R is incredibly simple and allows for easy manipulation and analyzation of data. It is a tool that handles anything from basic data manipulation to complex machine learning applications.

R-Studio is great for doing any form of statistical analysis. R is very easy to learn and everything seems to flow once you get the basics down. There are countless number of packages that you can use to make your analytics and data manipulation easier. There are even packages to allow you to use python libraries and tools.

No major complaints. Sometimes it's difficult to find the packages (and their documentation) easily. I often find myself googling things instead of using the built in help function.

Overall, R is an excellent data analysis platform, that allows for the creation of polished graphs, and includes a large number of packages that make data analysis easy and intuitive. It very rarely if ever crashes and allows for pulling data from different datasets. Some commands are more complicated than comparable code lines in other programs but usually any issues can be solved by a search engine inquiry.

I use R to analyze data for academic papers. Most recently, I used it to analyze a large dataset of survey responses, and what I like the most is that it can manage using data from different datasets all at once, which is great for large projects. I also think that R is very intuitive when it bounces back with an error to the user, because it explains what the issue is in more detail than other comparable data analysis programs. Lastly, I really like the fact that R very rarely, if ever crashes and that the data analysis code is saved whenever there is a package that requires the program to restart. The packages are also great and allow you to solve more complex data issues than other programs, and it R is particularly adequate for polished graphs, using the ggplot package.

My least favorite part about R is that it is sometimes surprisingly complicated in terms of the code required to complete simple tasks like rename a variable, but there are a lot of forums online for R support, so usually any issues are a search away. I also think R is not quite as good as other programs at giving detailed information on the results of data matching or reshaping data, but this is only a true issue for very specific research projects.

Not available, I just tried this software to see if it could recover my files.

The software doesn't work, it recovers nothing from a deleted harddisk. Comment of R-Studio:"Unfortunately we would not be able to help you to recover the data in this case. You may try to contact professional services data recovery company and see if they be able to help you."

Working with R Studio is great, it is easy to pick up for beginners as well as is a powerful IDE for advanced users. Code written in RStudio can be published and shared with ease.

Using RStudio allows a user to get the most out of R. It is very easy to install and use packages. New packages can be learned and understood very quickly with the help function and the examples that they provide. Images and graphics can be displayed, resized, exported as the user needs them to be.

Some of the defaults can be difficult to work with. Depending on what a user is planning on using R for certain defaults can be poor ways of displaying information, such as when to use scientific notation. Additionally, it is possible to perform actions that either cause infinite loops or could run for days if uninterrupted.

Excellent for data analysis for large data files.

Free, open-source, incredibly rich community of online users, there is seemingly a package for any type of work you are trying to do.

Somewhat steep learning curve, some packages don't work on government servers, overall straightforward syntax but some challenges.

With R I have actually used some of the predictive modeling to help make some financial decisions where I work and it has been very valuable for what our needs are.

I really like being able to make predictive models through R. Ive been using this software for about 8 months now and I feel like I am staring to get the hang of it. it requires just a good background in coding but the applications that you can use it for are great! Ive learned how to calculate discrete probability functions on R and its very accurate. oh and its free!

My biggest complaint about R is the huge learning curve that comes along with trying to learn it. I have to say that I was very frustrated at times. I had no previous experience in small amounts of coding or statistics but I must say that this software is good when you get the hang of it

It's been great so far. Coding in the software hasn't been too difficult to figure out.

I first started using this program to help make maps from the data that we were using in a 3PL company. Since then, I have been using this for general data visualization needs.

It took a while to figure out which libraries to use and bring in. Google is a pretty easy way to figure out which one works for what type of problem.

I am using it for almost 2 years and it makes me like the programming much more!

It is user friendly. Make using R programming easy with its features.

Sometimes is does not run properly and needs to be restarted

This IDE is great for developing and data science in R. It is easy to view the environment you’re working in to check versions of packages and variables. It’s also the industry standard for R, so there are tons of online reasources.

I frequently work on different projects, so having to constantly make a new workspace for each new project is tedious.

I had a great experience using this software. It has got exceptional statistical libraries which saves a lot of coding time.

It is an open-source software and the packages are great.

Not much to dislike the product. But the command window could have a little better.

A very good experience that is uncommon but if it happens, it needs to be done. It is a corrective type of maintenance in a way.

It is a very simple data recovery tool which can perform well for data deletion or damaged disks. I only used it once some while back to recover important data that was stored in an iPod Classic (in disk mode) which started failing. The process was flawless and fast.

Given the nature of the disk drives and as the data tends to be rewritten after a while in the spaces freed by the table of contents (TOC), it is mostly useful when the recovery happens almost immediately after the event, which limits the recovering power of real old data.

I spent a lot of time trying not to use R-Studio because I like to fight against the norm. I found that there are not other competitive IDE's for R. If time is spent to learn the hot keys and enhancements in R-Studio (such as the markdown and GitHub enhancements), one will not want to use other IDEs for R.

At first the product seems clunky until the shortcuts are learned. I should have watched a video for it when I was first using it.

I enjoyed R, has been my best data science tool ever since.

Very good programming tool for mathematician. Strong for data science and analytics task. Ever since I switched, it's no going back

Coding algorithms such as neural networks can be slow in R, and users need to be familiar with mathematics to use its vector and matrices manipulation

My experience with this software has been great and I highly recommend it.

I have been using R-studio for programming and data analysis and it is the best so far. It is free, easy to use, adept at handling big data and data visualization. It is easy to collaborate using Github.

I have been using R-studio for programming and I have not experienced any problems with this software.

That it is open source and super powerful.

You need some time to get used to it and it is more difficult to use in comparison to other (payed) alternatives.

R-studio is a very easy to navigate program that allows you to do every action in R but with the extra help and instruction. With this software, you can also adjust the settings to your liking so you can work comfortably. Very helpful tools allows questions to be answered quickly and informatively.

R-Studio sometimes has a few too many things going on at once so it sometimes gets hard to navigate and will lose where you are working.

R studio is a relief for one who needs to deal with more data.By giving commands we can handle them easly. If we need to get an average of 5000 data, we can find it by giving some appropriate commands.

It will take some time for the beginners to get familiar.

I am using this software for more than 1 year and I can say it has so many useful packages which makes everything easier in data analytics and data science.

I can not mention any specific problem for this software.

No need to toggle between tabs, all tabs in one place. Suggestions in-code are really helpful

Still can be made more user friendly by changing a few things

R-Studio is great for data science. Lots of packages to use, built in charts, functions, and many more that make it friendly and easy to use, but hard to master.

It's easy to setup, powerful, hard to master. It can do MANY things regarding data science. There are also lots of packages to use which make your life easier.

It is hard to pick up. The language is not universal.

It's a free software that can do heavy duty mathematical calculations, data analytics/programming, and can use add-ons like Latex to type-set mathematic symbols.

There is a learning curve and it's not easy in the beginning, but worth learning!

The overall experience with R-Studio is very nice.

R-Studio is an amazing integrated development environment built around the R programming language. The features in it and it's user friendliness makes it one of the most amazing software in the market for data engineers and data scientists.

Nothing to dislike when it comes to discussion about this development environment. Plotting is very easy with codes in this development. Plus, it has lots of packages which is a plus.

R is a great tool. The amount of packages that are offered within it is mind-boggling, and they all have countless capabilities. I use one called "psych" to do different statistical calculations on data. It's awesome.

The only cons in my opinion so far is that it can be challenging to learn. Syntax differs between packages, so that's just something you have to get used to. The only upside is that Google oftentimes can help you figure out what you're trying to do.

Overall it is a good IDE for R language and user-interface is best in it.

The best thing is user-interface and if you are dealing with heavy code R is there for your help. Variables are pre-defined there you can use autocomplete features to make coding convenient.

Sometimes the process gets freeze and hand and it leads to your work loss as it is not autosaved. Some features and extensions are not run in an optimized and smooth manner.

I like R and R Studio and find it the best for my statistical analysis

1. open source and free 2. powerful statistical features 3. accurate and useful packages for data analysis and visualization 4. user friendly 5. good features like R Markdown

1. hard to understand the errors and debugging 2. not able to work with large datasets 3. not very fast in comparison with Python