--- title: "Weather Data" format: html: toc: true toc-depth: 2 number-sections: true toc-location: left fig-cap-location: top code-fold: false code-tools: true theme: flatly page-layout: full editor: visual vignette: > %\VignetteIndexEntry{Weather Data} %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- ```{r, echo=FALSE, message=FALSE, warning=FALSE} # Ensure the temporary library from R CMD check is visible (esp. on Windows) libdir <- Sys.getenv("R_LIBS") if (nzchar(libdir)) { parts <- strsplit(libdir, .Platform$path.sep, fixed = TRUE)[[1]] .libPaths(unique(c(parts, .libPaths()))) } # now load your package suppressPackageStartupMessages(library(ecotourism)) ``` ## Introduction This vignette demonstrates the use of two datasets: `top_stations` and `weather`. After selecting an organism of interest, we linked each occurrence to its nearest weather station. We then counted the matches, identified the top three weather stations most closely associated with the organism’s occurrences, and downloaded the corresponding weather data for further analysis. ------------------------------------------------------------------------ This is the glimpse of your `top_stations` data : ```{r, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE} library(dplyr) library(ecotourism) data("top_stations") top_stations |> glimpse() ``` and this is `weather` data related to those top stations: ```{r, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE} data("weather") weather |> glimpse() ``` ------------------------------------------------------------------------ Map of Top Weather Stations ```{r echo=TRUE, eval=FALSE, fig.width=6, fig.height=4, message=FALSE, warning=FALSE} library(ggplot2) library(ggthemes) top_stations |> left_join(weather_stations) |> ggplot() + geom_sf(data = oz_lga) + geom_point(aes(x = stn_lon, y = stn_lat, color = organism), shape = 17, size = 3) + theme_map() ``` ------------------------------------------------------------------------