R packages are a collection of R functions, complied code and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose. When we start the R console, only the default packages are available by default. Other packages which are already installed have to be loaded explicitly to be used by the R program that is going to use them.
All the packages available in R language are listed at R Packages.
Below is a list of commands to be used to check, verify and use the R packages.

Check Available R Packages

Get library locations containing R packages

.libPaths()
When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.
[2] “C:/Program Files/R/R-3.2.2/library”

Get the list of all the packages installed

library()
When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.
Packages in library ‘C:/Program Files/R/R-3.2.2/library’:

base The R Base Package

boot Bootstrap Functions (Originally by Angelo Canty

for S)

class Functions for Classification

cluster “Finding Groups in Data”: Cluster Analysis

Extended Rousseeuw et al.

codetools Code Analysis Tools for R

compiler The R Compiler Package

datasets The R Datasets Package

foreign Read Data Stored by ‘Minitab’, ‘S’, ‘SAS’,

‘SPSS’, ‘Stata’, ‘Systat’, ‘Weka’, ‘dBase’, …

graphics The R Graphics Package

grDevices The R Graphics Devices and Support for Colours

and Fonts

grid The Grid Graphics Package

KernSmooth Functions for Kernel Smoothing Supporting Wand

& Jones (1995)

lattice Trellis Graphics for R

MASS Support Functions and Datasets for Venables and

Ripley’s MASS

Matrix Sparse and Dense Matrix Classes and Methods

methods Formal Methods and Classes

mgcv Mixed GAM Computation Vehicle with GCV/AIC/REML

Smoothness Estimation

nlme Linear and Nonlinear Mixed Effects Models

nnet Feed-Forward Neural Networks and Multinomial

Log-Linear Models

parallel Support for Parallel computation in R

rpart Recursive Partitioning and Regression Trees

spatial Functions for Kriging and Point Pattern

Analysis

splines Regression Spline Functions and Classes

stats The R Stats Package

stats4 Statistical Functions using S4 Classes

survival Survival Analysis

tcltk Tcl/Tk Interface

tools Tools for Package Development

utils The R Utils Package

Get all packages currently loaded in the R environment

search()
When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.
[1] “.GlobalEnv” “package:stats” “package:graphics”
[4] “package:grDevices” “package:utils” “package:datasets”
[7] “package:methods” “Autoloads” “package:base”

Install a New Package

There are two ways to add new R packages. One is installing directly from the CRAN directory and another is downloading the package to your local system and installing it manually.

Install directly from CRAN

The following command gets the packages directly from CRAN webpage and installs the package in the R environment. You may be prompted to choose a nearest mirror. Choose the one appropriate to your location.
install.packages(“Package Name”)

# Install the package named “XML”.
install.packages(“XML”)

Install package manually

Go to the link R Packages to download the package needed. Save the package as a .zip file in a suitable location in the local system.
Now you can run the following command to install this package in the R environment.
install.packages(file_name_with_path, repos = NULL, type = “source”)

# Install the package named “XML”
install.packages(“E:/XML_3.98-1.3.zip”, repos = NULL, type = “source”)

Load Package to Library

Before a package can be used in the code, it must be loaded to the current R environment. You also need to load a package that is already installed previously but not available in the current environment.
A package is loaded using the following command −
library(“package Name”, lib.loc = “path to library”)

# Load the package named “XML”
install.packages(“E:/XML_3.98-1.3.zip”, repos = NULL, type = “source”)

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Introduction to Data Science
• Need for Data Science
• What is Data Science
• Life Cycle of Data Science
• Applications of Data Science
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• Introduction to Machine Learning
• Introduction to Deep Learning
• Introduction to R&R-Studio
• Project Based Data Science
Introduction to R
• Introduction to R
• Data Exploration
• Operators in R
• Inbuilt Functions in R
• Flow Control Statements & User Defined Functions
• Data Structures in R
Data Manipulation
• Need for Data Manipulation
• Introduction to dplyr package
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• Getting summarized results with the summarise() function,
• Combining different functions with the pipe operator
• Implementing sql like operations with sqldf()
Visualization of Data
• Loading different types of datasets in R
• Arranging the data
• Plotting the graphs
Introduction to Statistics
• Types of Data
• Probability
• Correlation and Co-variance
• Hypothesis Testing
• Standardization and Normalization
Introduction to Machine Learning
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression
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• Intro to Logistic Regression
• Simple Logistic Regression in R
• Multiple Logistic Regression in R
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• ROC Curve
Classification Techniques
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• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
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• Decision Tree in R
• Information Gain
• Gini Index
• Pruning
Recommender Engines
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
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• User-Based Recommendation
• Item-Based Recommendation
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