Lightning Talks!

Name: Lightning Talks!
Start: 2019-01-22T18:00:00-08:00
End: 2019-01-22T20:00:00-08:00
Location: Coda

Hosted By

Gabriela de Q. and saishruthi s.

Details

Let's kick off 2019! Join the R-Ladies in our first ever Lightning Talks event. In this fun, informal setting, participants have approximately 5 to 20 minutes to speak about R, Data Science or any other topic!

AGENDA:
6pm - 6:30pm: Networking
6:30pm - 7:30pm: Lightning Talks
7:30pm - 8pm: Networking

Talks:

The making of a visual resume (by Diya Das)
How I learned to like ggplot and peered into the abyss of grid graphics
R in Jupyter Notebooks (by Viviana Marquez)
The "r" in Jupyter stands for the programming language "R". In this talk you will learn how to use R in Jupyter Notebooks as an alternative to R Studio.
sf paRks: Analyzing Park Maintenance with R (by Emily Vontsolos)
Using R from data import to visualization, the Parks Maintenance team has built a data pipeline using RForcecom, and tidyverse pkgs to clean, analyze, and visualize the data. While this system has advanced the analytical strength of reporting, there are also challenges with using R for old school, PDF-style, reports. These challenges, and some upcoming improvements will be explored during this talk.
Random Problems with R (by Kellie Ottoboni)
As part of my dissertation, I dug into the pseudo-random number generators and sampling algorithms used by common statistical pkgs. Along the way, I found an issue with the way R generates pseudo-random integers using the sample() function. I'll give an example where we'd like to generate integers uniformly on an interval, but sample produces 2x as many odd numbers as even ones.
Teaching visualization and ggplot2 (by Sara Altman)
Most data visualization resources focus either on the mechanics of a particular visualization tool or on the craft of creating effective visualizations. I’ll talk about teaching both—the mechanics and the craft—simultaneously. I’ll also discuss some ideas for improving your own visualizations.
Programming with dplyr: Quasiquotation and Quosures (by Ankur Bhatia)
Because most dplyr function use Nonstandard Evaluation, they don't follow the normal rules of R evaluation. This makes the goal of passing reference objects within dplyr code non-straightforward. However, through quasiquotation and quosures, R allows us to write reliable functions that incorporate dplyr verbs.

Speakers:

Diya is a biology postdoc at UC Berkeley and a fellow at the BIDS. She uses single-cell genomics to understand how regeneration works in mice.
Viviana Márquez is a Mathematician and Journalist from Colombia. Now pursuing a Master of Science in Data Science at the University of San Francisco.
Emily is a Performance Analyst for the SF Controller's Office. Her work varies and includes: the City's parks, Citywide demographics, homelessness, equity, and other department-specific program evaluations. She also administers a data analytics training program (including co-teaching the Intro to R course).
Kellie is a fifth year PhD candidate in Statistics at UC Berkeley. Her research is at the intersection of Stats and the Public Good, with projects ranging from evaluating the evidence for gender bias in teaching evaluations to developing stats methods for post-election audits.
Sara works at the Stanford Data Lab, which creates innovative, project-based curricula to teach the practical skills of DS. Previously, she was a Symbolic Systems masters student at Stanford, where she researched the helping behavior of young children
Ankur is a data scientist at Coda focused on Growth. Before that, he worked within Airbnb's Growth DS Team. His first experience with R was at UC Berkeley as an undergrad statistics student where he wrote a lot of code of questionable quality