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Course Description
This course is designed to supplement and build on the content covered at http://moderndive.com and the slides at http://bit.ly/soc301-slides. It assumes that you have completed the Introduction to R course on DataCamp.
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Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more- 1
Applying R Basics
FreeExploring the basics of R on a data set based on the fivethirtyeight.com "Most Police Don’t Live In The Cities They Serve" article.
- 2
Tidy Data
FreeLook into tidy data properties on data sets used in articles from fivethirtyeight.com in the fivethirtyeight R package
- 3
Scatter-plots & Line-graphs
FreeCreate and analyze different plots relating two numerical variables via the ggplot2 package using datasets in and derived from the fivethirtyeight R package
- 4
Histograms & Boxplots
FreeMake and interpret different plots relating one categorical variable to one numerical variable via the ggplot2 package using datasets in and derived from the fivethirtyeight R package
- 5
Barplots
FreeGenerate and examine barplots displaying one categorical variable or relationships between multiple categorical variables via the ggplot2 package using datasets in and derived from the fivethirtyeight R package
Distribution of one categorical variable100 xpDistribution of another categorical variable100 xpMovies over time100 xpBechdel pass/fail over time (stacked)100 xpBechdel pass/fail over time (side-by-side)100 xpBechdel pass/fail over time (faceted)100 xpBechdel pass/fail over time (proportional height)100 xpShowing off100 xp - 6
ggplot2 Review
FreeIdentifying and creating the appropriate plot based on different types of variables using the ggplot2 package
- 7
Filtering, Grouping, & Summarizing
FreeChoose a subset of rows and summarize a data frame in total and across different levels of other variables using the dplyr package
- 8
dplyr Review
FreeCreate a new column in a data frame, modify an existing column, and sort by one or more columns using the dplyr package. Review the main ideas of the Five Main Verbs of dplyr - filter, summarize, group_by, mutate, and arrange.
Chester Ismay
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Data Science Evangelist at DataRobot
Chester leads data science, machine learning, and data engineering in-person workshops for DataRobot University with DataRobot. He built (and helped instructors build) R, Python, SQL, and Spreadsheets courses for DataCamp first as a Curriculum Lead and then as Head of Content Development. He obtained a PhD in Statistics from Arizona State University and has taught courses and led workshops in mathematics, computer science, statistics, data science, and sociology. He is co-author of the fivethirtyeight R package and author of the thesisdown R package. He is also a co-author of ModernDive, an open-source textbook for introductory statistics and data science students using R.
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