R is one of the simplest programming languages and R-studio makes full use of its capabilities. The R community is amazing and provides immense support where needed.

Overall, I could write my desired code and do my job using powerful packages which were available.

Since it is open source there are many add ons that you can implement to the library to improve its functionality.

The problem of backwards compatibility of different versions of R and R packages is not an RStudio problem, and RStudio works with all versions of R and R packages.

It is super easy to use and convenient. Cheap compared to competitors.

Quite satisfied and content for R programming with RStudio.

The inability to create a collaboration project.

Ease of use, debugging, and GUI are the best in the buisiness when it comes to R IDEs.

RStudio is a delightful IDE for working with Rlang and data.

I can analyze and get beautiful and explanatory graphics in a few minutes. The coding part is very handy and it can be editing and saved so you do not lose any code or project for the future.

The user interface makes it extremely easy to organize and visualize data sets and it extremely useful for teaching. R studio is also easy to download and install and is free for individual users.

There are no major dislikes about R studio, being a free software it does depend on the open source community for continuing development, which can be arguably not beneficial to the software.

R studio is an improved version to manage and execute code in the R platform.

Highly recommended to the researchers who are handling large data set.

If you are looking for a free tool to do data analytics with RStudio will do what you need it to do. If ever given the option I will always use RapidMiner or another software that has a better GUI.

Overall, I love how powerful it is and like how many different packages are available.

Great open source software for data and statistical analysis, user friendly and minimized code writing, good supportive libraries and great infographs.

I have found using RStudio intuitive, easy, and fun to use.

The layout of the screen and the ability to break out separate windows is really great and makes using the software on a single screen or multiple screens very friendly.

As every programming language it may a bit difficult at the start to learn.

In my opinion, Rstudio provides the best environment to be used by statisticians and data scientists. The software is great and its supporting website provides a lot of tutorials.

I typically use RStudio to solve research issues such as data analysis of growth mindset. I would highly recommend it for free, convenient data analyses.

The very best second thing is this is open-source software( you do not need to buy it).

The ability to install missing R packages instantly, creating R Markdown documents, and integration with Shiny Web App are the most appealing features for me as a machine learning researcher.

A GUI interface for data manipulation and visualisation.

I also found it difficult to intergrate R studio with other languages such as python or C. It also seems catered to more of a data science analysis approach rather than programming and execution.

R-studio automatically updates periodically which can be a pain. Also, it cannot run multiple queries at the same time.

R Studio - larger learning curve than SPSS but will save you money.

Overall R studio is an excellent product and a great tool to supplement teaching statistics and algebra II at the high school level and any statistics and data science class at the college algebra.

It's a great software which generate outputs very easily using a coding system. And also it includes thousands of useful packages.

Also plotting graphs, curve fittings etc can be done with ease.

I really love how versatile it is in creating professional looking graphics and tables, as well as how intuitive the syntax is to learn.

Awesome light and dark mode for user preferences. Direct git integrations for faster and convenient coding with collaboration.

My overall experience with R-Studio has been positive and I have been able to effectively analyze a large amount of data and write papers on it.

Amazing matematical programming language. I use it often in my job to perform data analysis.

Rstudio can be very confusing at times, and as a code editor, sometimes the errors do not make sense.

Objects' explorer is a must in data science, and Rstudio's is particularly good for exploring and giving lot of information about every object in a glimpse. Multi-platform: GNU/Linux, Windows, Mac.

R Studio is my sought after Machine learning programme, I am so fascinated about how to use it scale business. All our credit models are built in R.

R is an amazing programming language designed to perform statistical analysis and calculations.

It is the swiss army tool of a data-scientist. Thanks to its huge community any doubt you may have can be replied easily by a simple search on internet, or by asking in forums.

It's clean, sleek interface is very intuitive and user friendly. It can handle large amounts of data and it suggests functions as you type, making it very beginner friendly.

Although GGPLOT package provides a lot of features for graphical representations, it is very difficult to handle.

The major disadvantage is the inability to use it for core code development, lack of flexibility in writing deep learning or neural network  models.

I love the price of R/Rstudio - free. Competitors such as SPSS/IBM are expensive for one year licenses.

It's easy to use, interactive and developer friendly.

I liked the most about Jupyter notebook is it is the best GUI interpreter for executing python code and i liked the Jupyter notebook shortcuts.

What i liked most is that Jupyter notebook allows you to go step by step trough your code. This allows me the user to effectively find and solve issues and errors and add markdown where required.

Last but not least, when it come to visualising data for instance using Python libraries, such as matplotlib or seaborn, Jupyter Notebook has a very clean and solid graph.

Additionally, there are many resources and extensions accessible thanks to the vibrant Jupyter Notebook community.

Shift+enter will execute the code in next line that is probably the best feature that helps while lerning python language.

I dislike that it starts up in your browser instead of as an app.

Application software development is a quality that will make you love this job for beginners.

You can integrate different codes, from python and R, making easy to document the progress in the development of new software.

Jupyter notebook is very intuitive and easy to use. As a student learning programming for the first time, Jupyter has been very helpful and a good introductory tool.

Overall its a very useful and friendly interface tool for learning python and R.

Multiple modes to save the notebook hence good for demos purposes, training, and storing in a repository for reference at a later date. Open source - good community support.

I love it and I use it way more than other IDEs for python.

The application is very easy to use. The interface is simple and user friendly.

The UI is very basic and few a times the kernal stops responding and have to interrupt and restart kernal.

Furthermore, with the Jupyter notebook, we plotted scientific plots to extract insights from the data and next built a model to predict disruptions in the business processes.

Runtime and stability still a problem as any other web-based applications. Missing auto-correct/format feature.

I started using Jupyter Notebooks while was learning to program on Python as a Data Scientist. I was pretty happy for its functionality and ease of use.

Writing the notebooks is easy and I can save it to share with others so that we can work on the same thing.

The front page, where you can see all the files you have, can be a bit messy.

Very impressed with the experience so far, you can learn/implement coding from anywhere that has a connection, not only for yourself but also easy to share/co-operate with your team.

I used this one for my web based financial modelling project. Worked like gem in the end, got a good score in the post graduation.

However, for advanced programmers, It could be improved by adding more features like integration with other software tools etc.

On some rare occasions, it doesn't execute properly or gives weird errors. Some notebooks aren't marked so one isn't sure if they are compatible with python 2 or 3.

The best part is I can run it easily from any browser or use it on the cloud.

It is a very user friendly software. The cool thing about it is that it can let you run your code line by line and it's executable using several different programming languages.

Overall experience using Jupyter notebook is best, I am using Jupyter notebook daily to write python codes.

With a wealth of features that make it a popular option for many users, Jupyter Notebook is an effective tool for data science, research, and education.

Web-based so very easy to implement. Friendly user interface really helps new comers who just got into coding.

The Jupyter Notebook has very less disadvantages one of those is the sometimes we need to manually close all the running kernals.

US Dollar

RStudio is an integrated development environment for R and Python. This software is ideal for data scientists, DevOps engineers, and IT admins. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging, and workspace management.

LongDescription

Graphical User Interface

For Developers

Collaboration Tools

Version Control

Continuous Integration

Code Review

Web/Mobile App Development

Debugging

Data Modeling

Code Editing

Code Development

Website Development

Self-Employed

2-10

11-50

51-200

201-500

501-1000

RStudio Desktop

JupyterLab is an open-source web application implementing the Jupyter interactive computing environment. As a multi-user system with many different roles and workflows, JupyterLab provides a flexible interface for configuring different types of work and exploring data. The modular design allows extensions to add functionality to the core components of JupyterLab, including workspace management, file storage, and the notebook interface for entering text, code and results.

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