Tag Archives: R programming language

R programming language: course available 24-7 online!

RData-segment_2bData Analysis and Visualization Using R: an introductory R programming course available online 24-7 through Princeton Coursera to current Princeton University community members around the globe.

Sometimes you need an R programming lesson on a Tuesday at 12:00am, or 1:00pm on a Sunday. What to do? Princeton Quantitative and Computational Biology graduate students David Robinson and Neo Christopher Chung, in association with Princeton Online/Princeton University Coursera, have created a multi-lesson searchable course based on the successful introductory R programming workshops taught by both Robinson and Chung over the past two years for the McGraw Center for Teaching and Learning and the J Street Library and Media Center.

The course is currently only available to current Princeton University members with a NetID, and does not appear on the Princeton University Coursera webpage.  Check out the course using the Princeton link, and login using your NetID and password.

Statistical Programming with R Workshop Series (Two Sessions!)

R is the de facto standard for statistical analysis in a wide range of disciplines such as 450985571v3computational biology, finance, sociology, political science and digital humanities. This two-part workshop will help participants to get started with R’s abilities, ranging from data structure to visualization. Designed for students without any programming experience, this course will better prepare you for introductory statistics courses and quantitative research at Princeton.

Part 1: Introductory Workshop in Statistical Computing with R
In the first session, you will become familiar with the R programming environment and learn how to work with variables, vectors and data frames. You’ll learn how to import data from a file, to filter it, and to extract summary statistics. You’ll then learn how to use the powerful ggplot2 package to visualize your data, including scatter plots, histograms and boxplots.

Part 2: Intermediate Workshop in Statistical Computing with R
In the second session, you’ll be introduced to R’s tools for statistics and exploratory data analysis. You’ll learn to use R’s built-in statistical functions to test hypotheses about your data, including computing correlations, comparing two samples, and performing linear regressions. You’ll then learn further methods of manipulating and summarizing data using the dplyr package, and learn the basics of exploratory data analyses.

PLEASE NOTE: The best way to learn R is to attend both sessions. The second session will assume students are familiar with both R data structures and the ggplot2 package. To meet the goals of each session, and out of respect for those who enrolled in both, the Instructor will not be able to review material for students not present for Part 1. If you absolutely must miss the first session, reviewing the material in Lessons 1 and 2 of the online course, and passing the corresponding interactive quizzes, would help acquire the necessary basis for Part 2.

Continue reading

The Productive Scholar: Turning Freshmen into Scientists: Hardware, Software, and Hands-on Technology in the Field

Topic: Turning Freshmen into Scientists: Hardware, Software, and Hands-on Technology in the FieldPS-FroshScientists-image2-web
Speakers: Adam Maloof and Frederik J. Simons

Time: Thursday, April 24, 4:30pm – 5:30pm (SPECIAL TIME!)
Location: HRC Classroom, 012 East Pyne, Lower Level

Refreshments will be provided. To register for this session: http://bit.ly/Frosh-Sci
(Registration is not required for attendance, however refreshments may be limited.)

For six years FRS 145/149/171/187 has taught students to define a hypothesis, collect data to test that hypothesis, analyze their data using quantitative techniques, and present their work in the form of scientific prose and figures.  Technology plays a central role in this mission, in the form of field instrumentation such as radar, magnetometry and GPS to collect data, and software such as Matlab and ArcGIS to analyze and present data. In this session Professors Adam Maloof and Frederick Simons will detail the lessons from their six year journey developing and refining their curriculum for turning Freshmen into scientists.

Adam Maloof is an Associate Professor of Geosciences. He is a field geologist who studies the rock record of the coevolution of animals and climate.

Frederik J. Simons is an Associate Professor of Geosciences. He is a geophysicist who specializes in the analysis of data from seismological networks and satellite gravity missions to study the structure and evolution of the Earth’s continents and their ice cover.

The Productive Scholar: Overview of Q-APS: Social Science Research Support for Scholars

Download the slides from this presentation: QAPS-Olmsted-slidesS2014

Topic: Overview of Q-APS (Program for Quantitative and Analytical Political Science): Social Science Research Support for Scholarsviews
Speaker: Jonathan Olmsted

Time: Thursday, April 17, 12:00pm – 1:00pm
Location: HRCC, 012 East Pyne, Lower Level

 

 

 

The Program for Quantitative and Analytical Political Science (Q-APS) offers training, consulting, and resources in support of social science and beyond. In this presentation, Jonathan Olmsted will outline and discuss examples of the ways Q-APS supports social science research at Princeton University for Princeton affiliates of all levels. The goal of this talk is to introduce these forms of support to a broader audience within the university.

SESSION RECAP: Jonathan provided a detailed overview of the various resources of Q-APS that are available to members of the campus. Though the focus of the presentation was on the social scientists Q-APS also works with humanists and those who, regardless of discipline, want to explore applying quantitative analysis to their research. Jonathan’s review included past and current examples of Q-APS’ support offerings including teaching…[more after the jump]

Continue reading

NEW! Workshop Series: Statistical Computing with R

R is the de facto standard for statistical analysis in a wide range of disciplines such as computational biology, finance, sociology, political science and digital humanities. This two-part workshop will help participants to get started with R’s abilities, ranging from data structure to visualization. Designed for students without any programming experience, this course will better prepare you for introductory statistics courses and quantitative research at Princeton.

Dates10/9  Part 1: Introductory Workshop in Statistical Computing with R
            11/13 – Part 2: Intermediate Workshop in Statistical Computing with R
Time: Wednesdays, 7pm – 9pm
Location: New Media Center (NMC), 1st Floor, Lewis Science Library

To Register for the workshop series, please fill out the registration form here. Or access the form via the QR code to the right. Limited space available.qrfree.kaywa.com

 

Continue reading