《数说噶玛兰单桶》

白州Bittersweet vs 山崎Smoky Batch the first

Glendronach

Hakushu

Kavalan

Whisky

Yamazaki

Author

Tony D

Published

January 4, 2024

“试问谁家出单桶数量最多？3千多个OB单桶的噶子毒咒算是独领风骚了。在他的面前出过900多桶OB单桶的多纳也只能算小弟。酒厂这样一桶一世界的卖，时常会给人有惊喜还有惊吓。坊间有个理论是选桶会更好，选剩的就叫野生桶。。。”

噶玛兰 7年 2015-2023 波本桶 for HNWS

酒精度：53.2% 桶：ex-Bourbon B150727021A

闻香：椰子油。释迦果。

品味：波本糖果。中段偏辣。尾韵果脯。这支闻香和好的处女桶可以一比。但喝起来就弱了。

打分：83【推荐一试】

噶玛兰 5年 2017-2022 马德拉桶 for HNWS

酒精度：59.4% 桶：Madeira D170327031A

闻香：坏掉的陈醋和装修味。有点臭。闻着就不太想喝了。

品味：喝起来也是木头装修味。尾韵有点红枣干。很久没喝到这么难喝的威士忌了。甚至在WB上都找不到这款酒。不会是难喝到没人愿意上传吧。

打分：66【可试可不试】

噶玛兰 12年 2008-2020 处女桶 for Drinker House

酒精度：57.1% 桶：Virgin N080128003

闻香：木桶带出了开心果。是处女桶那味了。

品味：这桶喝起来就略弱，没有闻香来的舒服。木头辛辣主调。没有极品噶酒处女桶的果酱感。

打分：85【推荐一试】

Show the code

library(tidyverse)  # data wrangling
library(RSelenium)  # activate Selenium server
library(rvest)      # web scrape tables
library(netstat)    # find unused port
library(data.table) # for the rbindlist function
library(readxl)
library(stringr)
library(janitor)
library(writexl)
#library(XLConnect)
library(openxlsx)
library(lubridate)
library(magick)
library(writexl)
library(cowplot)

#rstudioapi::writeRStudioPreference("console_max_lines", 800000)
options(max.print=8000000)


url_link='https://www.whiskybase.com/whiskies/distillery/256/kavalan'

distillery_name='kavalan'


url=url_link
page = read_html(url)

one_distillery <- html_nodes(page, "table") %>% html_table(fill = TRUE) %>% as.data.frame()

one_distillery[one_distillery == ''] <- NA

one_distillery002=one_distillery %>% fill('Var.2',.direction = "down")



#one_distillery003=one_distillery002
one_distillery003=one_distillery002 %>% filter(is.na(`Var.1`)==TRUE,is.na(`Whisky.listings`)==TRUE) %>%select(-one_of("Var.1","Shoplinks", "Var.10",'Var.12'))

bottle_link=page %>% html_nodes('.clickable')%>% html_attr('href') 

one_distillery004=cbind(one_distillery003,bottle_link)%>% rename('company'='Var.2')




####################   get distilled year  ##########################################
one_distillery004=one_distillery004 %>% mutate(vintage_year=as.numeric(str_extract(Name, "(\\d)+")))%>% 
  mutate(vintage_year=if_else(nchar(vintage_year)==4,vintage_year,0))


#write.csv(one_distillery004,'all kavalan.csv')
#write_xlsx(one_distillery004,"all kavala.xlsx")





# single cask by company

aa=one_distillery004 %>% mutate(abv=as.numeric(str_sub(Strength,1,4)))%>% filter(abv>50)%>% group_by(abv) %>% count()

bb=one_distillery004 %>% mutate(abv=as.numeric(str_sub(Strength,1,4)))%>% filter(abv>50)%>% group_by(abv) %>% 
  summarise(cask=n()) %>% mutate(percent=cask/sum(cask)) %>% arrange(desc(cask))


#sum(bb$cask)


library(ggpubr)
library(png)
library(jpeg)


url <- "https://images.squarespace-cdn.com/content/v1/6427f023b6c3226aec64fcc7/03a79706-1609-46f3-9c57-262aac5f5470/KAVALAN_LOGO_CMYK-10.png"
download.file(url, destfile = "rabbduck.png")
img=readPNG("rabbduck.png")

# by abv
q1=ggplot(bb, aes(x=abv, y=cask)) + background_image(img) +
  geom_bar(stat="identity",alpha=0.9)+ geom_text(aes(label=paste0(abv,'%')), position=position_dodge(width=0.9), vjust=-0.5)+ 
  geom_text(aes(label=paste0('(',cask,')')), position=position_dodge(width=0.9), vjust=+1.2)+
  labs(title = "Kavalan Solist single cask ABV and bottled cask", subtitle="*Data from whiskybase 13Jan2024",caption = "# Created by Drink Tony"
       ,x='ABV',y='bottled casks')+
  theme_classic()

#q1


#ggsave("abv.png",width = 25, height = 15, units = "cm")



############# compare with Glendronach
#url <- "https://www.glendronachdistillery.com/wp-content/uploads/2022/03/GlenDronach-MASTER-CMYK.png"
#download.file(url, destfile = "glendronach.png")

img2=readJPEG("./images/750x.jpg")



url_link='https://www.whiskybase.com/whiskies/distillery/101/glendronach'

distillery_name='glendronach'


url=url_link
page = read_html(url)

one_distillery <- html_nodes(page, "table") %>% html_table(fill = TRUE) %>% as.data.frame()

one_distillery[one_distillery == ''] <- NA

one_distillery002=one_distillery %>% fill('Var.2',.direction = "down")

one_distillery003=one_distillery002 %>% filter(is.na(`Var.1`)==TRUE,is.na(`Whisky.listings`)==TRUE) %>%select(-one_of("Var.1","Shoplinks", "Var.10",'Var.12'))

bottle_link=page %>% html_nodes('.clickable')%>% html_attr('href') 

one_distillery004=cbind(one_distillery003,bottle_link)%>% rename('company'='Var.2')




####################   get distilled year  ##########################################
one_distillery004=one_distillery004 %>% mutate(vintage_year=as.numeric(str_extract(Name, "(\\d)+")))%>% 
  mutate(vintage_year=if_else(nchar(vintage_year)==4,vintage_year,0))



summary001=one_distillery004 %>% filter(is.na(Casknumber)==FALSE
                                        ,is.na(Bottled)==FALSE
                                        ,company=='Distillery Bottling',vintage_year!=0)

ee=summary001 %>% mutate(abv=as.numeric(str_sub(Strength,1,4)))%>% group_by(abv) %>% 
  summarise(cask=n()) %>% mutate(percent=cask/sum(cask)) %>% arrange(desc(cask))


#sum(ee$cask)

# by abv
q2=ggplot(ee, aes(x=abv, y=cask)) + background_image(img2) +
  geom_bar(stat="identity",alpha=0.5)+ 
  #geom_text(aes(label=paste0(abv,'%')), position=position_dodge(width=0.9), vjust=-0.5)+ 
  #geom_text(aes(label=paste0('(',cask,')')), position=position_dodge(width=0.9), vjust=+1.2)+
  labs(title = "Glendronach single cask ABV and bottled cask", subtitle="*Data from whiskybase 13Jan2024",caption = "# Created by Drink Tony"
       ,x='ABV',y='bottled casks')+
  theme_classic()


#q2



library(gridExtra)
#grid.arrange(q1, q2, ncol = 2)

plot_grid(q1, q2)

q2=ggplot(ee, aes(x=abv, y=cask)) + background_image(img2) +
  geom_bar(stat="identity",alpha=0.5)+ 
  #geom_text(aes(label=paste0(abv,'%')), position=position_dodge(width=0.9), vjust=-0.5)+ 
  #geom_text(aes(label=paste0('(',cask,')')), position=position_dodge(width=0.9), vjust=+1.2)+
  labs(title = "Glendronach single cask ABV and bottled cask", subtitle="*Data from whiskybase 13Jan2024",caption = "# Created by Drink Tony"
       ,x='ABV',y='bottled casks')+
  theme_classic()

q2

“和多纳类似，噶子只给lmdw包过1桶，协会6桶。其他3千多桶都是自己的OB单桶。不少酒友也发现噶子的毒咒系列酒精度数都很神奇的集中在这5个数字：57.8%（738桶）；58.6%（666桶）；57.1%（483桶）；56.3%（334桶）；59.4%（313桶）。这5个酒精度既然已经涵盖了80%的独奏。对比看，图3右边的多纳单桶的酒精度数明显就更符合自然的正太分布。有朋友知道其背后的故事吗？”

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问谁领风骚 by 羅文 & 甄妮

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