––She very helpfully shared her code through Github so I was able to have a play with her code and use it to make some plots for other parts of the world and time frames.
Here is an example of a plot I have made which has eruptions in Indonesia between 1250 and 2020 with a volcano explosion index greater than 2.
Using this script it is possible to explore other places that have lots of earthquakes like Japan. This is a nice map of the volcanos in Japan.
START
## Inspiration from https://twitter.com/ijeamaka_a/status/1260660823229214724/photo/1
## https://github.com/Ijeamakaanyene/data_visualizations/blob/master/scripts/2020_10_volcanos.Rmd
library(dplyr)
library(tidyr)
library(ggplot2)
# download the data
volcano = readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/volcano.csv')
eruptions = readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/eruptions.csv')
# exploring the data a bit
## check countries with most eruptions...
erupt_by_country = left_join(eruptions, select(volcano, volcano_number, country, subregion),
by = c("volcano_number"="volcano_number")) %>%
group_by(country) %>%
count(sort = TRUE)
head(erupt_by_country)
## what size eruptions are most common...
count_erupt_size = left_join(eruptions, select(volcano, volcano_number, country, subregion),
by = c("volcano_number"="volcano_number")) %>%
group_by(vei) %>%
count(sort = TRUE)
## vei not available for lots of eruptions - historical I guess...
count_erupt_size
# create the bound data_set
all_eruptions_merged = left_join(eruptions, select(volcano, volcano_number,
country, subregion),
by = c("volcano_number"="volcano_number")) %>%
mutate(combo_year = as.numeric(paste(start_year, start_month, sep = ".")))
# allow some customization
## add your country, start year and end year
## and size of eruption from 1 to 7 or zero to see all.
my_country <- "Indonesia"
my_start_year <- 1250
my_end_year <- 2020
size <- 2
# filter merged data set...
eruptions_merged = all_eruptions_merged %>%
filter(country == my_country) %>%
filter(is.na(vei) == FALSE) %>%
filter(start_year > my_start_year) %>%
filter(start_year < my_end_year) %>%
filter(vei > size)
# Using for loop to create data needed to plot a geom_polygon
volcano_polygon_list = list()
years = unlist(eruptions_merged$combo_year)
volcano_ids = unlist(eruptions_merged$volcano_number)
veis = unlist(eruptions_merged$vei)
## There is most likely a better way of doing this.. but I cannot think of it!
## so for this to work on a wider than 20 year window we need to
## change the value for making the pyramid
## 0.25 is approx 1% of the number of decades
pyr_size <- (my_end_year - my_start_year)/100
for(i in 1:length(years)){
volcano_polygon_df = data.frame(
x = c(years[i], years[i] + pyr_size, years[i] + pyr_size*2),
y = c(0, veis[i], 0),
t = rep(volcano_ids[i], 3)
)
volcano_polygon_list[[i]] = volcano_polygon_df
}
# Converting into df and adding subregion information
volcano_polygon_df = volcano_polygon_list %>%
bind_rows() %>%
left_join(., select(eruptions_merged, volcano_number, subregion),
by = c("t" = "volcano_number"))
# create the plot
volcano_timeline = ggplot() +
geom_polygon(data = volcano_polygon_df, aes(x = x, y = y, group = t, fill = subregion),
alpha = 0.75, colour = "black") +
geom_segment(aes(y = 0, yend = 0, x = my_start_year, xend = my_end_year),
size = 1,
colour = "black",
arrow = arrow()) +
scale_x_continuous(limits = c(my_start_year, my_end_year),
expand = c(0.005, 0.005)) +
scale_y_continuous(expand = c(0, 0))
## add titles - based on the values put in...
volcano_timeline <- volcano_timeline +
labs(y = "Explosion Index", x = NULL, fill = NULL,
title = paste0("Volcanic Activity Timeline (vei>", size, ") ",
my_start_year, " to ", my_end_year, " within ", my_country),
subtitle = "Each triangle's height represents the erruptions volcanic explosion index.",
caption = paste0("Source: The Smithsonian Institution\n",
"Visualization: Paul Brennan | @brennanpcardiff adapted from Ijeamaka Anyene | @ijeamaka_a"))
## brown colour theme...
volcano_timeline + theme_bw() +
## separate out the colour scale to allow a change to this
scale_fill_manual(values = rcartocolor::carto_pal(n = 7, name = "BrwnYl"))
## Teal Green colour theme give the plot below...
volcano_timeline + theme_bw() +
## separate out the colour scale to allow a change to this
scale_fill_manual(values = rcartocolor::carto_pal(n = 7, name = "TealGrn"))
END
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