Applied programming for social scientists using R

SOST70051 Applied programming for social scientists using R 15 Semester 1 Postgraduate Taught Level 7 Alexandru Cernat Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The module will provide an introduction to how to use R efficiently. It will present the main building blocks of R, such as main types of objects used, as well as how to use them efficiently using loops and functional programing. The module will also cover how to “tidy” data or how to prepare it for visualization and analysis. The module will provide an introduction to how to use R efficiently. It will present the main building blocks of R, such as main types of objects used, as well as how to use them efficiently using loops and functional programing. The module will also cover how to “tidy” data or how to prepare it for visualization and analysis. The module will also present how to do exploratory data analysis with a focus on how to visualize data and highlight multivariate relationships. We will be using ggplot which is a very versatile package for visualization. Finally, the course will cover how to work with text and time data. The former is especially important as it is increasingly available in new forms of data (such a social media data). We will discuss how to manipulate text data and how to use it efficiently in R. The module has a focus on real world applications and we will discuss how to apply the new skills developed to different types of data. Each class will have a combination of lectures and practical exercises using real data.  Lecture Schedule (10 * 2hr presentation and computer lab or 4 days * 5 hour) <ol> <li style="text-align:justify">Introduction to R and Rstudio</li> <li style="text-align:justify">Objects and selection in R</li></ol>3.   Writing functions and loops in R4.   Using R efficiently: functional programming 5.   Introduction to tidy data6.   Introduction to visualization using ggplot7.   Introduction to text data in R8.   Reproducible workflows9.   Introduction to dynamic documents10. Recap The unit aims to: <ol style="list-style-type:lower-roman"> <li>Develop students’ understanding of, and practical skills of working with complex data using R.</li> <li>Develop skills to clean/tidy complex data.</li> <li>Develop skills to work efficiently with data using functions, loops and functional programming.  </li> <li>Develop the skills to visualize complex data.</li></ol>Develop the skills to work with text data Student should/will (please delete as appropriate) be able to Knowledge and Understanding: Understand how to use R to solve complex data management and visualization tasks. Know how to use R efficiently by implementing loops and functional programming. Know how to crate functions to help make data wrangling more efficient. Know how to visualize complex data using R and the ggplot language. Know how to work with text data efficiently.Intellectual skills: Skills in advanced data management and visualization. Skills in working with structured and unstructured data. Practical skills: Skills in using R with real world data. Transferable skills and personal qualities: Data handling, manipulation and visualization.  Knowledge of basic programming concepts. Written skills in the presentation of findings.    Teaching will take place in 2 hour blocks in computer lab and will include a combination of lecturing and computer practices. Alternatively the course can be run in 4 full days with five hours each using the same content. 0 Other 100% <table class="Table" style="border-collapse:collapse; border:solid windowtext 1.0pt" width="0"> <tbody> <tr> <td style="border:solid windowtext 1.0pt; width:274.75pt; padding:0cm 5.4pt 0cm 5.4pt" width="366"> <h3 style="text-align:justify">Assessment task (Please include opportunities for formative feedback)</h3> </td> <td style="border:solid windowtext 1.0pt; width:2.0cm; border-left:none; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="76">Length required</td> <td style="border:solid windowtext 1.0pt; width:77.95pt; border-left:none; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="104">Weighting within unit (if relevant)</td> <td style="border:solid windowtext 1.0pt; width:63.8pt; border-left:none; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="85">Feedback</td> </tr> <tr> <td style="border:solid windowtext 1.0pt; width:274.75pt; border-top:none; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="366">Formative Assessment </td> <td style="border-bottom:solid windowtext 1.0pt; width:2.0cm; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="76">1000 words</td> <td style="border-bottom:solid windowtext 1.0pt; width:77.95pt; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="104">0%</td> <td style="border-bottom:solid windowtext 1.0pt; width:63.8pt; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="85">100 words</td> </tr> <tr> <td style="border:solid windowtext 1.0pt; width:274.75pt; border-top:none; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="366">Research paper</td> <td style="border-bottom:solid windowtext 1.0pt; width:2.0cm; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="76">3000 words</td> <td style="border-bottom:solid windowtext 1.0pt; width:77.95pt; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="104">100%</td> <td style="border-bottom:solid windowtext 1.0pt; width:63.8pt; border-top:none; border-left:none; border-right:solid windowtext 1.0pt; padding:0cm 5.4pt 0cm 5.4pt" valign="top" width="85">300 words</td> </tr> </tbody></table> Y Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly UK Ltd. (http://adv-r.had.co.nz/) Grolemund, G. (2017). Hands-On Programming with R. Write Your Own Functions and Simulations.  O’Reilly UK Ltd. (https://rstudio-education.github.io/hopr/) Lectures 20 130