productivity with rstudio

1. data wrangling
   • tool: dplyr
2. documenting
   • tool: markdown
3. versioning
   • tool: github

library(Lahman)
library(plyr)

games <- ddply(Batting, "playerID", summarise, total = sum(G))
head(arrange(games, desc(total)), 5)

   playerID total
1  rosepe01  3562
2 yastrca01  3308
3 aaronha01  3298
4 henderi01  3081
5  cobbty01  3035

library(Lahman)
library(dplyr)

Batting %.%
  group_by(playerID) %.%
  summarise(total = sum(G)) %.%
  arrange(desc(total)) %.%
  head(5)

Source: local data frame [5 x 2]

   playerID total
1  rosepe01  3562
2 yastrca01  3308
3 aaronha01  3298
4 henderi01  3081
5  cobbty01  3035

• grammar of data manipulation: sequence of operations on a dataset, rather than setting temporary variables or nesting functions inside one another

• readable: minimal quoting needed, operations are easily aligned with another (especially with merge())

• fast: written largely in C++, faster than base functions

• generic: regardless of backend storage (data.frame, database, Goolge bigquery…)

• inline

  The Arithmetic mean is equal to \( \frac{1}{n} \sum_{i=i}^{n} x_{i} \), or the summation of n numbers divided by n.
  

  The Arithmetic mean is equal to \( \frac{1}{n} \sum_{i=i}^{n} x_{i} \), or the summation of n numbers divided by n.

• chunked

$$
\frac{1}{n} \sum_{i=i}^{n} x_{i}
$$

\[ \frac{1}{n} \sum_{i=i}^{n} x_{i} \]

• backup: offsite archive (if syncing with remote server)

• rewind: roll back changes, so you can experiment and/or clean up code without worry of loss

• document: associate changes of code and files with issues and messages

• collaborate: with others

• publish to web site (github free for public repositories)

1. sign up at github.com

2. install git and github

3. create or fork a repository, aka “repo”

4. clone from web to your local desktop

5. commit changes locally

6. push changes to your repo

7. pull request changes from your repo to upstream

where:

• [org] is an organization (eg ohi-science)
• [repo] is a repository in the orgranization (eg ohicore, ohiprep, etc.)
• [user] is your github username

• rpub: http://rpubs.com/bdbest/rprod

• raw: http://github.com/bbest/bbest.github.com/tree/master/talks/2014-04-03_R-productivity/index.Rpres

• rendered: http://bbest.github.io/talks/2014-04-03_R-productivity

• book: Reproducible Research with R and RStudio (2013) by Christopher Gandrud http://christophergandrud.github.io/RepResR-RStudio/

• dplyr: http://cran.r-project.org/web/packages/dplyr

• markdown: http://www.rstudio.com/ide/docs/authoring/using_markdown

• github: https://www.rstudio.com/ide/docs/version_control/overview