**Blog - Data Community DC**, and kindly contributed to R-bloggers)

Data Community DC and District Data Labs are excited to be hosting an Introduction to R Programming workshop on Saturday, September 26th. Register before 9/12/2015 to get an early bird discount!

## Overview

R is a powerful language for statistical computing. A prolific user community backs R with with an extensive library of packages. If you can think of it, somebody has already written a library for it. R also has a superb IDE, R Studio, facilitating reproducible research.

This course is for beginners to R programming. This is true whether a student is a veteran programmer just getting started with R, or someone with little programming experience.

## What You Will Learn

This course focuses on getting students started working on projects in R/R Studio. It emphasizes basic R data structures, data munging, basic statistics, and basic plot capabilities. This course also emphasizes using R Studio and R markdown to manage workflows for reproducible research. The final project of the course will be reporting a simple data analysis in an html file suitable for sharing.

## Course Outline

- Getting started: Installing R, R Studio, and R libraries
- Reproducible research: Setting up an R Studio Project and file structure
- Tour of R and R Studio: The IDE workspace, basic R data types, getting help, how R works
- Importing data: Importing tabular data, reading from databases, importing textual data using the “scan” function.
- Basic R data manipulation: vectorization and the apply functions
- Data munging: data manipulation with base R and the Hadleyverse
- Exporting data: saving .RData files, writing delimited data, writing to JSON
- Plotting with base R and ggplot
- Introduction to dynamic documents with R markdown
- Pulling it all together: your reproducible data analysis project

After this course you will have a working understanding of R programming and will be ready to take on your own projects. You will have been exposed to fundamentals in R and will know how to get help for your own research. You will have constructed a fully-reproducible project with R markdown suitable for reporting.

## Instructor: Tommy Jones

Tommy is a statistician, mathematician, or data scientist; depending on the problem or audience. He holds an MS in mathematics and statistics from Georgetown University and a BA in economics from the College of William and Mary. He is the Director of Data Science at 3e Services.

Tommy has previously performed economic and statistical modeling and analysis at the Science and Technology Policy Institute, the Federal Reserve Board, and the Institute for the Theory and Practice of International Relations. He has expertise in regression analyses, time series modeling and forecasting, natural language processing, data mining, and other quantitative techniques.

**leave a comment**for the author, please follow the link and comment on their blog:

**Blog - Data Community DC**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...