This module will provide you with the fundamental skills in

• basic programming in R
• reproducibility
• data wrangling
• data analysis

basis for

• Geospatial Data Analysis
• Geospatial Databases and Information Retrieval
• as well as Geographical Visualisation

One of the most widely used programming languages and an effective tool for (geospatial) data science

The lectures and practical sessions have been designed to follow the schedule below

• 101 Introduction
• 102 Data types
• 201 Selection and manipulation
• 202 Table operations
• 301 Reproducible analysis
• 111 Control structures and functions
• 501 Exploratory data analysis
• 502 Regression models
• 601 Unsupervised

Created in 1992 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand

• Free, open-source implementation of S
  • statistical programming language
  • Bell Labs

• Functional programming language
• Supports (and commonly used as) procedural (i.e., imperative) programming
• Object-oriented
• Interpreted (not compiled)

When values and operations are inputted in the Console, the interpreter returns the results of its interpretation of the expression

2

## [1] 2

"String value"

## [1] "String value"

# comments are ignored

R provides three core data types

• numeric
  • both integer and real numbers
• character
  • i.e., text, also called strings
• logical
  • TRUE or FALSE

R provides a series of basic numeric operators

5 + 2

## [1] 7

R provides a series of basic logical operators to test

5 >= 2

## [1] TRUE

Variables store data and can be defined

• using an identifier (e.g., a_variable)
• on the left of an assignment operator <-
• followed by the object to be linked to the identifier
• such as a value (e.g., 1)

a_variable <- 1

The value of the variable can be invoked by simply specifying the identifier.

a_variable

## [1] 1

An algorithm or effective procedure is a mechanical rule, or automatic method, or programme for performing some mathematical operation (Cutland, 1980).

A program is a specific set of instructions that implement an abstract algorithm.

The definition of an algorithm (and thus a program) can consist of one or more functions

• set of instructions that preform a task
• possibly using an input, possibly returning an output value

Programming languages usually provide pre-defined functions that implement common algorithms (e.g., to find the square root of a number or to calculate a linear regression)

Functions execute complex operations and can be invoked

• specifying the function name
• the arguments (input values) between simple brackets
  • each argument corresponds to a parameter
  • sometimes the parameter name must be specified

sqrt(2)

## [1] 1.414214

round(1.414214, digits = 2)

## [1] 1.41

• functions can be used on the right side of <-
• variables and functions can be used as arguments

sqrt_of_two <- sqrt(2)
sqrt_of_two

## [1] 1.414214

round(sqrt_of_two, digits = 2)

## [1] 1.41

round(sqrt(2), digits = 2)

## [1] 1.41

When creating an identifier for a variable or function

• R is a case sensitive language
  • UPPER and lower case are not the same
  • a_variable is different from a_VARIABLE
• names can include
  • alphanumeric symbols
  • . and _
• names must start with
  • a letter

A coding style is a way of writing the code, including

• how variable and functions are named
  • lower case and _
• how spaces are used in the code
• which libraries are used

# Bad
X<-round(sqrt(2),2)

#Good
sqrt_of_two <- sqrt(2) %>%
 round(digits = 2)

Study the Tidyverse Style Guid and use it consistently!

Libraries are collections of functions and/or datasets.

• installed in R using the function install.packages
• loaded using the function library
• every script needs to load all the library that it uses

install.packages("tidyverse")

library(tidyverse)

The meta-library Tidyverse contains many libraries, including stringr.

R provides some basic functions to manipulate strings, but the stringr library provides a more consistent and well-defined set

str_length("Leicester")

## [1] 9

str_detect("Leicester", "e")

## [1] TRUE

str_replace_all("Leicester", "e", "x")

## [1] "Lxicxstxr"

The Tidyverse also provide a clean and effective way of combining multiple manipulation steps

The pipe operator %>%

• takes the result from one function
• and passes it to the next function
• as the first argument
• that doesn’t need to be included in the code anymore

The two codes below are equivalent

• the first simply invokes the functions
• the second uses the pipe operator %>%

round(sqrt(2), digits = 2)

## [1] 1.41

sqrt(2) %>%
 round(digits = 2)

## [1] 1.41

An introduction to R

• Basic types
• Basic operators
• variables
• Libraries
• The pipe operator
• Coding style

In the practical session, we will see

• The R programming language
• Interpreting values
• Variables
• Basic types
• Tidyverse
• Coding style

More complex data types

• Vectors
• Factors
• Matrices
• Arrays
• Lists
• Data Frames