dtplyr
tidyexplain
Our great sponsors
| dtplyr | tidyexplain | |
|---|---|---|
| 24 | 1 | |
| 654 | 742 | |
| -0.2% | - | |
| 7.5 | 1.8 | |
| 2 months ago | over 2 years ago | |
| R | R | |
| GNU General Public License v3.0 or later | Creative Commons Zero v1.0 Universal |
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.
tidyexplain
What are some alternatives?
tidytable - Tidy interface to 'data.table'
ggsignif - Easily add significance brackets to your ggplots
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
gpx-viz - Personal project to visualize gpx tracks
tidypolars - Tidy interface to polars
ganttrify - Create beautiful Gantt charts with ggplot2
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 🚀
Datamancer - A dataframe library with a dplyr like API
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
dataiter - Python classes for data manipulation
forcats - 🐈🐈🐈🐈: tools for working with categorical variables (factors)
ggplot2-book - ggplot2: elegant graphics for data analysis