• Reading data into R {read.table()}
• Subsetting vectors {[ind]} and data frames {[row,col]}
• Creating logical tests for variables in your dataset
• Creating new variables
  • Binary
  • Categorical
  • Transforming, e.g. log(), exp(), sqrt()
• Summarizing variables
  • Basic statistics, e.g. mean(), sum(), sd()
  • One variable by levels of another variable: tapply()
  • Basic exploratory plots

You should feel comfortable doing most of the above

• We will be using multiple data sets in this lecture:
  • Salary, Monument, and Circulator from OpenBaltimore: https://data.baltimorecity.gov/browse?limitTo=datasets
  • Gap Minder - very interesting way of viewing longitudinal data
    • Data is here - http://www.gapminder.org/data/
  • http://spreadsheets.google.com/pub?key=rMsQHawTObBb6_U2ESjKXYw&output=xls

• One other data type that is the most generic are lists.
• Can be created using list()
• Can hold vectors, strings, matrices, models, list of other list, lists upon lists!
• Can reference data using $ (if the elements are named), or using [], or [[]]

> mylist <- list(letters=c("A", "b", "c"), 
+         numbers=1:3, matrix(1:25, ncol=5))

> head(mylist)

$letters
[1] "A" "b" "c"

$numbers
[1] 1 2 3

[[3]]
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

> mylist[1] # returns a list

$letters
[1] "A" "b" "c"

> mylist["letters"] # returns a list

$letters
[1] "A" "b" "c"

> mylist[[1]] # returns the vector 'letters'

[1] "A" "b" "c"

> mylist$letters # returns vector

[1] "A" "b" "c"

> mylist[["letters"]] # returns the vector 'letters'

[1] "A" "b" "c"

You can also select multiple lists with the single brackets.

> mylist[1:2] # returns a list

$letters
[1] "A" "b" "c"

$numbers
[1] 1 2 3

You can also select down several levels of a list at once

> mylist$letters[1]

[1] "A"

> mylist[[2]][1]

[1] 1

> mylist[[3]][1:2,1:2]

     [,1] [,2]
[1,]    1    6
[2,]    2    7

The split() function is useful for splitting data.frames

"split divides the data in the vector x into the groups defined by f. The replacement forms replace values corresponding to such a division. unsplit reverses the effect of split."

> dayList = split(circ,circ$day)

> # head(dayList)
> lapply(dayList, dim)

$Friday
[1] 164   7

$Monday
[1] 164   7

$Saturday
[1] 163   7

$Sunday
[1] 163   7

$Thursday
[1] 164   7

$Tuesday
[1] 164   7

$Wednesday
[1] 164   7

This is a brief introduction. The syntax is:

functionName = function(inputs) {
< function body >
return(value)
}

Then you would run the 4 lines of the code, which adds it to your workspace.

Here we will write a function that returns the second element of a vector:

> return2 = function(x) {
+   return(x[2])
+ }
> return2(c(1,4,5,76))

[1] 4

Note that your function will automatically return the last line of code run:

> return2a = function(x) {
+   x[2]
+ }
> return2a(c(1,4,5,76))

[1] 4

And if your function is really one line or evaluation, like here, you do not need the curly brackets, and you can put everything on one line:

> return2b = function(x) x[2]
> return2b(c(1,4,5,76))

[1] 4

Also note that functions can take multiple inputs. Maybe you want users to select which element to extract

> return2c = function(x,n) x[n]
> return2c(c(1,4,5,76), 3)

[1] 5

Let's write a function, sqdif, that:

1. takes two numbers x and y with default values of 2 and 3.
2. takes the difference
3. squares this difference
4. then returns the final value

> sqdif <- function(x=2,y=3){
+      (x-y)^2
+ }
> 
> sqdif()

[1] 1

> sqdif(x=10,y=5)

[1] 25

> sqdif(10,5)

[1] 25

Try to write a function called top() that takes a matrix or data.frame, and returns the first n rows and columns, with the default value of n=5.

Try to write a function called top() that takes a matrix or data.frame, and returns the first n rows and columns

> top = function(mat,n=5) mat[1:n,1:n]
> my.mat = matrix(1:1000,nr=100) 
> top(my.mat) #note that we are using the default value for n 

     [,1] [,2] [,3] [,4] [,5]
[1,]    1  101  201  301  401
[2,]    2  102  202  302  402
[3,]    3  103  203  303  403
[4,]    4  104  204  304  404
[5,]    5  105  205  305  405

You can use any function you want in apply statements. For example, from our split Circulator data

> lapply(dayList, top, n = 2)

$Friday
      day       date
5  Friday 01/15/2010
12 Friday 01/22/2010

$Monday
     day       date
1 Monday 01/11/2010
8 Monday 01/18/2010

$Saturday
        day       date
6  Saturday 01/16/2010
13 Saturday 01/23/2010

$Sunday
      day       date
7  Sunday 01/17/2010
14 Sunday 01/24/2010

$Thursday
        day       date
4  Thursday 01/14/2010
11 Thursday 01/21/2010

$Tuesday
      day       date
2 Tuesday 01/12/2010
9 Tuesday 01/19/2010

$Wednesday
         day       date
3  Wednesday 01/13/2010
10 Wednesday 01/20/2010

You can also designate functions "on the fly"

> lapply(dayList, function(x) x[1:2,1:2])

$Friday
      day       date
5  Friday 01/15/2010
12 Friday 01/22/2010

$Monday
     day       date
1 Monday 01/11/2010
8 Monday 01/18/2010

$Saturday
        day       date
6  Saturday 01/16/2010
13 Saturday 01/23/2010

$Sunday
      day       date
7  Sunday 01/17/2010
14 Sunday 01/24/2010

$Thursday
        day       date
4  Thursday 01/14/2010
11 Thursday 01/21/2010

$Tuesday
      day       date
2 Tuesday 01/12/2010
9 Tuesday 01/19/2010

$Wednesday
         day       date
3  Wednesday 01/13/2010
10 Wednesday 01/20/2010

> myList = list(a=1:10, b=c(2,4,5), c = c("a","b","c"),
+                 d = factor(c("boy","girl","girl")))
> tmp = lapply(myList,function(x) x[1])
> tmp

$a
[1] 1

$b
[1] 2

$c
[1] "a"

$d
[1] boy
Levels: boy girl

> sapply(tmp, class)

          a           b           c           d 
  "integer"   "numeric" "character"    "factor" 

> sapply(myList,function(x) x[1])

  a   b   c   d 
"1" "2" "a" "1" 

> sapply(myList,function(x) as.character(x[1]))

    a     b     c     d 
  "1"   "2"   "a" "boy"