R Programming Language
R is a programming language is widely used by data scientists and major corporations like Google, Airbnb, Facebook etc. For data analysis. This is a complete course on R for beginners and covers basics to advance topics like machine learning algorithm, linear regression, time series, statistical inference etc. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. The R programming language is an offshoot of a programming language called S. It was developed by Ross Ihaka and Robert Gentle-man from the University of Auckland, New Zealand. It was primarily adopted by statisticians and is now the de facto standard for statistical computing.
R Programming Language Similar To
R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. Free Download Udemy R Basics - R Programming Language Introduction. With the help of this course you can Learn the essentials of R Programming - R.
R Tutorial
We present you the R Tutorial, to learn R, the basics of R programming language, interfacing data to R from different data sources, creating charts and graphs, and extracting statistical information.
What is R programming language ?
R is an open source programming language. It has become one of the powerful choices for statistical analysis. R helps you to get big picture of your data by calculating statistical parameters like mean, standard deviation, correlation etc.
Features of R
- Open Source and Free to use.
- Works well for statistics.
- Command-Line Programming Language – But IDEs like R Studio and plugins to the popular IDEs like Eclipse, etc., are available.
- Easy sharing of Results or Analysis.
- Integration with other packages and programming languages.
- R programming is easy and informative.
R Tutorial Index
In our R Tutorial, we shall take you through the following topics :
- R Script File Basic Syntax – Understanding the basic syntax of R commands and R script file.
- R Data Types – Learn R basic data types with examples.
- R Variables – Learn R variables, rules followed to name a variable, commands to list down all the variables in the scope or delete any of them if necessary. Also learn the ways to assign a value to R Variable.
- R Operators – Learn R Operators : R Arithmetic Operators, R Relational Operators, R Logical Operators, R Assignment Operators, R Miscellaneous Operators with example R scripts.
- R Decision Making Statements
- R Loops
- R Strings
- R Lists
- R Arrays
- R Factors
- R Data Frames
- R Packages
- R Data Reshaping
Data Interfacing – from different sources of data to R language
- R CSV Files – Learn R functions to read CSV Files, analyze or filter data read from CSV Files, and write back filtered data to CSV Files.
- R Read Excel XLS XLSX Files
- R Binary Files
- R XML Files
- R JSON Files
- R Web Data
- R Database
Charts, Plots & Graphs – R Tutorial
- R Bar Charts
- R Boxplots
- R Histograms
- R Scatterplots
Statistical Analysis
- R Mode
- R Linear Regression
- R Multiple Regression
- R Logistic Regression
- R Normal Distribution
- R Binomial Distribution
- R Poisson Regression
- R Analysis of Covariance
- R Time Series Analysis
- R Nonlinear Least Square
- R Decision Tree
- R Random Forest
- R Survival Analysis
- R Chi Square Tests
Conclusion
With this R Tutorial, we have learnt the basics of R, how to interface data to R from different sources, create charts and graphs, and extract statistical information.
R Programming Language Tutorial
R is a popular programing language for statistics.
To install and run R in a Jupyter Notebook:
Start Navigator.
To install the R language and r-essentials packages,select Environments to create an new environment. Click Create.
Name the environment “r-tutorial”. Next to Packages, selectPython 3.7 and R. Select r from the dropdown menu. Click Create.
Open the environment with the R packageusing the Open with Jupyter Notebook option.
To create a new notebook for the R language, in the Jupyter Notebookmenu, select New, then select R.
Microsoft rdp api. Use the Microsoft Remote Desktop app to connect to a remote PC or virtual apps and desktops made available by your admin. The app helps you be productive no matter where you are. Getting Started Configure your PC for remote access first.
We will use dplyr to read and manipulate Fisher’s Iris multivariate data set in this tutorial. Copy and paste the following code into the first cell:
To run the code, in the menu bar, click Cell then select Run Cells,or use the keyboard shortcut Ctrl-Enter.
The iris data table is displayed.
Using ggplot, we can create a scatter plot comparing sepal length and width of three iris species. Click + to open a second cell, then copy and pastethe following code into the second cell:
To run the code, in the menu bar, click Cell then select Run Cells,or use the keyboard shortcut Ctrl-Enter.
R Programming Language Tutorial
For more resources on using R with Anaconda, seeUsing R language with Anaconda.