Skip to content

Latest commit

 

History

History
242 lines (163 loc) · 5.63 KB

README.md

File metadata and controls

242 lines (163 loc) · 5.63 KB

STS 98 - Lecture 2016.05.24

See notes.R to follow along in RStudio.

Also see the R input from the lecture.

Announcements

Assignment 5 now posted!

There will not be a second peer review.

Git Collaboration

Here are a few tips for using git as part of a team.

  1. Always pull before you push:

    git pull --rebase
git push origin master

    This guarantees you get changes your teammates have pushed to GitHub before you push your own changes. Git will warn you if there are any problems merging their changes with yours.

    The --rebase parameter to the pull command tells git to apply their changes to the repository before yours. This helps to avoid messy merge commits.

  2. Work on different files. Most of the time, git is smart enough to automatically merge changes you make, even if someone else on your team changed the same file.

    However, since we haven't gone over how to handle cases where git can't automatically merge changes, you're better off working with separate files (all in the same repository).

  3. Check out the Git documentation for tips on using git.

Reshaping Data

We left off discussing how to rearrange tabular data sets with the reshape2 package.

library("reshape2")

Reshape2 divides variables in a data set into:

  1. Identifier variables identify the subject being measured.

  2. Measurement variables represent measurements on the subject.

The melt() function melts a data set so that each row represents one measurement.

melt(smiths)

dogs = readRDS("data/dogs.rds")
melt(dogs)

Missing values can be dropped by setting na.rm = TRUE.

melth(smiths, na.rm = TRUE)

# Identifier variables are detected automatically, but `melt()` doesn't always
# get these right.
#
# To specify identifiers, use the `id.vars` parameter. Alternatively, to
# specify measurements, use the `measure.vars` parameter.

melt(dogs, id.vars = "name")

If some of the measurement variables are factors, R will warn that "attributes are not identical". You can safely ignore this warning.

When is it useful to melt data?

# 1. When variable names should actually be variables.

melt(dogs, measure.vars = c("rabies", "distemper"))

melt(dogs, measure.vars = c("rabies", "distemper"), variable.name = "vaccine",
  value.name = "date")

# 2. To _cast_ or aggregate the data into a new shape.

m = melt(dogs, id = "name")

dcast(m, name ~ variable)
dcast(m, ... ~ variable)

The dcast() function casts molten data sets. The second argument is a formula with the form:

IDENTIFIERS ~ variable

The IDENTIFIERS determine what each row in the reshaped data represents. To specify all identifiers, use ....

Casting is most useful for computing grouped statistics. In this case, the grouping variables should be treated as identifiers when you melt the data.

m = melt(dogs, measure.vars = c("age", "speed"))

dcast(m, breed ~ variable, mean)

dcast(m, breed + sex ~ variable, mean, na.rm = T)

String Operations

What if we want to find a specific kind of car in the vehicles data?

Use the function

grep(PATTERN, TEXT, fixed = TRUE)

to find the indexes of records that match.

v = readRDS("cl_vehicles.rds")
table(v$model)

grep("porsche 911", v$text, fixed = TRUE)

The ignore.case parameter can be used to make the search case insensitive, but does not work with fixed = TRUE.

indexes = grep("porsche 911", v$text, ignore.case = TRUE)
indexes
v[142, ]
v[indexes, ]

If you want to take a subset, use the grepl() function instead. The syntax is the same, but logical values are returned. TRUE means the pattern matched.

porsche = grepl("porsche 911", v$text, ignore.case = TRUE)
head(porsche)
nrow(v)
subset(v, porsche)

What if we want to replace part of a string?

Use

gsub(PATTERN, REPLACEMENT, TEXT, fixed = TRUE)

to replace all instances of a pattern. The "g" stands for "global". These functions search for an exact match, so you might need to try several different patterns to find everything.

gsub("_", " ", "Hello_world!", fixed = TRUE)

These functions also support matching with a more powerful search language called regular expressions.

Regular expressions are extremely useful and are covered in more advanced data studies classes (also STA 141).

Lattice Customization

How can we customize a lattice plot?

names(v)
class(v$date_posted)
drive_freq = table(as.Date(v$date_posted), v$drive)
drive_freq = as.data.frame(drive_freq)
names(drive_freq) = c("date", "drive", "freq")
head(drive_freq)

library("lattice")
class(drive_freq$date)
xyplot(freq ~ as.Date(date), drive_freq, groups = drive,
       type = "b", auto.key = TRUE)

What if we want to customize the legend?

key = list(space = "right", points = FALSE, lines = TRUE)

xyplot(freq ~ as.Date(date), drive_freq, groups = drive, type = "b",
  auto.key = key)

See ?simpleKey for a list of parameters that can be used in the key.

?simpleKey

What if we want to change the line colors and styles?

xyplot(freq ~ as.Date(date), drive_freq, groups = drive, type = "b",
  auto.key = key, col = 1:3)

The legend colors are not updated.

To change legend settings in lattice, we need to change the default lattice settings. We can see these visually:

show.settings()
settings = trellis.par.get()
str(settings)

Get a copy of the settings to change and then change them.

settings = trellis.par.get()
settings$superpose.line$col = 1:3

xyplot(freq ~ as.Date(date), drive_freq, groups = drive,