dtplyr
forcats
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| dtplyr | forcats | |
|---|---|---|
| 24 | 4 | |
| 654 | 537 | |
| -0.2% | 0.7% | |
| 7.5 | 3.1 | |
| 2 months ago | 2 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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dtplyr
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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/
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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/
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Millions of rows
FYI the developer of tidytable has been developing dtplyr for the Tidyverse. You might like that too!
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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.
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Best alternative to Pandas 2023?
https://dtplyr.tidyverse.org/ ?
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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.
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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.
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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:
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R process taking over 2 hours to run suddenly
Install the dtplyr package and change your code to:
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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.
forcats
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Using scale_x_discrete on graphs
Have a look at the forcats package https://forcats.tidyverse.org/
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[Q] 'pivot_longer' applied to an object of class "character"
Such a function wouldn't add anything that factor() or as.factor() don't already do, but the forcats tidyverse package does make it easier to work with factor variables afterwards: https://forcats.tidyverse.org/
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This chart has the days of the week on the x-axis, but they are all over the place, starting with Sunday and then going to Wednesday. My table has the correct sequence of days. So why is this happening to me? :\
Turn weekdays to factors to have some control over order. https://forcats.tidyverse.org/ https://r4ds.had.co.nz/factors.html#modifying-factor-order
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Frustration: One Year with R
This was fun to play around with. I made some very minor changes and posted at https://gist.github.com/hadley/d54895557fbb0fe0402d2277b9011....
It revealed to me that there's a buglet in `forcats::last()` (https://github.com/tidyverse/forcats/issues/303) and made me wonder if `pivot_longer()` should be able to rename the columns as you pivot them (https://github.com/tidyverse/tidyr/issues/1338)
What are some alternatives?
tidytable - Tidy interface to 'data.table'
cheatsheets - Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
ggplot2-book - ggplot2: elegant graphics for data analysis
tidypolars - Tidy interface to polars
ggplot2 - An implementation of the Grammar of Graphics in R
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 🚀
tidyr - Tidy Messy Data
Datamancer - A dataframe library with a dplyr like API
plotnine - A Grammar of Graphics for Python
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
COVID-19 - Plots and analysis relating to the pandemic