Data Cleaning and Visualisation Using R

SOST70023 Data Cleaning and Visualisation Using R 20 Semester 1 Postgraduate Taught Level 7 Myong Sook Kim Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 10.0 This highly practical course unit will provide an introduction to the main building blocks of the R and RStudio software and help students develop skills in working with R and RStudio in an efficient manner. The unit will cover data management and how to prepare (and tidy) data prior to visualisation and analysis. Various R extensions (or ‘packages’) will be introduced to facilitate different approaches to data exploration, visualisation, and investigation of relationships between variables. Incorporated practical examples will be based on real world data from across the social sciences.  This highly practical course unit will provide an introduction to the main building blocks of the R and RStudio software and help students develop skills in working with R and RStudio in an efficient manner. The unit will cover data management and how to prepare (and tidy) data prior to visualisation and analysis. Various R extensions (or ‘packages’) will be introduced to facilitate different approaches to data exploration, visualisation, and investigation of relationships between variables. Incorporated practical examples will be based on real world data from across the social sciences.  The unit aims to introduce students to statistical programming in R and RStudio and develop skills such as data tidying, visualisation, and reproducible work practices.   · State and define the basic concepts underpinning statistical programming;  · Organise complex data management tasks in R and RStudio;  · Outline the principles of the ‘grammar of graphics’;  · Identify and explain the value and limitations of statistical programming;  · Recognise the value of reproducible data practices;  · Critically assess data workflows.  · Devise advanced data management plans using statistical programming;  · Produce well-reasoned arguments for the suitability of various management and visualisation approaches given different data types;  · Evaluate ‘messy’ data and develop well-justified cleaning procedures.  · Execute data recoding and reshaping tasks in R and RStudio to extract new insights;  · Implement appropriate methods to quantify and assess relationships among variables in R and RStudio;  · Create reproducible workflows with different types of data.  · Manage, recode, and reshape data in R and RStudio;  · Summarise, visualise, and analyse data in R and RStudio;  · Create ‘clean’, consistent, and well-organised R code; · Produce text output in various formats using RStudio;  · Formulate, organise, express, and communicate data-driven opinions effectively;  · Communicate statistical information using suitable and effective graphics.  The course unit will consist of asynchronous videos, interactive activities, and guided online readings. In addition, there will be frequent synchronous sessions such as seminars, tutorials and/or practicals.In preparation for the final assignment, which will take the form of a research notebook, the students will also submit a draft notebook for which they will receive formative feedback. Additionally, they will be assessed based on a short infographic with accompanying text, as well as their contributions to a discussion forum.Students will also be provided with additional materials such as assigned readings, video demonstrations, and various other individual and group activities to supplement their learning.  0 Other 100% Non-assessed Draft Research Notebook (500 words)  Discussion Forum Contributions (10%)Visualisation Report (1500 words,25%)Research Notebook (2500 words, 65%)  Online feedback within 15 working days.  N Grolemund, G. (2017). Hands-On Programming with R. Write Your Own Functions and Simulations. O’Reilly UK Ltd. (<a href="https://rstudio-education.github.io/hopr/" rel="noreferrer noopener" target="_blank">https://rstudio-education.github.io/hopr/</a>).  Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly UK Ltd. (<a href="http://adv-r.had.co.nz/" rel="noreferrer noopener" target="_blank">http://adv-r.had.co.nz/</a>)  For Information and advice on Link2Lists reading list software, see: <a href="http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/" rel="noreferrer noopener" target="_blank">http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/</a>   0 0 173 There will be no on-campus contact hours and learning will take place fully online. Contact time – 27 hours