With (legacy) krangl (or dplyr in R) I could do `i...

# datascience

[holgerbrandl]

With (legacy) krangl (or dplyr in R) I could do irisData.groupBy("Species").summarizeAt({ all() }, SumFuns.mean) . How could I rewrite this to use kotlin-dataframe instead? Essentially I want to summarize all columns in a grouped data-frame to their mean. Conceptually, one may even want to use different aggregators here to not compute mean but also standard deviation or other aggregates at once. (cc @Jolan Rensen [JB])

[Jolan Rensen [JB]]

@Nikita Klimenko [JB]

[roman.belov]

@holgerbrandl probably I didn’t get the question, but generally everything is as easy as

[roman.belov]

For mean and std it looks like this

[holgerbrandl]

Thanks for the advice @roman.belov. Works great. Is it possible also to provide a column selector endsWith("Price") and to provide a custom aggregation (as lambda)? Similar to the swiss-army-knife in dplyr across https://dplyr.tidyverse.org/reference/across.html

[roman.belov]

endsWith is already here. Will add it to the docs

[roman.belov]

Regarding aggregation, there’s also already a quit broad syntax for custom aggregation : https://kotlin.github.io/dataframe/groupby.html#aggregation
Or do you have something different in mind?

[holgerbrandl]

Interestingly when evaluating your suggestion from above in a debug window it fails with

Cannot find local variable 'this@AggregateGroupedDsl' with type org.jetbrains.kotlinx.dataframe.aggregation.AggregateGroupedDsl

As part of a program, it works fine.

[holgerbrandl]

I've studied the documentation of aggregate @roman.belov. However, because the docs are presenting code only and do not include any data examples, I find it hard to understand/read (compared to the across docs from above). Also, the grammar of aggregate does not indicate to me how and if (a) column selection and (b) custom aggregates are possible. Neither can I find any example how to do so. Are you sure it's possible? It's an edge use-case I believe, so not supporting it would be fine I guess, although clearly dplyr::across emerged because there are obviously use-cases for a more flexible syntax.