Anaconda vs. Posit vs. Microsoft Visual Studio

I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.

Incentivized

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

Incentivized

When working with base C# code for desktop and web projects, then Microsoft Visual Studio is ideal as it provides the libraries and interfaces needed to quickly create, test and deploy solutions. It is when slightly more complex scenarios are required that issues can arise. The built-in integration for things like PowerBI Paginated Reports and dashboards is far from ideal.

Incentivized

• Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
• Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
• Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.

Incentivized

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Incentivized

• I like how the tabs and panels are organized; everything is gathered together so you can find what you're looking for quickly.
• If you can't find something, there's a thorough search engine that searches all of Visual Studio's features.
• The Team Foundation Server feature is fantastic since it allows us to easily deploy and roll back changes on the server.

• It can have a cloud interface to store the work.
• Compatible for large size files.
• I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.

Incentivized

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

Incentivized

• Would be great if there was a better way to save and restore open tabs.
• The built-in test runner can be improved, for example, by adding the ability to save a set of commonly run tests, etc.
• Better integration for Microsoft SQL server would be great.

Incentivized

It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.

Incentivized

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

Incentivized

VS is the best and is required for building Microsoft applications. The quality and usefulness of the product far out-weight the licensing costs associated with it.

Incentivized

I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.

Incentivized

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

Incentivized

I love the overall usability of Microsoft Visual Studio. I’ve been using this IDE for more than 20 years, and I’ve seen it evolve by leaps and bounds. Today, with AI and code-suggestion/completion features, developers no longer need to remember countless libraries, methods, or language syntax, or invest a huge amount of programming effort to complete a project. It truly offers everything a developer needs to program, debug, test, and deploy in a single IDE.

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

Incentivized

Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.

Incentivized

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

There are many resources available supporting Visual Studio IDE. Microsoft whitepapers, forum posts, and online Visual Studio documentation. There are countless demonstration videos available, as well. If users are having issues, they can call Microsoft Support, but depending on the company's agreement with Microsoft, the number of included support calls will vary from organization to organization. I've found that Microsoft support calls can be hit or miss depending on who you get, but they can usually get you with the right support person for your issue.

Incentivized

IT is very complicated to understand all the functions that the environment has if you are not familiar with this type of development environments. It is important to select a good in-person training to achieve to understand all the possibilities and the capacity of the application. In this case, you will be able to develop a lot type of different applications.

Incentivized

If you are not accustomed to develop in this type of development environments it would be complicated to follow all the parts of the course because if the course does not include a great tour with all the concepts to develop you will not have the option to understand all the functions.

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!

Incentivized

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

I personally feel Visual Studio IDE has [a] better interface and [is more] user friendly than other IDEs. It has better code maintainability and intellisense. Its inbuilt team foundation server help coders to check on their code then and go. Better nugget package management, quality testing and gives features to extract TRX file as result of testing which includes all the summary of each test case.

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

Incentivized

• It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
• By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
• There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.

Incentivized

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized

• Using the integration between Visual Studio and our source control service, the cost of re-work and losing code is drastically reduced.
• Paid versions of Visual Studio enable developers to be so much more productive than hacked-together open source solutions that it's hard to imagine developing in Windows without it.
• When combined with support subscriptions and the vast array of free online help options available, Visual Studio saves our developers time by keeping them coding and testing, not wasting their time trying to guess their way out of problems or spend endless hours online hoping to find answers.

Incentivized