Once you have figured it out, it’s actually a nice workflow. Don’t get me wrong, when I’m not publishing a paper, I quickly forget all commands, my whole setup etc. and start from scratch, cursing a lot and retracing my steps in the history, basically re-learning the framework.
I’d still never move away from ggplot2.
It absolutely improves with practice, and once you have settled on an aesthetic you like you can simply reuse the code, e.g. store all your color/line properties in a variable and just update each figure with that variable
My thesis had something like 30 figures, and at multiple points I had to do things like “put these all on a log scale instead” or “whoops, data on row 143,827 looks like it was transcribed wrong, need to fix it”
While setting everything up in ggplot took a couple hours, making those changes to 30 figures in ggplot took seconds, whereas it would have taken a monumental amount of time to do manually in excel
Honest question: do you think this could improve with practice? Or does the ggplot workflow necessarily makes it all slower?
Once you have figured it out, it’s actually a nice workflow. Don’t get me wrong, when I’m not publishing a paper, I quickly forget all commands, my whole setup etc. and start from scratch, cursing a lot and retracing my steps in the history, basically re-learning the framework. I’d still never move away from ggplot2.
It absolutely improves with practice, and once you have settled on an aesthetic you like you can simply reuse the code, e.g. store all your color/line properties in a variable and just update each figure with that variable
My thesis had something like 30 figures, and at multiple points I had to do things like “put these all on a log scale instead” or “whoops, data on row 143,827 looks like it was transcribed wrong, need to fix it”
While setting everything up in ggplot took a couple hours, making those changes to 30 figures in ggplot took seconds, whereas it would have taken a monumental amount of time to do manually in excel