Generating Tables Using Pander, knitr, and Rmarkdown

I use a pretty common workflow (I think) for producing reports on a day to day basis. I write them in rmarkdown using RStudio, knit them into .html and .md documents using knitr, then convert the resulting .md file to a .docx file using pander, which is really just a way of communicating with Pandoc via my R terminal. This workflow is great for many reasons that I won't get into, but one major shortcoming is how difficult it is to get a nice looking table out of it. I was working on a report today in which I am cleaning some data for my boss. I'll quickly replicate this for effect.
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R vs. Perl/mySQL - an applied genomics showdown

Recently I was given an assignment for a class I'm taking that got me thinking about speed in R. This isn't something I'm usually concerned with, but the first time I tried to run my solution (ussing plyr's ddply() it was going to take all night to compute.

Stop Sign Sampling Project

Post 1: Planning Phase

Welcome back to the blog y'all. It's been a while since my last post and I've got some fun stuff for you. I'm currently enrooled in a survey sampling methodology class and we've been given a semester-long project, which I will of course be doing entirely in R. My group's assignment is to estimate the proportion of cars that actually stop at a stop sign in Chapel Hill.
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A while ago I was asked to give a presentation at my job about using R to create statistical graphics. I had also just read some reviews of the Slidify package in R and I thought it would be extremely appropriate to create my presentation about visualization in R, in R. So I set about breaking in the Slidify package and I've got to give a huge shout out to Ramnath Vaidyanathan who created this package.

In class today we were discussing several types of survey sampling and we split into groups and did a little investigation. We were given a page of 100 rectangles with varying areas and took 3 samples of size 10. Our first was a convenience sample. We just picked a group of 10 rectangles adjacent to each other and counted their area. Next, we took a simple random sample (SRS), numbering the rectangles 1 through 100 and choosing 10 with a random number generator.

For a class I'm taking this semester on genomics we're dealing with some pretty large data and for this reason we're learning to use mySQL. I decided to be a geek and do the assignments in R as well to demonstrate the ability of R to handle pretty large data sets quickly.
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