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R Data Structures
WMG Training
Sept 2016
National Environmental Standards and Regulations Enforcement Agency
(NESREA)
Facilitator: Victor Ordu

Source: H Wickham (2014). Advanced R. Chapman & Hall, Boca Raton. http://adv-r.had.co.nz/

• Caveat
– Not everything I will tell you will be 100% correct
– Why?
• Stupid mistakes
• Updates
• New knowledge
• Multiple approaches, some better than others
• Continuous learning
– Moral: commit to personal growth

Notation
• Regular text with look like this…
• Highlighted items will look like this…
• this <- R_code(“will look like”)

Types of Data Structures
1. Vectors
2. Data frames
3. Matrices
4. Lists
5. Arrays

Kinds of Vectors
• Six (6) kinds:
1. Character
2. Integer
3. Double (or numeric)
4. Logical

Vectors
• Common characteristics
– All elements are of a particular data type (in lay
language, “type” would be numbers, words, etc.)
– One-dimensional
– The lowest vector is of length 1
– The largest … well, depends on the .Machine

Making vectors
• By assignment
– The concatenate function
• Latin: con – caten – atus (chain)
• Some call it “combine” function
• Indispensible in creating vectors
• You can ‘grow’ a vector
– You may ask how, much, much, much, much later

• Character vectors
– Strings are always placed in quotation marks when
coding i.e. “boy”, “NESREA”, “R is easy to
learn”, “A string can be a whole
sentence!”, “9”.
– Some character vectors are inbuilt into R e.g.
letters, LETTERS, month.abb, month.name
– Remember use quotation marks: “ ” or ‘ ’.
– We can create empty vectors with specific lengths e.g.
character(length = 10) or character(10)
– Limit approx. 231 (about 2 billion)
characters!

– Exercise
• Start a clean slate with rm(list = ls())
• Make a character vector Name containing full names
(both Surname and Given Name) of 10 adults
• Make a second vector Facility of names of 10
facilities (imaginary, please!)
• Use typeof() to check what type of vector Name is
• Confirm the type of Facility using
is.character()
• Note: We can use as.character() to convert
another vector to a character vector.

• Integer vectors
– 1L, 2L, 3L
• Why the ‘L’?
– Not numerical per se
– Wide range – max up to 2,147,483,647
– Exercise
• Make an integer vector Age of 10 adult subjects
• Make an integer vector StaffStrength for 10 facilities

• Numeric (double) vectors
– These are real numbers
– Story of the term double
– Some numeric vectors are inbuilt – mathematical
constants e.g. pi, exp(1),

• Logical vectors
– TRUE/FALSE (not true/false); T/F
– Zero is FALSE; any non-zero is TRUE
– Exercise
• Make a logical vector PermitSighted for 10 facilities.
• Make another one usingPPE for 10 individuals.
• Use str(), typeof(), is.logical(), to explore them.

Stats brief
• Types of variables
– Quantitative
– Qualitative
• Levels of measurement
– Nominal
– Ordinal
– Interval
– Ratio

Factors
• Integer values that are mapped to “strings”
• Used to represent categorical data
• Each category is called a level
• One of the most powerful uses of R
– Exercise
• Make a vector industryType using 3 categories – small,
medium, large – for 10 facilities only.
• Make a factor industryCategory by calling the function
factor() on industryType.
• Now use typeof(), is.factor, is.character,
is.integer() to review these 2 objects.

Things to note…
• Legal names
• Coercion
• Limits
• Common mistakes
– Confusing factors with characters

Homogenous Heterogeneous
1-dimension Atomic vectors Lists
2-dimensions Matrices Data frames
N-dimensions Arrays

> R toolbox
› help() or ?
› getwd(); setwd()
› ls()
› rm()
› save(); load()

> R toolbox
• An example – ls()
– It’s relatively easy to see all the objects at a glance
– Note that this function is called without any
arguments

> R toolbox
• But how do you deal with this?
Long list beyond screen

> R toolbox
– Extend use of functions by defining other
parameters (optional)
– Use ? to learn about a function’s uses &
arguments
– When more familiar, use others like args()

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