RStudio is a family of powerful and cost-effective undelete and data recovery software.
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 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.
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.
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.
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