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Data management and visualization with R (1 credit)Course DescriptionThis course is intended for students who are looking to improve their data analysis (including data management and visualization) skills with R programming language. The emphasis of the course will be on learning tools and techniques which are useful to students who will be doing data analysis and/or statistical programming in quantitative research or related applied areas.Times & LocationsOctober 8 - December 10 (10 weeks)Every Tuesdays from 9:45 - 11:00HHH 85Course PrerequisitesIntroductory statistics; ability to create bar graphs, line graphs, and scatter plots in MS Excel; and familiarity with principles of data visualization.Course Learning OutcomesAt the end of this course, students will be able to:Use R Studio to carry out R file and related database managementUse R to work with different types of databases and conduct basic data managementUse R to visualize data with different types of plotsCourse Materials & Readings1. Chang, W. (2012). R graphics cookbook: practical recipes for visualizing data. " O'Reilly Media, Inc.".Course format:Meets once a week for ten weeks of the semester. About half of the class time is spent in lecture and the remaining for doing in-class exercise.WorkloadAn in-class exercise will be assigned during each class for the students to practice what they have learned (each exercise is worth 6% of final grade);Students will use the knowledge from this course to complete a final project (data analysis for a interested research question and write a short report which is no more than five pages about it, 30% of final grade).Course OutlineUnit #Unit TitleLearning Challenges1Introduction to R StudioSet up R programming environment with R Studio;Familiar with the user interface of R Studio;Create and save R file;Print Hello, world;Using R Packages.2Introduction to RDifferent data types;Conditional statements;Loops;Functions.3Data manipulation (I)Import and save datasets of different types of files (.csv, .xls, .xlsx, .dta and so on);View data;Data selection;Conditional value assignment;4Data manipulation (II)Deal with missing values;Remove observation;Merge two datasets;Subset.5Data visualization with ggplot (I)Pie charts;Bar charts;Boxplots;Save plots.6Data visualization with ggplot (II)Histograms;Line graphs;Scatterplots.7R Statistics (I)Mean, median and mode;8R Statistics (II)Linear regression;Logistic regression.9Data visualization with ggplot (III)Multiple plots;Style.10Spatial visualizationBasics of spatial data;Structure of spatial data in R;Map plotting. ................
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