wrangle | data transformation package for deep learning | Machine Learning library

You can combine do.call() with rind() and lapply() to get a tidy format of summary(). t() transpose the output.

There is probably a more efficient way to do this, but we could do a series of ifelse statements using case_when from tidyverse. First, I remove any rows that just end with ;s__. Then, in the series of statements, I check to if a given taxonomic level is present, then if so, then return that in the desired format. Then, that is repeated across all taxonomic levels.

You can create a dataframe for each sub-object, concat them, and then compress the duplicates together with groupby(level=0) (for 0th index level) + sum:

No, not until closure HRTB inference is fixed. Current workarounds include using function pointers instead or implementing a helper trait on custom structs -- the helper trait is needed regardless of approach until higher-kinded types are introduced in Rust.

Playground

To avoid returning a Box, you would need the type parameter I to be generic over the lifetime 'a, so that you can use it with any lifetime (in a for<'a> bound, for example). Unfortunately, as discussed in a similar question, Rust does not yet support higher-kinded types (type parameters that are themselves generic over other type parameters), so we must use a helper trait:

I think you just need to pivot your data, order the days, and assign a grouping in the aesthetic:

We can use updateNumericInput with some stringr functions. For example, to set a decimal place after two digits:

You put every column name to lowercase with rename_all(tolower). Therefore, column Date doesn't exist but column date does. Replacing Date by date works.

You have to fix that for all other column names in mutate(). Also, you modify the variable City but it is not in the data you provide (perhaps you just forgot to include it).

I didn't run the app but this should fix your dplyr error.

Do either of these work for you?

Since you have only one value in each column for a group (Fruit, Order and Producer_ID) you can take sum(or max) of each column ignoring the NA values.

In base R with aggregate -