Introduction to Data Analysis and Graphics2

Session Two

Vector and Assignment, Data Objects and Data Importation

By the end of this session we will have knowledge on:

• Vectors and Assignment
• Data types
• Data structure and
• Importing data into R

• Simplest data structure in R is a vector. From a data point of view, a vector is collection of elements. These elements can be numeric values, alphabetical characters, logical, dates and time values.
• Vectors are created with function “c” which means “concatenate”. e.g. a numerical vector c(1, 5, 6, 8)
• Thee vectors can be named by using an assignment operator “<-” or function “assign()”. e.g. to assign vector c(1, 5, 6, 8) to name “num”; num <- c(1, 5, 6, 8) or assign(“num”, c(1, 5, 6, 8)). We often use “<-” for assignment, “assign” function is mostly used in developing functions
• A vector can be of any length begining from 1 to about 2.1474836 × 10⁹

R recognises seven data types, these are:

• Logical
• Integer
• Real/Double
• String/Character
• Factor

• These are vectors with only TRUE and FALSE values like c(TRUE, TRUE, FALSE, TRUE, FALSE)
• Can be considered as binary vectors in analysis
• Other than categorical variables with these values, these vectors are often created by binary operators like “<”, “>”, “<=”, >=, ==, =!, “|”, “||”, “&”, and “&&”

• These are basically positive and negative numbers without fractions {…, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, …}
• In R, integers are denoted with letter L e.g. c(-3L, 0L, 2L, 5L, 6L). Can confirm it's an integer vector with function is.integer(c(-3L, 0L, 2L, 5L, 5L))
• Example of a variable which can be considered to naturally have integers is “number of people” (you can't have a fraction of a person)
• Mathematically denoted by \( \mathbb{Z} \)

• A real number is any number along an infinitely number line
• They include fractions
• Denoted mathematically with \( \mathbb{R} \)
• Any numeric vector that does not have values followed by letter “L” are considered as double e.g. c(-3, 0, 2, 5, 6). Can confirm a vector is a real or double vector with funtion “is.double” e.g is.double(c(-3, 0, 2, 5, 6))

• Composed of alphabetical letters and word/text
• Denoted by single or double quotation marks
• R has a special vector with alphabetical letter; this is letters
• Example c("a", "b", "c"), letters, c('cats', 'and' , 'dogs')
• Can check whether a vector is a character vector with function is.character e.g. is.character(letters)

• These are vectors containing computer bytes or information on data storage units
• More of computer language (0's and 1's) than human readable language

• There two broad types of data structures in R
  • Atomic vectors
  • Generic (list) vectors
• These structures have three properties
  • Type
  • Length and
  • Attributes

• Function "type" is used to establish a vector's type, function "length" is used to determine length and function "attributes" is used to get additional information about a vector
  
• Atomic vectors and lists differ in their type as atomic vectors can only contain one data type while lists can contain any number of data types.

• Contains only one data type, they include 1 dimensional atomic vectors, 2 dimensional atomic vectors called “matrices” and multi-dimensional atomic vectors called “arrays”.
• Dimensionality can be considered as number of indices required to address any element in a vector e.g. vector “cat” requires one index to address any value, for example index “4” means fourth value which is Y
• Single variables are all atomic vectors of one dimension
• To check if a vector is either atomic or list, use is.atomic() or is.list(). Note there is a is.vector() but this checks if vector is named

• Two dimensional atomic vectors, they contain data of the same type
• Any atomic vector can be converted to a matrix by adding a dim attribute

Time series objects

• These are vectors used to store observations collected at given time points (interval) over a period time, e.g. observations collected every three three months for five year.
• Distiguishing feature in this data is time, interval is usually constant like three months (regular), but in other cases it might not be so (irregular)
• In R, time series data are numeric vectors with attribute class equal “ts” meaning time series

• Arrays are multi-dimensional atomic vectors.
• Matrices are two dimensional array.
• They are rarely used, but it's good to know they exist
• Created like matrices; "dim()" e.g. dim(a) <- c(6, 2, 2), or array() or as.array()

• Lists are data structure which can contain more than on type of data type.
• There are two types of lists; two dimensional lists called "data frames" and "lists"

• These are unique data structure
• Can contain any number and type of object, not just data. Can contain sub-lists hence also called recursive
• Created with function “list()”. Can also coerce other structures to a list with function “as.list()”
• We will create this structure in our next session

• Data importation also referred to as “reading in” data
• Reading data depends on type and location of file
• Sub-session interest, reading in local R, text, excel, database and other statistical program files
• Also discuss web scrapping

• Data created in R can be store in RData file
• This could be any data structure or a collection of data saved from an active working directory (workspace)
• Function “save.image()” used to store workspace, function “load” is used to read in any “.RData” (or even .Rhistory)

• read.table is R's core importing function
• Almost all other functions including contributed packages depend on this function
• Reads a file and creates a data frame from it
• It has a number of wrapper functions (functions which provide a convinience interface to another function like give pre-defined/default values, this make function calls more efficient)
• Wrapper functions include read.csv(), read.csv2(), read.delim, read.delim2

• Base R does not have a function to read in Excel based files
• But many contributed packages have functions to read them in
• Core reference in importing this type of files is one of R-projects manuals R Data Import/Export specifically chapter 9.
• Recommendation made is to try and convert Excel file in to “.csv” (comma-separated) or “delim” (tab-separated) file. Live demo (reading excel file)

• A bit of caution, database data tend to be large, R is not to good when it comes to large data, hence read in part of data or look for ways to increase memory allocated to R processes like using cloud.
• Most Relational Database Management Systems (RDMS) have data similar to R's dataframe where columns are called “fields” and rows are called “records”.
• Extracting part of relational database requires use of database quering sematics core of which is a SELECT statement.

• Other statistical softwares often used to read in data are SPSS, SAS, Stata and EpiInfo
• Like excel and database data, to read in these files a package must be used
• Recommended package is package "foreign" other packages include, "readstata3" and haven.

Live demo (reading SPSS and Stata data files)