This presentation will introduce you to a toolbox that will serve you well during your data quests.

• The R programming language
• The R studio IDE (Integrated Development Environment)
• R Markdown as documenting and reporting tool
• swirl as training environment

Wikipedia says:
"R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis"

• R is a programming language like any other.
• It knows about character data, numbers, lists, functions (…but has no dicts)
• Like Python, it has an interactive mode

• It has embedded advanced graphical support
• It has extensive support for statistical work

In R, EVERYTHING lives inside a vector. There are no singular variables of atomic data types, only vectors of length zero or greater:

x <- 42
x

[1] 42

x[1]

[1] 42

x[1][1][1][1]

[1] 42

You can use it

• to create reports in word, pdf or html
• you can use it to create presentations (such as this one)
• support for it is integrated in RStudio
• to embed R code that will be executed to generate your report

source http://rmarkdown.rstudio.com/

• Swirl is a very nice tool to train your R brain
  
• It is a framework for interactive lessons in R
• I recommend doing those!
• See swirlstats.com

• OK, before we're going into the gritty details of R, let me try to convince you why it could be nice to learn it.
• R has a very nice package of demo datasets
• ChickWeight is one of these - a dataframe with 578 weight measurements of chicks on different diets

head(ChickWeight)

  weight Time Chick Diet
1     42    0     1    1
2     51    2     1    1
3     59    4     1    1
4     64    6     1    1
5     76    8     1    1
6     93   10     1    1

• I want to explore weight trends over time for the different diets
• Done using the ggplot2 package in only a few lines of code
• I challenge you to try this in Excel!!

library(ggplot2)
#make nicer labels
ChickWeight$Diet <- factor(ChickWeight$Diet,
        labels = c("Diet 1", "Diet 2", "Diet 3", "Diet 4"))
ggplot(ChickWeight, aes(x = Time, y = weight)) + facet_grid(. ~ Diet) +
    geom_point(aes(color = Chick), alpha = 0.3, size = 1) + 
    geom_line(aes(color = Chick), alpha = 0.3) + 
    geom_smooth(method = "loess", size = 1) +
    theme(legend.position="none") +
    xlab("Time (days)") + ylab("Weight (grams)")

• picture on next slide

• This concludes the overview of the toolbox and demo use case
• In the next presentation we'll cover some basic R and then come back to visit R markdown