library(tidytuesdayR)
library(tidyverse)
library(extrafont)
# fonts()
my_col <- "#720000"
my_col2 <- "#9900bfbf"Libraries, fonts and colors
Load data and wrangling
tuesdata <- tidytuesdayR::tt_load(2021, week = 15)
forest <- tuesdata$forest
forest_area <- tuesdata$forest_area
brazil_loss <- tuesdata$brazil_loss
soybean_use <- tuesdata$soybean_use
vegetable_oil <- tuesdata$vegetable_oil
brazil_loss <- tuesdata$brazil_loss
slopes <- brazil_loss%>%
pivot_longer(cols=c(5,6,11,12,13),names_to="Predictors",values_to="values")
slopes <- slopes %>% select(year,fire,Predictors,values)Slope plot
slope_plot <- ggplot(slopes, aes(x = fire, y = values, color = Predictors) ) +
geom_point() +
geom_smooth(method = "lm", alpha = .15, aes(fill = Predictors)) +
theme_minimal() +
scale_y_continuous(name="Predictors", labels = scales::label_number_si(), limits=c(0,92000)) +
scale_x_continuous(name="Fire (hectares)", labels = scales::label_number_si(), limits=c(26000,537000)) +
annotate("curve", x = 400000, xend = 450000, y = 50000, yend = 75000,
color = "red", curvature = -0.5) +
annotate("text", x=500000, y= 75000, label="driving down: tree plantations \ndriving up: natural disturbances", colour=my_col) +
labs(x="Fire",
y="Predictors",
title = "Brazil Fire due to predictors",
subtitle = "flooding, mining, disturbances, plantations, infrastructures...",
caption = "Viz @fgazzelloni | DataSource: @ourworldindata | Brazil Fire predictors") +
theme(legend.position = "bottom",
legend.text = element_text(family="Trebuchet MS"),
legend.background = element_blank(),
legend.title = element_text(family="Trebuchet MS"),
legend.key = element_rect(fill = "white", colour = NA),
plot.title = element_text(family="Trebuchet MS", size =32,face="bold", hjust=0 ),
plot.subtitle = element_text(family="Trebuchet MS", size =20),
axis.title = element_text(family="Trebuchet MS", size =12),
strip.background = element_rect(colour = "black", fill = "white"),
strip.text.x = element_text(colour = "white", face = "bold"),
panel.spacing = unit(5, "lines"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "azure", color = NA),
panel.background = element_rect(fill = "azure")
) +
annotate("text", x = 280000, y = 80000,
family="Trebuchet MS",
label = "researchers at *Global Forest Watch* estimate that global deforestation \nin 2019 was around 5.4 million hectares. \n95% of this was in the tropics 33.12% in Brazil")Save final plot
ragg::agg_png(here::here("w15", "tidytuesday_slope.png"),
res = 320, width = 14, height = 8, units = "in")
slope_plot
dev.off()Read the image, attach the Tidytuesday logo and save it
library(ggimage)
library(magick)
tidy_logo<-image_read("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/static/plot_logo.png") %>%
image_resize("300x300")
tidy_slope <- image_read("tidytuesday_slope.png")
attached_logo <- image_composite(tidy_slope, tidy_logo,
operator="atop",
gravity="northeast") # tell R where to put the logo
image_write(attached_logo, path = "tidytuesday_slope.png", format = "png") # save final plot