1 What’s this all about?

1.1 Getting started

1.2 Versions of R

2 LURN… To Work R Blind

2.1 Screen readers

2.2 R and braille displays

2.3 Setting up R as a blind Windows user

2.3.1 Solution for the problem of the screen reader losing focus

2.3.2 Super fast default installation of R

2.4 Setting up R as a blind Mac user

2.5 Setting up R as a blind Linux user

2.6 Getting the most out of the R window

2.7 More advanced ways of running R programs

3 LURN… To Enter Data

3.1 Using R as a simple calculator

3.2 A simple set of numbers

3.3 A simple set of text values

3.4 Logical indicators

3.5 A note on subscripting

3.6 A patterned set of numbers

3.7 Less pattern and more repetition

3.8 An incomplete pattern

3.9 Dates and times

3.10 Larger data objects

3.11 Appropriate data labelling

3.12 Other approaches

4 LURN… To Import Data

4.1 Data from external files

4.2 Data stored in another directory

4.3 Data saved by other statistical software

4.4 Data from files stored on the internet

4.5 Data contained in contributed packages

4.6 Data cleanliness

4.7 Other packages

5 LURN… To Manipulate your Datasets

5.1 Sorting the data into a different order

5.2 Extracting data using information on one variable

5.3 Extracting data using information on more than one variable

5.4 Use of dplyr for data manipulation

6 LURN… To Export a Dataset

6.1 Creating external files

6.2 Exporting data for use in alternative software

7 LURN… To Create Simple Graphs

7.1 Histograms

7.2 Basic annotations to graphs

7.3 Other univariate summary graphs

7.3.1 Boxplots

7.3.2 Comparative boxplots

7.3.3 Dotplots

7.3.4 Simple line plots

7.4 Quantile-quantile plots

7.5 Scatter plots

7.6 Scatter plot matrices

7.7 Graphs for discrete valued variables

7.7.1 Bar charts

7.7.2 Pie charts

7.8 Closing

8 LURN… To Examine Data Numerically

8.1 Obtaining basic numerical summaries of data

8.2 Obtaining slightly more elegant summaries

8.3 Getting things printed how we want them

8.4 Correlation structure within a data set

8.5 Use of dplyr for data summarisation

8.6 Closing

9 LURN… To Save Results for Later Inspection

9.1 Copying and pasting graphs

9.2 Saving a graph using the menus

9.3 Saving a graph using commands within your R program

9.4 Saving output in a text file

9.5 Saving the entire R Console

10 LURN… To Become More Efficient in your Workplace

10.1 Managing R when you have multiple projects on the go

10.2 Batch processing commands

10.3 Running an R script without opening R under Windows

10.4 Using file associations in Windows

10.5 Don’t re-invent any wheels

11 LURN… To Do Basic Inference

11.1 Confidence intervals and hypothesis tests for the mean of one population

11.2 Confidence intervals and hypothesis tests for the difference of two population means

11.2.1 Two paired samples

11.3 Confidence intervals and hypothesis tests for a proportion from one population

11.4 Confidence intervals and hypothesis tests for the difference of two population proportions

11.5 Hypothesis tests and confidence intervals for Correlation coefficients

11.6 Testing the independence of two categorical variables

11.7 Testing the normality of a distribution

12 LURN… To Perform Regression Analyses

12.1 Data and suitable exploratory graphs

12.2 The simple regression model

12.3 Presenting the straight line model’s suitability in a graph

12.4 Model validation using diagnostic plots

12.5 Polynomial regression models

12.6 Presenting the polynomial model’s suitability in a graph

12.7 Multiple regression models

12.8 Indicator variables

13 LURN… To Perform Analyses of Variance (ANOVA)

13.1 Data and suitable exploratory graphs

13.2 One-way ANOVA

13.3 Model validation using diagnostic plots

13.4 Two-way ANOVA

13.4.1 Randomized complete block designs

13.4.2 Factorial design with two factors

13.4.3 Graphing the results for an experiment with two factors

13.4.4 What is an interaction?

13.4.5 Two-way ANOVA with no interaction

13.4.6 Two-way ANOVA with an interaction

14 LURN… To Perform Times Series Analyses

14.1 Time series objects in R

14.2 Time series plots

14.3 Smoothing of a time series using moving averages

14.4 Checking for stationarity

14.5 Autocorrelation and partial autocorrelation

14.6 Decomposition into seasonal and trend components

14.7 Exponential smoothing

14.8 Autoregressive models

14.9 Basic ARIMA models

14.10 Seasonality and ARIMA modelling

15 LURN… To Create Complex Graphs

15.1 More than one graph in a single window

15.2 Allowing different sized graphs to be included in a single window

15.3 Using colour or different symbols

15.4 Adding points to an existing graph

15.5 Using lines instead of points

15.6 Adding lines to an existing graph

15.7 Adding a curve to a graph

15.8 Adding text to an existing graph

15.9 Adding a legend for different information within a graph

15.10 More complex bar charts

15.11 Contour plots

16 Extending R beyond the base installation

16.1 Installing additional packages

16.2 Updating add-on packages

16.3 Using an enhanced graphical interface

16.4 Use of an integrated development environment (IDE)

17 LURN… To use the BrailleR add-on package

17.1 Creating a copy of the R console window

17.2 Text interpretation of graphs

17.3 Basic descriptions of variables

17.4 Altering R output to make it easier to read

17.5 Reading a scatter plot

17.6 Creation of graphs with an accessible interactive tool for exploration

17.7 What else?

18 LURN… To create maps

18.1 Creation of maps

18.2 Adding cities to a map

18.3 Using Google’s services to map locations of interest

19 LURN… To Use R as a Scientific Calculator

19.1 Trigonometric functions

19.2 Graphs of trigonometric functions

20 LURN… To Use R for Basic Calculus Tasks

20.1 Creating mathematical expressions

20.2 Plotting functions

20.3 Differentiation

21 LURN… To Use R for Linear Algebra

21.1 Basic notes on storage of vectors and matrices

21.2 Creating some simple matrix structures

21.3 Matrix and vector calculations

21.4 Inverting a matrix

21.5 Solving a set of linear equations

21.6 Determinants and traces

21.7 Eigenvalues and eigenvectors

22 LURN… To Solve Operations Research Problems

22.1 The Assignment Problem

22.2 The transportation problem

1.1 Getting started

1.2 Versions of R

2 LURN… To Work R Blind

2.1 Screen readers

2.2 R and braille displays

2.3 Setting up R as a blind Windows user

2.3.1 Solution for the problem of the screen reader losing focus

2.3.2 Super fast default installation of R

2.4 Setting up R as a blind Mac user

2.5 Setting up R as a blind Linux user

2.6 Getting the most out of the R window

2.7 More advanced ways of running R programs

3 LURN… To Enter Data

3.1 Using R as a simple calculator

3.2 A simple set of numbers

3.3 A simple set of text values

3.4 Logical indicators

3.5 A note on subscripting

3.6 A patterned set of numbers

3.7 Less pattern and more repetition

3.8 An incomplete pattern

3.9 Dates and times

3.10 Larger data objects

3.11 Appropriate data labelling

3.12 Other approaches

4 LURN… To Import Data

4.1 Data from external files

4.2 Data stored in another directory

4.3 Data saved by other statistical software

4.4 Data from files stored on the internet

4.5 Data contained in contributed packages

4.6 Data cleanliness

4.7 Other packages

5 LURN… To Manipulate your Datasets

5.1 Sorting the data into a different order

5.2 Extracting data using information on one variable

5.3 Extracting data using information on more than one variable

5.4 Use of dplyr for data manipulation

6 LURN… To Export a Dataset

6.1 Creating external files

6.2 Exporting data for use in alternative software

7 LURN… To Create Simple Graphs

7.1 Histograms

7.2 Basic annotations to graphs

7.3 Other univariate summary graphs

7.3.1 Boxplots

7.3.2 Comparative boxplots

7.3.3 Dotplots

7.3.4 Simple line plots

7.4 Quantile-quantile plots

7.5 Scatter plots

7.6 Scatter plot matrices

7.7 Graphs for discrete valued variables

7.7.1 Bar charts

7.7.2 Pie charts

7.8 Closing

8 LURN… To Examine Data Numerically

8.1 Obtaining basic numerical summaries of data

8.2 Obtaining slightly more elegant summaries

8.3 Getting things printed how we want them

8.4 Correlation structure within a data set

8.5 Use of dplyr for data summarisation

8.6 Closing

9 LURN… To Save Results for Later Inspection

9.1 Copying and pasting graphs

9.2 Saving a graph using the menus

9.3 Saving a graph using commands within your R program

9.4 Saving output in a text file

9.5 Saving the entire R Console

10 LURN… To Become More Efficient in your Workplace

10.1 Managing R when you have multiple projects on the go

10.2 Batch processing commands

10.3 Running an R script without opening R under Windows

10.4 Using file associations in Windows

10.5 Don’t re-invent any wheels

11 LURN… To Do Basic Inference

11.1 Confidence intervals and hypothesis tests for the mean of one population

11.2 Confidence intervals and hypothesis tests for the difference of two population means

11.2.1 Two paired samples

11.3 Confidence intervals and hypothesis tests for a proportion from one population

11.4 Confidence intervals and hypothesis tests for the difference of two population proportions

11.5 Hypothesis tests and confidence intervals for Correlation coefficients

11.6 Testing the independence of two categorical variables

11.7 Testing the normality of a distribution

12 LURN… To Perform Regression Analyses

12.1 Data and suitable exploratory graphs

12.2 The simple regression model

12.3 Presenting the straight line model’s suitability in a graph

12.4 Model validation using diagnostic plots

12.5 Polynomial regression models

12.6 Presenting the polynomial model’s suitability in a graph

12.7 Multiple regression models

12.8 Indicator variables

13 LURN… To Perform Analyses of Variance (ANOVA)

13.1 Data and suitable exploratory graphs

13.2 One-way ANOVA

13.3 Model validation using diagnostic plots

13.4 Two-way ANOVA

13.4.1 Randomized complete block designs

13.4.2 Factorial design with two factors

13.4.3 Graphing the results for an experiment with two factors

13.4.4 What is an interaction?

13.4.5 Two-way ANOVA with no interaction

13.4.6 Two-way ANOVA with an interaction

14 LURN… To Perform Times Series Analyses

14.1 Time series objects in R

14.2 Time series plots

14.3 Smoothing of a time series using moving averages

14.4 Checking for stationarity

14.5 Autocorrelation and partial autocorrelation

14.6 Decomposition into seasonal and trend components

14.7 Exponential smoothing

14.8 Autoregressive models

14.9 Basic ARIMA models

14.10 Seasonality and ARIMA modelling

15 LURN… To Create Complex Graphs

15.1 More than one graph in a single window

15.2 Allowing different sized graphs to be included in a single window

15.3 Using colour or different symbols

15.4 Adding points to an existing graph

15.5 Using lines instead of points

15.6 Adding lines to an existing graph

15.7 Adding a curve to a graph

15.8 Adding text to an existing graph

15.9 Adding a legend for different information within a graph

15.10 More complex bar charts

15.11 Contour plots

16 Extending R beyond the base installation

16.1 Installing additional packages

16.2 Updating add-on packages

16.3 Using an enhanced graphical interface

16.4 Use of an integrated development environment (IDE)

17 LURN… To use the BrailleR add-on package

17.1 Creating a copy of the R console window

17.2 Text interpretation of graphs

17.3 Basic descriptions of variables

17.4 Altering R output to make it easier to read

17.5 Reading a scatter plot

17.6 Creation of graphs with an accessible interactive tool for exploration

17.7 What else?

18 LURN… To create maps

18.1 Creation of maps

18.2 Adding cities to a map

18.3 Using Google’s services to map locations of interest

19 LURN… To Use R as a Scientific Calculator

19.1 Trigonometric functions

19.2 Graphs of trigonometric functions

20 LURN… To Use R for Basic Calculus Tasks

20.1 Creating mathematical expressions

20.2 Plotting functions

20.3 Differentiation

21 LURN… To Use R for Linear Algebra

21.1 Basic notes on storage of vectors and matrices

21.2 Creating some simple matrix structures

21.3 Matrix and vector calculations

21.4 Inverting a matrix

21.5 Solving a set of linear equations

21.6 Determinants and traces

21.7 Eigenvalues and eigenvectors

22 LURN… To Solve Operations Research Problems

22.1 The Assignment Problem

22.2 The transportation problem