updated 2017 README.md

2016-12-08_ggplot2_intro/README.md

@@ -1,8 +1,10 @@
 # ggplot2: An Introduction
 
-*Orange County R User's Group, December 2016 Meeting*
+* 2016-12-08
 
-*Author: Arun Rangarajan*
+* Speaker: Arun Rangarajan
+
+## Abstract
 
 This is a very basic introduction to R's ggplot2 plotting system presented at the OC R User Group on Dec 8, 2016.
 You can build HTML, PDF or EPUB books using the R Markdown file (with the `bookdown` package).

2017-01-26_error_and_condition_handling_in_R/README.md

@@ -1,8 +1,9 @@
 # Error and Condition Handling in R
 
-* Orange County R User's Group
 * 2017-01-26
-* Author: John Peach
+* Speaker: John Peach
+
+## Abstract
 
 Being an interactive language, it is common to not perform a lot of error checking in R. Even popular packages lack often lack the robustness seen in similar tools in other languages. This may be in part due to the background of R developers, the environments where R scripts are used, cultural norms or because the error handling system in R is a little different. Generally, a language handles errors in one of three ways. A function returns a special value on an error (i.e. C, bash) or it throws an exception that unwinds the call stack where the error is dealt with in a different scope (i.e. Java, python). The approach used in R is based on Lisp. In general, errors are handled by a 'condition system' that is powerful and flexible. 
 

2017-01-26_rstudio_conf_and_the_tidyverse/README.md

@@ -1,6 +1,8 @@
-#Speaker: Lawrence Wu, Data Analytics Manager at Payoff, Inc.
+# rstudio::conf and the tidyverse
 
-##Title: rstudio::conf and the tidyverse
+* 2017-01-26
+* Speaker: Lawrence Wu, Data Analytics Manager at Payoff, Inc.
 
-###Abstract
+
+## Abstract
 Lawrence just returned from the rstudio::conf conference. He will give an overview of what he saw at the conference, and speak about doing data analysis in the tidyverse.

2017-02-23_pred_modeling_intro/README.md

@@ -1,7 +1,9 @@
-#Speaker: Ryan Benz
+# An Introduction to Predictive Modeling in R
 
-##Title: An Introduction to Predictive Modeling in R
+* 2017-02-23
+* Speaker: Ryan Benz
 
-###Abstract
+
+# Abstract
 I will cover the basics of predictive modeling and how you can start building your own models in R, along with some tips, tricks and best practices. Predictive modeling is a big topic so I'll also provide some great resources for further learning.
 

2017-02-23_swirlify/README.md

@@ -1,8 +1,7 @@
-#Speaker: Pasha Safarzadeh
+# Swirl(ify)
 
+* 2017-02-23
+* Speaker: Pasha Safarzadeh
 
-##Title: Swirl(ify)
-
-
-###Abstract
+## Abstract
 Swirly and Swirlify are great tools for learning and teaching R. Swirl allows you to learn R within the R interface while Swirlify allows you to write Swirl lessons. I will go over the basics of Swirl and Swirlify so you can get started learning and writing lessons in R.

2017-04-27_r_programming_in_a_regulated_environment/README.md

@@ -1,15 +1,15 @@
-#Speaker: Guanjun Bella Feng
+# R Programming in a Regulated Environment
 
-##Title: R Programming in a Regulated Environment
+* 2017-04-27
+* Speaker: Guanjun Bella Feng
 
-
-###Abstract
+## Abstract
 Like most other programming departments operating in the biotech/pharmaceutical industry, Amgen Global Statistical Programming (GSP) primarily uses SAS to analyze and report data for clinical trials. Indeed, SAS is a well-established commercial software product that is the de facto standard in clinical trial environments. In recent years however, R has surged in popularity and has evolved to become a worthy programming language along with SAS. At Amgen, a group of programmers was assigned to investigate and establish R as a programming language within GSP. This group – the R Consultancy Group – has addressed package validation, reproducibility and other challenges. We are now beginning to implement R for the following:
 
-1) ggplot2 to create publishing-ready figures needed for Clinical Study Reports
-2) plotly to create dynamic graphs for department-level initiatives
-3) Shiny for data checks and product-level safety reporting
-4) R Markdown for a source code library for figures
-5) packrat for reproducibility
+1. ggplot2 to create publishing-ready figures needed for Clinical Study Reports
+2. plotly to create dynamic graphs for department-level initiatives
+3. Shiny for data checks and product-level safety reporting
+4. R Markdown for a source code library for figures
+5. packrat for reproducibility
 
-I will present an overview of our technical progress in these areas.
+I will present an overview of our technical progress in these areas.

2017-06-27_rrii/README.md

@@ -1,5 +1,7 @@
-#Speaker: 
+# RRII: Recent R Items of Interest
 
-##Title:
+* 2017-06-27
+* Speaker: Ryan Benz
 
-###Abstract
+## Abstract
+A survey of recent developments in the R Community

2017-08-24_basic_intro_to_image_processing/README.md

@@ -1,5 +1,8 @@
-#Speaker: Yemi O.
+# Basic Introduction To Image Analysis with R
 
-##Title: Basic Introduction To Image Analysis with R
+* 2017-08-24
+* Speaker: Yemi O.
 
-###Abstract
+
+## Abstract
+This talk covers some of the fundimentals of how images are stored and processed in R.

2017-11-30_intro_to_machine_learning_with_r/README.md

@@ -1,15 +1,13 @@
-#Speaker: Bella Feng
+# Machine Learning with R
 
-##Title: Machine-Learning-with-R
+* 2017-11-30
+* Speaker: Bella Feng
 
-###Abstract
+## Abstract
 This is a repository for all my presentation, notes, and projects related to "Machine Learning with R" book and things I gathered in learning ML and R.
-1. What is machine learning?
 
+1. What is machine learning?
 2. What are the use cases?
-
 3. The workflow of a machine learning project.
-
 4. What are the common machine learning algorithms and their strengths and weaknesses?
-
 5. Considerations in evaluating and improving a model.

2017-11-30_sales_to_date/README.md

@@ -1,6 +1,7 @@
-#Speaker: 
+# ggplot2 for sales
 
-##Title: ggplot2 for sales
+* 2017-11-30
+* Speaker: Peter Stemler
 
-###Abstract
-This short talk will cover how to create monthly and cumulative sales forecasts using tidyverse, forcats, lubridate, stringr, ggrepel and scales.
+## Abstract
+This short talk will cover how to create monthly and cumulative sales forecasts using tidyverse, forcats, lubridate, stringr, ggrepel and scales.