Upload the file to Github and read the file from Github

Ramendata<- "https://raw.githubusercontent.com/jayleecunysps/AssignmentforSPS/main/ramen-ratings.csv"
Ramendata <-read.csv(Ramendata)

Clean the data, selet and join

Ramendata <- Ramendata[!(Ramendata$Stars=="Unrated"),]
Ramendata$Stars <-as.numeric(Ramendata$Stars)
Ramendata <- Ramendata %>%  
select("Brand","Variety","Style","Country","Stars")

Vardata <- Ramendata %>%  
select("Variety","Stars") %>% 
filter_at(vars(1), any_vars(. > 0)) #remove blank

Branddata <- Ramendata %>%  
select("Brand","Stars") %>% 
filter_at(vars(1), any_vars(. > 0)) #remove blank

Styledata <- Ramendata %>%  
select("Style","Stars") %>% 
filter_at(vars(1), any_vars(. > 0)) #remove blank

Countrydata <- Ramendata %>%  
select("Country","Stars") %>% 
filter_at(vars(1), any_vars(. > 0)) #remove blank

Varrank <- aggregate(Stars ~ Variety, Vardata, mean)
Brandrank <- aggregate(Stars ~ Brand, Branddata, mean)
Stylerank <- aggregate(Stars ~ Style, Styledata, mean)
Countryrank <- aggregate(Stars ~ Country, Countrydata, mean)

Answer of the Benson Toi’s suggested analysis

We can use this data set to analyze the favorite favor, best brand, ramen style, and more.

please see the highest and lowest under the table below.

Varrank <- Varrank[order(Varrank$Stars, decreasing = TRUE), ]
head(Varrank,3)
##                                 Variety Stars
## 20        2 Minute Noodles Masala Spicy     5
## 40 Aloe Noodle Red Onion & Sesame Sauce     5
## 42          Aloe Noodle Vegetable Sauce     5
tail(Varrank,3)
##                                       Variety Stars
## 2227           Tiny Noodle With Oyster Flavor     0
## 2320               Vegan Pad Thai Noodle Soup     0
## 2355 Wei Wei A Instant Noodles Chicken Flavor     0
Brandrank <- Brandrank[order(Brandrank$Stars, decreasing = TRUE), ]
head(Brandrank,3)
##         Brand Stars
## 40 ChoripDong     5
## 48      Daddy     5
## 49    Daifuku     5
tail(Brandrank,3)
##          Brand Stars
## 238     Roland     0
## 306      Tiger     0
## 329 US Canning     0
Stylerank <- Stylerank[order(Stylerank$Stars, decreasing = TRUE), ]
head(Stylerank,3)
##   Style    Stars
## 1   Bar 5.000000
## 3   Box 4.291667
## 6  Pack 3.700458
tail(Stylerank,3)
##   Style    Stars
## 7  Tray 3.545139
## 4   Can 3.500000
## 5   Cup 3.498500
Countryrank <- Countryrank[order(Countryrank$Stars, decreasing = TRUE), ]
head(Countryrank,3)
##     Country    Stars
## 3    Brazil 4.350000
## 29  Sarawak 4.333333
## 4  Cambodia 4.200000
tail(Countryrank,3)
##        Country    Stars
## 24 Netherlands 2.483333
## 5       Canada 2.243902
## 25     Nigeria 1.500000