Where I start

This is an R Markdown documentof my research of Instant Noodle. My topic was begining with Maruchan Instant Lunch with Shrimp

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As beginging, my point to this project is to discover global diversity of instant noodles. After I did many research of data, the most “effective” data is not too much related to my main point. Then I am using this data of Global Demand of Instant Noodles, unfortunately, which is not have too much information.

My dataset is about expanding market of instant noodles which collected and estimated by World Instant Noodles Association (WINA). It include the global demand of instant noodles from 2009 to 2014.

Data

Here is a link for my data:

http://instantnoodles.org/noodles/expanding-market.html.

I use different part of data to make several different chart and to analyse the information. Here is my dataset in R and how I shape it:

library(ggplot2)
d<- read.csv("data/second data layout of Global Demand for Instant Noodles.csv")
#D is for only use the data in 2014, which is the row 216 to 258
D<-d[216:258,]

summary(d)
##        country         year          value      
##  Australia :  6   Min.   :2009   Min.   :    0  
##  Bangladesh:  6   1st Qu.:2010   1st Qu.:   90  
##  Brazil    :  6   Median :2012   Median :  300  
##  Cambodia  :  6   Mean   :2012   Mean   : 2366  
##  Canada    :  6   3rd Qu.:2013   3rd Qu.: 1970  
##  Chile     :  6   Max.   :2014   Max.   :46220  
##  (Other)   :222                  NA's   :7
str(d)
## 'data.frame':    258 obs. of  3 variables:
##  $ country: Factor w/ 43 levels "Australia","Bangladesh",..: 7 17 18 43 16 41 31 38 29 3 ...
##  $ year   : int  2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
##  $ value  : int  40860 13930 5340 4300 2280 4290 3480 2350 2550 1870 ...
summary(D)
##        country        year          value      
##  Australia : 1   Min.   :2014   Min.   :   10  
##  Bangladesh: 1   1st Qu.:2014   1st Qu.:  135  
##  Brazil    : 1   Median :2014   Median :  300  
##  Cambodia  : 1   Mean   :2014   Mean   : 2383  
##  Canada    : 1   3rd Qu.:2014   3rd Qu.: 1730  
##  Chile     : 1   Max.   :2014   Max.   :44400  
##  (Other)   :37
str(D)
## 'data.frame':    43 obs. of  3 variables:
##  $ country: Factor w/ 43 levels "Australia","Bangladesh",..: 7 17 18 43 16 41 31 38 29 3 ...
##  $ year   : int  2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
##  $ value  : int  44400 13430 5500 5000 5340 4280 3590 3070 2800 2360 ...

Global Demand for Instant Noodles in 2014

This is a bar chart of Global Demand for Instant Noodles in 2014:

ggplot(data=D, aes(x=country, y=value, fill=country)) +geom_bar(stat="identity")+theme(axis.text.x=element_text(angle=90,colour="black"))+xlab("Country/Region")+ylab("Global Demand for Instant Noodles-2014 Unit: 1 Million Packets (Bags/Cups)")

Global Demand for Instat Noodles Comparation

This is a line chart of Global Demand for Instat Noodles Comparation which compare every counrties’ change of demand through each year:

## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_path).

This is a line chart of Global Demand for Instat Noodles Comparation which compare every years’ change of demand:

## Warning: Removed 7 rows containing missing values (geom_point).

which is not a really clear way to show this kind of relation, so

as a bar chart:

Conclution

According to these charts, we could figure out that there have some countries have total different value compare to others, which is China and Indonesia. So I will search more information in these two regions and to check out the reason. This will be a interesing perspective to find some information about cultural diversity of instant noodles.