How to Download and Install R 3.6.3 on Your Computer
If you are looking for a free and open-source software environment for statistical computing and graphics, you might want to consider R. R is a programming language that is widely used by data analysts, researchers, and programmers for data manipulation, analysis, visualization, and machine learning. In this article, you will learn how to download and install R 3.6.3 on your computer, how to check if it is working properly, how to update it and its packages, and some useful features and packages that enhance R's capabilities.
What is R and what can it do?
R is a programming language that was created by Ross Ihaka and Robert Gentleman in 1993 as an implementation of the S statistical programming language. It has since evolved into a comprehensive software environment that offers a wide range of tools and functions for data manipulation, calculation, graphical display, and programming. R is extensible and allows users to create their own functions, packages, and extensions. R also has a rich package ecosystem that provides additional functionality for various domains, such as bioinformatics, econometrics, spatial analysis, text mining, web scraping, and more.
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R can perform many tasks related to data analysis and visualization, such as:
Importing data from various sources, such as files, databases, web APIs, etc.
Cleaning, transforming, reshaping, aggregating, filtering, and summarizing data.
Exploring data using descriptive statistics, tables, graphs, plots, etc.
Applying various statistical techniques and methods, such as hypothesis testing, regression analysis, clustering analysis, classification analysis, etc.
Creating interactive dashboards, reports, presentations, and web applications using tools like Shiny, R Markdown, flexdashboard, etc.
Developing custom functions, scripts, packages, and extensions using the R programming language.
Why use R 3.6.3 version?
R is constantly being updated and improved by the R Core Team and the R community. The latest stable version of R is 4.1.1 as of July 2021. However, some users may prefer to use an older version of R for various reasons, such as compatibility with existing code or packages, stability or performance issues with newer versions, or personal preference.
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R 3.6.3 is the third patch release of the R 3.6.x series that was released on February 29th 2020. It contains several bug fixes and minor improvements over the previous versions of R 3.6.x. Some of the notable changes include:
Improved accuracy of pchisq () for small df.
Fixed a bug in sample.int () that could cause incorrect results when sampling more than 2^31 values.
Fixed a bug in nlminb () that could cause convergence failures or incorrect results when bounds were active.
Fixed a bug in read.table () that could cause incorrect parsing of quoted fields containing embedded newlines.
Fixed a bug in writeClipboard () on Windows that could cause invalid UTF-8 output.
Updated several packages to their latest versions.
If you are using R 3.6.x series and want to have the most up-to-date version with bug fixes and improvements, you may want to download and install R 3.6.3 on your computer.
How to download and install R 3.6.3 on different operating systems
R can be downloaded R can be downloaded and installed on different operating systems, such as Windows, Mac OS X, and Linux. The process may vary slightly depending on the system and the version of R you want to install. Here are some general steps to follow for each system:
How to download and install R 3.6.3 on Windows
Go to the and click on the "Download R for Windows" link.
Click on the "base" link and then click on the "Download R 3.6.3 for Windows" link. This will download a file named R-3.6.3-win.exe.
Run the downloaded file and follow the instructions to install R on your computer. You can choose the default options or customize them as you wish.
Optionally, you can also download and install , a popular integrated development environment (IDE) for R that provides many features and tools to enhance your R experience.
How to download and install R 3.6.3 on Mac OS X
Go to the and click on the "R-3.6.3.pkg" link under the "Binaries for legacy macOS/OS X systems" section. This will download a file named R-3.6.3.pkg.
Double-click on the downloaded file and follow the instructions to install R on your computer. You may need to enter your administrator password and agree to the license terms.
Optionally, you can also download and install , a popular integrated development environment (IDE) for R that provides many features and tools to enhance your R experience.
How to download and install R 3.6.3 on Linux
The installation of R on Linux may vary depending on the distribution and version of Linux you are using. You may need to use a package manager or a command line tool to install R from a repository or a source file. Here are some general steps to follow for some common Linux distributions:
How to download and install R 3.6.3 on Ubuntu
Open a terminal window and type the following commands to add the CRAN repository for Ubuntu:
sudo add-apt-repository 'deb [12]( bionic-cran36/' sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 sudo apt update
Type the following command to install R 3.6.3 from the repository:
sudo apt install r-base
Optionally, you can also download and install , a popular integrated development environment (IDE) for R that provides many features and tools to enhance your R experience.
How to download and install R 3.6.3 on CentOS/RHEL
Open a terminal window and type the following commands to add the EPEL repository for CentOS/RHEL:
sudo yum install epel-release sudo yum update
Type the following command to install R 3.6.3 from the repository:
sudo yum install R
Optionally, you can also download and install , a popular integrated development environment (IDE) for R that provides many features and tools to enhance your R experience.
How to download and install R 3.6.3 from source on Linux
If you prefer to compile R from source or if your Linux distribution does not have a pre-built binary package of R 3.6.3, you can follow these steps:
Go to the and click on the "Download R for Linux" link.
Select your Linux distribution and follow the instructions to add the CRAN repository for your system.
Download the source code of R 3.6.3 from the repository or from .
Extract the downloaded file and navigate to the extracted folder in a terminal window.
Type the following commands to configure, compile, and install R:
./configure make sudo make install
Optionally, you can also download and install , a popular integrated development environment (IDE) for R that provides many features and tools to enhance your R experience.
How to check if R is installed and working properly
After installing R on your computer, you can check if it is working properly by opening a terminal window and typing the following command:
R --version
This should display the version of R that you have installed, along with some other information. For example, if you have installed R 3.6.3, you should see something like this:
R version 3.6.3 (2020-02-29) -- "Holding the Windsock" Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.5 LTS
If you see an error message or no output at all, it means that R is not installed correctly or not recognized by your system. You may need to check your installation steps or your system settings to fix the problem.
You can also launch R interactively by typing the following command:
R
This should open the R console, where you can type and execute R commands and see the results. For example, you can try typing the following command to print "Hello, world!" on the screen:
print("Hello, world!")
You should see something like this:
[1] "Hello, world!"
If you see the expected output, it means that R is working properly and you can start using it for your data analysis and visualization tasks. To exit the R console, you can type the following command:
quit()
How to update R and its packages
It is recommended that you keep your R installation and its packages up to date to ensure that you have the latest features, bug fixes, and security patches. You can update R and its packages in different ways depending on your operating system and your preferences.
How to update R on Windows
To update R on Windows, you can simply download and install the latest version of R from the . This will overwrite your existing installation of R with the new one. You may need to uninstall the old version of R first if you encounter any problems.
To update your packages on Windows, you can use the , which provides a function called updateR() that can check for new versions of R and update both R and its packages. To use this function, you need to install the installr package first by typing the following command in the R console:
install.packages("installr")
Then, you can load the package and run the updateR() function by typing the following commands:
library(installr) updateR()
This will open a dialog box that will guide you through the process of updating R and its packages.
How to update R on Mac OS X
To update R on Mac OS X, you can simply download and install the latest version of R from the . This will overwrite your existing installation of R with the new one. You may need to uninstall the old version of R first if you encounter any problems.
To update your packages on Mac OS X, you can use the , which provides a function called updateR() that can check for new versions of R and update both R and its packages. To use this function, you need to install the updateR package first by typing the following command in the R console:
install.packages("updateR") Then, you can load the package and run the updateR() function by typing the following commands:
library(updateR) updateR()
This will open a dialog box that will guide you through the process of updating R and its packages.
How to update R on Linux
To update R on Linux, you can use the same method that you used to install R, depending on your Linux distribution and version. For example, if you installed R from a repository using a package manager, you can use the same package manager to update R. If you installed R from source, you can download the latest source code and compile and install it again.
To update your packages on Linux, you can use the , which is a built-in function in R that can check for new versions of your installed packages and update them. To use this function, you need to type the following command in the R console:
update.packages()
This will prompt you to choose a CRAN mirror and then update your packages. You may need to enter your administrator password or confirm some messages during the process.
Some useful packages and features in R
R has many packages and features that can enhance your data analysis and visualization tasks. Here are some of them that you may find useful:
The tidyverse package
The is a collection of packages that share a common philosophy of data analysis and visualization based on the concept of tidy data. Tidy data is data that has a consistent structure, where each variable is a column, each observation is a row, and each value is a cell. The tidyverse package provides tools for importing, tidying, transforming, modeling, and visualizing data in a consistent and intuitive way. Some of the packages included in the tidyverse are:
dplyr: for data manipulation and transformation.
tidyr: for data tidying and reshaping.
ggplot2: for data visualization using the grammar of graphics.
readr: for data import from files.
tibble: for creating and working with tibbles, which are enhanced data frames.
stringr: for string manipulation and processing.
purrr: for functional programming and iteration.
forcats: for working with categorical variables.
To install the tidyverse package, you can type the following command in the R console:
install.packages("tidyverse")
To load the tidyverse package, you can type the following command:
library(tidyverse)
The magrittr package
The is a package that provides a pipe operator (%>%) that allows you to chain multiple functions together in a clear and readable way. The pipe operator takes the output of one function and passes it as the first argument to another function. This way, you can avoid nesting multiple functions or creating intermediate variables. For example, instead of writing something like this:
x
You can write something like this:
x % raise_to_power(2) %>% sum() %>% sqrt()
The magrittr package also provides some other useful operators and functions, such as %<>% (two-way pipe), %T>% (tee pipe), %$% (exposition pipe), . (placeholder), and %>% (compound assignment pipe).
To install the magrittr package, you can type the following command in the R console:
install.packages("magrittr")
To load the magrittr package, you can type the following command:
library(magrittr) The data.table package
The is a package that provides an enhanced version of data frames, called data tables, that are faster, more memory-efficient, and more convenient to work with. Data tables have many features that make them superior to data frames, such as:
Fast and concise syntax for data manipulation and transformation, using the [i, j, by] notation.
Fast aggregation, grouping, and joining operations, using the special symbols .N, .SD, .BY, etc.
Fast and flexible subsetting, filtering, and updating of data, using the special symbols .I, .( ), :=, etc.
Automatic indexing and keying of data for faster access and sorting.
Ability to add and modify columns by reference without copying the entire data.
Ability to work with large data sets that do not fit in memory using the fread() and fwrite() functions.
To install the data.table package, you can type the following command in the R console:
install.packages("data.table")
To load the data.table package, you can type the following command:
library(data.table)
Conclusion
In this article, you have learned how to download and install R 3.6.3 on your computer, how to check if it is working properly, how to update it and its packages, and some useful features and packages that enhance R's capabilities. R is a powerful and versatile language for statistical computing and graphics, with many benefits that make it popular among data analysts, researchers, and programmers. You can use R for various tasks related to data manipulation, analysis, visualization, and machine learning. You can also extend R's functionality by using its rich package ecosystem or creating your own functions, packages, and extensions. R is a free and open-source software environment that you can download and use for any purpose.
If you want to learn more about R and how to use it for your data analysis and visualization projects, you can check out some of these resources:
: The official website of R that provides information about R, its development, documentation, downloads, etc.
: The official journal of R that publishes articles about new developments and applications of R.
: A website that aggregates blogs and news about R from various sources.
: A website that provides a forum for R users to ask questions, share tips, and connect with other R users.
: A website that provides a list of packages related to specific topics or domains in R.
FAQs
What is the difference between R and RStudio?
R is the programming language and software environment that provides the core functionality for statistical computing and graphics. RStudio is an integrated development environment (IDE) that provides a user-friendly interface and additional tools for working with R. You can use R without RStudio, but you cannot use RStudio without R.
How do I install a package in R?
To install a package in R, you can use the install.packages() function in the R console. For example, to install the ggplot2 package, you can type:
install.packages("ggplot2")
You may need to choose a CRAN mirror from which to download the package. You can also install packages from other sources, such as GitHub or Bioconductor, using other functions or tools.
How do I load a package in R?
To load a package in R, you can use the library() function in the R console. For example, to load the ggplot2 package, you can type:
library(ggplot2)
This will make the functions and objects from the package available for use in your current session. You may need to load a package every time you start a new session or script.
How do I update a package in R?
To update a package in R, you can use the update.packages() function in the R console. For example, to update all your installed packages, you can type:
update.packages()
This will check for new versions of your installed packages and update them if This will check for new versions of your installed packages and update them if available. You may need to choose a CRAN mirror from which to download the updates. You can also update specific packages by specifying their names as arguments to the function.
How do I uninstall a package in R?
To uninstall a package in R, you can use the remove.packages() function in the R console. For example, to uninstall the ggplot2 package, you can type:
remove.packages("ggplot2")
This will remove the package and its dependencies from your library. You may need to confirm some messages during the process.
I hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy coding! 44f88ac181
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