Visit to Hobart

R
rinat
mapview
TAS
lightbox
Author

José R. Ferrer-Paris

Published

June 16, 2025

Modified

March 8, 2026

In January 2023 I visited Hobart with my family. During this family holiday I made some observations that I later uploaded to iNaturalist.

Bruny Island

Tools and Libraries

In this contribution I will use my R skills to provide an overview of these observations of flora and fauna. Here is the list of R packages I will use:

library(rinat)
library(dplyr)
library(lubridate)
library(sf)
library(mapview)
library(leafpop)

Relative paths for files

I use the library here to manage relative paths of the project:

here::i_am("regions/Hobart.qmd")
here() starts at /Users/z3529065/proyectos/CES/code-4-iNat

I save the downloaded inat data into this data folder at the root of the project folder:

if (!dir.exists(here::here("data")))
    dir.create(here::here("data"))
inat_obs_data <- here::here("data", "inat-obs.rds")

Query iNat

This will query all the observations from my iNaturalist user. I will save this to a data folder:

if (file.exists(inat_obs_data)) {
    user_obs <- readRDS(inat_obs_data)
} else {
    user_obs <- get_inat_obs_user("NeoMapas",maxresults = 5000) |> 
        mutate(dts=date(datetime), year=year(dts), month=month(dts))
    saveRDS(user_obs, inat_obs_data)
}

Filtering the data

For this post, I am focusing on the observation made between the 17 and the 24 of January 2023:

hobart_obs <- filter(user_obs, 
  dts %in% sprintf("2023-01-%s", 17:24))

This is the number of observations that I have uploaded for those dates:

nrow(hobart_obs)
[1] 35

And this is the approximate number of species (or other taxa) included in those observations:

n_distinct(hobart_obs$species_guess)
[1] 30

Spatial attributes and map of locations

I can make this object spatially explicit using the function st_to_sf from package sf:

hobart_obs_xy <- st_as_sf(hobart_obs,
  coords=c("longitude","latitude"), 
  crs=4326)

We select only some columns to display in the map, and we jitter the coordinates slightly to display some overlapping points:

hobart_obs_xy <- select(hobart_obs_xy, 
  scientific_name,
  iconic_taxon_name,
  species_guess, 
  place_guess, 
  quality_grade,
  dts) |>
  st_jitter(amount = .005)

This example uses mapview to create a quick map with different layers for each day:

mapview(hobart_obs_xy,
    map.types = c("OpenStreetMap.DE", "Esri.WorldImagery"),
    layer.name = c("Visit to Hobart"),
    zcol = "dts", burst = TRUE)

That is very handy to dig into the areas explored during this trip, use the controls to show or hide groups of observations and the buttons on the right side to zoom to a particular group.

This is the end

That’s a fantastic summary of our trip! I hope you enjoyed and find the code useful. Cheers.

Session info

Here is the information for this R session:

sessionInfo()
R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.3

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

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

time zone: Australia/Sydney
tzcode source: internal

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

other attached packages:
[1] leafpop_0.1.0   mapview_2.11.2  sf_1.0-24       lubridate_1.9.4
[5] dplyr_1.2.0     rinat_0.1.9    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6            xfun_0.52               ggplot2_4.0.2          
 [4] raster_3.6-32           htmlwidgets_1.6.4       lattice_0.22-7         
 [7] leaflet.providers_2.0.0 vctrs_0.7.1             tools_4.5.2            
[10] crosstalk_1.2.1         generics_0.1.4          stats4_4.5.2           
[13] curl_7.0.0              tibble_3.3.1            proxy_0.4-29           
[16] pkgconfig_2.0.3         KernSmooth_2.23-26      satellite_1.0.5        
[19] RColorBrewer_1.1-3      S7_0.2.1                leaflet_2.2.2          
[22] uuid_1.2-1              lifecycle_1.0.5         compiler_4.5.2         
[25] farver_2.1.2            textshaping_1.0.1       terra_1.8-93           
[28] codetools_0.2-20        htmltools_0.5.8.1       maps_3.4.3             
[31] class_7.3-23            yaml_2.3.10             pillar_1.11.1          
[34] jquerylib_0.1.4         classInt_0.4-11         brew_1.0-10            
[37] tidyselect_1.2.1        digest_0.6.37           rprojroot_2.0.4        
[40] fastmap_1.2.0           grid_4.5.2              here_1.0.1             
[43] colorspace_2.1-2        cli_3.6.5               magrittr_2.0.4         
[46] base64enc_0.1-3         dichromat_2.0-0.1       leafem_0.2.4           
[49] e1071_1.7-17            withr_3.0.2             scales_1.4.0           
[52] sp_2.2-0                timechange_0.3.0        rmarkdown_2.29         
[55] httr_1.4.7              png_0.1-8               evaluate_1.0.3         
[58] knitr_1.50              rlang_1.1.7             Rcpp_1.1.1             
[61] glue_1.8.0              DBI_1.2.3               svglite_2.2.1          
[64] jsonlite_2.0.0          R6_2.6.1                plyr_1.8.9             
[67] systemfonts_1.2.3       units_1.0-0

Footnotes

  1. Check this posts’ source code for extra configuration details, and see the quarto documentation for instructions on how to activate this in your own quarto documents.↩︎