ArcGIS is the only tool we have the geocodes addressed on premises without sending data over the internet. We could have explored that option with Spotfire but it was out of our price range. I also tried to do this manually using PostgreSQL which also has a free implementation …
ArcGIS is more robust than QGIS, but often slower and more memory intensive. QGIS is also free, while providing at least 90% of the functionality. Although it might be difficult to get used to the interface differences between the two programs, QGIS is a worthy competitor …
I've used ArcGIS and ESRI for similar analysis and while both have their advantages, RStudio is much better suited for running advanced statistics and processing large volumes of data. It can also produce quality maps, however, for visually attractive maps and graphs, ArcGIS is …
RStudio absolutely offers everything that SPSS does at zero cost. Yes, there is a bit of learning curve in terms of you needing to equip yourself with R language but that's a good thing as you learn and apply more complex statistical tools and techniques on your datasets. …
RStudio is the only GUI which is web-client based. It is also by far the easiest to install out of the the 4 GUIs ive listed avaliable for R. Finally I would say the cleanest and most sleek GUI would be RStudio, which makes it easier for other people I work with to understand.
I'm very grateful to be able to use it, and I have a master's degree with a focus in Geospatial Analysis. There can be a bit of a learning curve, and I try to build user-friendly ways for volunteers to see & collect data. Meanwhile, if a colleague is less confident with building such a system, it may be more difficult for them to implement.
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.
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.
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.
Simply because the program deserves it. It seems to me that it is a fundamental tool for the storage, analysis, and interpretation of medium and large-scale phenomena, unmanageable with traditional engineering software. Its versatility in the handling of the different "layers" with which the data is handled and interpolation tools, make this software a powerful ally both for companies and for the educational part of the universities.
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.
Once set up, the tools are extremely easy to use. I had a staff member develop a tool for field data collection, that included an external and internal dashboards to monitor progress in days. The field workers that collected the data, barely knew how to use a computer, and within minutes they could use the application that was configured for them.
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
Unlike other platforms (ex: EMSI), there is no "help desk" new users can easily call into for troubleshooting or errors, and so you have to spend LOTS of time trying workarounds. This is also because the help center blog posts are usually pretty confusing, and many times do not include images or videos to help you along. Any such changes would be immensely useful!
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.
My students love the "drop" feature in Google Maps, but besides that it truly doesn't compare. I love that you can add, delete, or change layers to this map to better understand its larger affect. There are many more ways to manipulate maps on ArcGIS than on Google Maps. I can also add personal details and information if I want to create a specific map, something that I am unable to do with Google
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.
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).
