Hi,
I would like to use Python in R. Does anybody here know how to use Python on win 10 ?
Mainly I want to do it because of that reason:
https://community.rstudio.com/t/please-share-some-use-cases-why-we-would-use-reticulate-python-and-r/94575
I started to use google Colab but would prefer to do it in RStudio.
In Colab keyboard shortcuts don't work, there is no environment pane, etc.
I would be grateful for any hints how to use it in RStudio.
You can install just python from here
Or you can install the Anaconda distribution witch includes other software as well like JupyterLab, Jupyter Notebooks, etc.
Once you have Python installed in your system, you can use it from RStudio in some ways:
reticulate package.Hi, thank you for reply,
Just a few questions:
Thank you in advance for reply.
No, objects are stored in separate environments but you can access R objects from within Python code using the r object (e.g. r.x would access to x variable created within R from Python).
You usually would read it in R and access the content in Python as explained above. Still, it s possible that a Python library I'm unaware of exists that allows reading directly from these formats.
From the documentation:
Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability.
I'm not aware of such a tool but that doesn't mean it doesn't exist.
There is no tidyverse in Python since it is an R package but very likely there are Python libraries implementing similar functionality.
Python, like R, is open-source with a lot of independent contributors, so functionalities depend on the specific library you are using, there are plotting libraries based on ggplo2 like this one for example:
For this I can say that chatGPT is a good tools, but not is perfect. Im try in occasion but not is 100 % perfect. Is a tool for start with basics things.
Put in chatGPT this and see the results: make a histogram in R and python.
Can you please show an example how to do it ?
Can you provide a reproducible example for an specific use case? The exact way depends on how you are using Python in RStudio.
Otherwise there are plenty of examples in the documentation
This is not code translation per se, it is freely creating code in both languages to accomplish the same task but it is not translating one into the other.
Yes, I can,
I want to find best theoretical distribution for the "serving" data:
library(fitdistrplus)
data(groundbeef)
serving <- groundbeef$serving
using that approach (package distfit):
https://towardsdatascience.com/how-to-find-the-best-theoretical-distribution-for-your-data-a26e5673b4bd
There is "serving" dataframe in R. There is no R package called distfit. There is a distfit package in Python. What to do next , what would be the best way to do it in RStudio ?
I don't want to install unnecessary packages on my Windows setup so I have tested this on Posit Cloud but it should be very similar for you:
library(reticulate)
library(fitdistrplus)
#> Loading required package: MASS
#> Loading required package: survival
data(groundbeef)
serving <- groundbeef$serving
# You only need to do this once
#conda_create("my_env")
#conda_install("my_env", "distfit")
use_condaenv("my_env")
np <- import("numpy")
distfit <- import("distfit")
X <- np_array(serving, dtype='int64')
dfit <- distfit$distfit(method='parametric', todf=TRUE)
dfit$fit_transform(X)
#> $model
#> $model$name
#> [1] "gamma"
#>
#> $model$score
#> [1] 0.0003573497
#>
#> $model$loc
#> [1] -2.20644
#>
#> $model$scale
#> [1] 17.63285
#>
#> $model$arg
#> $model$arg[[1]]
#> [1] 4.301304
#>
#>
#> $model$params
#> $model$params[[1]]
#> [1] 4.301304
#>
#> $model$params[[2]]
#> [1] -2.20644
#>
#> $model$params[[3]]
#> [1] 17.63285
#>
#>
#> $model$model
#> <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x7f726d698eb0>
#>
#> $model$bootstrap_score
#> [1] 0
#>
#> $model$bootstrap_pass
#> NULL
#>
#> $model$color
#> [1] "#e41a1c"
#>
#> $model$CII_min_alpha
#> [1] 25.00746
#>
#> $model$CII_max_alpha
#> [1] 142.0396
#>
#>
#> $summary
#> name score loc scale arg
#> 1 gamma 0.0003573497 -2.20644 17.63285 4.301304
#> 2 beta 0.0003606702 5.400327 336.2081 2.670049, 10.487716
#> 3 genextreme 0.0003626564 57.57065 29.49865 0.03931055
#> 4 dweibull 0.0003783728 67.92109 32.61583 1.487238
#> 5 lognorm 0.000392554 9.963838 52.78125 0.8290418
#> 6 t 0.0004097055 72.87649 35.60593 37.17807
#> 7 norm 0.0004128576 73.6378 35.82408 NULL
#> 8 loggamma 0.0004144338 -8538.13 1221.852 1151.153
#> 9 pareto 0.00055225 -34359738358 34359738368 539926600
#> 10 expon 0.0005522501 10 63.6378 NULL
#> 11 uniform 0.0005753646 10 190 NULL
#> params model
#> 1 4.301304, -2.206440, 17.632847 <environment: 0x55d9afd77a00>
#> 2 2.670049, 10.487716, 5.400327, 336.208112 <environment: 0x55d9afd7ad78>
#> 3 0.03931055, 57.57065027, 29.49864839 <environment: 0x55d9afd7a2c0>
#> 4 1.487238, 67.921091, 32.615829 <environment: 0x55d9afd7d638>
#> 5 0.8290418, 9.9638375, 52.7812468 <environment: 0x55d9afd7cb80>
#> 6 37.17807, 72.87649, 35.60593 <environment: 0x55d9afd7c0c8>
#> 7 73.63780, 35.82408 <environment: 0x55d9afd7f440>
#> 8 1151.153, -8538.130, 1221.852 <environment: 0x55d9afd7e988>
#> 9 539926600, -34359738358, 34359738368 <environment: 0x55d9afd7ded0>
#> 10 10.0000, 63.6378 <environment: 0x55d9afd81248>
#> 11 10, 190 <environment: 0x55d9afd80790>
#> bootstrap_score bootstrap_pass color
#> 1 0 NULL #e41a1c
#> 2 0 NULL #e41a1c
#> 3 0 NULL #377eb8
#> 4 0 NULL #4daf4a
#> 5 0 NULL #984ea3
#> 6 0 NULL #ff7f00
#> 7 0 NULL #ffff33
#> 8 0 NULL #a65628
#> 9 0 NULL #f781bf
#> 10 0 NULL #999999
#> 11 0 NULL #999999
#>
#> $histdata
#> $histdata[[1]]
#> [1] 0.0035018649 0.0045793618 0.0215499378 0.0021549938 0.0175093245
#> [6] 0.0002693742 0.0086199751 0.0013468711 0.0070037298 0.0008081227
#> [11] 0.0002693742 0.0000000000 0.0008081227
#>
#> $histdata[[2]]
#> [1] 17.30769 31.92308 46.53846 61.15385 75.76923 90.38462 105.00000
#> [8] 119.61538 134.23077 148.84615 163.46154 178.07692 192.69231
#>
#>
#> $size
#> [1] 254
#>
#> $alpha
#> [1] 0.05
#>
#> $stats
#> [1] "RSS"
#>
#> $bins
#> [1] "auto"
#>
#> $bound
#> [1] "both"
#>
#> $name
#> [1] "popular"
#>
#> $method
#> [1] "parametric"
#>
#> $multtest
#> [1] "fdr_bh"
#>
#> $n_perm
#> [1] 10000
#>
#> $smooth
#> NULL
#>
#> $weighted
#> [1] TRUE
#>
#> $f
#> [1] 1.5
#>
#> $n_boots
#> NULL
#>
#> $random_state
#> NULL
Created on 2023-02-12 with reprex v2.0.2
Yes, it is, thank you. This output looks a bit messy, is it possible to put it in like broom:tidy or something to get that output in more orderly way ?
I understand from that code of yours and output as well, that whilst writing in *.py file in R we change R library to Python's import and Python's dot to R $ ? Is it correct ?
One more question, does Python recognize R objects ? I mean I want mtcars in Python, how do I convert it to Python's dataframe ?
I suppose it is, but I have never worked with that specific python library and it's output so me finding out how is pretty much the same as you finding out how on your own. Have in mind that I'm importing Python modules into R so the output is just an R object.
I think I don't understand what you mean, if you write code in a .py file it means you are writing Python code, ergo, you have to use Python syntax which is completely independent from R.
reticulate makes interoperability possible, when you are importing python modules into R (like I did in my example) the data sharing is transparent to the user but if you are writing Python code you can access R objects trough the special r object (as long as you are running Python using reticulate) so it would be something like r.mtcars, given that mtcars is already loaded into the global environment of the R session.
I must have mixed up something, but what I meant was for example that in R there is library(tidyverse), but in Python we do have import pandas, in R it was written up there in your post:
dfit$fit_transform(X)
but in Python it is:
dfit.fit_transform(X)
This is what I meant, apologies for confusion as this is all new to me.
Thank you for your kind replies.
Not sure if this is helpful but when you use Posit Cloud there's a built in function that allows you to code with Python. It's free and on the cloud
Can you please, explain it more, I never used Posit Cloud.
Posit Cloud is a service that provides an RStudio or Jupyter Notebooks session running on the cloud, I believe @rtoppenh is referring to the latter.
Jupyter Notebooks is similar to using rmarkdown or Quartobut it doesn't have that much interoperability among languages