Events  Classes  Deals  Spaces  Jobs 
    Sign in  
35-hour comprehensive introduction to R. Learn how to load, save transform data, write functions, generate graphs fit basic statistical models w/ data.
Saturday, April 21, 2018 at 10:00 AM   $2190
NYC Data Science Academy, 500 8th Ave, Ste 905

Sign up for our awesome New York
Tech Events weekly email newsletter.
This course is a 35-hour program designed to provide a comprehensive introduction to R. Youll learn how to load, save, & transform data as well as how to write functions, generate graphs, & fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis & visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating & analyzing data of various types, creating advanced visualizations, generating reports, & documenting your codes.


Basic knowledge about computer components
Basic knowledge about programming


Unit 1: Basic Programming with R

Introduction to R

What is R?
Why R?
How to learn R
RStudio, packages, & the workspace

Basic R language elements
Data object types
Local data import/export
Introducing functions & control statements
In-depth study of data objects
Functional Programming

Unit 2: Basic Data Elements

Data transformation


Character manipulation
String manipulation
Dates & timestamps
Web data capture
API data sources
Connecting to an external database

Unit 3: Manipulating Data with dplyr

Subset, transform, & reorder datasets
Join datasets
Groupwise operations on datasets

Unit 4: Data Graphics & Data Visualization

Core ideas of data graphics & data visualization
R graphics engines
Big data graphics with ggplot2

Unit 5: Advanced Visualization

Customized graphics with ggplot2

Coordinate systems
Axis labels

Other plotting cases
Violin Plots
Pie charts
Mosaic plots
Hierarchical tree diagrams
scatter plots with multidimensional data
Time-series visualizations
R & interactive visualizations

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.
© 2019 GarysGuide      About   Terms   Press   Feedback