Posit is far better than Jupyter Notebook and Minitab in this regard that Posit is actually capable of doing all kind of analytical stuffs like data pre-processing, wrangling, validation and visualization. On the other hand, Jupyter Notebook can be used for python programming …
Posit is way way way more reliable than Excel for anything more involved than a quick spreadsheet. Faster speeds, greater charting abilities, flexible functionality and more efficient memory usage. Python is still my go-to for anything that needs integration, but Posit beats …
RStudio is free and so that is the main reason that I use it. I like that it is open source and so there are lots of support on the internet. I tried SAS JMP and Python in a text editor but RStudio was better than either of those options for cost and code flexibility …
These all work synergistically and fulfill slightly different roles. In general this is determined by complexity of task and the degree of training and expertise of the end user. RStudio works well for organisations looking to move into doing more complex analytics. In general …
I don't really know another program as powerful as Excel. I've used Google Doc programs but do not feel they come close. So far, anytime I've needed a table of some sort for data, whether it's budget oriented or information off a survey, the best system has been Excel. We do web audits on occasion and we create an Excel worksheet featuring every URL of the pages we're auditing, notes, data about the content, information about files attached to the page and other information to help us determine what pages need updating, deleting or otherwise. We also use Excel primarily to export our Google Analytics to in order for us to create reports for clients that need to see specific information about their traffic.
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.
It is very good at embedded formulas and tying cells to one another
It allows me to compare deals terms on a side-by-side basis and talk my clients through it easily.
It is very helpful as well in terms of allowing me to filter/sort results in many different ways depending on what specific information I am most interested in prioritizing.
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.
Excel offers collaboration features that allow multiple users to work on the same spreadsheet, but managing changes made by different users can be challenging. Excel could improve its features by offering more granular control, better tracking of changes, and more robust conflict resolution tools.
Itcan be a barrier to productivity when importing and exporting data from other applications or file formats. To improve its features, it should offer better support for standard file formats and more robust error handling and reporting tools.
Excel can be challenging for finance students and working professionals, but it can be improved by offering more robust tutorials, better documentation, and more user communities and support forums.
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.
Excel remains the industry standard for spreadsheets and has maintained simple and straight-forward formula writing methods. Although there is a learning curve to do more complex calculations, there are countless help sites and videos on the Internet for almost any need.
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.
I'm giving it a 7 because it is my go to. But the fact other prefer Google Sheets when working with a team does get irritating. I've used the online version of Microsoft Excel that other teams can get into and it still seems behind Google Sheets. It's a little clanky and slow? If that's even a term.
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
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
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.
Out of Microsoft Excel, Microsoft Power BI, IBM SPSS, and Google Sheets, Microsoft Excel is by far the most common tool used for anything data-related across organizations. Accordingly, our organization has also implemented Microsoft Excel as a first-step tool. We recently adopted Microsoft Power BI (the free version), and use it occasionally (mostly for creating dashboards), but it is less commonly understood by stakeholders across our organization and by our clients. Accordingly, Microsoft Excel is more user-friendly and because of its popularity, we can easily look up how to do things in the program online. Google Sheets is a comparable alternative to Microsoft Excel, but because it's cloud-based and we have sensitive data that needs to be protected, we chose against using this software. Finally, a few users (including myself) have access to and utilize IBM's SPSS. For my role, it's a helpful tool to do more rigorous analyses. However, because of its cost and limited functionality as a simple spreadsheet, we only use it for more complex analyses.
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.
Each user can use it to whatever level of expertise they have. It remains the same so users can contribute to another's work regardless of whether they have more or less expertise
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.
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).
