Countries with most expensive wine

Countries with most expensive wine

Every morning while taking subway i check my twitter account to see whats happening in the R world. Last week i came across a post and ended up learning about TidyTuesday initiative. The concept is simple the link is updated every monday with a new dataset and lets users to create visuals in R and post on twitter using the hashtag #tidytuesday.

I had participated in something similar called makeovermondays. This was specifically for tableau. But i ended up using R for generating visuals and it worked out well.

Initiatives like these allows us to get different perspectives about data and understand how other people are perceiving the same data. Anyways i tried my hand at this weeks Wine dataset. In this post i will provide the code i used and in next post i will try to break up my code and explain what my code is doing.

library(ggplot2)
 library(dplyr)
 library(packcircles)
 data <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv", stringsAsFactors = FALSE)

 prices <- data %>% group_by(country) %>% 
                summarise(n = n(), prc = max(price, na.rm = TRUE))-> data_f
 
data_f <- na.omit(data_f)
 packing <- circleProgressiveLayout(data_f$prc, sizetype='area')
 data_f = cbind(data_f, packing)      
 dat.gg <- circleLayoutVertices(packing, npoints=50)
# Make the plot 
ggplot() + 
 # Make the bubbles 
geom_polygon(data = dat.gg, aes(x, y, group = id, fill=as.factor(id)), colour = "black", alpha = 0.6) + 
# Add text in the center of each bubble + control its size geom_text(data = data_f, aes(x, y, size=prc, label = country)) + scale_size_continuous(range = c(1,4)) + 
# General theme: 
theme_void() +  
theme(legend.position="none",text=element_text(color = "white"),       plot.background = element_rect(fill = "black")) + 
coord_equal()

Leave a Reply

WordPress.com.

Up ↑

%d bloggers like this: