lidR VS ggplot2

Hey all :) I am currently computing a DSM for Los Angeles (~10600 km²) based on the 2015 - 2016 LARIAC Lidar data (it's part of NOAA). So far they provide the raw LiDAR data, a DEM at 3ft resolution and building heights polygons based on their LiDAR data. However, since I need a DSM for a hobby project, I am currently computing one with the lidR R package. It's at 3 ft resolution to match it with the DEM. I am definitely no pro at this kind of work but from a first glance the results look good.

If you use R, there are many tools for not only converting point cloud types, but also creating DEM/DSM, feature detection, and more. Check out the lidR package! LasTools also does this (and does it VERY well) but the unrestricted tools that preserve the data quality cost $$. I actually do like the way they set up LasTools pricing though - it’s simple and only a few grand. But nothing beats free!

People here are going to focus on Python, but I cannot recommend using the R programming language as a GIS enough. There is an amazing library for working with lidar data called lidR that I think would be worthwhile. Also, the r4ds book is another great starting point for learning R for data analysis and general programming.

And this seemed promising, but I didn't understand how `~length(Z)` behaves. Based on the example https://github.com/Jean-Romain/lidR/blob/4612963e6f73ead5840715434f55eaa46ec5ce24/R/voxel_metrics.R#L67

It's my first time working with LiDAR data. I am using the R package lidR.

ggplot2

I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.

Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.

I used the ggplot2 package in R to create these figures.

This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.

Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2.

Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R).