# LIBRARIES
library(tidyverse)
library(ggpubr)
library(reshape2)
library(ggcorrplot)
library(ggcharts)
library(tmaptools)
library(prismatic)
library(patchwork)
library(gridExtra)
library(ggflags)
library(showtext)
library(camcorder)
library(ggtext)
# FONTS
font_add_google("Luckiest Guy","ramp")
font_add_google("Bebas Neue","beb")
font_add_google("Fira Sans","fira")
font_add_google("Raleway","ral")
font_add_google("Bitter","bit")
showtext_auto()
# DATA
coffee_ratings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-07/coffee_ratings.csv')
coffee_ratings = data.frame(coffee_ratings, stringsAsFactors = FALSE)
coffee_ratings <- coffee_ratings %>%
filter(country_of_origin %in% c("Brazil", "Vietnam", "Colombia", "Indonesia", "Ethiopia"))
coffee_ratings <- coffee_ratings %>%
mutate(iso3 = countrycode::countrycode(country_of_origin,
origin = "country.name.en",
destination = "iso3c"))
data2 <- tibble(
count = c(1,1,1,1,1),
n = c(1,2,3,4,5),
country =c("Brazil", "Vietnam", "Colombia", "Indonesia", "Ethiopia"),
code =c("br","vn","co","id","et"))
data1 <- coffee_ratings %>%
mutate(iso3 = countrycode::countrycode(country_of_origin,
origin = "country.name.en",
destination = "iso3c"))
# PLOT
p1 <- ggplot(data=coffee_ratings) +
#annotation_custom(grob = g, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) +
xlim(69,90) +
geom_violin(aes(y = country_of_origin, x = total_cup_points), color=NA, alpha=.95 ) +
geom_jitter(aes(y = country_of_origin, x = total_cup_points),
height = .2, size = 4,
alpha = .5,
color = "#daa520") +
geom_boxplot(aes(y = country_of_origin, x = total_cup_points),
alpha = 0,
outlier.shape = NA,
coef = 0,
color = "#daa520",
fill = "red") +
geom_vline(xintercept = mean(coffee_ratings$total_cup_points),
linetype = 2,
size = 0.5, col="red") +
geom_curve(x = 83.5, y = 5, xend = 87, yend = 5.1)+
annotate("text", label = "Valoración media\nde todos los países productores", x = 87, y = 5.3, family = "fira") +
geom_segment(aes(x = mean(total_cup_points),
y = country_of_origin,
yend= country_of_origin, xend= 83,
col="red" )) +
geom_point(aes(y = country_of_origin, x = mean(total_cup_points)),
size = 4, col = "red") +
labs(title = "\nPuntuación por taza de café (CQI: 2010 - 2018)",
subtitle = "Mayores productores de café del mundo\nDatos: Coffee Quality Institute (CQI) 2010-2018\n\nMedidas de calidad: aroma, sabor, regusto, acidez, cuerpo, balance,\nuniformidad, limpieza de la taza, dulzura, humedad y defectos\n",
caption = "") +
xlab("\nPuntuación por taza") +
ylab(" ") +
geom_flag(data=data2, aes(y=country, x=70,
country=code), size=25 ) +
theme(legend.position = "none",
panel.grid.major = element_line(size = 0.1, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.1, linetype = 'solid',
colour = "white"),
axis.title.x=element_text(size=16, family = "fira"),
plot.title=element_text(size=25, family = "bit", color = "#190706"),
axis.text.y = element_text(color = "#190706", size=14, family = "fira"),
axis.text.x = element_text(color = "#190706", size=12, family = "fira"),
)
#p1En este gráfico destacan los cinco países con mayor producción de café en el mundo, tomando en cuenta varias medidas de calidad: aroma, sabor, regusto, acidez, cuerpo, balance, uniformidad, limpieza de la taza, dulzura, humedad y defectos.
