ggplot2 VS sf

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).

sf

Before learning about geostatistics, do you feel comfortable working with and exploring geospatial data? If not, I'd highly recommend getting comfortable with the sf package in R. It's an implementation of the OpenGIS standard in R tidyverse. The OpenGIS standard defines specific data types and functions for geospatial data, which means that you can read e.g. PostGIS documentation and use the same functions in R.

Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)

R supports working with spatial data really well; you should look into the sf-package: https://r-spatial.github.io/sf/

A lot of the books are out if date because geospatial has been rewritten from the ground up to be dramatically improved. I would focus on the sf package: https://r-spatial.github.io/sf/. I would also find a PostGIS book which sf shares many functions and learn to the the database when appropriate.

Are you using the sf package? I envision running a loop that creates a vector feature for each cell of the raster grid, intersects that feature with the underlying shapefile, multiplies the area of each intersected portion by its value, and assigns the raster the mean of those values. Kind of a lot to set up, but I'm not a master at this so maybe someone else knows a more straightforward method

I found this: https://github.com/r-spatial/sf/issues/1595

Now that I explained the concept, I will show some R code using the sf library to achieve this. sf stands for simple features and it's a very nice library for working with geospatial data.