Wrapping Up ggplot + Grammar of Data Manipulation

What up with the geom_bar(stat = "identity") code from last time?

The default stat for bar plots is “bin”. Meaning if the data are already binned, we don't want to re-bin them again, so we set stat="identity" meaning take the numbers as they are.

See R code.

The qplot() command (Chapter 2 in ggplot text) describes a way to make “quick” plots, such as simple histograms and scatterplots.

It's built using all the grammar of graphics and you add layers.

See R code.

Go to R code.

• Slides from MATH 141
• Click on “Raw” to download PDF

We now discuss a grammar for data manipulation. Other terms for “data manipulation” include:

• data wrangling
• data munging
• The New York Times takes a rather pessimistic view.

Most data manipulations can be achieved by the following verbs on a “tidy” data frame:

1. filter: keep rows matching criteria
2. summarise: reduce variables to values
3. mutate: add new variables
4. arrange: reorder rows
5. select: pick columns by name

Each of these is a command from the dplyr package.

The beauty of this “grammar” (and the grammar of graphics) is that it is programming language/software agnostic.

Even if later on your don't end up using R, the previous five verbs is still how you would think about manipulating your data.

• piping: the %>% command, described as “then”.
• boolean algebra: statements that evaluate to TRUE or FALSE.
• grouping: define groupings on a categorical variable via the group_by() command that is useful for summarise()'ations.

The %>% command, described as “then”. This saves you from a morass of nesting.

For example ex: say you want to apply functions h() and g() and then f() on data x. You can do

• f(g(h(x))) OR
• h(x) %>% g() %>% f()

This allows for sequential breaking down of tasks, allowing you and more importantly others to understand what you are doing!

• == equals
  • Ex: 5 == 3 yields FALSE
• != not equal to
  • Ex: 5 != 3 yields TRUE
• | or
  • Ex: 5 < 3 | 5 < 10 yields TRUE
• & and
  • Ex: 5 < 3 | 5 < 10 yields FALSE
• %in% is x in y?
  • Ex: c(1, 3, 2) %in% c(1, 2) yields TRUE FALSE TRUE

We need to install the dev_tools package. Unfortunately, you can't just download it from CRAN (i.e. directly from RStudio as we've been doing). Follow the instructions here. Beforehand

• Mac users: Install Apple's software development kit Xcode. Huge file!
• Windows users: Ensure you have R v. 3.1.2 (run sessionInfo() in R), and install Rtools32.exe

Then run

devtools::install_github("hadley/rvest")
library(rvest)

and ensure the package rvest loads.

if (!require("rvest")) devtools::install_github("hadley/rvest")
library(rvest)
webpage <- html("http://en.wikipedia.org/wiki/List_of_Stanley_Cup_champions")
stanley.cup <- webpage %>% html_nodes("table") %>% .[[3]] %>% html_table()

Get comfortable with this: dplyr cheat sheet from the folks at RStudio.