An Introduction to Spatial Data Analysis in R and Visualisation in R- a newly finalized (May 2017) PDF document providing an excellent series of tutorials on the use of R for Spatial Data Analysis (by Guy Lansley and James Cheshire, UCL):An Introduction to Spatial Data Analysis in R.pdf

An Introduction to Spatial Data Analysis and Visualisation in R (2016):CDRC Tutorials (self register for access)- excellent resource created by Guy Lansley and James Cheshire, UCL

ESRI Applied Analysis Case Studies page:ESRI Applied Analysis Case Studies

GIS in Ecology Case Studies page:Ecology Analysis Case Studies

GIS and Big Data presentation, Oct 2014- the following PDF (Powerpoint presentation) by Prof Mike Goodchild provides a series of slides on the role of Big Data in GIS and GIS Science:GIS and Big Data Presentation

GIS, Big Data and Data-driven Geography Paper - GeoJournal 2014- the following PDF by Prof Harvey Miller and Prof Mike Goodchild provides a more detailed discussion Big Data in GIS and GIS Science:GIS and Big Data Paper

PPDAC Methodology Example- the following document provides an example of applying the PPDAC methodology to Noise Mapping :noise.pdf

Consumer Data Research Book - April 2018 (192 pages)- this book, available as an open access PDF and paperback, provides a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors:Consumer Data Research

Original Powerpoint files- A set of Powerpoint files (zipped) covering each main Chapter (based on the 3rd edition but still usable with more recent editions):Download Powerpoint files(22Mb zip file)

Original Dataset files - source files- A set of files (zipped) covering data used in each main Chapter plus original Distance Transforms paper (based on the 3rd edition but still usable with more recent editions):Download Dataset files(2Mb zip file)

Original Dataset files - description- A brief description of the datasets and related diagrams in the text:Download Documentation for datasets

QUESTIONS

Please note that when responding to these questions, illustrations from the database being used to practice must be utilized. Where possible optics (images of reference while the studio are critical)

1. Define R programming and explain its applications.
2. With the aid of illustrations, list out some of the function that R provides.
3. Explain how you can start the R commander GUI.
4. In R how you can import Data.
5. Mention what does not 'R' language do.
6. Explain how R commands are written.
7. How can you save your data in R.
8. Mention how you can produce co-relations and covariances.
9. Explain the t-tests in R.
10. Explain what is With () and By () function in R is used for.
11. What are the data structures in R that is used to perform statistical analyses and create graphs.
12. Explain general format of Matrices in R.
13. Explain what is transposing in R.
14. Explain how data is aggregated in R.
15. What is the function used for adding datasets in R.
16. Explain how you can make a table in R without an external file.