dtplyr
tidypolars
Our great sponsors
dtplyr | tidypolars | |
---|---|---|
24 | 7 | |
654 | 309 | |
-0.2% | - | |
7.5 | 8.0 | |
2 months ago | 3 months ago | |
R | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dtplyr
-
Tidyverse 2.0.0
Can’t say I’ve used it, but isn’t that what dtplyr is supposed to provide?
https://dtplyr.tidyverse.org/
-
Error when trying to use dtplyr::lazy_dt, "invalid argument to unary operator"
# I am trying to follow the example at https://dtplyr.tidyverse.org/
-
Millions of rows
FYI the developer of tidytable has been developing dtplyr for the Tidyverse. You might like that too!
-
fuzzyjoin - "Error in which(m) : argument to 'which' is not logical"
If you need speed, you should consider using dtplyr (or tidytable), or even dbplyr with duckdb.
-
Best alternative to Pandas 2023?
https://dtplyr.tidyverse.org/ ?
-
R Dialects Broke Me
If you want data.table speed, but using dplyr/tidy then dtplyr is a good package to have handy. Personally I love R, and choose R + NodeJS as my gotos for everything I do, and use Python only when I have to.
-
Merging csv from environment.
Also, that dataset is quite big, and the "base" Tidyverse will be excessively slow. You should supplement the "base" Tidyverse packages (i.e. dplyr and tidyr) with either dtplyr or dbplyr (+ duckDB). I'd suggest starting with dtplyr, which should handle 10M+ rows fine.
-
mutate ( ) function is only working in code chunk I run it in. It does not change the column in my data frame other than in that one code chunk.
If you want, there's a "substitute" for dplyr called dtplyr (also part of the Tidyverse), which "translates" your dplyr/tidyr code into data.table behind the scenes, and allows you to make your modifications apply directly to the original dataset by default:
-
R process taking over 2 hours to run suddenly
Install the dtplyr package and change your code to:
-
DS student here: why use R over Python?
Get the best of both worlds (tidyverse + data.tables) with dtplyr, a data.table backend for dplyr.
tidypolars
-
Modern Polars: a side-by-side comparison with Pandas
I recommend trying tidypolars
-
Modern Polars: an extensive side-by-side comparison of Polars and Pandas
There’s a tidypolars package that appears to be well-maintained https://github.com/markfairbanks/tidypolars
-
R Tidyverse / dplyr is life changing!
tidypolars is one I’ve seen. Still very new, but it’s built on top of polars (which is a little more like dplyr to begin with), so it’s much faster than pandas.
-
Introducing tidypolars - a Python data frame package with syntax familiar to R tidyverse users
If I misunderstood your question - feel free to open a discussion with a small code example and we can talk through how you can do it in tidypolars.
- Introducing tidypolars - a Python data frame package for R tidyverse users
-
Tidyverse appreciation thread
Try out tidypolars. It's really close to tidyverse syntax and it's a lot faster than pandas as well
What are some alternatives?
tidytable - Tidy interface to 'data.table'
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
db-benchmark - reproducible benchmark of database-like ops
Datamancer - A dataframe library with a dplyr like API
extendr - R extension library for rust designed to be familiar to R users.
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
dataiter - Python classes for data manipulation
pandoc - Universal markup converter