4 ms·

> Whereas "framework" generally means tightly coupled components, in a "do things my way or the highway" fashion. Using that definition how would lubridate be

by kgwgk 4y ago

> Whereas "framework" generally means tightly coupled components, in a "do things my way or the highway" fashion. Using that definition how would lubridate be part of the tidyverse "framework"? > Technically speaking, it's not "impossible" to mix tidyverse with base R, but in general if you wanted to mix and match tidy vs base R syntax, you'd find yourself working "against" the tidy api rather than alongside it most of the time. I don't know, wrapping readxl::read_excel with a as.data.frame or as.data.table call works beautifully for me.

RosanaAnaDana 4y ago
I think he's almost certainly speaking of the %>% group_by convention that is basically demanded of within the tidyworld. I load magrittr and ggplot and toss the rest out. Right to left reading order is all I really want from that world. Data tables are such a superior convention to tibbles that it's not even funny (I need a tibbles like I need another hole in the head).
- HFguy 4y ago
  What is wrong with tibbles?
  - Bootvis 4y ago
    They are not data.tables and sometimes I prefer working with data.tables.
  - kuhewa 4y ago
    Their print method annoys me and I don't like the benevolent paternalism of attempting to prevent you from modifying indexes of the dataframe.
  - nequo 4y ago
    I was (wrongly) guessing performance. Tibbles are ok for smaller data sets where data transformations don’t need to be in place and there’s no risk of running out of RAM. Data tables perform better, at the cost of what I think is a less ergonomic interface. See benchmarks here (compare data.table with dplyr): https://h2oai.github.io/db-benchmark/ https://h2oai.github.io/db-benchmark/
- kuhewa 4y ago
  But base r already has split() which does the same thing, turns a dataframe into a list of dataframe divided by values of a column. With the base r pipe operator and base functions like subset, aggregate, transform, split, you can basically replicate that convention while loading exactly no tidyverse packages. You can also use group_by(data, column) without a pipe if wanting to smuggle in a specific dplyr function.

by kgwgk 4y ago

RosanaAnaDana 4y ago

I think he's almost certainly speaking of the %>% group_by convention that is basically demanded of within the tidyworld. I load magrittr and ggplot and toss the rest out. Right to left reading order is all I really want from that world. Data tables are such a superior convention to tibbles that it's not even funny (I need a tibbles like I need another hole in the head).

HFguy 4y ago
What is wrong with tibbles?
- Bootvis 4y ago
  They are not data.tables and sometimes I prefer working with data.tables.
- kuhewa 4y ago
  Their print method annoys me and I don't like the benevolent paternalism of attempting to prevent you from modifying indexes of the dataframe.
- nequo 4y ago
  I was (wrongly) guessing performance. Tibbles are ok for smaller data sets where data transformations don’t need to be in place and there’s no risk of running out of RAM. Data tables perform better, at the cost of what I think is a less ergonomic interface. See benchmarks here (compare data.table with dplyr): https://h2oai.github.io/db-benchmark/ https://h2oai.github.io/db-benchmark/
kuhewa 4y ago
But base r already has split() which does the same thing, turns a dataframe into a list of dataframe divided by values of a column. With the base r pipe operator and base functions like subset, aggregate, transform, split, you can basically replicate that convention while loading exactly no tidyverse packages. You can also use group_by(data, column) without a pipe if wanting to smuggle in a specific dplyr function.