Registering and Accesing Posit Cloud and Installing R and RStudio
Author
Kamarul Imran Musa
Published
August 1, 2025
Posit Cloud
Posit Cloud lets you access Posit’s powerful set of data science tools right in your browser – no installation or complex configuration required.
Posit Cloud lets you access Posit’s powerful set of data science tools right in your browser–no installation or complex configuration required.
Get To Know Cloud
Do, share, teach and learn data science using the RStudio IDE or Jupyter Notebooks, directly from your browser. You can share projects with your team, class, workshop or the world. Teach or learn data science in R or Python with students or colleagues.
There’s nothing to install or maintain and no dedicated hardware or annual purchase contract is required. Individual users, instructors and students only need a browser - we handle everything else.
Register for free Posit Cloud account and access our course
Steps:
Click POSIT Cloud folder for our workshop at this link
Proceed with registration if required
Free registration
Join the space
Join the space
Go to Members area
Members area
Create new RStudio project
Create a new RStudio project
RStudio interface
RStudio interface
Personal Workspace
Personal workspace is your Workspace, which is your personal workspace on Posit Cloud.
Only you can work on content in Your Workspace, but you can choose to let content here be viewed (and copied) by other Posit Cloud users.
By default, any content you create can only be accessed by you. To share content with others, open the content, click on the settings button (the gear in the upper right) and change Access to Everyone.
You can also copy the content’s address from your browser and distribute the link however you wish.
Shared Spaces
You can also do work in a shared space on Posit Cloud. A shared space is an area where a group of people can collaborate together - only the members of a shared space can access the space and its content.
To learn more about shared spaces, please take a look at the Posit Cloud Guide.
R Programming Language
Introduction to R
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
The R environment
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular matrices,
a large, coherent, integrated collection of intermediate tools for data analysis,
graphical facilities for data analysis and display either on-screen or on hardcopy, and
a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
There are some terms we have to be familiar:
packages
codes
parameters
argument
CRAN
CRAN (Comprehensive R Archive Network) is the primary repository for R packages, and it hosts thousands of packages that users can download and install to extend the functionality of the R Programming Language.
These packages are created by R users and developers from around the world and cover a wide range of topics and applications.
It functions as a robust repository, hosting a diverse collection of R packages and related software, making it an essential cornerstone for statisticians, data scientists, and researchers worldwide. In this comprehensive exploration, we will delve deep into the significance of CRAN and its pivotal role in nurturing the growth of the R programming language.
Understanding CRAN
CRAN is a network of servers storing R packages.
R is an open-source programming language for statistical computing.
The packages on CRAN enhance data analysis capabilities.
CRAN serves as the primary platform for sharing packages with the R community.
The Importance of CRAN
Central Hub: CRAN acts as the central hub for R packages, a place where users can easily access, download, and install packages without the need for extensive searches across various websites or sources. This seamless access streamlines the process of enhancing R’s capabilities, enabling users to find and install packages effortlessly.
Quality Assurance: One of CRAN’s standout features is its steadfast dedication to quality assurance. Package maintainers undergo rigorous review processes when submitting their packages to CRAN. This meticulous examination ensures that packages meet the highest standards, including thorough documentation, best practices, and adherence to CRAN’s guidelines. As a result, users can have full confidence in the quality and dependability of packages on CRAN.
Version Management: CRAN maintains a comprehensive history of package versions, allowing users to access and install specific versions of packages. This feature is crucial for ensuring the reproducibility of data analysis and research, ensuring that code performs as intended, even as packages evolve over time.
Diverse Selection of Packages: CRAN hosts a vast array of packages covering a wide range of domains. From statistical modeling and machine learning to data visualization and manipulation, CRAN’s repository caters to the needs of beginners and experienced users alike. Whatever your data analysis requirements, you’re likely to discover a package that streamlines and enhances your workflow on CRAN.
Community Collaboration: Beyond being a distribution platform for packages, CRAN fosters a vibrant community of R developers, maintainers, and users. Developers can collaborate on packages, share their expertise, and contribute to the ongoing enrichment of R’s ecosystem. Users can seek help, report issues, and engage in discussions, fostering a sense of camaraderie and support that bolsters the entire community.
R Packages
R codes are contained with a package. CRAN provides the list of all R packages.
Look at CRAN TaskViews if you want to know R packages that are grouped based on certain topics such as Bayesian, Causal Inference, Epidemiology and many others.
There are also many more packages that are not listed on CRAN but hosted on other repositories such as GitHub.
Installing R Packages
If you need to use certain R packages, you have to install it first. There are two ways of installing R packages into your R IDE:
Writing codes in R Console: Type install.packages("tidyverse"), then click ENTER
Install R package
Using GUI in the Package pane
Install the R package
Now procedd to install these packages
tidyverse
haven
here
gtsummary
R functions
We have to write R scripts to perform desired task in R. R scripts consists a set of R codes. Users write R scrips in the Console pane or in R Script file or other R editor such as Quarto document.
Call:
lm(formula = weight ~ Time, data = ChickWeight)
Coefficients:
(Intercept) Time
27.467 8.803
Arguments in R functions
Now, let’s type a question mark in front of a function.
For example, type a question mark infront of lm. Then click ENTER.
?lm
A window will appear. It will describe the detailed information about the function. You can see the default paramaters for such function, such as:
lm(formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, contrasts = NULL, offset, ...)
You will see arguments for each parameters:
formula
data
subsets
weight
and others
RStudio IDE
RStudio is an integrated development environment (IDE) for R and Python. It includes a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging, and workspace management. RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux).
There are a few ways to use RStudio:
Install it on your computer
Use it on the Cloud (POSIT, Amazon, Microsoft, Saturn Cloud)
Install and use it on a server (RStudio Workbench)
