RStudio is a family of powerful and cost-effective undelete and data recovery software.
Lo mejor a la hora de mapear con RStudio es la facilidad con la que puedes adecuar tus datos a lo que necesitas expresar. Siendo un lenguaje tan rápido de aprender, con Rstudio y sus herramientas solo debes tener claro cómo plasmar tu idea para que los otros lo entiendan, sin duda RStudio te ayudará en ello, solo hay que aprovecharlo.
Un aspecto a mejorar es el diseño de los gráficos, en este caso de los mapas. Con los cambios que existen en los distintos software de este tipo, Rstuido debe fijar su norte a una mayor calidad en los gráficos que genera.
Al trabajar con estudios de mercado, necesito encontrar distintas formas de expresar las variables que uso. Por ello con un simple NPS en escala nacional tuve la oportunidad de graficarlo en un mapa geográfico el cual me ayudó a ver el comportamiento de este indicador en el país, incluyendo por Estados y Municipios.
RStudio es increíble. Me encanta el hecho de que sea de código abierto, y funciona a la perfección con R. Hay tantos paquetes desarrollados por otros que se pueden obtener fácilmente de CRAN o Bioconductor, por lo que realizar análisis es muy intuitivo y fácil de usar. Todas las funciones también vienen con buena documentación y un montón de ejemplos para seguir a lo largo, que fue tremendamente útil cuando estaba empezando como un usuario principiante lats año.
A veces encuentro que escribir tus propios paquetes o programas para hacer cálculos no es tan sencillo. Por ejemplo, intenté escribir una función que clasificara un conjunto de productos y realizara un cálculo de similitud entre los proveedores. Escribir nuevas funciones desde cero fue un poco difícil para mí, ya que no encontré muy buena documentación que fuera fácil de seguir. He tenido que confiar en los paquetes de otros, que también han funcionado bien.
Definitivamente recomendable - es gratis para todos, lo que es especialmente un gran beneficio para los estudiantes. Utilícelo más en clases para exponer a los estudiantes a una variedad de tareas de visualización, modelado y minería de datos.
Estoy usando RStudio para ejecutar análisis predictivos, específicamente estoy tratando de predecir las ventas para una semana determinada basado en predictores tales como el número de visitas a nuestro sitio web, los enlaces en los que los usuarios hacen clic, su historial de compras, etc. RStudio tiene muy buenas funciones para construir modelos de regresión y aprendizaje de máquina para este tipo de trabajo. Me di cuenta de que el mayor beneficio es que es gratuito. Anteriormente usaba Stata, por lo que tuve que pagar.
RStudio is an amazing statistical software to use today. It is not only good for basic data analysis and visualization but also for more sophisticated statistical modeling and inference. I use RStudio for both my research projects and teaching and students love it as well. The best part about RStudio is that it is open-source (hence free). It also has several libraries with built-in functions that are useful.
The main thing that I dislike about R and RStudio is the file handling aspect. Sometimes files that are read into RStudio need to be manipulated in some ways to apply certain techniques (like ANOVA or Analysis of Variance). I wish this was simpler as in other software packages like SPSS. Another minor thing is that the learning curve may be a little steeper for undergraduate students (every function and use is not as intuitive).
Definitely recommend - it is free for all, which is especially a huge benefit for students. Use it more in classes to expose students to a variety of visualization, modeling and data mining tasks.
I use RStudio to perform both simple and complex statistical analyses for my research projects and also teach courses on Business Statistics and Data Science using it. Students love it and it is relatively easy to learn if you have some programming background. RStudio has enabled me to apply different types of statistical and data mining algorithms to different types of data easily and obtain the results that are eay to understand, present, and interpret.
RStudio is incredible. I love the fact that it is open source, and works seamlessly with R. There are so many packages developed by others that can be easily obtained from CRAN or Bioconductor, so performing analytics is very intuitive and user-friendly. All the functions also come with good documentation and plenty of examples to follow-along, which was tremendously helpful when I was starting out as a beginner user lats year.
I sometimes find that writing your own packages or programs to do computations is not as straightforward. For instance, I tried to write a function that would rank a set of products and perform a similarity calculation between the vendors. Writing new functions from scratch was a bit hard for me as I did not find very good documentation that was easy to follow. I have had to rely on other's packages, which has worked out well too.
I'm using RStudio to run predictive analytics, specifically I'm trying to predict sales for a given week based on predictors such as number of visits to our website, what links users click on, their past purchase history, etc. RStudio has very good functions to build regression and machine learning models for such work. I realized that the biggest benefit is that it is free of cost. I was previously using Stata for which I had to pay.
I recently started to work with R and statistical analysis for my PhD project. RStudio is basically R with buttons and more useful resources. The auto save sessions are amazing and you don't have to be worried about your computer shut down or anything that can happen somethings, you are not gonna lose your data. To install packages is super easy and there is a lot of them out there. The console is super user friendly and the script screen is pretty clear. Even for beginners like me, RStudio looks easy.
One thing I don't like about the RStudio is the lack of auto updating R feature. When a new R is released, you have to download and install it manually, RStudio does not recognize a new version of R. And you also have to copy and paste your packages from the old R to the new directory, RStudio cannot handle it yet.
If you have to run lot of data in R and you are a beginner or not a bioinformatics guy, use RStudio, the interface is good and user friendly, everything is pretty clear in the screen, you will know when something is wrong with your code and RStudio gives you some hint to make the code work.
RStudio helped me to analyze my RNA-seq data in one week, and I never worked with this kind of data before. It looks clear and easy, just type and run your script, do it all over again and your work are done.
The community associated with the tool and language is very strong. Also the extensive work and updation of packages make it very easy to use and apply functionalities. Caret and dplyr are very versatile packages. If one learns to use ggplot 2 package, they can even create interactive dashboards like one from tableau.
There is not much to dislike about the language but the constant and frequent updates in few packages makes one re-learn the concepts related to it again and again.
It is difficult to learn, but there is a lot of material available online to woke through it. I learned through data camp, ISLR and participated in online kagggle competitions. It takes time but once crossed the threshold its a cake walk then on.
RStudio was mostly used for data wrangling. This data cleaning part took the maximum part of processing the whole data. Once the concepts were learned it was easy to use and modify the data for further work on it. Statistical knowledge coupled with the knowledge of this tool can work wonders. One can build regression models and do predictive analysis. There are many applications towards improving and understanding the data for the further applications.
I really like the user interface design. It is it very intuitive with different sections like code, console, file paths, libraries. Another good point is the easy way of searching and installing new libraries that make your work more simple and fast
I wouldn't know what to say. I think this tool doesn't have any cons. It is made for any kind of user for beginners and also advanced users and can be used in some many different fields
Very easy to use but in case you have some problems, you can check the official documentation or even non official. There are a lot of documentation on the Internet.
Fast and efficient R programming. It is currently the best and more most famous framework for R development used for big data and business intelligent projects
I don't even know where to begin. Rstudio is highly customizable, allowing you to choose how to display your console, plots, workspace variables, etc within the IDE. The newer versions of Rstudio allow for version control with git as well, which is very advantageous for collaborative projects. Plotting is also easy, and you can easily scroll through your plot history to compare many plots. You can easily inspect your datasets and variables as well. Installation is very simple and takes a few moments. I use Rstudio with my PC and Mac, and I've never experienced problems with either. In addition to an IDE, you can also write latex and markdown documents integrated with R code and output. I love this feature, because I produce many reports in which I need to show R code and output. Also Rstudio is free!
Although Rstudio is an excellent IDE, that comes at a cost of overhead. Compiling large lateX documents, for example, takes much longer if you run them through Rstudio versus the command line.
I would highly recommend Rstudio to a wide variety of people--from students to instructors, business professionals, and anyone who'd like to get some programming experience in a user friendly IDE. I especially recommend Rstudio to those who are on a budget, because Rstudio is free! I would also recommend it for analysts who wish to use version control on collaborative projects, because Rstudio integrates with github.
I use Rstudio extensively daily to analyze, process, and manipulate data. It is an invaluable resource. I would not be as productive or efficient without it. I really love that I can produce high quality reports with R code. This helps others understand my work and makes my research reproducible.
What I like the most about R-studio is the variety of packages that it covers. Most statisticians are well-versed with R so most of their academic papers end up being a package in R which makes it a very useful software for statistics. R-studio is easy to use and it is quite friendly. I also like the visualization packages such as ggplot2 which generates fancy figures.
I think R is very good for statistics but it's quite limiting for many other tasks such as data-wrangling, deep learning, and machine learning packages. If you are working in those areas I suggest using Python instead.
Consider making Machine Learning and Deep learning packages more user friendly and provide a good tutorial on that so that you can resemble something like Scikit-learn)
I have been working with R-studio for a while now. I often run ANOVA models ore regressions. I also use it for mapping (ggmap) and visualization because I think the visualizations are great. Although, after the advent of Tableau, I barely use R-studio for visualization. Because of it being an open-source software, it is still a common software preferred by many companies including Mckinsey.
RStudio has been a complete game changer for me at work. Not only does it provide a slick interface for utilizing the R programming language, it makes implementing new packages and combining multiple programming languages simple and efficient. The Help tab save a great deal of time that would otherwise be spent on Google. The Plots tab makes exporting plots quick and versatile. Personally, I could go on for a long time on how great this product is. Shout out to the Monokai theme; it makes coding a dream.
There are some issue with saving plots and their size and scale. I can program a plot to have a specific scale, but open it in the Plots tab differently and the saved file changes. Especially when I put multiple plots onto the same image there are scale issues. I have spent more time than I would like to admit adjusting scales.
Check out the documentation and Stack Overflow with questions. This GUI is highly powerful and worth the time to acclimate to. I would also recommend the theme "Monokai," which is a dark theme. It makes the coding softer on the eyes.
I complete probably 85%-90% of my work with RStudio. We run off an IBM AS400 server and I constantly pull and manipulate data through an ODBC connection using SQL queries and then dump the data into Excel. Data wrangling that would take literal days in Excel take mere seconds once a program is written. We recently updated our accounting software and we had to verify that our data transferred correctly. The accountants would have had to check hundreds of thousands of lines of data manually over the course of days (in at least 3 different stages), but I was able to complete the whole process in a few hours (coding, formatting, and running scripts combined). Highly powerful.
R studio is easy to navigate and use for analysis even with little to no experience with R programming. It allows you to easily manage and visualize all the objects in your environment, whether you are performing simple analysis and graphing or if you are running complex models or even writing your own R packages.
It could use a few more thorough tutorials that shows you exactly the basics of working through a project in Rstudio, where all the features are located, etc. But many tutorials exist online that can serve the same purpose if need be
I am using R studio to manage and edit different data structures, perform computational statistical analysis and visualize the results of these analyses
Its has a very good GUI where one can easily write R code for writing machine learning program. You can see the values of global variable create various programs etc.Also it is very fast. Rstudio is great tool for programming R code for machine learning programming. It has a very good GUI and is a great tool for Data Analysis and Data Science. It is freely available and very easy to download and setup.
As of now I did not find anything bad in Rstudio. One thing that I can say is that it can be more colorful.
Rstudio is great tool for programming R code for machine learning programming. It has a very good GUI and is a great tool for Data Analysis and Data Science. It is freely available and very easy to download and setup.
Writing code for machine learning project which is the part of Data Science initiative.
1. I liked the User interface and MacOS application. Very Dynamic and Fluid .
2. I've used online tools as well provided by Rstudio and found them really helpful.
3. Admin portal seems so good. Connecting apps is very easy as well.
I did't came across any issues while using RStudio on my macOS. Everything worked well as advertised. Satisfied.
1. Information about Various models is must.
2. Reading manual before using is always a good idea,
1. making ML models and using them in production ,
2. The very important part was Deploying over cloud which is just a breeze with help of Studio.
3. Used and tried hands on Predictive Analytics and found it very intelligent.
Separating the console and clean script allows the user to have a working script with which the problem solve and perfect, as well as a clean, final script. RStudio brings packages to your fingertips and makes installing them a breeze. The ability to also view one's dataset while working is also invaluable to the user. Generally, the RStudio interface is the same, easy to use interface as R so if you're familiar with one, it should be easy to learn the other.
In order to save packages to your user library, you must download and save them. It would be nice if the system library included more widely used pre-downloaded packages.
RStudio is a great resource as it is open source software and constantly improving and evolving. As a free software, it can be essential for any business which analyzes data, spatial or not.
I work in geospatial analysis. R is a great tool because you can use it for geospatial statistics. RStudio simply makes working in R ten times easier by creating a clean script as well as a separate working space. RStudio also allows me to save and install packages with a click instead of having to download them each time I use the program.
The interface is nice and the modeling features are nice. I like R, I like Rstudio, better than SAS and better than a lot of other software. I like R. This sort of thing is quite used for research purposes and whatnot but it could be used for other things as well. Like for education, finance, and a lot of other sectors
Interface could be more customized and faster. R could be better, could be better documented, could do with more python like features, get more stuff, etc. Could improve on speed and other aspects as well.
Go for it
Modeling and statistical analysis, Can't talk about specifics due to NDA, but mostly stats and analysis of data and whatnot. It is quite nice actually. Very revolutionary and will bring lots of benefits to the world. This is what research is really about. Funding is an issue, but it wouldn't be an issue if everyone knew what we were doing. The world will be a better place with our research and it would really be out there. Space, it will be reachable with the amount of work we do with gravity. That's the biggest problem. Gravity. If we solve gravity, we can get to space and we can get to other planets, like what Elon musk wants to do. But the government doesn't like having people out there without their control so they restrict our funding. That is not good.
The software is simple to learn, and there are many guides available as needed. I also like how simple it is to get help with using different packages within the program. I also like the availability of RMarkdown that can be used to turn codes into html.
Sometimes I struggle with adding commas and keeping my symbols/punctuation straight. With Stata, punctuation and symbols aren't as necessary when inputting the commands.
Definitely make sure to use the resources that are all over the internet for help with RStudio. Almost anything you could possibly need help on is out there, and will make your life a lot easier! I also think it's good to have a good background in another stats program first because then the manipulations that you will need to do will make a lot more sense.
I am able to quickly and easily find the data results and answers that I am looking for when my data sets have upwards of 200,000 observations. This is very useful and saves time.
Rstudio está diseñado para ofrecer un entorno de desarrollo integrado. RStudio es la razón por la que elegí la programación en R. Es fácil de usar y el diseño es ideal. También me gusta que sea fácil actualizar paquetes. Lo más sobresaliente es que no solo ofrece la finalización automática del código, sino también la finalización automática de la ruta del archivo. Aunque aprender a programar en R sea algo complejos al principio, con RStudio es más fácil y merece la pena.
En general, no tengo quejas sobre RStudio. Lo único que puedo mencionar es que tarda un poco en cargar el entorno. Toma tiempo procesar conjuntos de datos grandes y que podrían implementar el uso de múltiples procesadores. Aún así, sigue siendo una de las mejores interfaces para usar R.
Ten paciencia con el lenguaje R. Es complejo y lleva su tiempo aprenderlo pero una vez que lo dominas no hay nada mejor y RStudio te da todas las las facilidades posibles para conseguirlo.
Utilizo RStudio para análisis estadísticos complejos como modelos de ecuación estructural, análisis bayesianos y modelos jerárquicos. Fundamentalmente lo utilizo para la investigación.
Visualization is the best option provided for large data sets. The help function for all the code which we required is really nice. The packages are easy to understand. The interface is quite simple and straightforward.It is easy to install. There are proper documentation available to understand each and every thins. The scrips i made for my project were easy to use by everyone. It is open source, easy not only for employees but also for students who are in their learning phase can easily use R for their academic purpose.For R users, there is active group which can help an user to understand things very easily. This group helped me in providing me a lot in understanding the errors.
Nothing much. It is quite easy to access but one need to know about the right libraries to be installed. I didn't dislike anything about this product as particular.Some memory management issue i have faced sometimes. It can quickly absorb all the memory and space available. Other than that i never faced any issue with R.
It's an amazing tool for data analysis. The data cleaning part done on R helped me a lot in reducing my daily hours of cleaning the data. The scripts made on R studio is really easy and helpful.One is able to understand the concept and functions easily with help feature.
Large data sets for providing analysis to senior leadership. The visualization helped me in providing good result to leadership. The data cleaning scrips reduce the man hours for data scientists and for me as well.
RStudio's interface is clean, minimalist, and makes writing code easy. Being able to view plots/files, variables, workspace, and command line all at once is helpful. Highlighted text (depending on values, variables, functions etc.) makes singling out comments, files, and numbers easy; this feature is expanded when working in R Markdown and is visually simple. The ability to preview and knit R Markdown documents is useful.
RStudio tends to slow down considerably when editing an R Markdown or R Notebook file; I find myself having to write in a basic R script document then transfer over to Markdown once finished. Some useful features are somewhat hidden (ie, "Set Working Directory" is a tiny option in a small menu; it would be nice if this were more notable). The export image feature isn't great, and I usually use an outside utility to preserve image fidelity.
RStudio helps our data analysis move quickly. Benefits include increased efficiency and a lower learning curve for writing R code.
It's fairly easy to import data into R Studio. It is helpful to be able to write multiple scripts simultaneously in the Editor. The console displays the results of the script outcome and/or displays any errors.
The Console does not save the output. It would be helpful if the console saved the output in different sections/windows for each corresponding script.
I would definitely recommend R Studio to others, but it is import to learning the programming language first. It is also very important to learn how to clean up the data first through the appropriate script.
I analyzed MLB sport related data. There were two datasets imported into R Studio, one for players and one for teams. I cleaned up the data. Dataframes were then created to merge and store the data. By creating the data frames and running the scripts, the sports stats became easier to analyze.
Rstudio has a great interface: data browsing is the best I have seen in any IDE, including MATLAB. It has integration with git. As a allows for creating proper packages, ready with documentation and and all whistles and bells. The same time it is sample and intuitive, making it beginner-friendly.
It's problems is really a continuation of this IDE advantages: e.g. interface is minimal and simple, but it would be nice to create multiple separate windows for browsing data or have R-console detached.
It is free and frankly the only option for R IDE
Created and submitted two Bioconductor packages. Explored certain problems in random matrix theory.
RStudio makes work easier with When entering RStudio we see the screen divided into four windows, that multiplatform R, works on Mac, Windows, and Numbers UNIX Numbers. This means that you can work with your data, figures, analysis and more importantly using your instructions, R is free software, there is a large community of volunteers working to improve it, which allows it to be molded and addressed to specific issues. Creating programs and packages that work in the environment R. Programs such as R-studio, Java GUI for R, R-commander, RKWard, among others, and with more than 6000 packages indexed in CRAN, Biocoductor, GitHub and R-Forge
R has a vast documentation of help, description of packages and functions, it is difficult to find specific information at any given time, R is an online programming language of command, which does not involve the use of menus like other statistical programs, this makes many people who are not familiar with programming, it is very difficult to migrate to R. But this is more than a disadvantage, because programming will better understand the basis of statistics and data analysis, compared to other people who do not use R.
First, because it is a language with a complex learning curve, but very robust and effective for the handling of statistical data, for developers specialized in these languages, it can be simple. In addition, R is a programming language that is constantly evolving and that has extensive documentation, ease in the preparation of data, with this technique is very simple, largely because it automates many processes by programming sequences of commands, R works with any type of file, R is a language that allows the implementation of additional packages that give a huge data management capacity, It is open source and free.
Basically, I can simplify the steps when updating a database, reduce working time, once it is scheduled, as a statistical platform, it offers me all the techniques of data analysis. In addition to programming new methods and routines in an easy and robust way, I can do any database immediately, I can perform all the data analysis and even read files of different formats.
The ability to load data into the workspace interactively and being able to view the data instantaneously, without having to export it first. This is a brilliant feature that more IDEs should include. I also enjoy being able to snippets of code at a time, not the entire file.
I wish it had a built-in terminal along with the R environment, like with PyCharm. This way one can run 2 codes at once, or be running one code while playing around with another at the same time.
The data visualization capabilities are good, but if you plan to use RStudio solely for this purpose, exporting plots can get a little tedious and viewing the plots interactively can be error-prone, in my experience. Python is more powerful for visualization but less convenient.
Everything from principal component analysis for hugely multivariate databases, to data visualization (e.g. plotting temperature gradient time series' over the entire US), to simple data analysis and file I/O.
I like that RStudio (a user interface for R) is free & allows for the ability to download & install packages in the console itself. You can even connect to Google Visualization Charts, which open up more possibilities with data visualization.
I find that it isn't 100% user friendly - I can never remember where to save exactly what I would like (my R script in the editor or the entire disc image), & to this day, I still forget how to set my working directory permanently. I also dislike that due to being open source, every package is different in coding (even if written by the same source). This makes similar functions tricky to do vs in SAS where you just change which proc function, etc you are using. I find that it can be a bit clunky, but overall, still decent for being free & open source.
Make sure to really research packages to ensure writing code correctly as they can vary so much!
It's great to have a free option as a resource for working with large datasets. SAS can be pricey, but since R & RStudio are free, it is a great substitute. It allows to anyone with any software budget access their data & be able to conduct many different levels of analysis. I don't personally utilize RStudio every day, as I am more primarily a SQL & SAS user, but it is very handy to have RStudio around for when I need it.
I love the way it has integrated code completion, help manuals for any package and also the possibility to integrate custom tools to its addins. IT also allows us to view what's loaded in the global environment and even see the structure of objects.
Not being able to run parallel processes within the editor. It all must be handled by R and not even all the operations can be scaled. There should be more addins available. Code formatting is inconsistent and makes your code unreadable at some point.
There are other tools out there that might be easier to use for neofites. But I strongly recommend this tool for anybody wanting to learning predictive analytics or datascience. That being said, I would nto recommend it for data exploration since Excel or SAP Lumira is more powerfull than this. Trifacta is also another good tool to explore your data. But when it comes to modeling, math enthousiasts will love RStudio.
I am EAM analyst and I need to perform regression testing and analysis. I also need to build models for our reliability group.
- Easy to navigate
- Fantastic to install/uninstall packages
- Great sense of customisation
- Stable with rare crash
- Fast in-memory engine
- Open source
- Free license with option to go pro
- Slow when sample size is high
- Needs a bit of redesign to get closer to something like Zeppelin or Jupyter. This IDE style is a bit of old school.
- It does not have cool visualisation solution. Everything must be coded.
This IDE is great to start with R. However, it keeps data in memory so its performance is highly dependent of the volume of RAM you have on your machine. It can easily crash when data is large.
This is dedicated to R as name suggests with lots of features. If you are in enterprise environment, you can even deploy your model on RStudio Server where you can schedule your task on a certain plan. Surly you have to pay for enterprise a very reasonable fee (I think it is $1000).
R code development, description analysis, data cleansing, feature engineering, modelling
I like that it has a nice GUI. When an image is generated, it pops up in the corner and the user has the option to zoom in and can save it from the GUI rather than typing into a command line. You can see what it looks like before you save. You can also see characters and strings you have created in the upper right corner of the GUI, so keeping track of what you've created in R is much easier. The drop down menus for installing packages from both CRAN and from source are also really nice!
It can be a little clunky sometimes, though it could be my computer making it that way.
Data analysis is much easier, and due to the graphical nature of RStudio, it's easier to see what I've done as far as creating objects and strings, and also images.
R studio does not have much format to follow. I can easily build in my own function and do application. What's more, I can knit the result in several different ways. The packages with instruction and explanation are useful for new user to understand the usage.
I can treat R studio as a calculator and notebook. It can show the results very fast and easy to operate.
It can add the Greek sign, matrix and several mathematical symbol as the comments. Therefore, we can knit an entire report at once with nice format, clear output and code.
It also have nice plot package such as ggplot, which can output a nice and colorful plot.
For the traditional plot, there is not too much choices to edit them in a nice way.
For the result, it does not show everything in one step. You have to run different codes and packages to get what you want.
It is kind of hard to contribute existed data in a new way. For example, if we want to inverse the entire data, treat one column and row, it is kind of hard to do it.
And R studio distinguish the capital word and lower case character, which we need to pay attention to this point every time.
R studio usually saving a lot of records in the environment, which, in my opinion, is a waste of memory. If the history can be cleaned after we quit the software, it may be better.
I often use R studio to do data analysis.
clear output and easy code typing
It can give you the result with complete format.
Rstudio is extremely user friendly and has everything you could possibly want from R. It breaks it down into 4 panels. The first panel is simple vanilla R. The other one is a scripting place for you to write and edit R code nicely. The other one is your environment which shows you which variables and functions you have created (trust me your gonna want to know this) and the last one shows your directory, packages, and much more. R is complicated on its own and the IDE here helps break it down and become more user friendly. Trust, your gonna want to use this for R
Nothing really, they update R studio every while and the team that does this is remarkable. One thing I disliked was that they did not help show the directory of your files (trust this was a pain trying to figure out and get wording right), but they recently updated it to include an import dataset button which figures out your file and gives you correct directory along with the code. It is really good.
TRY IT TRY IT TRY IT TRY IT TRY IT TRY IT
What business problems are you solving with R Studio? What benefits have you realized?
The problems that are being solved is a working station for data analysis and a way to utilize a statistical programming language like for proper analysis. This IDE is really friendly and has much better features than other IDE's ive tried. You would not be disappointed.
- RStudio can effectively run R packages and is simple to use while trying to perform statistical analysis for huge amount of data.
- It is open source and available for free.
- The workspace can be easily saved and organised as a Rhistory file which can be used for referencing later.
- The autocomplete feature is pretty quick and reliable.
- The panels are neatly divided and can be resized according to need.
- The main problem with RStudio is that it is lacking behind in Data Visualization as compared to its competitors. Many Data Visualization software like Tableau, Qlikview available in the market can give you stunning visuals which help understand data much easier but Rstudio has not come upto par with the variety of charts that are available in these tools.
- There are instances of Rstudio crash which are quite frequent.
If you are working on R, Rstudio is the the way to go.
- Rstudio helps tackle data science problems from almost every industry like healthcare, finance, retail, etc
- Instead of using R command line, you can use Rstudio which can help you plot multiple graphs which are saved in different windows and you can view them together.
- You can build R scripts which are easier to share and run on Rstudio.
The best thing about RStudio is how user friendly the console is. I can save my code and its easy to run the required lines repeatedly. I do not have to rewrite it. It is easy to install all the required packages.
When I want to connect the RStudio with PowerBI its not possible. I need to have R Console
Anytime RStudio is better than R console
I do most of work on data table using RStudio. It takes only few functions to perform any joins or aggregations on a data unlike Java or any other language. I like the visualization part of dataset which is very convenient.
Very helpful IDE for R-programming especially for newcomers to R. Highlights errors in real-time that is not available in the R software itself. Most helpful features are the R-Markdown and R-presentation that enables saving the work (source code and output) in MS Word file or a PDF and thus it is presentable to those not using the software. The project can be saved as R-presentation and can be demonstrated online on any computer by uploading the project to Rpubs.com for free. R has very helpful user community that provides useful solutions to many problems.
The user needs to have a good idea of many packages to work efficiently (unlike other software such as SAS). Some of the packages cannot be downloaded directly from CRAN mirrors (on the fly using the install.packages command) or from GITHUB. These packages have to be downloaded locally and then installed before loading them. some of the figures/plots cannot be shown on R-presentation or R-HTML although these are generated on the local computers. Generating the pdf in the R-markdown needs installation of additional software (MiKTeX) not provided with R-Studio installation.
Plenty of R-packages are available and the user may need to consider various options before finalizing the analysis and visualization.
I used RStudio for principal component analysis and analysis of observational data using propensity score matching in R.
Best part about R-Studio is its simplicity. Being able to navigate inputs as tabs, manage your environment, and constantly see what data frames you have helps users with coming back to projects and being able to pick up where they left off with little downtime. It's also nice to navigate packages while keeping your console and code blocks open. Obviously, its nice that its open source as well and that it can deal with very large data sets, but that is unique to R, not RStudio.
Honestly there isn't too much I dislike about the program. There are some shortfalls in the ease-of-use if you aren't familiar with R, but in that case you shouldn't be using such a program. The only reason I didn't rate it 10 is due to the fact I've found it prone to crashing.
This has helped me work with other colleagues in R, which has revolutionized how we deal with large datasets. Just working in R console seems daunting to most users, but when they look at the interface of RStudio, it is much more approachable and therefore more people will use it.
R-Studio provides users with a complete data recovery software with undelete capabilities. Initially developed for data recovery experts, data specialists, IT professionals, and system administrators, R-Studio Data Recovery software is clearly built for advanced and experienced computer users. The software may be a bit confusing for the less experienced users but it also comes with a step by step wizard to help simplify the learning and implementation process.
1. Hard to change the size of the windows automatically. E.g. When I use SAS I have the keys set up so that one key runs the analysis, goes to the log window and zooms it to the full screen. AFAIK, in RStudio you have to use the mouse to do this.
2. Use of projects is kind of complex.
3. Some key combinations that are nearly universally used for one thing are used for something else in RStudio e.g. ctrl+R doesn't open a find and replace, it adds a line
Machine learning, predictive analytics
RStudio has a well-designed interface that is user friendly & easy to get on with. It is coed-based, so it takes some time to learn this language - which is something a little bit intimidating to those who haven't worked as a coder before. However, it is not hard to learn, and once proficient, you can do so many things in R that takes forever in Excel or SPSS.
Extra point: RStudio is incredibly powerful, that it conjures up data within seconds.
It might be mu delusion, but when dataset is super large, RStudio seems to be operating tiny bit slower than R.
If you want to be proficient in R - start with using RStudio.
I work in a neuroscience lab, where we intensively use R to analyze neuro-signal data. By using RStudio, we were able to train all our research assistants so that they can use R to analyze data, and this makes everything so much easier. In the past, we have to use Excel or SPSS to analyze data, and putting data into Excel or SPSS has never stopped being a huge pain for everyone. Now with RStudio, just writing down some codes and it automatically drags the data we want. Also, in the past, after the data analysis, we need to pull out the data and create graphs, put them in powerpoint slide. If data changes, we have to start all over again, which will take forever. Now with RStudio, we simply create graphs and slides within R, and if our database changes, all those graphs and slides change automatically. It works so well, that we cannot think of going back to excel & powerpoint - there' simply no point of doing that.
- Ease of use for first-time learners of R
- Availability in multiple platforms (Mac OS X, Linux, Windows)
- Very comprehensive IDE / Interface
- Ease of installing R Packages
- Debugging capabilities
- Quadrant View (IDE)
- Git Integration
- Sweave / KnitR Support
- Project workspaces
- User Management (in the Commercial Version)
- Commercial edition price is very high (starts at $ 10,000)
- Support is only available via email and response times can be quite high
- Since the support is so limited, I didn't think that the high cost was justified. The main reason we wanted to use it was so that we can get enterprise support which was almost non-existent. When you have a production issue and need to get something fixed, there is no phone support to help you and you need to wait for someone to respond via email.
- The licensing is not perfect. We had a HPC server and every now and then RStudio would stop working because it detected some change in hardware when nothing had in fact happened. This would cause immense issues as no one would be able to access RStudio until it was fixed. Since the support was via email, it meant that there was no option other than informing the business/statistics groups that we were 'waiting to hear back from RStudio'.
- The account manager was hard to get a hold of when we...
Consider the benefits of using the open-source version vs commercial prior to making the purchase
It's a 'must-have' if you are using R for development
- Enterprise acceptance of RStudio
- Open Source Version has been very stable and is a much better interface to demonstrate R functionalities than using the terminal
- Have used it regularly since it started
The RStudio packs a number of important features that a standard R does not have. These include the ability to use pull-down menus to install packages, plot charts, import data, and execute blocks of the code. RStudio also offers a very nice editor for coding.
Despite all advantages, RStudio still stands far from commercial packages like STATA or EVIEWS in terms of its user-friendliness. Having built-in STATA-like abilities to perform regular statistics tasks, like OLS, GMM, time series tools, etc. using pull-down-menus, and duplicated by a code would be immensely helpful.
Economics and Financial analysis of large data. R is very helpful because it is free, massively parallelizable, and has many useful tools other packages do not have.
The capabilities with the rStudio packages as well as the Shiny Deployment platform make rStudio a fantastically powerful platform for processing, analyzing and visualizing data.
There is definitely a learning curve just like with any programming language. The good news, however is that you can easily make sure that you get up to speed with a good deal of online training that is either low cost or free. That plus the learning curve is far less steep than with Python (however, the power from a systematic perspective is limited as well).
I would highly recommend that you take a look at RSTudio as it implements a very easy way for you to ensure you are able to get acquainted with true data analysis; however, it is not without competition. There are many other suites but you should check to see if integrations for R are available for your organization's data sources and if you can easily get data out of those systems to even begin analysis. If you can't get the data out it does you no good to use a system like rStudio. Regardless, the power and flexibility definitely helps here, but you also may want to investigate Python and some of the more robust capabilities available there if you are looking to build a more scalable system that can deal with greater data prep and magnitude/velocity requests across the board.
Nimble, effective data analysis and visualization.
Predictive forecasting and data plotting.
Manipulation and Data Preparation
There are a couple different reasons. 1) Being able to use have all the many features of the R language in a succinct interface enhances productivity so well. 2). The R community and the packages behind the R language give any user the necessary tools to do anything under the sun. It is just finding the right package and then understanding how it should be utilized within the current application one is using. 3). The aesthetic of the UI feels natural and helps the experience to be easy and straightforward.
It is built on a web-based platform, there are times that the connection between the R kernel and the IDE. It can be annoying.
There is a learning curve to the scripting language of R and look overwhelming to those who have not had a background in coding. However, RStudio is the best way to start as it aids the user by giving easy visibility to the data and the R package environment which are both essential elements of the R language.
Well, most of our business data problems have been solved using Excel. Being that Excel is a wonderful tool it has helped. However, it has been difficult to reproduce our analysis. Of course, there is the ability to save multiple copies of the data, but at the end of the day, there isn't a way to easily version an excel file which R and Rstudio have been able to give the RMarkdown and R Notebook structure.
RStudio is a free and open-source integrated development environment (IDE) for R, one of the most programming language for statistical computing and graphics. What I like most about this tool is a capacity in data preprocessing.
What I like the least about RStudio is that if you perform too complex calculations, the computation time increases a lot.
In my opinion, RStudio is an essential tool for any data analyst due to its capacity and a large number of libraries developed for this platform.
Thanks to RStudio, I have been able to perform the data preprocessing in a simple way. As a consequence, the predictive models obtained next have more accuracy.
RStudio makes using R for data science a breeze. I especially love the graphs and charts that I can make using R in RStudio. I am able to preview and format multiple graphs at once and tweak them in just the right way. RStudio also makes it easy to install packages and munge through data.
Not so much of a dislike as much of a preference, but I usually use Python to preprocess my data and then use R to visualize it and run algorithms. I find that preprocessing with Python is much easier.
RStudio is great for visualizing and running predictive algorithms on your data.
RStudio helps me to visualize my data and understand how individuals interact with my product. The graphs that come out of R make it easy to present my findings to other people in the company.
This is a free Integrated Development Environment, which makes is quicker and easier to program in R. I find it much cleaner and easier to use than the native R console.
There is really nothing that you can dislike about a product that is free and makes life easier. The product works well and has no obvious weaknesses.
It's free - no reason not to try (and use) it.
RStudio allows me to organize my R scripts and manage my data analysis projects quickly and easily. It provides a much better user interface and experience/
R Studio provide the best user interface of the whole package of software. It's amazing the amount of plugins you can use for this tool and generate great samples of work. The great thing about being open source allow everyone who is willing to work with data to use this powerful tool with the option of upgrade the tool.
The only thing I don't like is the option of language, so far is limited to some languages and even if they have Spanish for example, the translation per se is incomplete.
If you're starting use the R programming, then this is the best software you can use to keep familiarizing with the environment that offers R.
Also, if you're considering to teach about data or you work at anything that works with data, consider use R and R Studio.
I'm currently working in Social Media Analytics and I use R Studio to apply analysis of all social media. I use for example for Facebook the plugin "RFacebook" and is very effective at the time of give me all the specification I need to work with; also there is for Twitter, which you can use only if you're linking the option of API with the tool.
R-Studio is an free integrated development environment (IDE) that is generally easy to use and learn for novice and power users alike. The multiple framed windows allows the user to multi-task and seamlessly access data streams, work on the R-Code and view the results within the console or the built-in graphical interface.
R-Studio comes with a vast database of add-on packages that can easily be installed within software and allows the users to greatly take advantage of the R-language to many applications beyond just pure Statistical Research.
The built-in HELP command provides syntax and context on the add-on package. However, it can be confusing to understand depending on which package you use. There does not seem to be a standard tutorial for these packages and I usually end up finding the resource I need from a web search externally.
There is no cost of using R-Studio and there is quite a lot of forums and community support around the R-language and its add-on packages. However, there is a steep-learning curve when it comes to R (but not uncommon with other programming language). But it is not the most easily adaptable to cloud-based Big Data clusters such as Hadoop where the supported language is Python.
I used R-Studio to mine Twitter feeds and analyze the sentiment of these feeds all through using R-Studio's built-in packages. The reproduce-ability of the code and the flexibility it offers is priceless. And did I mention it's also a freeware?
There's a terminal for executing R commands and you can view various plots that you'd like to see. For example, recently I've had to make a lot of violin plots of various groups within an agricultural dataset, and RStudio was able to effectively show me all the variation within the groups quickly and effectively, and is able to parse all that data, probably over a gigabyte
R itself isn't too nice to use, for certain cases. For example, I've had to stitch reports together but it was somewhat more annoying to do. This may be a critical drawback if you create many reports in the day.
Doesn't necessarily help you learn R, so learn that first before using RStudio.
Data analysis of various data sets
its open source and also its so innovative and also data analysis is very efficient while using it. it has very flexible tools to do what ever we want and mould it in a way to enhance the techniques and get the desired output in many ways of representation. It has several packages or plugins focused on data visualization. In the case of R, two of the most commonly used are ggplot2, a library that allows bar, point, line, area, maps and scale charts. ggplot2 depends on other packages that need to be downloaded and installed, such as ‘itertools’, ‘iterators’, ‘reshape’, ‘proto’, ‘plyr’, ‘RColorBrewer’, ’digest’ and ‘colorspace’. Another plugin for R programmers to make visualizations with big data is rgl, which enables the creation of 3D graphics in real time.
nothing much about it as we started learning since a week. It has new techniques which help me solve the problems within less time.R is a slow programming language: this tends to be one of the recurrent complaints mentioned by developers when asked about the drawbacks of programming with R. Although this is widely acknowledged by almost everyone, it's also true that some programmers explain this lack of speed by the fact that many of the packages used to add features are not developed in R, but in other syntaxes such as Fortran and C++.
it is very much helpful while using big data and computations on it
we want to do some data analysis with it for the IRS site and couldn't download and perform good operations with node so we switched from node to r language and it worked quite well.
RStudio allows you to keep all your most-used R windows connected in one screen, with easy navigation. It also includes numerous customisation options for window placement, aesthetics like text/background colour scheme, and easy package download & organisation.
Really, there's not much to dislike. The lack of sync between R and Rstudio updates is annoying, but that's not really avoidable since they're two completely different pieces of software.
So far my preferred GUI for implementing R software
I use RStudio daily for data analysis and visualisation. It allows for increase in organisation of data and decrease in time spent on analyses.
I like that a lot of the packages are open source. Plenty of resources for reference and help. You can google pretty much any question and you will find many answers. It also makes beautiful graphs fit for publication.
The way the working directory has to be set can be confusing at first. The syntax is a learning curve. But once you realize that there are several different ways to write the code and that you can google anything it is quite powerful.
There are several packages and ways to complete a task. Installation of packages can take a while.
Not for business but for school. Analysis of genetic sequences. Great for publication type graphs and data.
R studio is very friendly software for user who are coding in R programming. Best Integrated Development Environment for coding in R. Visualizing and analyzing the data is best option in R studio. Packages it provides are very useful and easy to reload like in Eclipse. we can not only create new files but can also import data sets and also visualize them.
Even though the system looks and feels very technical, users will be able to navigate easily through everything once they figured out that everything is listed in tabs and on a directory tree.
Many Pre-loaded algorithms to visualize the data for example algorithms like K-means, SVM, Regression etc.
While loading the software, it takes some time. R studio also struck sometimes while importing the data sets as the file size is larger.
Graphics and UI look can be improved as it looks basic and have to improve the resolution of the graphs.
Graphics can only be generated by coding, it would be nice to have options to touch up the produced plots manually, similar to GraphPad.
Best IDE for R programming
I'm currently doing my research on Time series where i use R studio for analysis and visualizing the data. we import data sets and do statistical analysis on it like predicting prices of stocks and we use support vector machine algorithm mostly.
Best I D E to code in R programming.
The flexibility. The range of libraries allows the user to do any analysis, and the open-source is perfect to implement your own scripts and analysis. The community is very strong and the backup and the possibility of getting help is very high. You can also contact with the own authors of the packages and libraries to ask questions.
The need of writing code could be a problem if you are not familiar with programming. The possibility of having a bug in the library could be a problem, but the majority of the packages are constanly updtating and solving problems like this.
Do not hesitate, RStudio is the right option to data analysis and data visualization
Statistical analysis and data visualization
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