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You can use it for a range of tasks including to write and run code, find help, view plots, and examine data. RStudio is an integrated development environment (IDE) that has a great deal of helpful functionality.
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You can download the free version of RStudio Desktop here. We will be using RStudio for assignments in this class. Please also consider using Slack (see below for more information) - may of your will share the same questions. You can also post your own questions on StackOverflow, but make sure to read up on the guidelines first.
There is sometimes a bit of an art to finding the right way to phrase a search regarding a coding problem but in many cases you can use an error message and quickly find an answer. If you get stuck working on any problems I highly recommend searching for help on StackOverflow, an online community devoted to coding advice.
If you have the time, I would recommend starting to read (or skim) through the chapters listed in the syllabus and using RStudio to test out some of the examples (if you use the online version of the book you can easily copy over the code). I think it is a really well organized and easy to follow introduction to the fundamentals of R. The textbook focuses on using R to work with data, drawing upon a set of packages known as the tidyverse, of which the first author Hadley Wickham is the lead developer. I have indicated the relevant chapters each week. We will work through most of this book over the first few weeks of the semester. The main textbook we will be using this semester is R for Data Science (R4DS) by Hadley Wickham and Garrett Grolemund (which Bail also uses in the videos discussed above). The last video also covers Github and RMarkdown (discussed below).
They were created as a resource for participants in the Summer Institute in Computational Social Science without much prior experience. These videos are intended to serve as an introduction to programming in R for a social science audience. You can find a Twitter thread and a link to the materials here.
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The most useful thing you can do to prepare for this course is to familiarize yourself with R.Ĭhris Bail at Duke has recorded a series of short videos introducing R. The first virtual meeting take place on Monday January 25th, Week 2 of class. I will still be holding virtual office hours during week 1. You will need to go through all of the material discussed below by our first class meeting on January 25th.Ī note on scheduling: Since this class is on Mondays (and the semester starts on a Tuesday) there will be no class meeting on Week 1, but you should complete the required readings on the syllabus. Below are some helpful resources for learning about R, RStudio, RMarkdown, Github, and Slack - all of which will be used throughout the course.
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All of the books are available for free online. Here you will find links to all of the readings for the semester. I have an up-to-date copy of the syllabus here.
Please read it carefully and ensure you have completed the checklist by the second week of classes. This section will help to familiarize students with the tools we will be using throughout the semester. 85, year, transform =ax.transAxes, fontweight = "bold") # Plot every year's time series in the background sns.lineplot( data =flights, x = "month", y = "passengers", units = "year", estimator = None, color = ".7", linewidth = 1, ax =ax, ) # Reduce the frequency of the x axis ticks ax.set_xticks(ax.get_xticks()) # Tweak the supporting aspects of the plot g.set_titles( "") g.set_axis_labels( "", "Passengers") g.tight_layout() plt.This Wiki contains information pertaining to SOC577 Computational Sociology, Spring 2021, Rutgers University. # import matplotlib.pyplot as plt import seaborn as sns sns.set_theme(style = "dark") flights = sns.load_dataset( "flights") # Plot each year's time series in its own facet g = sns.relplot( data =flights, x = "month", y = "passengers", col = "year", hue = "year", kind = "line", palette = "crest", linewidth = 4, zorder = 5, col_wrap = 3, height = 2, aspect = 1.5, legend = False, ) # Iterate over each subplot to customize further for year, ax in g.axes_ems(): # Add the title as an annotation within the plot ax.text(.
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