15,327
R queries
2022-12-19
RTutor
23
Analyzed data using natural language interface.
Generated by ChatGPT
RTutor is an AI-based tool for data analysis. It provides a natural language interface for users to interact with their data. It can be used to generate R and Python code for various statistical analyses and generate reports in HTML format.
It is built on OpenAI’s powerful text-davinci-003 language model and can translate natural language into R and Python code.It supports data files in CSV, TSV/tab-delimited text files, and Excel formats.
It can also detect data types automatically, convert numeric columns to factors, and generate descriptive summaries and plots. It can also generate code for correlation, GGpairs, and other analyses.RTutor also supports natural language processing in dozens of human languages, including Chinese, Ukrainian, Arabic, Hindi, Spanish, German, French, Luxembourgish, Vietnamese, Portuguese, Japanese, Italian, Persian and more.It can also be used to answer generic questions without mentioning column names as it can detect and understand context.
RTutor is released as a prototype for testing and improvement.RTutor is a personal project of Steven Ge and is freely available for academic and non-profit organizations only.
Commercial use beyond testing is not allowed.
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RTutor was manually vetted by our editorial team and was first featured on December 21st 2022.
Pros and Cons
Pros
Generates R and Python code
Supports CSV, TSV, Excel formats
Auto-detects data types
Generates descriptive summaries
Generates correlation, GGpairs analyses
Supports numerous human languages
Detects and understands context
Detects data types automatically
Auto-convert numeric to factors
Available for academic, non-profit
Generates HTML reports
Prototype for testing, improvement
Translates natural language to code
Generic question answering
Generates code chunks
Data frame auto-loading as 'df'
Voice input optional
Supports multiple languages
Code refinement for analytics
Data visualization and exploration
Generates and runs Python code
Auto convert column as row names
Generates HTML report
Creates interactive plots
Filtering options for data
Provides code chunk records
Sends error messages and results
Cons
Only supports CSV, TSV, Excel formats
Commercial use not allowed
Still in prototype/testing phase
Requires data preparation in Excel
Does not execute large datasets (>10MB)
Accuracy of generated code not guaranteed
Generates code only in R and Python
Does not include installed R packages
Can fail due to server load
Q&A
What is RTutor?
RTutor is an artificial intelligence-based tool for data analysis. It employs a natural language interface for users to interact with their data. It can translate natural language into R and Python code, and execute numerous statistical analyses. RTutor can generate descriptive summaries and plots, and works equally well with data files in various formats such as CSV, TSV/tab-delimited text files, and Excel. It has a multilingual functionality, supporting several global languages.
How does RTutor translate natural language into R and Python code?
RTutor uses OpenAI's text-davinci-003 language model to translate natural language into R and Python code. Requests structured in natural language are processed through the AI model, which subsequently generates R and Python code. This code is then cleaned up and executed in a Shiny environment, displaying results or error messages as necessary.
How does RTutor support different languages for data analysis?
RTutor leverages the powerful capabilities of OpenAI’s text-davinci-003 language model, which supports various languages. This makes it possible for RTutor to process natural language instructions in many global languages, including but not limited to Chinese, Ukrainian, Arabic, Hindi, Spanish, German, French, Luxembourgish, Vietnamese, Portuguese, Japanese, Italian, and Persian.
What file formats does RTutor support for data analysis?
RTutor can analyse data files in CSV, TSV/tab-delimited text files, and Excel formats. Once uploaded, these data files are automatically loaded into RTutor as a data frame referred to as 'df'.
How does RTutor generate reports in HTML format?
RTutor logs multiple requests to produce an R Markdown file that includes the executable R code. This file can be knitted into an HTML report, enabling record keeping and reproducibility of the analysis and results.
Can I use RTutor to generate Python code?
Yes, besides R code, RTutor can also generate Python code from instructions given in natural language. As per recent updates, Python code generation and execution is one of the features RTutor boasts.
What statistical analyses can RTutor perform?
RTutor can perform various statistical analyses including but not limited to descriptive summaries, plots, correlation analysis, and GGpairs analysis. It can also generate code for these analyses in R and Python for further execution. The type of analysis RTutor performs largely depends on the natural language command given by the user.
How can RTutor help with data types detection and conversion?
RTutor is equipped with a feature that auto-detects data types and ensures they are appropriately cast for analysis. It can check if the data types are correct in terms of numeric columns juxtaposed with categories (factors or characters). If required, users can instruct RTutor to convert the data type by communicating, for example, 'Convert cyl as numeric' or 'Convert year as factor'.
How does RTutor's automatic numeric columns to factors conversion function work?
RTutor's automatic numeric columns to factors conversion function is specially designed to improve data analysis. Numerical columns that have a limited number of unique values can be automatically converted to factors. This function is crucial because data types can significantly impact the analysis and plots.
How does RTutor generate descriptive summaries and plots?
RTutor generates descriptive summaries and plots based on the natural language command provided by the users. It processes the command, converts it into R or Python code, executes the code, and presents the summaries and plots as part of the result. Various aspects of exploratory data analysis such as distributions, basic plots, or simple models, can be obtained as per user's requirements.
How does RTutor's correlation analysis feature work?
RTutor's correlation analysis feature is cleverly built in. When a user requests a correlation analysis in natural language, RTutor generates corresponding R or Python code and executes it. The results include a correlation matrix or a correlation plot representation depending upon the specific command given by the user.
Can RTutor generate GGpairs analysis code?
Yes, RTutor can generate GGpairs analysis code. Users can request this form of analysis in natural language, following which RTutor generates the corresponding R or Python code and executes it to provide a GGpairs analysis result.
What kind of generic questions can RTutor answer?
RTutor can answer most generic questions related to data analysis, statistics, and data science concepts. However, the defining attribute of RTutor is its ability to generate R and Python codes to answer questions without necessarily mentioning column names, as it can detect and understand the context based on the rest of the information provided.
Who is Steven Ge and what is his role in RTutor?
Steven Ge is the creator of RTutor. As a personal project, he developed RTutor to provide a natural language interface for users to interact with their data, and to generate R and Python code for different statistics analyses.
Why is RTutor only available for academic and non-profit organizations for commercial use?
RTutor is only available for academic and non-profit organizations for commercial use due to the licensing agreement. The tool is freely available for testing, academic and non-profit purposes under the CC BY-NC 3.0 license. Commercial use beyond testing is not permitted. For commercial use beyond testing, it's incumbent upon the interested parties to contact Steven Ge.
How does RTutor incorporate OpenAI's text-davinci-003 language model?
RTutor deeply incorporates OpenAI's text-davinci-003 language model in its core functionalities. It uses this powerful language model to interpret and process users' requests and then generates R and Python code accordingly. This allows RTutor to implement practically any command, ranging from a simple data interrogation to an intricate data analysis technique, in any of the many supported languages.
How efficient is RTutor in generating R and Python scripts?
RTutor is highly efficient as it uses OpenAI’s powerful text-davinci-003 language model. The model can generate and evaluate R and Python scripts quickly and efficiently from instructions given in dozens of human languages.
How can RTutor interact with data without mentioning column names?
RTutor can interact with data without mentioning column names by leveraging advanced natural language processing capabilities. It's able to detect and understand context based on the other information present in the query. This includes being able to run queries without specifying column names, as the AI is capable of interpreting the data set's internal structure and matching the user's natural language queries to the appropriate data points.
How does RTutor's Temperature setting affect the AI's performance?
RTutor's 'Temperature' setting influences the AI's exploration vs exploitation balance. A higher 'Temperature' setting makes the AI more aggressive in seeking alternative solutions. This means that the AI is more likely to propose diverse potential code outcomes for the same natural language request.
How is the 'Continue from this chunk' feature used in RTutor?
The 'Continue from this chunk' feature in RTutor enables a user to build on their current code. It allows for iterative data wrangling and analysis, where certain steps such as removing rows, adding columns, or log-transforming, are followed by subsequent analyses. By selecting the 'Continue from this chunk' checkbox, the current R code is inserted before the next chunk and gets executed, allowing users to expand their work without starting from scratch.
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