ramen_ratings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-04/ramen_ratings.csv")
library(tidyverse)
library(gganimate)
library(ggthemes)I have not posted regarding #TidyTuesday in a while, so here it is. It is all about manipulating the dataset and generating necessary plots.
Just an animated plot where for the top 10 brands how stars change with style and country. Ramen is with highest ratings in Japan, Taiwan and South Korea. #TidyTuesday GitHub Code : https://t.co/lPQrYK8aoN pic.twitter.com/IQ3okDflYK
— Amalan Mahendran (@Amalan_Con_Stat) June 5, 2019
ramen_ratings %>%
count(stars,sort = TRUE) %>%
ggplot(.,aes(fct_inorder(as_factor(stars)),n,label=n))+
geom_col(fill=blues9[5])+
geom_text(vjust=-0.5,color=blues9[9])+ylab("Count / Frequency")+
xlab("Stars")+ggtitle("Stars Distribution")+
theme_economist()+
theme(axis.text.x = element_text(angle = 45))
Brand versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(brand,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Brands Distribution by Stars",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars < 2)%>%
count(brand,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Brands Distribution by Stars",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Style versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(style,sort = TRUE) %>%
top_n(10) %>%
ggplot(.,aes(x=fct_inorder(style),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Style")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Styles Distribution by Stars",
subtitle = "Top 10")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars < 2)%>%
count(style,sort = TRUE) %>%
top_n(5) %>%
ggplot(.,aes(x=fct_inorder(style),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Style")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Styles Distribution by Stars",
subtitle = "Top 5")+
theme_economist()+ coord_flip()
Country versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(country),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Countries Distribution by Stars",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars < 2)%>%
count(country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(country),y=n,label=n))+geom_col(fill=blues9[5])+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.5,color=blues9[9])+
ggtitle("Countries Distribution by Stars",
subtitle = "Top 20")+
theme_economist()+ coord_flip()
Brand and Style versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(brand,style,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n,fill=style))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Brand Distribution by Stars but for Styles",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars <2)%>%
count(brand,style,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n,fill=style))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Brand Distribution by Stars but for Styles",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Brand and Country versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(brand,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n,fill=country))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Brand Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars <2)%>%
count(brand,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(brand),y=n,label=n,fill=country))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Brand")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Brand Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Style and Country versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(style,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(country),y=n,label=n,fill=style))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Style Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars <2)%>%
count(style,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=fct_inorder(country),y=n,label=n,fill=style))+
geom_col(position = "dodge")+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.25,color=blues9[9],
position = position_dodge(width = 1))+
ggtitle("Style Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Brand, Style and Country versus Stars
Rating where stars is 4 or above 4
ramen_ratings %>%
subset(stars >=4)%>%
count(brand,style,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=country,y=n,label=n,fill=style))+
geom_col(position = "dodge")+facet_wrap(~brand)+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.05,color=blues9[9],size=3.5,
position = position_dodge(width = 1))+
ggtitle("Style Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Rating where stars is below 2
ramen_ratings %>%
subset(stars <2)%>%
count(brand,style,country,sort = TRUE) %>%
top_n(25) %>%
ggplot(.,aes(x=country,y=n,label=n,fill=style))+
geom_col(position = "dodge")+facet_wrap(~brand)+
ylab("Count / Frequency")+xlab("Country")+
geom_text(hjust=-0.05,color=blues9[9],size=3.5,
position = position_dodge(width = 1))+
ggtitle("Style Distribution by Stars but for Country",
subtitle = "Top 25")+
theme_economist()+ coord_flip()
Special Plot
#summarytools::dfSummary(ramen_ratings)
spe_plot<-ramen_ratings %>%
subset(brand=="Nissin" | brand=="Nongshim" |
brand=="Maruchan" | brand=="Myojo" |
brand=="Samyang Foods" | brand=="Mama"|
brand=="Paldo" | brand=="Indomie"|
brand=="Ottogi" | brand=="Sapporo Ichiban") %>%
remove_missing() %>%
ggplot(.,aes(brand,as_factor(stars),color=style))+
geom_jitter()+theme_economist()+
xlab("Brand")+ylab("Stars")+
ggtitle("How Stars change for Brands",subtitle="{closest_state}")+
transition_states(country,transition_length = 1,state_length = 2)+
enter_fade()+exit_fade()
animate(spe_plot,nframes=44,fps=2)
THANK YOU