R objects

1
R objects

• All R entities exist as objects
• They can all be operated on as data
• We will cover
• Vectors
• Factors
• Lists
• Data frames
• Tables
• Indexing
• R packages and datasets

2
Vectors

• Think of vectors as being equivalent to a single
  column of numbers in a spreadsheet
• You can create a vector using the c( ) function
  (concatenate) as follows
• x lt- c( )
• For example
• x lt- c(1,2,4,8) creates a column of the numbers
  1,2,4,8

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Vectors

• Other ways of creating columns of numbers
  (vectors)
• The seq function
• seq(1,10,1) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
• seq(1,4,0.5) 1, 1.5, 2, 2.5, 3, 3.5, 4
• xy
• 110 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
• 2 110 2, 4, 6, 8, 10, 12, 14, 16, 18, 20
• The rep function
• rep(2,4) 2, 2, 2, 2

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Indexing
Referencing (indexing) specific cells in a
column
Example if x is the vector 1, 2, 5 then x 1
1, x 2 2, x 3 5 and x 12 1,
2 first two listed items in x x 23 2,
5 2nd 3rd listed items in x x xgt2 5 use
of gt and lt characters
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Performing simple operations on vectors

• In R, when you carry out simple operations ( -
  /) on vectors that have the same number of
  entries, R just performs the normal operations on
  the numbers in the vector, entry by entry
• If the vectors dont have the same number of
  entries, then R will cycle through the vector
  with the smaller number of entries

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Performing simple operations on vectors
Example
7
Performing simple operations on vectors
Examples
8
Performing simple operations on vectors
Example
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Performing simple operations on vectors
Vectors (columns of numbers) can be assigned by
putting together other vectors, for example
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Functions

• R functions take arguments (information that you
  put into the function which goes between the
  brackets) and can perform a range of tasks
• In the case of the help function the task is to
  display information from the R documentation
  files
• A comprehensive list of R functions can be
  obtained from the R reference manual under the
  help menu

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Simple statistic functions
R comes with some useful functions
sqrt ( ) square root mean ( ) arithmetic
mean hist ( ) calculating plotting histograms
R also comes with pre-loaded datasets, which
well discuss later.
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Basic statistic functions on vectors
gt X1 lt- c(1.1, 4.3, 5, 2, 1, 4, 9.5) gt
sum(X1) sum 26.9 gt mean(X1) mean 3.842857 gt
median(X1) median 4 gt var(X1) variance
8.762857 gt sd(X1) standard deviation
2.960212 gt summary(X1) Min. 1st Qu. Median
Mean 3rd Qu. Max. 1.000 1.550 4.000 3.843
4.650 9.500 gt quantile(X1) 0 25 50 75
100 1.00 1.55 4.00 4.65 9.50
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Mixing vectors and scalars

• R has the very convenient feature of having
  operators that work with vectors
• It is even possible to mix vectors and scalars
• For example

gt X1 lt- c(1.1, 4.3, 5, 2, 1, 4, 9.5) gt X1 1 1
2.1 5.3 6.0 3.0 2.0 5.0 10.5 gt X1 2 1 2.2 8.6
10.0 4.0 2.0 8.0 19.0
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Vectors to record data
gt x c(45,43,46,48,51,46,50,47,46,45) gt
length(x) 1 10 gt x c(x,48,49,51,50,49)
append values to x gt length(x) 1 15 gt
x16 41 add to a
specified index gt length(x) 1 16 gt mean(x) 1
47.1875 gt x1720 c(40,38,35,40) add
to many specified indices gt length(x) 1 20 gt
mean(x) 1 45.4
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Factors

• A factor is a vector that encodes information
  about the group to which a particular observation
  belongs
• Categorical data is often used to classify data
  into various levels or factors
• To make a factor is easy, using the factor
  function

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Factors smoking survey example
A survey asks people if they smoke or not. The
data is Yes, No, No, Yes, Yes gt
xc("Yes","No","No","Yes","Yes") gt x
print out values in x 1 "Yes"
"No" "No" "Yes" "Yes" gt factor(x)
print out value in factor(x) 1 Yes No
No Yes Yes Levels No Yes
notice levels are printed.
Notice the difference in how R treats factors
with this example
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Factors student height example
Suppose the recorded height of South African and
British students are as follows heights lt-
c(1.7,1.95,1.63,1.54,1.29) You make a new vector
fac_heights, to record the nationality that each
observation pertains to fac_heights lt-
factor(c(GB, SA, GB, GB, SA))
Useful when testing for differences between groups
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Factors gender survey example
Consider a survey that has data on 691 females
and 692 males gt gender lt- c(rep("female",691),
rep("male",692)) create vector gt gender lt-
factor(gender) change vector to
factor

• Once stored as a factor, the space required for
  storage is reduced
• Values female and male are the levels of the
  factor
• gt levels(gender) assumes gender is a factor
• 1 "female" "male"

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Lists
A set of objects (e.g. vectors) can be combined
under a single name as a list (similar to a
spreadsheet in Excel)
Example x lt- c (1, 7, 8, 9, 10) y lt- c (red,
yellow, blue, green) example_list lt- list
(size x, colour y)
Note vectors can consist of characters (i.e.
letters/words) instead of numbers, but never
numbers AND characters
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Data frames

• The function data.frame( )
• This is a special kind of list, in which the
  entries in a specific position in the elements of
  the list correspond to one another
• Each element of the list has the same length
• It is a rectangular table, with rows and columns

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Data frames

• Example 1
• Simple data frames can be created
• Enter the following information at the prompt
  line
• h lt- c (150, 170, 168, 179, 130)
• w lt- c (65, 70, 72, 80, 51)
• patient_data lt- data.frame (weightw, heighth)
• Type in patient_data to see whats just been
  created

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Access of elements in data frames

• Individual elements can be accessed using a pair
  of square brackets and by specifying their
  index, or name
• Here are some ways to access a cell, row or
  column
• patient_dataheight accesses a column
• patient_data , i accesses the ith column
• patient_data i, accesses the ith row
• patient_dataheight i i is the cell position
  in height column
• patient_data i, j looking for the jth cell in
  the ith column

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Data frames

• More complex tables can be created
• Data within each column must have the same type
  (e.g., number, text), but different columns may
  have different types like a spreadsheet, as in
  the example

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Data frames
Accessing specific cells, or data
Note "" is a shortcut minus "-" sign means not.
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Tables

• We often view categorical data with tables
• The table function allows us to look at tables
• Its simplest usage is table(x) where x is a
  categorical variable

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Tables
Example smoking survey
A survey asks people if they smoke or not. The
data is Yes, No, No, Yes, Yes gt
xc("Yes","No","No","Yes","Yes") gt table(x) x No
Yes 2 3
The table command simply adds up the frequency of
each unique value of the data
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R packages and datasets

• View a list of R packages library()
• Access datasets with the data function
• data( ) provides a list of all the datasets
• data (Titanic) loads the Titanic dataset
• summary (Titanic) provides summary information
  about the Titanic dataset
• attributes(Titanic) provides more information
• Titanic dataset name will display the data
• List all datasets in a package, e.g.,
  data(package'stats')

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Working through some examples

• List preloaded datasets in R data( )
• Display the women dataset women
• Now lets access specific data
• Access data from each column
• womenheight or women ,1
• womenweight or women ,2
• Access data from individual rows
• women1, or women10, etc.
• Try it.

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Working through some examples

• Now that you can access sample data, lets work
  with it
• Get the mean weight and height of the women in
  our example..
• Remember the help function help(mean)
• Also, R can show an example example(mean)

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Common useful functions
print() prints a single R object cat()
prints multiple objects, one after the
other length() number of elements in a vector,
or of a list mean() median() range() unique(
) gives the vector of distinct
values sort() sort elements into
order order() xorder(x) orders elements of
x rev() reverse the order of vector elements