Introduction
R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software.
Some of the advantages of R are:
r download ios
It's open-source. No fees or licenses are needed, so it's a low-risk venture if you're developing a new program.
It's platform-independent. R runs on all operating systems, so developers only need to create one program that can work on competing systems. This independence is yet another reason why R is cost-effective!
It has a rich package ecosystem. There are thousands of packages available on CRAN (the Comprehensive R Archive Network) that provide functions and tools for various domains and tasks, such as data wrangling, visualization, machine learning, web scraping, etc.
It has excellent graphical capabilities. R can produce high-quality plots and charts that can be customized and exported in various formats.
It has a large and active community. There are many online resources, forums, blogs, books, courses, and events that can help you learn and improve your skills in R.
Installation
To use R on your iOS device, you will need to install two software: R itself and RStudio. RStudio is an integrated development environment (IDE) that provides a user-friendly interface and many features to make coding in R easier and more efficient.
Here are the steps to install R and RStudio on iOS:
Go to the and click on "Download R for (Mac) OS X".
Click on the link for the .pkg file of the latest R version and save it.
Double click the downloaded file and follow installation instructions.
Go to and click on the download button for RStudio Desktop.
Click on the link for the iOS version of RStudio and save the .dmg file.
Double click the downloaded file and drag the RStudio icon to your Applications folder.
Examples
Now that you have installed R and RStudio on your iOS device, you can start coding in R. Here are some examples of how to use R for different purposes:
Data manipulation
R has many packages that can help you manipulate data in various ways, such as filtering, transforming, summarizing, joining, reshaping, etc. One of the most popular packages for data manipulation is dplyr, which provides a consistent set of verbs that operate on data frames or tibbles (a modern reimagining of data frames).
Here is an example of how to use dplyr to filter rows of a data frame based on some conditions:
```r # Load dplyr package library(dplyr) # Create a data frame of cars cars % filter(type == "Sedan", mpg > 25) `` The output of the code above is: model type price mpg --- --- --- --- Toyota Sedan 25000 30 BMW Sedan 40000 25 Tesla Sedan 45000 100 As you can see, dplyr makes it easy to filter data with logical expressions. You can also use other verbs, such as select, mutate, arrange, group_by, and summarize, to perform other data manipulation tasks. For more information on dplyr, you can check out the . Plotting
R has many packages that can help you create beautiful and informative plots and charts from your data. One of the most popular packages for plotting is ggplot2, which implements the grammar of graphics, a system for describing and building graphs.
Here is an example of how to use ggplot2 to create a scatter plot of the cars data frame:
r programming compiler app ios
r programming language for ios
r studio download for ios
r project for statistical computing ios
r code editor for ios
r programming compiler app store
r programming tutorial for ios
r console for ios
r interpreter for ios
r programming compiler iphone
r programming compiler ipad
r programming compiler ipod touch
r download videos photos stories ios
r download instagram stories ios
r download tiktok without watermark ios
r download youtube video audio ios
r download snapchat stories ios
r download twitter fleets ios
r download routinehub ios
r download shortcut best shortcut ios
r download media grabber api ios
r download gluebyte scripts codes ios
r download facebook photo video ios
r download linkedin pinterest sharechat imgur ios
r download soundcloud twitch tumblr vk ios
how to install r on ios device
how to run r code on ios device
how to use r on iphone ipad ipod touch
how to learn r programming on ios device
how to test r code snippets on ios device
how to update r on ios device
how to uninstall r on ios device
how to get support for r on ios device
how to access cran mirror from ios device
how to read frequently asked questions about r on ios device
how to follow news from the r foundation on ios device
how to support the r foundation with a renewable subscription on ios device
how to follow the r foundation on mastodon or linkedin from ios device
benefits of using r programming compiler app on ios device
features of using r programming compiler app on ios device
reviews of using r programming compiler app on ios device
ratings of using r programming compiler app on ios device
alternatives of using r programming compiler app on ios device
comparisons of using r programming compiler app on ios device
recommendations of using r programming compiler app on ios device
problems of using r programming compiler app on ios device
solutions of using r programming compiler app on ios device
tips of using r programming compiler app on ios device
tricks of using r programming compiler app on ios device
hacks of using r programming compiler app on ios device
```r # Load ggplot2 package library(ggplot2) # Create a scatter plot of price vs mpg ggplot(cars, aes(x = price, y = mpg)) + geom_point() + labs(title = "Price vs MPG of Cars", x = "Price ($)", y = "Miles per Gallon") ``` The output of the code above is:
As you can see, ggplot2 makes it easy to create plots with layers of aesthetics and geoms. You can also use other geoms, such as bars, lines, histograms, boxplots, etc., to create different types of plots. For more information on ggplot2, you can check out the . Machine learning
R has many packages that can help you apply machine learning techniques to your data, such as classification, regression, clustering, dimensionality reduction, etc. One of the most popular packages for machine learning is caret, which provides a consistent interface and tools for building and evaluating predictive models.
Here is an example of how to use caret to train and test a linear regression model on the cars data frame:
```r # Load caret package library(caret) # Split the data into training and testing sets set.seed(123) # for reproducibility train_index <- createDataPartition(cars$price, p = 0.8, list = FALSE) train_set <- cars[train_index, ] test_set <- cars[-train_index, ] # Train a linear regression model on the training set model <- train(price mpg, data = train_set, method = "lm") # Test the model on the testing set predictions <- predict(model, newdata = test_set) performance <- postResample(predictions, test_set$price) performance ``` The output of the code above is: RMSE Rsquared MAE --- --- --- 3989.6 0.8869 2985.8 As you can see, caret makes it easy to train and test models with various methods and parameters. You can also use other methods, such as knn, svm, rpart, etc., to create different types of models. For more information on caret, you can check out the . Tutorials
If you want to learn more about R and practice your skills interactively, there are many online tutorials that can help you. Here are some of the best ones:
: DataCamp offers courses and projects on various topics and levels of R programming, such as introduction to R, data visualization with ggplot2, machine learning with caret, etc.
: RStudio Cloud is a platform that allows you to create and share R projects online without installing anything. You can also access many free tutorials and courses from RStudio and other instructors.
: Swirl is a package that turns R into an interactive learning environment. You can install swirl and choose from many courses that teach you R concepts and skills in the R console.
Conclusion
In this article, I have given you a brief overview of R, its applications, and its installation guide for iOS. I have also shown you some examples of how to use R for data manipulation, plotting, and machine learning. Finally, I have suggested some online tutorials that can help you learn more about R and practice your skills.
I hope you have found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading!
FAQs
What are some of the disadvantages of R?
Some of the disadvantages of R are:
It has a steep learning curve. R has many features and functions that can be overwhelming for beginners.
It has a low performance. R is not very efficient or fast compared to other languages, especially for large datasets or complex computations.
It has an inconsistent syntax. R has many different ways of doing the same thing, which can cause confusion and errors.
What are some of the alternatives to R?
Some of the alternatives to R are:
Python: Python is another popular language for data science and machine learning, with many libraries and frameworks that provide similar or better functionality than R.
Julia: Julia is a newer language that aims to combine the ease of use of R with the speed and performance of C.
SAS: SAS is a commercial software that provides a comprehensive suite of tools for data analysis and reporting.
How can I update R and its packages?
To update R on your iOS device, you can follow these steps:
Go to the and download the latest version of R for iOS.
Double click the downloaded file and follow installation instructions.
Open RStudio and go to Tools > Check for Package Updates.
Select all packages and click Install Updates.
How can I get help in R?
To To get help in R, you can use the following methods:
Use the help function. You can type ? followed by the name of a function, package, or topic to get the documentation and examples.
Use the help.search function. You can type ?? followed by a keyword to search for help pages that contain that keyword.
Use the RStudio Help tab. You can browse or search for help topics in the RStudio IDE.
Use online resources. You can visit websites such as , etc., to ask questions or find answers from other R users.
How can I share my R code and results?
To share your R code and results, you can use the following methods:
Use R Markdown. R Markdown is a format that allows you to create dynamic documents that combine code, output, text, and graphics. You can export your R Markdown files as HTML, PDF, Word, or other formats.
Use Shiny. Shiny is a package that allows you to create interactive web applications that run R code and display output. You can host your Shiny apps on your own server or on .
Use GitHub. GitHub is a platform that allows you to host and share your code repositories online. You can also use GitHub Pages to publish your HTML files generated by R Markdown or Shiny.
44f88ac181