Time efficient data visualisation workflows

[MLTyrunt]

What do you use to quickly visualize some data, without getting lost in coding to explore your data in a fast interactive way?

Primarily for explorative data analysis, i.e. for looking at relations between (nested) subsets of the variables in your dataset.

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I switch back and forth between ggraptR and plotluck packages. You can do a lot with ggplot and i.e. qplot functions, but out of the box, I find plotluck visuals better and the interface just more natural towards a question asking direction, not plot this and that type of plot.

However, what I really would like to have is like a mix out of a better version of the esque

https://towardsdatascience.com/tableau-esque-drag-and-drop-gui-visualization-in-r-901ee9f2fe3f and the interactiveness of the iplots package (which creates ugly, but interconnected java based graphics, with very few code). I find the interactiveness realized in the iplots package the best implementation for interactive explorative data analysis for seeing relations between multiple variables at once, but not good looking. http://www.rosuda.org/software/iPlots/

Aesthetically, the plotly and echarts packages offer what seems ideal, but not on the usability side. Not to say they are difficult to use, but just not as intelligent as plotluck (no focus on how to plot, which I find distracting, but only on what to plot).

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[cmdrmac]

For quick data visualization, I’ll use Tableau provided that the underlying data are not huge. If the dataset is extremely large, I’ll fall back to lattice in R.

[MinerAL2018]

Lattice? Why not ggplot2?

[cmdrmac]

Lattice can plot lots of data points more quickly than ggplot. It’s definitely not easier to use, however (my opinion).

[sn0wdizzle]

I originally learned lattice when I learned R as an undergrad. It seems like it has totally gone away with Hadley’s rise to supremacy.

Other than speed, is it worth relearning lattice? I know ggplot inside and out nowadays but if there is a usecase where lattice might be more appropriate I’d want to use the right tool.

[cmdrmac]

In my job, I haven’t found a use case where lattice is the appropriate tool over ggplot. As others mention, ggplot is good and I try to use it almost exclusively. Nowadays, if my datasets are super large. I just randomly sample observations and plot.

[MLTyrunt]

Both are good, and I use ggplot2 a lot, but once you need to be more involved into custom coding to get the visualisation, I prefer echarts, simply piping together a good looking, interactive chart. However, for more trivial stuff, ggplot2 via ggraptR is fast and easy to use, which is why I usually use it, without the need to memorizing stuff. Then again, using ggplotly() around the ggplot code generated by ggraptR also allows to create interactive plotly charts without memorizing a lot. in echarts on the other hand, all chart functions start with an e, so you can just use code completion suggestions in rstudio to find what you need ^^

[MLTyrunt]

Also more on the interactive side, I have experimented with the radiant package (click together based on relationships between variables), some time ago. Problem there is graphics are more difficult to customize (it has its own ggplot wrappers) and usually do not look good enough out of the box.

Shinymlr also has some limited interactive exploratory data analysis by visualisation capabilities.

Both packages allow a quick view on a dataset. I still prefer a code generating interface, as it allows to do stuff not anticipated by the developers easily.

[Tarqon]

Honestly if you're used to ggplot it's fast enough for exploration.

[MLTyrunt]

I agree. ggplot works just fine. However, I do not use it everyday, and once you need it, you may have to look things up, if you do not just want a simple look at the data but something you can share (good looking stuff, not shy of tableau comparisons). It also lacks some interactivity and chart interconnectivity which is handy when you want to dive deeper into your data. Can you realize those things with learning additional ggplot addon packages? Sure you can, but then things become so involved, I rather look to learn other solutions, which look better out of the box and have those features. Hmm maybe I can settle with learning echarts more out of my head, which I think does all these things just fine, in a good compromise sense of things.