• Evan Tachovsky: Maps of Cleveland

    Online event

    Evan Tachovsky is data scientist, cartographer, and a product of Northeast Ohio. He is going to share how he used R to create the maps for the book "Cleveland in 50 Maps" from Belt Publishing. Talk Summary: - A gentle introduction to spatial analysis in R using sf and the tidyverse. - Exploring a bit of the data scraping and munging required to build the datasets. - Producing publication quality maps with R and things to think about when collaborating with designers. https://beltpublishing.com/products/cleveland-in-50-maps Note: this talk will be recorded and posted on our YouTube channel.

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  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want.

    1
  • Yixuan Qiu: Prettier R Graphs and Documents with {showtext}+{prettydoc}

    Prettier R Graphs and Documents with {showtext}+{prettydoc} Description: In this talk I will introduce two R packages that aim at making your plots and R Markdown documents prettier. First, the {showtext} package makes it easy to use your favorite fonts in R plots with virtually any graphics device. The function interface is device-independent, and fonts can either come from a local machine or from an online repository such as Google Fonts. In the second part, I will introduce the {prettydoc} package that provides a number of alternative themes for R Markdown documents. They are designed to be good-looking yet small in size. There are also some unique features of {prettydoc} such as code highlighting options and offline math expressions. At the end of the talk, I will mention how to extend the two packages to fit your own need, for example creating a font package, or adding a new theme for {prettydoc}. Bio: Yixuan Qiu is an assistant professor in the School of Statistics and Management at Shanghai University of Finance and Economics (SUFE). His research interests include statistical computing, deep learning models, and data visualization. Yixuan is an active software developer in the R community, and has authored and maintained many popular packages including RSpectra, RcppNumerical, recosystem, showtext, and prettydoc. Before joining SUFE, he worked as a postdoctoral researcher in the Department of Statistics and Data Science at Carnegie Mellon University. He obtained his PhD in statistics from Purdue University in 2018. Note: this talk will be recorded and posted on our YouTube channel.

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  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want.

    4
  • Emily Zabor: Creating presentation-ready summary tables with {gtsummary}

    Creating presentation-ready summary tables with the {gtsummary} package in R Presenting the results of summary statistics and regression models in a reproducible fashion is an essential task for many data analysts. However, the tools for creating well-formatted tables in R have been either limited in scope or cumbersome to use. The {gtsummary} package was written to work with the {gt} package from RStudio to create presentation-quality tables using sensible defaults but with high customizability, helping reduce the need for post-hoc table formatting. This talk will introduce users to the {gtsummary} package, from the basic functions for summary tables to details of customization options. Emily is a biostatistician at Cleveland Clinic in the Department of Quantitative Health Sciences and The Taussig Cancer Institute where she collaborates with oncologists on applied clinical research and does methodologic research on statistical methods for clinical trial design and survival analysis. Before joining the Cleveland Clinic, Emily worked as a research biostatistician at Memorial Sloan Kettering Cancer Center in New York, and was a founding board member of the R-Ladies NYC chapter. Emily earned her MS is biostatistics from the University of Minnesota in 2010 and her DrPH in biostatistics from Columbia University in 2019. Note: this talk will be recorded and posted on our YouTube channel.

    3
  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want.

    2
  • Liz Nelson: Production R Shiny Made Easy-- the Golem Framework

    R Shiny has made web app development for data science approachable and increasingly popular. The Golem package provides a framework and tools to make producing replicable, production R Shiny applications easier than ever. Come learn about the Golem package and how to get your own production-quality Shiny app up and running. Liz Nelson is a data scientist and developer currently getting her Masters in computer science and public policy at U Chicago. She has a passion for civic tech and improving government services with ethical and collaborative application of technology.

    2
  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want.

    1
  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want. If you want an idea to work on, you could look at the kaggle housing price data from our last meeting (https://www.kaggle.com/c/house-prices-advanced-regression-techniques). See some of the groups code for this at https://github.com/ClevelandRUserGroup/RTalksMarch2020.

    4
  • Virtual R Café

    Online event

    This is a chance to hang out with other R coders for a bit. It will be informal with no set topic. Bring something to work on and feel free to share or ask questions if want. If you want an idea to work on, you could look at the kaggle housing price data from our last meeting (https://www.kaggle.com/c/house-prices-advanced-regression-techniques). See some of the groups code for this at https://github.com/ClevelandRUserGroup/RTalksMarch2020.

    4