Wherein we all learn to be Data Ninjas, Mondays from 1:30-2:30 in the Data Basement at Penn State (Spring 2018)
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README.md updated README Apr 30, 2018

README.md

Social Data Analytics Workshop Series: Introduction to Data Science with R

Hi! My name is Matt Denny, I am a grad student in the Department of Political Science and Social Data Analytics, an NSF Big Data Social Science IGERT Fellow, and the instructor for this workshop series. I will be posting all relevant materials for this workshop series on this page.

Preliminaries

You can email me at mdenny@psu.edu or matthewjdenny@gmail.com with any questions. There are lots of additional materials available on my website at: http://www.mjdenny.com/, but you will only need to look at the stuff linked to from this page in oder to be successful in this workshop series. To download all of the materials associated with this workshop series, you will want to start by downloading a GUI client for Git.

You will then want to clone this repo onto your computer using either the

https://github.com/matthewjdenny/SoDA-Workshop-Series-Introduction-to-Data-Science.git

link and your client or by clicking the "Clone in Desktop" button on the right hand side of the page. If you want to directly edit the files posted here and track your changes, you can copy individual files into another directory and create your own Git repo with the files in it. If you are not sure what any of the above meant, don't worry! We will go over using Github at the beginning of the first workshop, so there is no need to spend too much time trying to figure Github out. If you are at some point during this workshop series, and it is also not essential for you to learn the material. If you want to learn more about GitHub and how to use it, check out this [Github pictorial]. Welcome to these workshops!

Workshop Schedule

  1. 8-31-17: Introduction and Installing and Setting up R and RStudio:

  2. 9-7-17: Getting Started with R Programming:

    • In this workshop, we will get started writing our first R commands. In particular, we will learn about defining variables, writing comments, doing some basic math, comparing variables, and printing information to the R console. This is the stuff I use most often in my day-to-day workflow, and will form the foundation for the more advanced programming concepts we learn throughout this series.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  3. 9-14-17: Basic Programming and Data Structures in R:

    • In this workshop, we will wrap up covering some programming basics in R, and then get into the five basic data structures we will be using throughout this workshop series. Values, vectors, matrices, data.frames and lists provide a powerful and flexible toolbox for representing social science data. We will learn how these different data structures can be used separately and together to represent complex social science datasets.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  4. 9-21-17: WORKSHOP POSTPONED: I am getting over a nasty cold and am not sure my voice can last for an hour, so we are postponing this workshop until next week, see you all then!

  5. 9-28-17: Data Structures and Subsetting in R:

    • In this workshop, we will keep covering basic data structures in R: dealing with matrices, data.frames, and list objects. We will also learn about subsetting and indexing these objects, so we can begin to access and chop up the data structures we create. This workshops is where you should finally start to see some of the data structures you are more familiar with (like spreadsheets), and learn how to extract useful bits of information from those data.
    • You can download the R script for this workshop here.
  6. 10-5-17: NO WORKSHOP: SoDA Job Candidate will be visiting so there will be no workshop this week.

  7. 10-12-17: Data I/O and R Packages:

    • In this workshop, we will finally move beyond the basic functionality provided by defaul in R, and learn about how to extend that functionality by using R packages. We will make use of one such package (rio), to read in (and write out) data from many different formats to R (SAS, Sata, Excel, Minitab, SPSS, etc.).
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  8. 10-19-17: Looping and Conditionals:

    • In this workshop we will begin to give R a "brain". First, we will teach it how to repeat tasks for us, such as adding numbers together. Next, we will learn the syntax for executing code only if some condition(s) are met. Finally, we will combine these concepts so that we can automate the process of checking through data and taking some actions dependent on what the program finds. We will then apply our skills to a real-world example.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the example data for this workshop here
  9. 10-26-17: Functions:

    • In this workshop, we will start to think about writing R code that is useful for more than just the specific task we are doing at the moment. In R, functions are way of taking some code that works for one task, and making it easily applicable to other similar tasks. We have already encountered functions (like sum()), and we will learn how to define our own functions. Functions also allow us to build in automatic checks that we are not making data management mistakes, and we will go through a function template to make this easier going forward. Functions are great, lets learn how to use them!
    • You can find the slides for this workshop here.
    • You can download the first R script for this workshop here.
    • You can download the second R script for this workshop here.
  10. 11-2-17: Managing Multiple Datasets:

    • In this workshop, we will begin to synthesize what we have learned so far and apply it to managing multiple complex datasets. The primary activity we will cover in this workshop will be a worked example managing 11 large relational datasets and extracting useful information from them using loops and conditionals. We will also make use of loops and lists to help us store and easily access the 11 datasets we will be working with. Finally, we will start to put our data management skills together with what we know about functions to automate and generalize the process of reading in and cleaning data.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the data for this workshop here. Be sure to unzip the file when you download it and save the extracted folder somewhere where you can find it.
  11. 11-9-17: Working with Text Data:

    • In this workshop, we will cover the basics of manipulating and representing text data in R. Learning how to chop up, manipulate, and extract bits of text from string (text) data is a critical skill for working with messy data, web scraping, and for conducting text as data analyses. We will start by diving into the stringr libraries for extracting a replacing parts of strings, and then work with the quanteda library for forming and manipulating document-term matrices. The skills we cover here will form the basis for a series of lectures on web-scraping this spring.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  12. 11-16-17: Long and Wide Data: * In this workshop, we will cover transforming data between long and wide formats -- two common representations of panel data. Data in Long format are often of the form where each row represents an observation of a particular individual at a particular time point. Data in wide format have one row for each individual, but lots of columns recording time-varying variables at different time points. Different analysis tasks often require that we be able to switch between these two representations, and this workshop shows you how to make the conversion in both directions. * You can find the slides for this workshop here. * You can download the R script for this workshop here. * You can download the data for this workshop here.

  13. 11-30-17: Dealing With Messy Data: * In this workshop, we will go over the basics of cleaning up spreadsheet data containing errors or other formatting problems. This is one of the areas where R really shines, as it allows one to automate the process of cleaning data using loops, conditionals, and user defined functions. Data cleaning is often more of an art than a science (because every dataset comes with its own unique challenges), but in this workshop I will take you through some of the greatest hits I have encountered in messy data, all packed into one example dataset. * You can find the slides for this workshop here. * You can download the R script for this workshop here. * You can download the data for this workshop here.

  14. 12-7-17: Data Management by Example:

    • In this workshop we will try to wrap up what we have covered this semester in the form of some review and an exercise. We will try out some practical data management tasks on an example dataset after I review the key concepts we have covered so far this semester. This will be the last workshop for the Fall semester, so I will leave you all with an additional assignment to try out for yourselves over Winter break before we resume the workshop series in the Spring.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the solution R script for this workshop here.
    • You can download the data for this workshop here.
  15. Winter Break: Managing Multiple Datasets Homework:

    • This optional homework assignment should be completed before our first meeting in the Spring semester. It covers working with multiple datasets and using loops and conditionals to work with large data.frames.

    • You can download the assignment here.

    • You can download the solution here.

    • You can download the data here.

  16. 1/29/18: Review of R Programming and Managing Multiple Datasets Homework:

    • In our first workshop of the Spring semester, we will quickly review what we covered in the Fall semester, and spend some time going over the solution to the winter break assignment. If you are new to the workshops, this will give you a chance to see what you need to know to move forward with the workshops. If you are returning, it will give you a chance to get your head back in the game, and ready to continue learning R for data science.

    • You can find the slides for this workshop here.

    • You can download the assignment here.

    • You can download the solution here.

    • You can download the data here.

  17. 2/12/18: Introduction to Web Scraping with R:

    • This week, we will cover the basics of automated data collection from the internet, including legal issues. We will start out by taking the basic approach of treating web pages as text documents, and will use the tools we have developed for working with text data to extract useful information from HMTL webpages. We will then apply what we have learned in an example scraping Google Scholar. This workshop will set the stage for the next several weeks of workshops.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  18. 2/19/18: Scraping Twitter:

    • This week, we will wrap up our introduction to web scraping and begin working with the Twitter streaming API. Twitter is a wonderful free resource for collecting large volumes of real time social data, but requires a fair bit of setup to get working. We will cover all of the basics of getting access to the Twitter API, and then go over some examples collecting Tweets containing a keyword and from a geographic area.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  19. 2/26/18: Advanced Web Scraping:

    • In this workshop we will wrap up our Twitter scraping tutorial and move on to more advanced topics in web scraping. More specifically, we will cover streaming the 1% Twitter sample, as well as collecting Tweets from a geographic location. We will then move on to covering some practical considerations for scraping a large corpus of Tweets/webpages and dive into an example, time permitting.
    • You can find the slides for this workshop here.
    • You can download the first R script for this workshop here.
    • You can download the second R script for this workshop here.
    • You can download the data for this workshop here.
  20. 3/12/18: Overview of Big Data and HPC in Social Science Research:

    • In this workshop we will briefly wrap up our discussion of more advanced web scraping techniques and then launch into an overview of big data and HPC in social science research. Our main goal will be to get everyone acquainted with the strategies and tools available for working with large and complex datasets in R (and at Penn State), and when to make use of them. More specifically, we will discuss efficient programming, parallelization, programming languages, and computer hardware, and how all of these fit together to accomplish complex data management tasks.
    • You can find the slides for this workshop here.
    • You can download the R script we will wrap up covering in the first few minutes of this workshop here.
    • You can download the data for this workshop here.
  21. 3/19/18: Basics of Efficient Programming:

    • In this workshop, we will cover the basics of efficient programming, and go over some classic pitfalls to avoid. We will cover growing datastructures (and why we avoid them!), subsetting, using builtin functions, and sparse matrices, among other tricks for working more efficiently in R. Our goal will be to write code that uses less resources (time and memory) to complete the same task, without requiring significant additional programming on our part.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the data for this workshop here.
  22. 3/26/18: Parallelization and Sparse Data:

    • In this workshop we will focus on efficient data structures for representing sparse data (datasets containing lots of zeros), and processing data in parallel. The majority of the workshop will be spent going over the basics of implementing parallelization across several different types of data management problems. My goal will be to give participants the tools to speed up their computation across many related tasks, without having to get too deep into the weeds with programming.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the data for this workshop here.
  23. 4/23/18: Plotting:

    • In this workshop we will cover the basics of plotting in R. This will include how to put your data in a format where it can be plotted, and how to control a number of aesthetic features of R plots, including coloring, titles, legends, axis labels, etc. We will make use of both the base R graphics, and ggplot2, an add-on package for generating beautiful plots in R.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
  24. 4/30/18: Advanced Plotting:

    • In this workshop, we will take what we have already learned about plotting in R, and apply it to a number of example datasets to make publication quality plots. Here, the goal will be mastering some of hte more advanced features of ggplot2, in order to effectively communicate complex data, while also having total control over the aesthetics of the plots.
    • You can find the slides for this workshop here.
    • You can download the R script for this workshop here.
    • You can download the data for this workshop here, here, and here .