• Defining R and how it works
  
• Exploring R and Rstudio
  
• R graphics
  • for teaching
    
  • for exploratory analysis
    
  • advanced
    
  • interactive graphs
    
  • maps
    
• R reports and presentations
  
• R for exercises and homework
  
• Extra: R summary statistics
  

• R is a language and environment for statistical computing and graphics

• An effective data handling and storage facility;

• A large, coherent, integrated collection of intermediate tools for data analysis;

• Graphical facilities for data analysis and display either on-screen or on hardcopy;

• Simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

• You have the freedom to run the program as you wish, for any purpose (freedom 0).

• You have the freedom to study how the program works, and change it so it does your computing as you wish (freedom 1).

• You have the freedom to redistribute copies so you can help your neighbor (freedom 2).

• You have the freedom to distribute copies of your modified versions to others (freedom 3).

• R foundation

• R Mailing Lists

• Packages

• Go to www.r-project.org
• Downloads: CRAN
• Set your Mirror: Anywhere in Spain is fine.
• Select 'Download R for Windows'.
• Select base.
• Select R 3.2.3 for Windows
  • The others are if you are a developer and wish to change the source code.

Adapted from 'R for Stata users' by Muenchen and Hilbe

• Comprehensible repertoire of statistical techniques
• R is directly accessible from many statistics packages like SAS, SPSS and Stata.
• Every aspect of R is open for anyone to modify in any way they like.
• R also offers the very flexible and powerful Grammar of Graphics approach. Developers have even gone so far as replacing R’s core graphical system.
• R is free. This helps to attract developers and is a major reason that there are so many add-on packages for it.

Adapted from 'R for SPSS and SAS users' by Robert Muenchen

• It takes most statistics packages at least 5 years to add a major new analytic method;
• You can do all your data management with any software you prefer, and learn just enough R to import a file and run the procedure you need.
• SAS or SPSS requires using a different language such as C or Python to program functions; R uses its own language
• R’s graphics are extremely flexible and are of publication quality.
• R is free.

According to Roger Peng, Associate Professor at John Hopkins University:

• Memory management;
• Speed;

• Efficiency;

• Is R imprecise?
  R packages go through “Alpha,” “Beta,” and “Release Candidate” testing phases, which are open to the public. Each phase has tighter restrictions on modifications of R.

Problem = Showing overlapping variation using distributions

Problem = Showing overlapping variation using Venn diagrams

Problem = Visualize Likert type questions quickly

Problem = Visualizing statistical techniques

Problem = Plotting distributions next to scatterplots

Problem = Making graphical summaries

Problem = Exploring your data visually in a fast way

## Run this in your console
library(openintro)
edaPlot(mtcars)

Problem = Getting QUALITY visualizations with almost no work.

• Let's go to the console to get to know ggvis

  Advantages of ggvis over other graph packages:

• HTML and Javascript graphics are the ones actually doing the work behind ggvis.

For more graphs, go here

Let's play around!

## Run this in your console
library(ggvis)
span <- waggle(0.2,1)
span <- left_right(0.2,1,step = 0.1)

mtcars %>%
    ggvis(~mpg, ~disp) %>%
    layer_lines() %>%
    layer_smooths(span=span)

• First 'span' variable will adjust the smoothness automatically
• Second 'span' variable will allow you to adjust smoothness with the left-right arrows

Problem = Getting QUALITY visualizations exportable to websites

## Run this in your console
library(plotly)
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
plot_ly(d, x = carat, y = price, text = paste("Clarity: ", clarity),
        mode = "markers", color = carat, size = carat)

For more, check out plotly

• Shiny has also interactive graphs which can be included in websites
• Simpler example

• rCharts
  

Crime density in Baltimore

You can get summary or regression statistics using the stargazer package

With the swirl package you can program exercises and homeworks that students can do anywhere.
Try it out yourself:

## Run this in your console
library("swirl")
swirl()

Try it out yourself:

## Run this in your console
library(stargazer)
m1 <- lm(mpg ~ hp, data=mtcars)
m2 <- lm(mpg ~ hp + drat + am, data=mtcars)
stargazer(m1, m2, type="text",
 dep.var.labels=c("Miles/(US) gallon","Fast car (=1)"),
 covariate.labels=c("Gross horsepower","Rear axle ratio","Four foward gears"))