Looking to create stunning, data-rich maps in R? The tidyterra package makes it simple to integrate spatial data with ggplot2, bringing the power of the tidyverse to geospatial analysis. With tidyterra, you can work with spatial data just like any other data set in ggplot2. ✔️ Ease of Use: Simplifies map creation by integrating with ggplot2, using familiar functions and syntax, which reduces the complexity of working with spatial data. ✔️ Flexible Visualizations: Supports a variety of geospatial formats, allowing you to combine spatial data with other data sets, and work seamlessly with both raster and vector data. ✔️ Efficient Data Handling: Leverages dplyr-like functions for filtering, summarizing, and joining, which streamlines data processing and enables smooth integration between spatial and non-spatial data. ✔️ Beautiful Maps: Create detailed, customizable maps with less code and more flexibility, using the grammar of graphics you already know from ggplot2. The visualizations shown here are taken from the package website and demonstrate how tidyterra simplifies creating detailed maps using ggplot2: dieghernan.github.io/tidyter… If you’d like to learn more about ggplot2 and how to create stunning visualizations, check out my online course on “Data Visualization in R Using ggplot2 & Friends!” Click this link for detailed information: statisticsglobe.com/online-c… #RStats #DataVisualization #R4DS #database #DataAnalytics #DataViz #ggplot2 #Statistics

Working with environmental data often means searching through multiple sources and handling messy file formats. The forestdata R package solves this by bringing global forest, land-use, and canopy height datasets directly into R. It makes exploring large-scale ecological data much simpler and fully integrated into your workflow. With forestdata you can: 🔹 Access global canopy height maps 🔹 Explore tree allometry and crown architecture databases 🔹 Work with forest inventories and land cover datasets 🔹 Load everything in tidy or spatial formats for analysis and visualization This opens up new opportunities to study forests, land use, and environmental change, all within R. The visualization below shows an example created with the forestdata R package by Adrián Cidre González. You can find more details on the package website: cidree.github.io/forestdata For more practical insights on R, Python, statistics, and data science, check out my newsletter. More information: statisticsglobe.com/newslett… #RStats #datastructure #statisticians #DataAnalytics