The folks over at RStudio have been killing it lately. A few months ago they released their integration with knitr and RMarkdown. We use it here at tumblr for report generation and I absolutely love it.
Most recently, they released Shiny, which lets you build interactive visualizations in the browser with minimal effort. I haven’t played around with it yet but I’m really looking forward to doing so.
If you’ve built any interesting Shiny apps yet, leave a link in the comments. I’d love to see them.

The folks over at RStudio have been killing it lately. A few months ago they released their integration with knitr and RMarkdown. We use it here at tumblr for report generation and I absolutely love it.

Most recently, they released Shiny, which lets you build interactive visualizations in the browser with minimal effort. I haven’t played around with it yet but I’m really looking forward to doing so.

If you’ve built any interesting Shiny apps yet, leave a link in the comments. I’d love to see them.

jeffreyhorner:

R Flavored Markdown is a plain-text formatting syntax for creating documents that can be rendered to HTML. In fact it’s like HTML, but simpler. R Flavored Markdown is a variant of original Markdown with a few additional features

This is a really exciting step towards reproducible research. The markdown code below creates this output:

# Normal Distributions Functions in R

Density, distribution function, quantile function and random
generation for the normal distribution with mean equal to ‘mean’
and standard deviation equal to ‘sd’.

Use them this way:

```{r}
     dnorm(x, mean = 0, sd = 1, log = FALSE)
     pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
     qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
     rnorm(n, mean = 0, sd = 1)
```

The math behind the code:

$$latex  
f(x) = \frac{1}{(\sigma\sqrt{2 \pi})} e^{-(\frac{(x - \mu)^2}{2 \sigma^2})}
$$

There is still room at the NY Open Statistical Meetup tomorrow to hear Jeff and others talk about creating dynamic reports in R.

This is a pretty interesting site with 90 2-minute R tutorial videos. I’ve only watched a couple but the narration is pretty high-octane as far as programming tutorial videos go.

Some examples:

  • 013 how to read spss, stata, and sas files into r
  • 029 how to run analyses across multiple categories of a data table with the tapply and aggregate functions in r
  • 083 how to plot residuals from a regression in r (assuming you know some fancy statistics)
  • 085 how to export or save a plot in r
  • 089 how to run a block of commands at start-up to do stuff like setting your CRAN mirror permanently with r