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chatgpt
Interface to ChatGPT from R
Stars: 310
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The ChatGPT R package provides a set of features to assist in R coding. It includes addins like Ask ChatGPT, Comment selected code, Complete selected code, Create unit tests, Create variable name, Document code, Explain selected code, Find issues in the selected code, Optimize selected code, and Refactor selected code. Users can interact with ChatGPT to get code suggestions, explanations, and optimizations. The package helps in improving coding efficiency and quality by providing AI-powered assistance within the RStudio environment.
README:
Meme by Programming Jokes I IT Humor & Memes
Install the current released version of {chatgpt}
from
CRAN:
install.packages("chatgpt")
Or install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("jcrodriguez1989/chatgpt")
You need to setup your ChatGPT API key in R.
First you will need to obtain your ChatGPT API key. You can create an API key by accessing OpenAI API page -don’t miss their article about Best Practices for API Key Safety-.
Then you have to assign your API key for usage in R, this can be done just for the actual session, by doing:
Sys.setenv(OPENAI_API_KEY = "XX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
Or you can do it persistent (session-wide), by assigning it in your
.Renviron
file. For it, execute usethis::edit_r_environ()
, and in
that file write a line at the end your API key as
OPENAI_API_KEY=XX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
The {chatgpt} R package provides a set of features to assist in R coding. Current existing addins:
- Ask ChatGPT: Opens an interactive chat session with ChatGPT
- Comment selected code: Comment the selected code
- Complete selected code: Complete the selected code
- Create unit tests: Create testthat unit tests for the selected code
- Create variable name: Create a name for a variable that would be assigned the result of this code
- Document code (in roxygen2 format): Document a function definition, in roxygen2 format
- Explain selected code: Explain the selected code
- Find issues in the selected code: Find issues in the selected code
- Optimize selected code: Optimize the selected code
- Refactor selected code: Refactor the selected code
Note: When no code is selected, it will use the whole file’s code.
Error in gpt_get_completions:
list(message = "You exceeded your current quota, please check your plan and billing details.", type = "insufficient_quota", param = NULL, code = NULL)
To fix this, you need to provide a billing method in OpenAI. More information can be found in this article.
> cat(ask_chatgpt("What do you think about R language?"))
As an AI language model, I dont have personal opinions, but I can provide you factual information about R language. R is a popular programming language used for statistical computing, data analysis, and graphical visualization. It provides a wide range of tools for analyzing data, including linear and non-linear modeling, time-series analysis, and classification methods, among others. R is also an open-source language, which means it is freely available to use for everyone. Due to its extensive package ecosystem and user-friendly syntax, R is widely preferred by data analysts, statisticians, and researchers.
> cat(comment_code("for (i in 1:10) {\n print(i ** 2)\n}"))
# This is a for loop that initializes the variable i to 1 and iterates until it reaches 10
for (i in 1:10) {
# The code block within the loop will execute once for each iteration
# This line prints the result of i raised to the power of 2 using the ** operator
print(i ** 2)
}
> cat(complete_code("# A function to square each element of a vector\nsquare_each <- function("))
square_each <- function(x){
x_squared <- x^2
return(x_squared)
}
# Example usage
my_vector <- c(1, 2, 3, 4, 5)
square_each(my_vector) # Output: 1 4 9 16 25
> cat(create_unit_tests("squared_numbers <- function(numbers) {\n numbers ^ 2\n}"))
Sure, heres a full testthat file with test cases for the given R code:
{r}
library(testthat)
# Define the function
squared_numbers <- function(numbers) {
numbers ^ 2
}
# Test case for a vector of one number
test_that("squared_numbers works with one number", {
expect_equal(squared_numbers(2), 4)
})
# Test case for a vector of multiple numbers
test_that("squared_numbers works with multiple numbers", {
expect_equal(squared_numbers(c(2, 3, 4)), c(4, 9, 16))
})
# Test case for an empty vector
test_that("squared_numbers works with an empty vector", {
expect_error(squared_numbers(NULL))
expect_warning(squared_numbers(NaN))
})
# Test case for a non-numeric vector
test_that("squared_numbers works with a non-numeric vector", {
expect_error(squared_numbers("test"))
expect_error(squared_numbers(TRUE))
})
In these test cases, were testing:
- whether the function works with a vector of one number
- whether the function works with a vector of multiple numbers
- whether the function gracefully handles an empty vector- whether the function gracefully handles a non-numeric vector
For the first two test cases, were using expect_equal() to check that the functions output matches the expected output. For the next two test cases, were using expect_error() and expect_warning() to check that the function throws the expected errors and warnings.
You can save this code as a file with a .R extension, for example squared_numbers_test.R, and then run the tests with the test_file() function from the testthat package:
{r}
library(testthat)
test_file("squared_numbers_test.R")
This will output a summary of the test results, showing how many tests passed, skipped, and failed.
> cat(create_variable_name("sapply(1:10, function(i) i ** 2)"))
Here is an example of a good variable name for the result:
squared_values <- sapply(1:10, function(i) i ** 2)
The variable name squared_values indicates that the result of the code is a set of values that have been squared.
> cat(document_code("square_numbers <- function(numbers) numbers ** 2"))
Id be happy to help! Heres an example of how you could document the "square_numbers" function using roxygen2 format:
{r}
# Square numbers
#
# This function takes a vector of numbers and returns their squares.
#
# @param numbers A numeric vector to be squared
#
# @return A numeric vector representing the squares of the input numbers
#
# @examples
# square_numbers(1:5)
#
# @export
square_numbers <- function(numbers) {
numbers ** 2
}
This includes a brief description of the function, an explanation of the input parameter, a description of the output, an example usage of the function, and the @export tag, which indicates that this function should be available to users of the package.
> cat(explain_code("for (i in 1:10) {\n print(i ** 2)\n}"))
This is a for loop that will run 10 times, with the value of i ranging from 1 to 10.
During each iteration of the loop, the value of i is squared using the ** (exponentiation) operator, and the result is printed using the print() function.
So, each time the loop runs, the output will be:
1
4
9
16
25
36
49
64
81
100
This loop can be used to generate the sequence of squares from 1 to 10.
> cat(find_issues_in_code("i <- 0\nwhile (i < 0) {\n i <- i - 1\n}"))
The code creates an infinite loop since the condition inside the while loop (i < 0) is never met when the initial value of i is 0. To fix it, either initialize i with a negative value like i <- -1 or change the comparison operator inside the loop to >, e.g. while (i > 0) { i <- i - 1 }.
> cat(optimize_code("i <- 10\nwhile (i > 0) {\n i <- i - 1\n print(i)\n}"))
The code can be optimized using a for loop which is a more efficient and concise loop for iterating over a sequence of values. Heres the optimized code:
R
for (i in 9:0) {
print(i)
}
This code does the same thing as the original code, but its more efficient because it eliminates the need for the while loop and the extra variable assignment. The for loop directly iterates over the sequence from 9 to 0 and prints each value.
> cat(refactor_code("i <- 10\nwhile (i > 0) {\n i <- i - 1\n print(i)\n}"))
Here is the refactored R code:
i <- 10
for (i in 9:0) {
print(i)
}
This can also be done using a while loop:
i <- 10
while (i > 0) {
print(i - 1)
i <- i - 1
}
Both versions will produce the same output as the original code.
If you want {chatgpt} not to show messages in console, please set the
environment variable OPENAI_VERBOSE=FALSE
.
If you want {chatgpt} addins to take place in the editor -i.e., replace
the selected code with the result of the addin execution- then you sould
set the environment variable OPENAI_ADDIN_REPLACE=TRUE
.
To change the language that ChatGPT responds in, the
OPENAI_RETURN_LANGUAGE
environment variable must be changed. E.g.,
Sys.setenv("OPENAI_RETURN_LANGUAGE" = "Español")
cat(chatgpt::explain_code("for (i in 1:10) {\n print(i ** 2)\n}"))
#> El código utiliza un bucle "for" para imprimir los cuadrados de los números del 1 al 10.
#>
#> La sintaxis "for (i in 1:10)" indica que el bucle se va a ejecutar 10 veces, y que la variable "i" va a tomar valores desde 1 hasta 10.
#>
#> Dentro del bucle, "print(i ** 2)" calcula el cuadrado del valor actual de "i" y lo imprime en la consola. El operador "**" se usa para elevar un número a una potencia.
#>
#> Entonces, la salida del código será:
#>
#> [1] 1
#> [1] 4
#> [1] 9
#> [1] 16
#> [1] 25
#> [1] 36
#> [1] 49
#> [1] 64
#> [1] 81
#> [1] 100
In order to run ChatGPT queries behind a proxy, set the OPENAI_PROXY
environment variable with a valid IP:PORT
proxy. E.g.,
Sys.setenv("OPENAI_PROXY" = "81.94.255.13:8080")
.
To replace the default OPENAI's API URL ("https://api.openai.com/v1"
), you can set the OPENAI_API_URL
environment variable with the URL you need to use.
E.g., Sys.setenv("OPENAI_API_URL" = "https://api.chatanywhere.com.cn")
.
ChatGPT model parameters can be tweaked by using environment variables.
The following environment variables can be set to tweak the behavior, as documented in https://beta.openai.com/docs/api-reference/completions/create .
-
OPENAI_MODEL
; defaults to"gpt-3.5-turbo"
-
OPENAI_MAX_TOKENS
; defaults to256
-
OPENAI_TEMPERATURE
; defaults to1
-
OPENAI_TOP_P
; defaults to1
-
OPENAI_FREQUENCY_PENALTY
; defaults to0
-
OPENAI_PRESENCE_PENALTY
; defaults to0
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Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.
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minio
MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
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mage-ai
Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.
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AiTreasureBox
AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.
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tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.
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airbyte
Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.
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labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.