Kiama Coastal walk

Tools and Libraries

I will be using R with the following selection of libraries in this blog post:

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

• rinat: Access iNaturalist data with R.

• dplyr: A Grammar of Data Manipulation.

• DT: An R interface to the DataTables library

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

here::i_am("regions/Kiama-walk.qmd")

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")

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)
}

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

user_obs_xy <- st_as_sf(user_obs,coords=c("longitude","latitude"), crs=4326)

For this post, I am focusing on the observation made on the 9 March 2025:

kiama_walk_obs <- filter(user_obs_xy, 
  dts %in% c("2025-02-08", "2025-02-09", "2025-02-10"))

This is the number of observations for that day:

nrow(kiama_walk_obs)

[1] 61

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

n_distinct(kiama_walk_obs$species_guess)

[1] 48

Table of observations

We can create a table of observations:

st_drop_geometry(kiama_walk_obs) |> 
  select(scientific_name, common_name, quality_grade, url) |>
  mutate(`url` = 
    sprintf('<a href="%s" target="_blank">iNat link</a>', url)) |> 
  datatable(style='bootstrap4', escape = FALSE) |>
  formatStyle(c('common_name', 'quality_grade', 'url'),  
    color = 'maroon', backgroundColor = 'mistyrose') |>
  formatStyle('scientific_name', 
    color = 'maroon', backgroundColor = 'mistyrose', fontStyle = 'italic')

Here I am using the datatable function from package DT, and setting the style to bootstrap4 to match the style of this website.

Map of locations

A quick map with the default image of each observation can be made with mapview:

mapview(kiama_walk_obs,
    map.types = c("OpenStreetMap.DE", "Esri.WorldImagery"),
    layer.name = c("Kiama coastal walk"),
    popup =
     popupImage(kiama_walk_obs$image_url, src = "remote"))

That’s fantastic!

Session info

sessionInfo()

R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.4.1

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] DT_0.34.0       leafpop_0.1.0   mapview_2.11.4  sf_1.1-0       
[5] lubridate_1.9.5 dplyr_1.2.1     rinat_0.1.10   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6            xfun_0.57               ggplot2_4.0.3          
 [4] raster_3.6-32           htmlwidgets_1.6.4       lattice_0.22-9         
 [7] leaflet.providers_3.0.0 vctrs_0.7.3             tools_4.5.2            
[10] crosstalk_1.2.2         generics_0.1.4          stats4_4.5.2           
[13] curl_7.1.0              tibble_3.3.1            proxy_0.4-29           
[16] pkgconfig_2.0.3         KernSmooth_2.23-26      satellite_1.0.6        
[19] RColorBrewer_1.1-3      S7_0.2.2                leaflet_2.2.3          
[22] uuid_1.2-2              lifecycle_1.0.5         compiler_4.5.2         
[25] farver_2.1.2            textshaping_1.0.5       terra_1.9-11           
[28] codetools_0.2-20        htmltools_0.5.9         maps_3.4.3             
[31] class_7.3-23            yaml_2.3.12             jquerylib_0.1.4        
[34] pillar_1.11.1           classInt_0.4-11         brew_1.0-10            
[37] tidyselect_1.2.1        digest_0.6.39           rprojroot_2.1.1        
[40] fastmap_1.2.0           grid_4.5.2              here_1.0.2             
[43] cli_3.6.6               magrittr_2.0.5          base64enc_0.1-6        
[46] dichromat_2.0-0.1       leafem_0.2.5            e1071_1.7-17           
[49] withr_3.0.2             scales_1.4.0            sp_2.2-1               
[52] timechange_0.4.0        rmarkdown_2.31          httr_1.4.8             
[55] otel_0.2.0              png_0.1-9               evaluate_1.0.5         
[58] knitr_1.51              rlang_1.2.0             Rcpp_1.1.1-1.1         
[61] glue_1.8.1              DBI_1.3.0               svglite_2.2.2          
[64] jsonlite_2.0.0          R6_2.6.1                plyr_1.8.9             
[67] systemfonts_1.3.2       units_1.0-1