Excellent tutorial Milos, but at the end of the video the map that is on LinkedIn in yellow with those brown population peaks is not there... forgive me, as I haven't done the tutorial yet. Thank you in advance and congratulations once again.
Hi Ivan, thank you so much for your kind words! I’m glad you enjoyed the tutorial. Just to clarify, the tutorial is intended as a demo, and I often change color palettes for different projects. The map you mentioned with the yellow and brown population peaks on LinkedIn might not be included in this specific tutorial. Feel free to reach out if you have any questions while going through the tutorial. Thanks again for your support!
Amazing. Can we improve on the texture and quality with blender and GIS , combining with Rayshader. On twitter several relief and 3d maps are extremely well rendered and of high quality. I was wondering how do they do that
Great tutorial as always! I wanted to ask for your opinion on for what industries/jobs do you think making such R plots are best geared towards? For instance, it should be easy to use 'drag and drop' features from many dashboarding tools in the market to visualize population density. These graphs you make are very beautiful, however it makes me think who should invest the time and energy to create these in R and who should use easy drag and drop dashboarding tools
Thank you for your kind words and your interesting question. I think making R maps is just one part of a larger journey that involves working with APIs and data wrangling. All those skills combined, make you an attractive candidate for data analyst positions in literally any company. That's because the data is messy so companies are always in need of skillful data wranglers. Now back to your question about the job where mapping is required. Remote sensing aside, any company that analyzes the geolocations of their customers will be in search of geospatial analysts. Here, I mean both GIS companies such as Carto, TomTom or Esri but also more general players such as Airbnb, Google, Meta and Microsoft. Of course, these are not the only domains where R plots can be useful. There are many other fields and sectors that can benefit from the power and flexibility of R for data visualization. However, I agree that there are also situations where drag and drop tools might be more convenient or appropriate than R. For example, some possible scenarios are: - When speed is more important than customization: Drag and drop tools can help to create simple and standard charts quickly and easily, without requiring much coding or tweaking. This can be useful when the goal is to get a quick overview of the data or to communicate a simple message. - When the data is not too complex or messy: Drag and drop tools can handle well-structured and clean data without much difficulty, but they might struggle with more complex or messy data that requires more preprocessing or transformation. This can be a problem when the data is not in a suitable format or has missing values, outliers, or errors. - When the audience is not familiar with R: Drag and drop tools can produce charts that are more accessible and intuitive for people who are not familiar with R or coding. This can be an advantage when the audience is not very technical or does not have much background knowledge about the data or the analysis.
