Anaconda vs. Posit

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

• Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
• Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
• I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.

Incentivized

• RStudio does an excellent job providing a clean user interface for R or Shiny applications
• RStudio integrates natively with version control software
• Users can program with either R or Python
• RStudio has a command line built in, eliminating the need for a separate program for a REPL

Incentivized

• More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
• Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
• R Studio Hangs a lot when open from Anaconda Navigator.

Incentivized

• Ability to scale across the company is limited based on the users license, cannot share a dashboard to the general view of the company.
• Ability to retain session - not simple method to customize view per user (e.g., once session is ended, the users will return next time to the baseline view).
• Ability to enable communication between multiple users - leave notes, tag other users, or share specific view.

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 other platform that meets our needs. Even if it was terrible we would still use it but fortunately for us it is a very solid project with a great support team. I hope in the future to expand our use and get more licences as well as upgrade to RStudio workbench but for now we are very happy.

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

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

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

One of the main competitors to Anaconda can be Google products such as Colab. Colab gives you the flexibility to handle large datasets gives it an edge over Anaconda. But again, the ease of access and usability of Anaconda stacks up against Colab. Besides, Anaconda relies more on your machine which makes it safe to use.

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 think that RStudio scales pretty well based on the size of the datasets I'm using. It has multithreading capabilities unlike some other statistical analysis programs which is very useful in cutting down on time. The format of RStudio's syntax also makes it very easy to replicate regardless off the scale of the analysis and data set

Incentivized

• Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
• Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
• Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.

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