TyT2025W19: Seismic Events at Mount Vesuvius

By Johanie Fournier, agr., M.Sc. in rstats tidymodels tidytuesday eda viz

May 11, 2025

The data of this week Tidytuesday challenge inspire me to make an aminated map of the seismic events at Mount Vesuvius. The dataset contains information about seismic events detected at the famous Mount Vesuvius in Italy. It comes from the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV)’s Data Portal and can be explored along with other seismic areas on the GOSSIP website. The raw data was saved as individual CSV files from the GOSSIP website and some values were translated from Italian to English.

Get the data

tuesdata <- tidytuesdayR::tt_load(2025, week = 19)

vesuvius <- tuesdata$vesuvius

Explore the data

skimr::skim(tuesdata$vesuvius)


Name	tuesdata$vesuvius
Number of rows	12027
Number of columns	11
_______________________
Column type frequency:
character	3
numeric	7
POSIXct	1
________________________
Group variables	None

Data summary

Variable type: character

skim_variable	complete_rate	min	max	n_unique
area	1	14	14	1
type	1	10	10	1
review_level	1	7	11	2

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
event_id	0	1.00	15494.12	11014.20	102.00	5965.50	14981.00	21387.50	40802.00	▇▅▇▂▂
latitude	3433	0.71	40.82	0.00	40.80	40.82	40.82	40.82	40.86	▁▇▂▁▁
longitude	3433	0.71	14.43	0.00	14.35	14.42	14.43	14.43	14.48	▁▁▃▇▁
depth_km	3433	0.71	0.41	0.50	0.01	0.14	0.24	0.43	9.35	▇▁▁▁▁
duration_magnitude_md	399	0.97	0.18	0.56	-2.00	-0.20	0.10	0.50	3.10	▁▇▇▁▁
md_error	399	0.97	0.30	0.00	0.30	0.30	0.30	0.30	0.30	▁▁▇▁▁
year	0	1.00	2018.88	3.28	2011.00	2016.00	2019.00	2022.00	2024.00	▂▆▅▇▇

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
time	0	1	2011-04-20 00:27:24	2024-12-31 17:02:32	2019-05-11 22:23:43	11953

We have a date variable and an indication og the magnitude of the seismic event.

Animated Visualization

library(gganimate)
library(rgeoboundaries)

# Gulf of Naples in Campania, Italy
naples <- gb_adm3("Italy") |> 
  filter(shapeName == "Napoli")

#Create the base map
base_map <- ggplot() +
 geom_sf(data=naples, fill="lightgrey", color="white") +
 geom_point(data=vesuvius, 
            aes(x=longitude, 
                y=latitude, 
                group=year, 
                color=duration_magnitude_md,
                size=duration_magnitude_md*1000,
                alpha = 50)) +
  transition_time(year) +
  ggtitle('Seismic Events Magnitude at Mount Vesuvius {frame_time}') +
  shadow_mark() +
  scale_color_gradient(low = "pink", high = "darkred")+
  theme(legend.position = "none", 
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.x=element_blank(),
        axis.ticks.y=element_blank(),
        axis.line.x=element_blank(),
        axis.line.y=element_blank(),
        plot.title=element_text(hjust=0.5))
num_years <- max(vesuvius$year) - min(vesuvius$year) + 1

# Save the animation as a GIF
anim <- gganimate::animate(base_map, nframes = num_years, duration=20)
anim_save("ggmap_animation.gif", animation = anim)

# Read and display the saved GIF animation
animation <- magick::image_read("ggmap_animation.gif")
print(animation, info = FALSE)

Session Info

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Toronto
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] reticulate_1.40.0    jofou.lib_0.0.0.9000 tidytuesdayR_1.1.2  
 [4] tictoc_1.2.1         KrigR_0.9.4          ncdf4_1.23          
 [7] ecmwfr_2.0.2         rgeoboundaries_1.3.1 terra_1.8-10        
[10] sf_1.0-19            pins_1.4.0           fs_1.6.5            
[13] timetk_2.9.0         yardstick_1.3.2      workflowsets_1.1.0  
[16] workflows_1.1.4      tune_1.2.1           rsample_1.2.1       
[19] parsnip_1.2.1        modeldata_1.4.0      infer_1.0.7         
[22] dials_1.3.0          scales_1.3.0         broom_1.0.7         
[25] tidymodels_1.2.0     recipes_1.1.0        doFuture_1.0.1      
[28] future_1.34.0        foreach_1.5.2        skimr_2.1.5         
[31] gganimate_1.0.9      forcats_1.0.0        stringr_1.5.1       
[34] dplyr_1.1.4          purrr_1.0.2          readr_2.1.5         
[37] tidyr_1.3.1          tibble_3.2.1         ggplot2_3.5.1       
[40] tidyverse_2.0.0      lubridate_1.9.4      kableExtra_1.4.0    
[43] inspectdf_0.0.12.1   openxlsx_4.2.7.1     knitr_1.49          

loaded via a namespace (and not attached):
  [1] rstudioapi_0.17.1   jsonlite_1.8.9      magrittr_2.0.3     
  [4] magick_2.8.5        farver_2.1.2        rmarkdown_2.29     
  [7] vctrs_0.6.5         memoise_2.0.1       hoardr_0.5.5       
 [10] base64enc_0.1-3     gh_1.4.1            htmltools_0.5.8.1  
 [13] progress_1.2.3      curl_6.1.0          parallelly_1.41.0  
 [16] KernSmooth_2.23-26  httr2_1.1.0         plyr_1.8.9         
 [19] zoo_1.8-12          stars_0.6-7         cachem_1.1.0       
 [22] ggfittext_0.10.2    lifecycle_1.0.4     iterators_1.0.14   
 [25] pkgconfig_2.0.3     Matrix_1.7-2        R6_2.5.1           
 [28] fastmap_1.2.0       digest_0.6.37       reshape_0.8.9      
 [31] colorspace_2.1-1    furrr_0.3.1         timechange_0.3.0   
 [34] httr_1.4.7          abind_1.4-8         compiler_4.4.2     
 [37] intervals_0.15.5    proxy_0.4-27        bit64_4.6.0-1      
 [40] withr_3.0.2         backports_1.5.0     viridis_0.6.5      
 [43] DBI_1.2.3           MASS_7.3-64         lava_1.8.1         
 [46] rappdirs_0.3.3      classInt_0.4-11     tools_4.4.2        
 [49] units_0.8-5         zip_2.3.1           future.apply_1.11.3
 [52] nnet_7.3-20         glue_1.8.0          grid_4.4.2         
 [55] snow_0.4-4          generics_0.1.3      gtable_0.3.6       
 [58] countrycode_1.6.0   tzdb_0.4.0          class_7.3-23       
 [61] data.table_1.16.4   hms_1.1.3           sp_2.1-4           
 [64] xml2_1.3.6          pillar_1.10.1       vroom_1.6.5        
 [67] splines_4.4.2       lhs_1.2.0           tweenr_2.0.3       
 [70] lattice_0.22-6      bit_4.5.0.1         FNN_1.1.4.1        
 [73] renv_1.0.7          survival_3.8-3      tidyselect_1.2.1   
 [76] pbapply_1.7-2       gitcreds_0.1.2      gridExtra_2.3      
 [79] svglite_2.1.3       crul_1.5.0          xfun_0.50          
 [82] hardhat_1.4.0       timeDate_4041.110   stringi_1.8.4      
 [85] DiceDesign_1.10     yaml_2.3.10         evaluate_1.0.3     
 [88] codetools_0.2-20    httpcode_0.3.0      automap_1.1-12     
 [91] cli_3.6.3           rpart_4.1.24        systemfonts_1.2.1  
 [94] repr_1.1.7          munsell_0.5.1       spacetime_1.3-2    
 [97] Rcpp_1.0.14         doSNOW_1.0.20       globals_0.16.3     
[100] png_0.1-8           parallel_4.4.2      gower_1.0.2        
[103] prettyunits_1.2.0   GPfit_1.0-8         listenv_0.9.1      
[106] viridisLite_0.4.2   ipred_0.9-15        xts_0.14.1         
[109] prodlim_2024.06.25  e1071_1.7-16        gstat_2.1-2        
[112] crayon_1.5.3        rlang_1.1.5         cowplot_1.1.3

Posted on:: May 11, 2025

Length:: 4 minute read, 673 words

Categories:: rstats tidymodels tidytuesday eda viz

Tags:: eda rstats tidymodels tidytuesday viz

See Also:: Heatmap to Visualize Spatio-Temporal Data; Side by side interactive map with {leaflet} and {leaflet.extras2}; Using {pollen} and {vegperiod} to analyze temperature, GDD, and vegetation period

Get the data

Explore the data

Animated Visualization

Sign up for the newsletter

Session Info