea Yes 0.0 F
## 89 King's Boba Yes 0.0 F
## 90 Brown Sugar Milk Yes 0.0 F
## 91 Lavender Milk Tea No 0.0 F
## 92 Honey Green Tea No 1.0 F
## 93 Green Milk Tea No 1.0 F
## 94 Almond Milk Tea No 0.0 F
## 95 Almond Milk Tea No 1.0 F
## 96 Ovaltine Milk Tea No 1.0 F
## 97 Matcha Green Tea Yes 1.0 F
## Comments
## 1 yummmmmmmmmmmmmyyyy They have BOGO sometimes.
## 2 only because I never had red bean anywhere else xD and its good
## 3 their black tea is yummers
## 4 pretty solid imo c: it's just that their boba can be hard sometimes
## 5 i really like it the ice cream makes the drink creamy; only downside is their boba aint that gud
## 6 solid drink with lots of boba
## 7 v niceee; top list of things I'd get from there
## 8 freakin gooood; boba is alright tho
## 9 yummmerssss no boba lol
## 10 it was yum c: ; i agree, it really is yumm - nm
## 11 yummies; the boba is nice and chewy
## 12 yummy!!! light, fresh peachy taste with strong oolong kick at the end
## 13 Always consistent with this drink! My go to
## 14 it slaps
## 15 yummmmmm
## 16 Very good! Just that the cup is very small :c not that we're trying to drink a lot >.<
## 17 the drinks are yummerz, i didnt try their boba but their snacks are good they give a lot.
## 18 SO GOOD; PERFECT, MILKY
## 19 Yummy!
## 20 Very good~ I've never had Ceylon tea before, but it's very yummy! Would recommend getting it a little less sweet though~
## 21 i live by this i swear by this my newest obsession no one has better wintermelon than ding tea
## 22 IT SLAPSSSS -priscilla and Reviewer D
## 23 Really good but you can tell a change in consistency
## 24 pretty good tea and it has BOGO happy hour 1.50 1-5 pm but its in rowland
## 25 their tea is good, they had some moments where they didn't treat me right and I don't ususally get their boba so idk. But the last time I tried their boba it was hard as a rock.
## 26 Reviewer D freaking loved it with boba yummy
## 27 for those days when you want something milky, it's yummy
## 28 yummyskis jasmine milk tea
## 29 drink was good 2x amazing it gets an extra 0.5 points
## 30 can get a bit too much sometimes but still a good noms
## 31 Lotssss of boba; the drink is a bit too sweet tho
## 32 Drink was good in the end; was a tad annoying to mix it
## 33 I usually just get their tea and its not bad. A little sweet or not too sweet. Kinda inconsistent but its okay. Their boba is kinda tasteless.
## 34 The Pasadena one tasted like a coffee drink :///// and their cup is narrower than usual...
## 35 Tastes how a brown sugar milk should
## 36 The only reason it's B-tier is because its $6 for a small :/
## 37 Their drink isn't bad and their boba is meh but its cheap and their fried combo is yummers. but gotta get a little extra sweet for the drink bc its kinda watered down sometimes.
## 38 Boba was small and it fulfills my need for a brown sugar milk drink
## 39 I like it but sometimes not good? Boba is small/different than usual, also can be expensive :c
## 40 Hella yummy, but don't know much about their other drinks or if their boba is good
## 41 Tastes more like Assam milk tea, but still good; don't know about their boba
## 42 Refreshing!!! PLUS for drive thru
## 43 Not bad for a non boba drink!! And it's low calorie and cheapish c:
## 44 pretty basic drink i think it's a JMT w/ caramel, but i usually get it if idk wat to get from here
## 45 I've never gotten their boba but idk if they even have boba. But their drink hasn't disappointed me yet. But sometimes I crave boba so its not a good "go to" place.
## 46 yummmmyyyyy (for those no boba days)
## 47 i like it and their boba is sweet imo
## 48 Nice texture; boba nice and chewy
## 49 pretty good? the boba is edible the drink tastes not flowery but more TEA- E
## 50 Drink is yummy i like it but the boba could be better.. more on the harder side
## 51 was gonna give it an 8, but their cup annoyed me so much with all the spilling >:( drink itself was not bad
## 52 drink very yum, but boba hard :c. i got it when they had the 20% off promo, but normally it'd prolly be close to 6 bucks
## 53 yummy but far so B-tier
## 54 inconsistent............ but cheap... would go if desparate and lazy
## 55 i like it. never had their boba but i got 10% off :) and their cups are cute
## 56 was a hot day and felt great to consume
## 57 quite yummy, but a litttttttttle watered down, so not a super strong oolong taste
## 58 not bad! Again, a little bland, but still p good
## 59 pretty good! Regular sweetness is a litttttle too sweet though
## 60 it could be a little sweeter, but i think overall not bad! their boba is chewy so i like (obvs boba isnt sweet but alas)
## 61 too expensive for a small cup; drink itself is just alright
## 62 Would be something I'd get if there wasn<U+0092>t anything else I want
## 63 they use to be fine but they changed their tea and it sucks now. Can't remember their boba that well.
## 64 Gave them another chance, but they disappointed me. Helllllllaaalujah sweet and tasted like just powder mixed with water instead of milk after a while>:(
## 65 Used to be 10/10 for that strong yummy jasmine taste, but they changed. >:(
## 66 It was ok but don<U+0092>t think I'll get again; they make boba at 10am LOL
## 67 hehe I made my own drink here and their tea wasn't bad. Boba was meh but they don't exist near us anymore so too bad.
## 68 Was super thirsty when I got it so it was really refreshing; Started to taste bitter in the end
## 69 Meh; just average
## 70 their drink for our first time was so good, even the boba was sweet but they got worse and worse everytime we went.
## 71 the only drink i get at here i quite like it tbh but never really craving it yafeel? it's just there
## 72 don't go for their boba but their wintermelon is pretty gud like ding tea
## 73 boba is kinda hard (too chewy) not that sweet, drink is nice but the place is farrr its in Monrovia
## 74 meh. i got this after the ice all melted so it was watered down and its quite far. strong tea taste, pretty cheap
## 75 oh boy. they gave me tea with no sugar so i had to go back and ask for a lot of sugar. the custard bread was good. Their cookies and cream one is just an oreo and chocolate cream. misleading. w.e. and their boba custard was alright but the boba had no taste.
## 76 idk.. it's a bit bland, more tea-y. also tastes like sip and that brings bad memoriez
## 77 was a bit yogurty
## 78 i don't rly hate it... but it's weird? idk i dont like it either.. but it's pretty cheap c:
## 79 Boba was tasteless and I didn<U+0092>t enjoy the yam bits; if it was just the drink, it would be ok
## 80 Actually really super sweet
## 81 I probably just don't like mixed fruit teas >.< I don't know why I got it. It's very pineapple and passion fruit flavored
## 82 Kinda tastes like cereal like Ovaltine Milk Tea from Wushiland, but this one is actually sweet enough
## 83 Just very very sweet. The pudding was good though and not super sweet. But the liquid is very very sweet ;.;
## 84 tasted weird...
## 85 didn't really like it and they have me mini boba dont like that
## 86 the drink wasn't memorable but the pudding was TRASH. The pudding texture was like soaked-up sponge cake.
## 87 Reviewer Ds drink tasted like medicine, and was undrinkable after drinking 1/4 of it
## 88 Regular sugar tastes like water. Boba experiene was like chewing hard air.
## 89 it was like $6 when I got it, small AF cup, and it tasted like SOYSAUCE
## 90 just lol.
## 91 Just don't it tastes like medicine and herbs
## 92 tasted bland and had a bitter aftertaste. was BOGO but if i had to pay full price for that ill sharpen my pitchfork and stab em
## 93 so damn bland. tastes like straight up milk
## 94 god
## 95 tastes like they blended those chinese almond cookies with milk. no thanks
## 96 Very bland and it tastes like chinese cereal. Like the milk that is flavored with unsweetened corn flakes
## 97 No thank you- Check data types of variables
# looking into the structure
str(boba)## spec_tbl_df [97 x 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Shop : chr [1:97] "85 Degrees" "Wushiland" "Wushiland" "Wushiland" ...
## $ City : chr [1:97] "Alhambra" "San Gabriel" "San Gabriel" "San Gabriel" ...
## $ Distance : num [1:97] 7.1 3.8 3.8 3.8 5.7 3.3 7.1 18 2.9 5.2 ...
## $ Reviewer : chr [1:97] "Reviewer C" "Reviewer E" "Reviewer E" "Reviewer E" ...
## $ Price : num [1:97] 3 4.74 3.99 3.99 5.74 3.99 4.75 5.1 7.5 4.25 ...
## $ Drink Name: chr [1:97] "Sea Salt Mountain Tea" "Red Bean Milk Tea" "Assam Black Tea" "Jasmine Green Milk Tea" ...
## $ Boba : chr [1:97] "No" "No" "Yes" "Yes" ...
## $ Rating : num [1:97] 9 10 10 8.5 8.5 9 8 8 10 9 ...
## $ Rank : chr [1:97] "A" "A" "A" "A" ...
## $ Comments : chr [1:97] "yummmmmmmmmmmmmyyyy They have BOGO sometimes." "only because I never had red bean anywhere else xD and its good" "their black tea is yummers" "pretty solid imo c: it's just that their boba can be hard sometimes" ...
## - attr(*, "spec")=
## .. cols(
## .. Shop = col_character(),
## .. City = col_character(),
## .. Distance = col_double(),
## .. Reviewer = col_character(),
## .. Price = col_double(),
## .. `Drink Name` = col_character(),
## .. Boba = col_character(),
## .. Rating = col_double(),
## .. Rank = col_character(),
## .. Comments = col_character()
## .. )The data type for each variable appears to be correctly identified. It would only be necessary to change the variables’ data type if the analysis required for a specific type.
- Remove columns: Rank and Comments
# running head() to see final results
boba <- select(boba, -c("Rank", "Comments"))
head(boba)## # A tibble: 6 x 8
## Shop City Distance Reviewer Price `Drink Name` Boba Rating
## <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <dbl>
## 1 85 Degre~ Alhambra 7.1 Reviewer~ 3 Sea Salt Mountain T~ No 9
## 2 Wushiland San Gabr~ 3.8 Reviewer~ 4.74 Red Bean Milk Tea No 10
## 3 Wushiland San Gabr~ 3.8 Reviewer~ 3.99 Assam Black Tea Yes 10
## 4 Wushiland San Gabr~ 3.8 Reviewer~ 3.99 Jasmine Green Milk ~ Yes 8.5
## 5 Wushiland San Gabr~ 5.7 Reviewer~ 5.74 Ice Cream Green Mil~ Yes 8.5
## 6 Wushiland San Gabr~ 3.3 Reviewer~ 3.99 Jasmine Green Milk ~ Yes 9The first few rows is seen to view the final result of the ‘boba’ data set after it has been cleaned. We now view the data set without the ‘Rank’ and ‘Comments’ variables.
Exploratory Data Analysis
This section will cover answering the research questions we encountered in the beginning of the project.
RQ1: Which City is the most to least visited?
# group cities together and summarize the amount of reviewers and distance for each city
# graph City and Reviewer frequency into a bar plot
city_freq <- boba %>%
group_by(City) %>%
summarise(
reviews = length(Reviewer),
average_distance = mean(Distance))
city_freq %>%
ggplot(aes(reorder(City, reviews) , y = reviews, fill = City)) +
geom_bar(stat = "identity") +
ggtitle(" Distribution of reviewers who visited each city") +
xlab("City") +
ylab("Reviewers") +
theme(plot.title = element_text(hjust = 0.5), legend.position = 'none') +
coord_flip()
RQ1: Analysis
The most frequently visited city is San Gabriel with 20 visits, while multiple cities with the least of 1 visit are: West Covina, Monrovia, Little TOkyo, Koreatown, and Hacienda Heights.
RQ2: What is the average Distance Reviewers travel to each City?
# Graph City and the average distance into a bar graph
city_freq %>%
ggplot(aes(reorder(City, average_distance) , y = average_distance, fill = City)) +
geom_bar(stat = "identity") +
ggtitle("Average distance to each city") +
xlab("City") +
ylab("Average Distance")+
theme(plot.title = element_text(hjust = 0.5), legend.position = 'none') +
coord_flip()
RQ2: Analysis
The longest average Distance Reviewer(s) traveled to get a milk tea drink is around 33 miles to Costa Mesa, and the shortest average Distance to travel is around 1 mile to El Monte.
RQ3: Is there a correlation between how far Reviewers travel and the cost of milk tea drinks?
# create scatter plot of the Price and Distance
boba %>%
ggplot(aes(Distance, Price)) +
geom_point() +
ggtitle("Relationship Between Distance vs. Price") +
theme(plot.title = element_text(hjust = 0.5), legend.position = "none") +
geom_smooth(method = 'lm', se = FALSE)## `geom_smooth()` using formula 'y ~ x'
RQ3: Analysis
There is no correlation between the distance and price value of milk tea drinks. It appears that, no matter the distance, the price of a drink will range around the same with the exception of outliers.
RQ4: How are the Ratings in each City?
# create boxplots for each City based on Ratings
ggplot(boba, aes(x=City,y=Rating)) +
geom_boxplot() +
ggtitle("Ratings for all cities visited") +
theme(axis.text.x = element_text(angle=60, hjust=1), plot.title = element_text(hjust = 0.5)) 
RQ4: Analysis
Some Cities do not have a complete boxplot due to the fact that they have only one data that was observed. So, we only know how well those specific Cities does in terms of Rating set by one Reviewer.
Cities that do have a complete boxplot tells us how Reviewers rate them with the drinks they gathered. We can see how overall some Cities have 3 types of distributions- symmetrical, positively and negatively skewed. Since distributions vary across Studios, we can look into each studio and interpret how ratings are distributed individually.
RQ5: Which City receives the most Boba consumption?
# create a groupby side by side barplot with each city based on the boba variable
boba %>%
ggplot(aes(x = City,fill = Boba)) +
geom_bar(position = position_dodge()) +
ggtitle("Boba Consumption in Each City") +
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values = c("darkolivegreen", "darksalmon"))
RQ5: Analysis
The bar graph shows a side by side frequency comparison of Reviewers who do and do not get Boba toppings in each City. The City with the most Boba consumption is in San Gabriel with a frequency of 10 out of the total 20 visits. The city with the least Boba consumption is in Alhambra with an estimated frequency of 10 out of the total 14 visits
RQ6: Which Reviewers get Boba toppings in their drink?
# create a groupby side by side comparison between reviewers based on the boba variable
got_boba <- boba %>%
ggplot(aes(x = Boba, fill = Reviewer)) +
geom_bar(position = position_dodge()) +
ggtitle("Reviewers who did and did not get boba toppings in their drinks") +
labs(x = "Reviewers choice to get boba toppings", y = "Drinks with boba toppings") +
theme(plot.title = element_text(hjust = 0.5))
got_boba + scale_fill_brewer(palette = "Spectral")
RQ6: Analysis
The bar graph shows a side-by-side comparison between reviewers who did and did not get boba toppings in their drink. Reviewer B has the highest frequency of getting boba toppings of about 18 out of 21 drinks. Reviewer C has the highest frequency of not getting boba toppings of about 20 out of 23 drinks.
RQ7: How much do Reviewers Pay for their drinks?
# create histogram to view frequency of prices for each reviewer
par(mfrow = c(2,3))
a_price <- hist(boba$Price[boba$Reviewer == "Reviewer A"], main = "Reviewer A Price Frequency", xlab = "Price", col = 'lightcoral')
b_price <- hist(boba$Price[boba$Reviewer == "Reviewer B"], main = "Reviewer B Price Frequency", xlab = "Price", col = 'lightcyan')
c_price <- hist(boba$Price[boba$Reviewer == "Reviewer C"], main = "Reviewer C Price Frequency", xlab = "Price", col = 'khaki1')
d_price <- hist(boba$Price[boba$Reviewer == "Reviewer D"], main = "Reviewer D Price Frequency", xlab = "Price", col = 'moccasin')
e_price <- hist(boba$Price[boba$Reviewer == "Reviewer E"], main = "Reviewer E Price Frequency", xlab = "Price", col = 'lavenderblush')
# create boxplot for each reviewer based on price variable to compare between eachother
ggplot(boba, aes(x=Reviewer,y=Price, fill = Reviewer)) +
geom_boxplot() +
theme_classic() +
theme(axis.text.x = element_text(angle=60, hjust=1)) +
theme(legend.position = 'none', plot.title = element_text(hjust = 0.5)) +
scale_fill_manual(values = c("lightcoral", "lightcyan", "khaki1", "moccasin", "lavenderblush")) +
ggtitle("Reviewer's prices on milk tea drinks")
RQ7: Analysis
A histogram is shown on how much each reviewers pay for their drinks. It appears that between all Reviewers, they have a high frequency to pay around $4-6 for their drinks while having a few lower or higher frequencies to pay within that range.
A boxplot is another visualization to show the price reviewers would pay for milk tea drinks. We can see a symmetrical, positive and negative skewed distribution. All reviewers have around the same 75th percentile, and, with the exception of one reviewer, have around the same 25th percentile. This allows us to interpret comparisons where each reviewer would pay for their drinks, and whether they fall positively or negatively distributed.
Conclusion
The exploratory data analysis has shown Reviewers’ experience when getting milk tea drinks around the San Gabriel Valley.
Each city varied in the amount of visits, where San Gabriel had 20 visits whereas West Covina, Monrovia, Little TOkyo, Koreatown, and Hacienda Heights only had 1 visit. It was important to take note the cities with 1 visit since each city was explored. It allowed the analysis portion to consider any limitations and/ or reductions when research questions were answered.
The average distance to travel to each city between all reviewers ranged from a minimum of 1 mile to a maximum of 33 miles. The ratings for the cities have different distributions, where we found symmetrical and positively and negatively skewed distributions. Since there is a wide range of cities, it is found easier to look into each city for their rating distributions and compare between other cities. It was also seen there was no correlation between the distance and prices when traveling. So, no matter the distance, the prices would vary around the same.
Each reviewer had different frequencies on whether they got boba toppings or not. The consumption of boba toppings contributed to a comparison frequency for each city. We were able to see which city had the most to least boba toppings. Lastly, the price frequency reviewers paid for their drinks was also seen. Each reviewer showed us they frequently paid around $4-6 to pay.
Limitations
Sugar and ice levels preference. Each reviewer was given options of the amount of sugar and ice levels they wanted in their drinks. This causes the rating to be more subjective due to the reviewers taste preference.
Travel distance. The experience to travel to get a milk tea, may be a factor to how reviewers rate their drinks. In other words, reviewers may think the distance may or may not be worth to get a drink- affecting the rating they provide.