ggplot2 VS box

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).

As for organising code within a project, as mentioned packages really don’t allow this beyond collation order. The best solution in this space is the ‘box’ package which implements a fully-featured module system for R. ‘box’ notably gets used by some folks to implement large-scale Shiny applications; if this is what you’re after, I would recommend the ‘rhino’ framework, which builds upon Siny and ‘box’.

You can write amazing, clean, modular code with the box package.

I would look into box https://github.com/klmr/box if you haven’t heard of it already

Shapeless plug: check out the already mentioned ‘box’, I think it’s strictly superior to ‘import’ (but I’m biased).

By contrast, R code doesn’t need to change the working directory at all! Having to do so hides other flaws in the code. For instance, when trying to load code or data, use the tools provided by R. That is, write packages and use system.file or, when not writing packages, use ‘box’.

‘box’ fixes that.

I think your use-case is best addressed by the ‘targets’ package. But I would also recommend checking out the ‘box’ package for a more general way of structuring R projects in modules which isn’t supported well natively by R (disclaimer: I wrote that package). Writing R code as modules fundamentally side-steps the issue of having to deal with absolute paths. Instead, all code and data are either contained in the module or can be accessed relative to the working directory.

However, using ‘box’, as recommended in another comment, allows you to achieve the same effect with less (and cleaner) code, by declaring your imports locally with the box::use function.

If you're writing you're own code you can use packageName::functionName(), or the box package. Which is definitely useful on larger codebases.