Our April meetup with Data Philly focused on the increasingly popular topic of Open Science. See below for the article that R Ladies Philly members Alice, Karla, and Amy co-wrote! This article originally appeared on Technical.ly Philly.
What is open science? It’s like open data, but for research. Organizers of the R-Ladies Philly meetup offer an explainer based on talks from their most recent meetup.
Many are familiar with the concept of open source in software development, but what is open science?
In February and March, R-Ladies Philly continued a longstanding community data project with the Philadelphia Animal Welfare Society (PAWS) - Philly’s largest no-kill shelter. Last year, we helped PAWS analyze data from volunteer engagement to identify patterns associated with continued volunteering. This year, we worked with PAWS to understand important facets of their sheltering system, including 1) animals’ trajectories at PAWS, 2) the adoption process, 3) geospatial patterns of adoptions, and 4) public engagement via social media.
Our January meetup marked an important event in R-Ladies Philly history - our first ‘birthday’! We honored this occasion by celebrating what our community has achieved over the past year, and planning for an exciting 2019.
Recap of our first year Alice and Karla started off by reiterating the founding principles of R-Ladies Philly: to create a welcoming, accepting, and representative community for R users in the Philadelphia area who espouse our values of diversity and gender equity.
We collaborated with Women in Kaggle Philly to provide an Introduction to Machine Learning (ML).
ML is the science of getting computers to learn from data without explicit programming. ML has led to advances in speech recognition, tumor identification, self-driving cars, and many other arenas. You probably interact with ML every day!
This meetup featured a lightning talk introducing some key concepts followed by a hands-on Kaggle competition tutorial.
A brief introduction to Machine Learning Tamera Lanham, a data scientist at Elsevier, gave a lightning talk introducing ML.
Geographic Information Systems or GIS are specialized technology used for spatial data that require mapping. In our November meetup, R-Ladies Philly member and GIS Specialist Mary Lennon introduced R-Ladies Philly to manipulating and plotting spatial data from our favorite city (Philly!) in our favorite language (R!)!
How can we access and store spatial data? First, Mary explained that GIS data can be stored in different formats including Shapefiles, GPX, and GeoJSON.
Shiny is an R Package that combines the computational power of R with the interactivity of the web to enable users to create interactive web apps (and dashboards!) in R. For our October meetup, Dr. Mine Çetinkaya-Rundel led a workshop introducing the basics of building dashboards using Shiny, and a demo on transitioning from dashboards to standalone apps. In case you missed the Meetup, all materials (code! slides! more!) can be found on the github repo Mine created.
In our September meetup hosted at the Penn Dental School, R-Ladies LA founder and recent Philadelphia transplant Dr. Katie Scranton led an interactive workshop on making websites (like this one!) using R and the blogdown package.
Overview Katie started off by providing a broad overview of how blogdown works. Blogdown generates websites using Rmarkdown documents, and each blogdown website consists of a single folder of static files. Once created, your blogdown website can then be hosted on any web server to make it ‘live.
Data cleaning is the process of preparing your data for analysis; ensuring that it is technically correct and in the desired format. Data cleaning can often be more time-consuming than the actual analysis! This was our second meetup on the topic. Click here for a recap of our first data cleaning meetup in June.
Reshaping data We began with an introduction to reshaping data from Alice. The presentation was based on the DataCamp tutorial Long to Wide Data in R.
Our June meetup Our June meetup was about cleaning data using R. Data cleaning is the process of preparing your data for analysis; ensuring that it is technically correct and in the desired format. Data cleaning can often be more time-consuming than the actual analysis!
We were live tweeting the event! Check out some tweets below.
Introduction Darina and Katerina provided an introduction to data cleaning, with a focus on the process of ensuring the data is technically correct.
Our May Event! The May R-Ladies meetup took place on May 16, 2018 at Drexel’s LeBow School of Business. Thanks for hosting us again, Drexel, and thanks to our sponsors Datacamp and the R consortium!
Our topic for May was version control using the popular resources Git and GitHub, and we focused on integrating version control with R.
Intro Darina and I started with an example to illustrate the utility of version control in collaborating on a document or project over time.