load("./data/wine150_tidy")
wine150_tidy %>%
select(country, points_avg_country) %>%
filter(country %in% c("Australia", "China", "Georgia", "India", "Israel", "Japan", "Lebanon", "New Zealand", "South Korea", "Turkey")) %>%
mutate(country = fct_reorder(country, desc(points_avg_country))) %>%
unique() %>%
mutate(text_label = str_c("Country: ", country, "\nAverage Points: ", points_avg_country)) %>%
plot_ly(
x = ~country, y = ~points_avg_country, color = ~factor(country), text = ~text_label,
type = "bar", colors = "viridis") %>%
layout(
xaxis = list(title = "Asian-Pacific Countries"),
yaxis = list(title = "Average Wine Points", range = (c(80,88))),
title = "Average Wine Points of Asian-Pacific Countries")By comparing wine review data of the 10 countries in the Asian-Pacific region, we could see that Turkey has the highest average points (88.1) and South Korea has the lowest average points (81.5). There might be many explanations for this result. For grapes, the colder weather in South Korea may be less favorable than the warmer weather in Turkey. In addition, there may be a culture explanation. From the bar plot, we find that South Korea, China and Japan have the lowest average wine points, and according to the cultures of these three East Asian countries, wine is not even their most popular alcoholic beverage.
load("./data/wine150_tidy")
wine150_tidy %>%
select(points, country, winery, variety, points_avg_variety, points_avg_winery) %>%
filter(country %in% c("Australia", "China", "Georgia", "India", "Israel", "Japan", "Lebanon", "New Zealand", "South Korea", "Turkey")) %>%
mutate(winery = fct_reorder(winery, desc(points_avg_winery))) %>%
filter(as.numeric(winery) <= 20) %>%
arrange(winery) %>%
plot_ly(
x = ~winery, y = ~points, color = ~factor(winery),
type = "box", colors = "viridis") %>%
layout(
xaxis = list(title = "Asian-Pacific Wineries"),
yaxis = list(title = "Wine Points"),
title = "Top 20 Asian-Pacific Wineries: Highest Professional Recognition")One of the most important factors of wines is the producer. Usually, the high reputations of prestigious wineries serve as guarantees of great wine qualities. After calculating average wine points for Asian-Pacific wineries, we have shown the top 20 Asian-Pacific wineries with the highest average wine points, with Campbells being No.1 (94.92). The box plot has also provided quartiles of wine points for each winery.
load("./data/wine150_tidy")
wine150_tidy %>%
select(points, country, winery, variety, points_avg_variety, points_avg_winery) %>%
filter(country %in% c("Australia", "China", "Georgia", "India", "Israel", "Japan", "Lebanon", "New Zealand", "South Korea", "Turkey")) %>%
mutate(winery = fct_reorder(winery, points_avg_winery)) %>%
filter(as.numeric(winery) <= 20) %>%
arrange(winery) %>%
plot_ly(
x = ~winery, y = ~points, color = ~factor(winery),
type = "box", colors = "viridis") %>%
layout(
xaxis = list(title = "Asian-Pacific Wineries"),
yaxis = list(title = "Wine Points"),
title = "Bottom 20 Asian-Pacific Wineries: Lowest Professional Recognition")We have also shown the bottom 20 Asian-Pacific wineries with the lowest average wine points. It is clear that Nirvana has received the lowest average wine points (80). In addition, the quartiles of wine points for each winery have also been provided with box plot.
load("./data/wine150_tidy")
wine150_tidy %>%
select(points, country, winery, variety, points_med_variety, points_med_winery) %>%
filter(country %in% c("Australia", "China", "Georgia", "India", "Israel", "Japan", "Lebanon", "New Zealand", "South Korea", "Turkey")) %>%
mutate(variety = fct_reorder(variety, desc(points_med_variety))) %>%
filter(as.numeric(variety) <= 10) %>%
arrange(variety) %>%
plot_ly(
x = ~variety, y = ~points, color = ~factor(variety),
type = "violin", colors = "viridis") %>%
layout(
xaxis = list(title = "Grapes for Asian-Pacific Wines"),
yaxis = list(title = "Wine Points"),
title = "Most Favorable 10 Grapes for Asian-Pacific Wines: Highest Professional Recognition")Wines’ qualities are predominantly determined by their raw materials: different grapes. Therefore, by calculating the median wine points of all kind of grapes for Asian-Pacific wines, we have found out the top 10 kinds of grapes for Asian-Pacific wines with the highest median wine points, with Cabernet-Shiraz being No.1 (96). The violin plot has also provided quartiles of wine points for each kind of grape.
load("./data/wine150_tidy")
wine150_tidy %>%
select(points, country, winery, variety, points_med_variety, points_med_winery) %>%
filter(country %in% c("Australia", "China", "Georgia", "India", "Israel", "Japan", "Lebanon", "New Zealand", "South Korea", "Turkey")) %>%
mutate(variety = fct_reorder(variety, points_med_variety)) %>%
filter(as.numeric(variety) <= 10) %>%
arrange(variety) %>%
plot_ly(
x = ~variety, y = ~points, color = ~factor(variety),
type = "violin", colors = "viridis") %>%
layout(
xaxis = list(title = "Grapes for Asian-Pacific Wines"),
yaxis = list(title = "Wine Points"),
title = "Least Favorable 10 Grapes for Asian-Pacific Wines: Lowest Professional Recognition")We have also shown the bottom 10 kinds of grapes for Asian-Pacific wines with the lowest median wine points. It is clear that Meoru has received the lowest median wine points (81.5). In addition, the quartiles of wine points for each kind of grape have also been provided with violin plot.