What Do You Know About Instant Noodle?
by Reyna Sondakh
14 July 2019
Brief History of Instant Noodle
Instant noodles were born in Japan on 25 August 1958 and invented by Momofuku Ando of Nissin Foods under the brand name Chikin Ramen. Ando has enabled mass-production of instant noodles by establishing the entire process of industrial method of manufacturing: noodle-making, steaming, seasoning, and dehydrating in oil heat. The product that becomes ready to eat just in two minutes by adding boiling water was dubbed “a magic ramen,” and became an instant popular sensation.
Instant Noodles Around The World
Initially, instant noodles were gaining popularity across East Asia, South Asia and Southeast Asia, where they are now firmly embedded within the local cultures of those regions. Spreading first to Asia and then to Americas, Europe and Africa, instant noodles have become accepted globally.
# Data Input and Checking Data
noodle <- read.csv("data_input/ramen-lists.csv")
str(noodle)## 'data.frame': 2580 obs. of 7 variables:
## $ Review..: int 2580 2579 2578 2577 2576 2575 2574 2573 2572 2571 ...
## $ Brand : Factor w/ 355 levels "1 To 3 Noodles",..: 193 122 195 339 38 252 8 110 236 137 ...
## $ Variety : Factor w/ 2413 levels "\"A\" Series Artificial Chicken",..: 2195 1448 458 723 1957 1112 2058 1375 816 2232 ...
## $ Style : Factor w/ 8 levels "","Bar","Bowl",..: 6 7 6 7 7 7 6 8 7 7 ...
## $ Country : Factor w/ 38 levels "Australia","Bangladesh",..: 19 33 37 33 17 31 19 19 19 30 ...
## $ Stars : Factor w/ 51 levels "0","0.1","0.25",..: 37 7 16 19 37 47 39 37 3 18 ...
## $ Top.Ten : Factor w/ 39 levels "","\n","2012 #1",..: 1 1 1 1 1 1 1 1 1 1 ...# Inspecting Data & Data Cleaning, eliminate the unrated data (row 33, 123, 994)
noodle$Stars <- as.numeric(as.character(noodle$Stars))
noodle <- noodle[-c(33,123,994),]
str(noodle)## 'data.frame': 2577 obs. of 7 variables:
## $ Review..: int 2580 2579 2578 2577 2576 2575 2574 2573 2572 2571 ...
## $ Brand : Factor w/ 355 levels "1 To 3 Noodles",..: 193 122 195 339 38 252 8 110 236 137 ...
## $ Variety : Factor w/ 2413 levels "\"A\" Series Artificial Chicken",..: 2195 1448 458 723 1957 1112 2058 1375 816 2232 ...
## $ Style : Factor w/ 8 levels "","Bar","Bowl",..: 6 7 6 7 7 7 6 8 7 7 ...
## $ Country : Factor w/ 38 levels "Australia","Bangladesh",..: 19 33 37 33 17 31 19 19 19 30 ...
## $ Stars : num 3.75 1 2.25 2.75 3.75 4.75 4 3.75 0.25 2.5 ...
## $ Top.Ten : Factor w/ 39 levels "","\n","2012 #1",..: 1 1 1 1 1 1 1 1 1 1 ...# Creating New Variable For Region and Sub-Region
levels(noodle$Country)## [1] "Australia" "Bangladesh" "Brazil" "Cambodia"
## [5] "Canada" "China" "Colombia" "Dubai"
## [9] "Estonia" "Fiji" "Finland" "Germany"
## [13] "Ghana" "Holland" "Hong Kong" "Hungary"
## [17] "India" "Indonesia" "Japan" "Malaysia"
## [21] "Mexico" "Myanmar" "Nepal" "Netherlands"
## [25] "Nigeria" "Pakistan" "Philippines" "Poland"
## [29] "Sarawak" "Singapore" "South Korea" "Sweden"
## [33] "Taiwan" "Thailand" "UK" "United States"
## [37] "USA" "Vietnam"noodle$Sub.Region[noodle$Country=="Australia"]<-"Australia"
noodle$Sub.Region[noodle$Country=="Bangladesh"]<-"South Asia"
noodle$Sub.Region[noodle$Country=="Brazil"]<-"South America"
noodle$Sub.Region[noodle$Country=="Cambodia"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Canada"]<-"North America"
noodle$Sub.Region[noodle$Country=="China"]<-"East Asia"
noodle$Sub.Region[noodle$Country=="Colombia"]<-"South America"
noodle$Sub.Region[noodle$Country=="Dubai"]<-"Middle East"
noodle$Sub.Region[noodle$Country=="Estonia"]<-"North Europe"
noodle$Sub.Region[noodle$Country=="Fiji"]<-"Oceania"
noodle$Sub.Region[noodle$Country=="Finland"]<-"North Europe"
noodle$Sub.Region[noodle$Country=="Germany"]<-"West Europe"
noodle$Sub.Region[noodle$Country=="Ghana"]<-"West Africa"
noodle$Sub.Region[noodle$Country=="Holland"]<-"West Europe"
noodle$Sub.Region[noodle$Country=="Hong Kong"]<-"East Asia"
noodle$Sub.Region[noodle$Country=="Hungary"]<-"Central Europe"
noodle$Sub.Region[noodle$Country=="India"]<-"South Asia"
noodle$Sub.Region[noodle$Country=="Indonesia"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Japan"]<-"East Asia"
noodle$Sub.Region[noodle$Country=="Malaysia"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Mexico"]<-"North America"
noodle$Sub.Region[noodle$Country=="Myanmar"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Nepal"]<-"South Asia"
noodle$Sub.Region[noodle$Country=="Netherlands"]<-"West Europe"
noodle$Sub.Region[noodle$Country=="Nigeria"]<-"West Africa"
noodle$Sub.Region[noodle$Country=="Pakistan"]<-"South Asia"
noodle$Sub.Region[noodle$Country=="Philippines"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Poland"]<-"Central Europe"
noodle$Sub.Region[noodle$Country=="Sarawak"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="Singapore"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="South Korea"]<-"East Asia"
noodle$Sub.Region[noodle$Country=="Sweden"]<-"North Europe"
noodle$Sub.Region[noodle$Country=="Taiwan"]<-"East Asia"
noodle$Sub.Region[noodle$Country=="Thailand"]<-"Southeast Asia"
noodle$Sub.Region[noodle$Country=="UK"]<-"West Europe"
noodle$Sub.Region[noodle$Country=="United States"]<-"North America"
noodle$Sub.Region[noodle$Country=="USA"]<-"North America"
noodle$Sub.Region[noodle$Country=="Vietnam"]<-"Southeast Asia"
noodle$Sub.Region <- as.factor(noodle$Sub.Region)
levels(noodle$Sub.Region)## [1] "Australia" "Central Europe" "East Asia" "Middle East"
## [5] "North America" "North Europe" "Oceania" "South America"
## [9] "South Asia" "Southeast Asia" "West Africa" "West Europe"noodle$Region[noodle$Sub.Region=="Australia"]<-"Australia/Oceania"
noodle$Region[noodle$Sub.Region=="Central Europe"]<-"Europe"
noodle$Region[noodle$Sub.Region=="East Asia"]<-"Asia"
noodle$Region[noodle$Sub.Region=="Middle East"]<-"Asia"
noodle$Region[noodle$Sub.Region=="North America"]<-"America"
noodle$Region[noodle$Sub.Region=="North Europe"]<-"Europe"
noodle$Region[noodle$Sub.Region=="Oceania"]<-"Australia/Oceania"
noodle$Region[noodle$Sub.Region=="South America"]<-"America"
noodle$Region[noodle$Sub.Region=="South Asia"]<-"Asia"
noodle$Region[noodle$Sub.Region=="Southeast Asia"]<-"Asia"
noodle$Region[noodle$Sub.Region=="West Africa"]<-"Africa"
noodle$Region[noodle$Sub.Region=="West Europe"]<-"Europe"
noodle$Region <- as.factor(noodle$Region)
levels(noodle$Region)## [1] "Africa" "America" "Asia"
## [4] "Australia/Oceania" "Europe"str(noodle)## 'data.frame': 2577 obs. of 9 variables:
## $ Review.. : int 2580 2579 2578 2577 2576 2575 2574 2573 2572 2571 ...
## $ Brand : Factor w/ 355 levels "1 To 3 Noodles",..: 193 122 195 339 38 252 8 110 236 137 ...
## $ Variety : Factor w/ 2413 levels "\"A\" Series Artificial Chicken",..: 2195 1448 458 723 1957 1112 2058 1375 816 2232 ...
## $ Style : Factor w/ 8 levels "","Bar","Bowl",..: 6 7 6 7 7 7 6 8 7 7 ...
## $ Country : Factor w/ 38 levels "Australia","Bangladesh",..: 19 33 37 33 17 31 19 19 19 30 ...
## $ Stars : num 3.75 1 2.25 2.75 3.75 4.75 4 3.75 0.25 2.5 ...
## $ Top.Ten : Factor w/ 39 levels "","\n","2012 #1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sub.Region: Factor w/ 12 levels "Australia","Central Europe",..: 3 3 5 3 9 3 3 3 3 10 ...
## $ Region : Factor w/ 5 levels "Africa","America",..: 3 3 2 3 3 3 3 3 3 3 ...A Glimpse About Instant Noodle Around The World
nrow(noodle)## [1] 2577summary(noodle)## Review.. Brand Variety Style
## Min. : 1 Nissin : 381 Beef : 7 Pack :1528
## 1st Qu.: 645 Nongshim: 98 Chicken : 7 Bowl : 481
## Median :1289 Maruchan: 76 Artificial Chicken: 6 Cup : 450
## Mean :1289 Mama : 71 Vegetable : 6 Tray : 108
## 3rd Qu.:1934 Paldo : 66 Yakisoba : 6 Box : 6
## Max. :2580 Myojo : 63 Miso Ramen : 5 : 2
## (Other) :1822 (Other) :2540 (Other): 2
## Country Stars Top.Ten Sub.Region
## Japan : 352 Min. :0.000 :2536 East Asia :1189
## USA : 323 1st Qu.:3.250 \n : 4 Southeast Asia: 758
## South Korea: 307 Median :3.750 2012 #1 : 1 North America : 390
## Taiwan : 224 Mean :3.655 2012 #10: 1 West Europe : 115
## Thailand : 191 3rd Qu.:4.250 2012 #2 : 1 South Asia : 61
## China : 169 Max. :5.000 2012 #3 : 1 Australia : 22
## (Other) :1011 (Other) : 33 (Other) : 42
## Region
## Africa : 3
## America : 401
## Asia :2011
## Australia/Oceania: 26
## Europe : 136
##
## For this reports, we’re trying to analyze 2577 variant of instant noodles which distributed in 38 different countries in 5 continents. Based on the summary, we can find that Japan has the most variant of instant noodles in the world and followed by USA and South Korea. Since instant noodles founded in Asia, it’s not astonishing to see that at least 2011 different kinds of instant noodles can be found in all around Asia.
graphics::pie(xtabs(~ Region, noodle))
Based on the pie chart above, we can see clearly that most variant of instant noodles can be found in Asia, with America and Europe following behind.
The Most Recognizable Brand
Based on the data summary, Nissin alone has 381 different products, which makes them the most recognizable instant noodles brand around the world. Nongshim from South Korea and Maruchan from United states can’t be leave aside as the second and the third brands which have more instant noodles variant.
nissin <- as.data.frame(sort(prop.table(table(droplevels(noodle[noodle$Brand == "Nissin","Country"]))),decreasing = T))
names(nissin)[1] <- paste("Country")
nissin## Country Freq
## 1 Japan 0.291338583
## 2 USA 0.249343832
## 3 Hong Kong 0.175853018
## 4 Singapore 0.070866142
## 5 Germany 0.057742782
## 6 Mexico 0.047244094
## 7 Thailand 0.044619423
## 8 Colombia 0.015748031
## 9 India 0.015748031
## 10 Brazil 0.013123360
## 11 China 0.007874016
## 12 Indonesia 0.005249344
## 13 Hungary 0.002624672
## 14 Philippines 0.002624672All Nissin’s 381 different products are distributed in 14 different countries, with around 70% of the products centered around Japan, USA and Hong Kong.
The Packaging of Instant Noodles
graphics::pie(xtabs(~ Style, noodle))
xtabs(~ Style + Region, noodle)## Region
## Style Africa America Asia Australia/Oceania Europe
## 0 0 2 0 0
## Bar 0 1 0 0 0
## Bowl 0 78 401 0 2
## Box 0 1 5 0 0
## Can 0 1 0 0 0
## Cup 0 107 280 17 46
## Pack 3 161 1267 9 88
## Tray 0 52 56 0 0We can find several type of packaging for instant noodles, but the most common one is the “pack” style, followed by bowl and cup.
What Do Customers Think About The Instant Noodles?
There can be a lot of instant noodle in the market, but how do we know whether it’s good or not? The easiest way to find out each instant noodle tastiness is by finding reviews and ratings from other customers.
africa.mean <- (mean(noodle$Stars[noodle$Region == "Africa"]))
america.mean <- (mean(noodle$Stars[noodle$Region == "America"]))
asia.mean <- (mean(noodle$Stars[noodle$Region == "Asia"]))
australia.mean <- (mean(noodle$Stars[noodle$Region == "Australia/Oceania"]))
europe.mean <- (mean(noodle$Stars[noodle$Region == "Europe"]))
region.mean <- cbind(Region=c("Africa","America","Asia","Australia/Oceania","Europe"))
region.mean <- cbind(region.mean,as.data.frame(c(africa.mean,america.mean,asia.mean,australia.mean,europe.mean)))
names(region.mean)[2] <- paste("Mean")
region.mean## Region Mean
## 1 Africa 2.833333
## 2 America 3.359414
## 3 Asia 3.752797
## 4 Australia/Oceania 3.251923
## 5 Europe 3.169485graphics::barplot(xtabs(Mean ~ Region, region.mean))
Based on data, most of instant noodles in Asia can be categorized as delicious, because the average of the instant noodle rating in Asia is 3.75. We can say that you can go wrong when you pick any instant noodle in Asia. On the other hand, you need to do more research when you want to buy instant noodle in Africa, since the average rating for its’ instant noodles is 2.83.
noodle$Stars.Range <-c("0-1", "1-2", "2-3", "3-4", "4-5")[findInterval(as.numeric(as.character(noodle$Stars)) , c(0, 1, 2, 3, 4, Inf) )]
xtabs(~ Stars.Range + Region, noodle)## Region
## Stars.Range Africa America Asia Australia/Oceania Europe
## 0-1 0 17 32 0 5
## 1-2 1 24 67 1 10
## 2-3 0 53 174 6 17
## 3-4 2 168 780 10 83
## 4-5 0 139 958 9 21graphics::barplot(xtabs(~ Stars.Range + Region, noodle))
table(droplevels(noodle$Country),noodle$Stars.Range)##
## 0-1 1-2 2-3 3-4 4-5
## Australia 0 1 6 9 6
## Bangladesh 0 0 0 3 4
## Brazil 0 0 0 0 5
## Cambodia 0 0 0 2 3
## Canada 7 7 12 12 3
## China 10 10 12 71 66
## Colombia 0 0 1 5 0
## Dubai 0 0 0 3 0
## Estonia 0 0 0 2 0
## Fiji 0 0 0 1 3
## Finland 0 0 0 3 0
## Germany 0 0 0 22 5
## Ghana 0 0 0 2 0
## Holland 0 0 0 4 0
## Hong Kong 1 3 14 41 78
## Hungary 0 0 1 6 2
## India 0 2 5 17 7
## Indonesia 0 2 5 37 82
## Japan 4 7 20 105 216
## Malaysia 0 1 9 51 94
## Mexico 0 0 0 11 14
## Myanmar 0 0 2 7 5
## Nepal 0 1 0 10 3
## Netherlands 3 1 3 7 1
## Nigeria 0 1 0 0 0
## Pakistan 0 1 1 6 1
## Philippines 2 3 6 22 14
## Poland 0 0 0 2 2
## Sarawak 0 0 0 0 3
## Singapore 0 0 7 37 65
## South Korea 3 6 25 128 145
## Sweden 0 0 0 3 0
## Taiwan 5 16 20 81 102
## Thailand 5 9 27 96 54
## UK 2 9 13 34 11
## United States 0 0 0 1 0
## USA 10 17 40 139 117
## Vietnam 2 6 21 63 16Asia - The Heaven of Instant Noodle
asia <- subset(noodle, Region == "Asia")
str(asia)## 'data.frame': 2011 obs. of 10 variables:
## $ Review.. : int 2580 2579 2577 2576 2575 2574 2573 2572 2571 2570 ...
## $ Brand : Factor w/ 355 levels "1 To 3 Noodles",..: 193 122 339 38 252 8 110 236 137 291 ...
## $ Variety : Factor w/ 2413 levels "\"A\" Series Artificial Chicken",..: 2195 1448 723 1957 1112 2058 1375 816 2232 421 ...
## $ Style : Factor w/ 8 levels "","Bar","Bowl",..: 6 7 7 7 7 6 8 7 7 7 ...
## $ Country : Factor w/ 38 levels "Australia","Bangladesh",..: 19 33 33 17 31 19 19 19 30 34 ...
## $ Stars : num 3.75 1 2.75 3.75 4.75 4 3.75 0.25 2.5 5 ...
## $ Top.Ten : Factor w/ 39 levels "","\n","2012 #1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sub.Region : Factor w/ 12 levels "Australia","Central Europe",..: 3 3 3 9 3 3 3 3 10 10 ...
## $ Region : Factor w/ 5 levels "Africa","America",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Stars.Range: chr "3-4" "1-2" "2-3" "3-4" ...summary(asia)## Review.. Brand Variety Style
## Min. : 2.0 Nissin : 234 Artificial Chicken : 6 Pack :1267
## 1st Qu.: 620.5 Mama : 71 Beef : 5 Bowl : 401
## Median :1309.0 Paldo : 66 Yakisoba : 5 Cup : 280
## Mean :1281.4 Nongshim: 62 Artificial Beef Flavor: 4 Tray : 56
## 3rd Qu.:1914.5 Myojo : 57 Artificial Spicy Beef : 4 Box : 5
## Max. :2580.0 Indomie : 52 Chicken : 4 : 2
## (Other) :1469 (Other) :1983 (Other): 0
## Country Stars Top.Ten Sub.Region
## Japan :352 Min. :0.000 :1971 East Asia :1189
## South Korea:307 1st Qu.:3.250 \n : 4 Southeast Asia: 758
## Taiwan :224 Median :3.750 2012 #1 : 1 South Asia : 61
## Thailand :191 Mean :3.753 2012 #10: 1 Middle East : 3
## China :169 3rd Qu.:4.500 2012 #2 : 1 Australia : 0
## Malaysia :155 Max. :5.000 2012 #3 : 1 Central Europe: 0
## (Other) :613 (Other) : 32 (Other) : 0
## Region Stars.Range
## Africa : 0 Length:2011
## America : 0 Class :character
## Asia :2011 Mode :character
## Australia/Oceania: 0
## Europe : 0
##
## Asia as the origin of instant noodles can be said as the heaven of instant noodles. There are at least 2011 different varieties of instant noodles, which more half of those variants can be found in East Asia. Furthermore, we can also see that Southeast Asia also not fall behind from East Asia. It has at least 758 variants of instant noodles, which are higher that the sum of instant noodles variants in 4 other regions. For instant noodles in Asia, in terms of tastiness, with a strong 3.75 stars rating on average, you hardly can go wrong when you choose instant noodles in Asia.
noodle.sub.asia <- as.data.frame(sort(table(droplevels(asia$Sub.Region)),decreasing = T))
names(noodle.sub.asia)[1]<-paste("Sub.Region")
noodle.sub.asia## Sub.Region Freq
## 1 East Asia 1189
## 2 Southeast Asia 758
## 3 South Asia 61
## 4 Middle East 3graphics::barplot(xtabs(Freq ~ Sub.Region, noodle.sub.asia))
noodle.asia <- as.data.frame(sort(table(droplevels(asia$Country)),decreasing = T))
names(noodle.asia)[1]<-paste("Country")
noodle.asia## Country Freq
## 1 Japan 352
## 2 South Korea 307
## 3 Taiwan 224
## 4 Thailand 191
## 5 China 169
## 6 Malaysia 155
## 7 Hong Kong 137
## 8 Indonesia 126
## 9 Singapore 109
## 10 Vietnam 108
## 11 Philippines 47
## 12 India 31
## 13 Myanmar 14
## 14 Nepal 14
## 15 Pakistan 9
## 16 Bangladesh 7
## 17 Cambodia 5
## 18 Dubai 3
## 19 Sarawak 3graphics::barplot(xtabs(Freq ~ Country, noodle.asia))
What About Instant Noodle in Indonesia?
Instant noodle firstly introduced in Indonesia in 1968 with the brand Supermie. Nowadays, instant noodles become a comfort food, which is a significant part of the Indonesian diet.
ina <- subset(noodle, Country == "Indonesia")
ina <- ina[,-c(5)]
names(ina)## [1] "Review.." "Brand" "Variety" "Style" "Stars"
## [6] "Top.Ten" "Sub.Region" "Region" "Stars.Range"str(ina)## 'data.frame': 126 obs. of 9 variables:
## $ Review.. : int 2549 2507 2463 2438 2417 2399 2374 2075 2052 1922 ...
## $ Brand : Factor w/ 355 levels "1 To 3 Noodles",..: 195 112 195 343 284 112 343 171 112 172 ...
## $ Variety : Factor w/ 2413 levels "\"A\" Series Artificial Chicken",..: 717 1800 718 1269 187 1372 1262 1301 1631 878 ...
## $ Style : Factor w/ 8 levels "","Bar","Bowl",..: 7 7 7 7 7 7 7 7 6 6 ...
## $ Stars : num 4.5 4 3.25 5 3.75 4 4.5 4.5 3.5 1.5 ...
## $ Top.Ten : Factor w/ 39 levels "","\n","2012 #1",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Sub.Region : Factor w/ 12 levels "Australia","Central Europe",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ Region : Factor w/ 5 levels "Africa","America",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Stars.Range: chr "4-5" "4-5" "3-4" "4-5" ...summary(ina)## Review.. Brand Variety
## Min. : 44.0 Indomie :52 Mi Goreng Jumbo Beef : 2
## 1st Qu.: 724.2 ABC :12 100 Green Chilli Soto Flavour: 1
## Median : 868.5 Mi Sedaap:12 Beef : 1
## Mean : 991.5 SuperMi : 8 Bihun Kuah Rasa Baso Sapi : 1
## 3rd Qu.:1260.8 GaGa : 7 Chicken : 1
## Max. :2549.0 Sarimi : 7 Chicken Curry : 1
## (Other) :28 (Other) :119
## Style Stars Top.Ten Sub.Region
## Pack :104 Min. :1.500 :120 Southeast Asia:126
## Cup : 21 1st Qu.:3.750 \n : 2 Australia : 0
## Box : 1 Median :4.000 2012 #1: 1 Central Europe: 0
## : 0 Mean :4.067 2012 #2: 1 East Asia : 0
## Bar : 0 3rd Qu.:4.500 2012 #5: 1 Middle East : 0
## Bowl : 0 Max. :5.000 2013 #3: 1 North America : 0
## (Other): 0 (Other): 0 (Other) : 0
## Region Stars.Range
## Africa : 0 Length:126
## America : 0 Class :character
## Asia :126 Mode :character
## Australia/Oceania: 0
## Europe : 0
##
## nrow(ina)## [1] 126At least, there are 126 variant of instant noodles which you can find in Indonesia, which most of them are Indomie. We can say that most of the instant noodles in Indonesia are delicious, since the average star rating for instant noodles in Indonesia is 4.06. It’s even higher that the average star rating for instant noodles in awhole Asia.
noodle.ina <- as.data.frame(sort(table(ina$Stars.Range), decreasing = T))
noodle.ina## Var1 Freq
## 1 4-5 82
## 2 3-4 37
## 3 2-3 5
## 4 1-2 2graphics::barplot(xtabs(Freq ~ Var1,noodle.ina))
noodle.ina2 <- as.data.frame(sort(table(droplevels(ina$Brand)), decreasing = T))
noodle.ina2## Var1 Freq
## 1 Indomie 52
## 2 ABC 12
## 3 Mi Sedaap 12
## 4 SuperMi 8
## 5 GaGa 7
## 6 Sarimi 7
## 7 Eat & Go 5
## 8 Super Bihun 4
## 9 Pop Bihun 3
## 10 Healtimie 2
## 11 La Fonte 2
## 12 Nissin 2
## 13 Salam Mie 2
## 14 Tropicana Slim 2
## 15 World O' Noodle 2
## 16 Cap Atoom Bulan 1
## 17 Maitri 1
## 18 Mie Sedaap 1
## 19 President 1table(droplevels(ina$Brand),ina$Stars.Range)##
## 1-2 2-3 3-4 4-5
## ABC 0 0 3 9
## Cap Atoom Bulan 0 0 1 0
## Eat & Go 0 0 2 3
## GaGa 0 0 4 3
## Healtimie 0 0 1 1
## Indomie 1 5 10 36
## La Fonte 0 0 1 1
## Maitri 0 0 0 1
## Mi Sedaap 0 0 3 9
## Mie Sedaap 1 0 0 0
## Nissin 0 0 1 1
## Pop Bihun 0 0 2 1
## President 0 0 0 1
## Salam Mie 0 0 0 2
## Sarimi 0 0 4 3
## Super Bihun 0 0 2 2
## SuperMi 0 0 1 7
## Tropicana Slim 0 0 2 0
## World O' Noodle 0 0 0 2prop.table(table(droplevels(ina$Brand),ina$Stars.Range== "4-5"))##
## FALSE TRUE
## ABC 0.023809524 0.071428571
## Cap Atoom Bulan 0.007936508 0.000000000
## Eat & Go 0.015873016 0.023809524
## GaGa 0.031746032 0.023809524
## Healtimie 0.007936508 0.007936508
## Indomie 0.126984127 0.285714286
## La Fonte 0.007936508 0.007936508
## Maitri 0.000000000 0.007936508
## Mi Sedaap 0.023809524 0.071428571
## Mie Sedaap 0.007936508 0.000000000
## Nissin 0.007936508 0.007936508
## Pop Bihun 0.015873016 0.007936508
## President 0.000000000 0.007936508
## Salam Mie 0.000000000 0.015873016
## Sarimi 0.031746032 0.023809524
## Super Bihun 0.015873016 0.015873016
## SuperMi 0.007936508 0.055555556
## Tropicana Slim 0.015873016 0.000000000
## World O' Noodle 0.000000000 0.015873016graphics::barplot(table(ina$Stars.Range,droplevels(ina$Brand)))
Out of 82 variants of instant noodles in Indonesia which rated between 4 and 5, 28.57% of them is accounted for Indomie’s instant noodles. So, we can say that Indomie is the most loved instant noodles brand in Indonesia, which followed by Mie Sedaap and ABC who are tied for the second most loved.
Conclusion
Now, what do I know about instant noodle?
I know that it was founded by Momofuku Ando of Nissin Foods under the brand name Chikin Ramen in Japan on 25 August 1958 Sixty One years since its’ inception, Japan still the biggest producer of instant noodles, which has at least 352 variant of instant noodles. Nissin is the biggest noodle producers which has 381 different products that distributed in 14 countries. Asia is the heaven of instant noodles, because you can find 2011 out of 2577 instant noodles variant all across Asia. Most of instant noodles in Asia are the delicious one, because, on average, Asia’s instant noodles got 3.75 star for it’s taste. However, don’t forget about Indonesia, with average of 4.06 star rating, Indonesia became one of the Asian countries which has a higher star rating than the average of Asia’s star rating.