diff --git a/_site.yml b/_site.yml index 2c6f5b1..530a452 100644 --- a/_site.yml +++ b/_site.yml @@ -39,8 +39,10 @@ navbar: href: slides/data-wrangling-2.html - text: "Class 10: Visualizing proportions" href: slides/visualizing-proportions.html - - text: "Class 11: Getting to know your data" + - text: "Class 11: Getting to know your data 1" href: slides/know-your-data-1.html + - text: "Class 11: Getting to know your data 2" + href: slides/know-your-data-2.html - text: "Class 12: Getting things into the right order" href: slides/getting-things-in-order.html - text: "Class 13: Figure design" diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 415394b..ea0796d 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -2388,7 +2388,8 @@

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Class 8: Data wrangling 1 Class 9: Data wrangling 2 Class 10: Visualizing proportions -Class 11: Getting to know your data +Class 11: Getting to know your data 1 +Class 11: Getting to know your data 2 Class 12: Getting things into the right order Class 13: Figure design Class 14: Color spaces and color-vision deficiency diff --git a/docs/index.html b/docs/index.html index ca49510..616d230 100644 --- a/docs/index.html +++ b/docs/index.html @@ -2466,7 +2466,8 @@

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Class 8: Data wrangling 1 Class 9: Data wrangling 2 Class 10: Visualizing proportions -Class 11: Getting to know your data +Class 11: Getting to know your data 1 +Class 11: Getting to know your data 2 Class 12: Getting things into the right order Class 13: Figure design Class 14: Color spaces and color-vision deficiency diff --git a/docs/schedule.html b/docs/schedule.html index 17a0f06..2d4d6f3 100644 --- a/docs/schedule.html +++ b/docs/schedule.html @@ -2466,7 +2466,8 @@

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Class 8: Data wrangling 1 Class 9: Data wrangling 2 Class 10: Visualizing proportions -Class 11: Getting to know your data +Class 11: Getting to know your data 1 +Class 11: Getting to know your data 2 Class 12: Getting things into the right order Class 13: Figure design Class 14: Color spaces and color-vision deficiency diff --git a/docs/search.json b/docs/search.json index f8eab31..9226237 100644 --- a/docs/search.json +++ b/docs/search.json @@ -6,13 +6,13 @@ "description": "Data Visualization in R", "author": [], "contents": "\nThis is the home page for SDS 375, Data Visualization in R. All course materials will be posted on this site.\nInstructor: Claus O. Wilke\nMeeting times: TTH 3:30pm to 5:00pm\nVenue: UTC 4.110\nSyllabus: click here\nUpcoming lectures and assignments: click here\nComputing requirements\nFor students enrolled in this course, you only need a working web browser to access the edupod server, located at: https://edupod.cns.utexas.edu/\nIf you are using the edupod server, stop reading here. Everything is pre-installed and no further action is needed.\nTo run any of the materials locally on your own machine, you will need the following:\nA recent version of R, download from here.\nA recent version of RStudio, download from here.\nThe following R packages:\nbroom, cluster, colorspace, cowplot, distill, gapminder, GGally, gganimate, ggiraph, ggdendro, ggdist, ggforce, ggplot2movies, ggrepel, ggridges, ggthemes, gifski, glue, knitr, learnr, naniar, margins, MASS, Matrix, nycflights13, palmerpenguins, patchwork, rmarkdown, rnaturalearth, rnaturalearthhires, scales, sf, shinyjs, sp, tidyverse, transformr, umap, xaringan\nYou can install all required R packages at once by running the following code in the R command line:\n\n\n# first run this command:\ninstall.packages(\n c(\n \"broom\", \"cluster\", \"colorspace\", \"cowplot\", \"distill\", \"gapminder\", \n \"GGally\", \"gganimate\", \"ggiraph\", \"ggdendro\", \"ggdist\", \"ggforce\",\n \"ggplot2movies\", \"ggrepel\", \"ggridges\", \"ggthemes\", \"gifski\", \"glue\",\n \"knitr\", \"learnr\", \"naniar\", \"margins\", \"MASS\", \"Matrix\",\n \"nycflights13\", \"palmerpenguins\", \"patchwork\", \"rmarkdown\", \"rnaturalearth\",\n \"scales\", \"sf\", \"shinyjs\", \"sp\", \"tidyverse\", \"transformr\", \"umap\",\n \"xaringan\"\n )\n)\n\n# then run this command:\ninstall.packages(\n \"rnaturalearthhires\", repos = \"https://packages.ropensci.org\", type = \"source\"\n)\n\n\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.\n\n\n\n", - "last_modified": "2024-02-19T16:39:54-06:00" + "last_modified": "2024-02-19T16:43:19-06:00" }, { "path": "LICENSE.html", "author": [], "contents": "\nMIT License\nCopyright (c) 2021 Claus O. Wilke\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the “Software”), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\nTHE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n\n", - "last_modified": "2024-02-19T16:39:55-06:00" + "last_modified": "2024-02-19T16:43:19-06:00" }, { "path": "schedule.html", @@ -20,7 +20,7 @@ "description": "", "author": [], "contents": "\n\nContents\nLectures\nHomeworks\nProjects\nReuse\n\nLectures\n1. Jan 16, 2024—Introduction\n\nMaterials:\n\nSlides\nWorksheet (Solutions are available here)\n2. Jan 18, 2024—Aesthetic mappings\n\nMaterials:\n\nSlides\nWorksheet\n3. Jan 23, 2024—Telling a story, Visualizing amounts\n\nMaterials:\n\nSlides: Telling a story\nSlides: Visualizing amounts\nWorksheet\n4. Jan 25, 2024—Coordinate systems and axes\n\nMaterials:\n\nSlides\nWorksheet\n5. Jan 30, 2024—Visualizing distributions 1\n\nMaterials:\n\nSlides\nWorksheet\n6. Feb 1, 2024—Visualizing distributions 2\n\nMaterials:\n\nSlides\nWorksheet\n7. Feb 6, 2024—Color scales\n\nMaterials:\n\nSlides\nWorksheet\n8. Feb 8, 2024—Data wrangling 1\n\nMaterials:\n\nSlides\nWorksheet\n9. Feb 13, 2024—Data wrangling 2\n\nMaterials:\n\nSlides\nWorksheet\n10. Feb 15, 2024—Visualizing proportions\n\nMaterials:\n\nSlides\nWorksheet\n11. Feb 20, 2024—Getting to know your data\n\nMaterials:\n\nSlides 1\nSlides 2\nWorksheet 1: Data cleaning and inspection\nWorksheet 2: Missing values\n12. Feb 22, 2024—Getting things into the right order\n\nMaterials:\n\nSlides\nWorksheet\nHomeworks\nAll homeworks are due by 11:00pm on the day they are due. Homeworks need to be submitted as pdf files on Canvas.\nHomework 1 (due Jan 25, 2024)\n\nMaterials:\n\nR Markdown template\nHTML\nHomework 2 (due Feb 1, 2024)\n\nMaterials:\n\nR Markdown template\nHTML\nHomework 3 (due Feb 8, 2024)\n\nMaterials:\n\nR Markdown template\nHTML\nHomework 4 (due Feb 29, 2024)\nHomework 5 (due Mar 7, 2024)\nHomework 6 (due Apr 4, 2024)\nHomework 7 (due Apr 11, 2024)\nProjects\nAll projects are due by 11:00pm on the day they are due. Projects need to be submitted on Canvas. Please carefully read the submission instructions for each project.\nProject 1 (due Feb 15, 2024)\n\nMaterials:\n\nInstructions\nProject Template (Rmd)\nProject Template (HTML)\nGrading rubric\nExample project\nProject 2 (due Mar 21, 2024)\nProject 3 (due Apr 18, 2024)\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.\n\n\n\n", - "last_modified": "2024-02-19T16:39:55-06:00" + "last_modified": "2024-02-19T16:43:19-06:00" }, { "path": "syllabus.html", @@ -28,7 +28,7 @@ "description": "", "author": [], "contents": "\n\nContents\nCourse title and instructor\nPurpose and contents of the class\nPrerequisites\nTextbook\nTopics covered\nComputing requirements\nCourse site\nAssignments and grading\nLate assignment policy\nOffice hours\nEmail policy\nSpecial accommodations\nAcademic dishonesty\nSharing of Course Materials is Prohibited\nClass Recordings\nReuse\n\nCourse title and instructor\nTitle: SDS 375 Data Visualization in RSemester: Spring 2024Unique: 56690, TTH 3:30pm–5:00pm, UTC 4.110\nInstructor: Claus O. WilkeEmail: wilke@austin.utexas.eduOffice Hours: Mon. 9am - 10am (open Zoom), Thurs. 10am - 11am (open Zoom), or by appointment\nTeaching Assistant: Alexis HillEmail: alexis.hill@utexas.eduOffice Hours: Wed. 2pm - 3PM (open Zoom), Thurs. 11am - 12pm (open Zoom), or by appointment\nPurpose and contents of the class\nIn this class, students will learn how to visualize data sets and how to reason about and communicate with data visualizations. A substantial component of this class will be dedicated to learning how to program in R. In addition, students will learn how to compile analyses and visualizations into reports, how to make the reports reproducible, and how to post reports on a website or blog.\nPrerequisites\nThe class requires no prior knowledge of programming. However, students are expected to have successfully completed an introductory statistics class taught with R, such as SDS 320E, and they are expected to have some basic familiarity with the statistical language R.\nTextbook\nThis class draws heavily from materials presented in the following book:\nClaus O. Wilke. Fundamentals of Data Visualization. O’Reilly Media, 2019.\nAdditionally, we will also make use of the following books:\nHadley Wickham, Danielle Navarro, and Thomas Lin Pedersen. ggplot2: Elegant Graphics for Data Analysis, 3rd ed. Springer, to appear.\nKieran Healy. Data Visualization: A Practical Introduction. Princeton University Press, 2018.\nAll these books are freely available online and you do not need to purchase a physical copy of either book to succeed in this class.\nTopics covered\n\nClass\nTopic\nCoding concepts covered\n1.\nIntroduction, reproducible\nworkflows\nRStudio setup online, R Markdown\n2.\nAesthetic mappings\nggplot2 quickstart\n3.\nTelling a story\n\n4.\nVisualizing amounts\ngeom_col(), geom_point(),\nposition adjustments\n5.\nCoordinate systems and\naxes\ncoords and position scales\n6.\nVisualizing distributions\n1\nstats, geom_density(),\ngeom_histogram()\n7.\nVisualizing distributions\n2\nviolin plots, sina plots, ridgeline plots\n8.\nColor scales\ncolor and fill scales\n9.\nData wrangling 1\nmutate(), filter(), arrange()\n10.\nData wrangling 2\ngroup_by(), summarize(), count()\n11.\nVisualizing proportions\nbar charts, pie charts\n12.\nGetting to know your data\nhandling missing data, is.na(), case_when()\n13.\nGetting things into the\nright order\nfct_reorder(), fct_lump()\n14.\nFigure design\nggplot themes\n15.\nColor spaces, color vision\ndeficiency\ncolorspace package\n16.\nFunctions and functional\nprogramming\nmap(), nest(), purrr package\n17.\nVisualizing trends\ngeom_smooth()\n18.\nWorking with models\nlm, cor.test, broom package\n19.\nVisualizing uncertainty\nfrequency framing, error bars, ggdist package\n20.\nDimension reduction 1\nPCA\n21.\nDimension reduction 2\nkernel PCA, t-SNE, UMAP\n22.\nClustering 1\nk-means clustering\n23.\nClustering 2\nhierarchical clustering\n24.\nVisualizing geospatial\ndata\ngeom_sf(), coord_sf()\n25.\nRedundant coding, text\nannotations\nggrepel package\n26.\nInteractive plots\nggiraph package\n27.\nOver-plotting\njittering, 2d histograms,\ncontour plots\n28.\nCompound figures\npatchwork package\n\nComputing requirements\nProgramming needs to be learned by doing, and a significant portion of the in-class time will be dedicated to working through simple problems. All programming exercises will be available through a web-based system, so the only system requirement for student computers is a modern web browser.\nCourse site\nAll materials and assignments will be posted on the course webpage at:\nhttps://wilkelab.org/SDS375\nAssignment deadlines are shown on the schedule at: https://wilkelab.org/SDS375/schedule.html\nAssignments will be submitted and grades will be posted on Canvas at:\nhttps://utexas.instructure.com\nParticipation via presence in class and in online discussions will also be tracked on Canvas.\nR compute sessions are available at:\nhttps://edupod.cns.utexas.edu\nNote that edupods will be unavailable due to maintenance approximately two hours per month, usually on a Thursday afternoon between 4pm and 6pm. Specific maintenance times are published in advance here:\nhttps://wikis.utexas.edu/display/RCTFusers\nAssignments and grading\nThe graded components of this class will be homeworks, projects, peer-grading, and participation. Each week either a homework, a project, or a peer-grading is due. Homeworks will be relatively short visualization problems to be solved by the student, usually involving some small amount of programming to achieve a specified goal. They are graded by the TA. Projects are larger and more involved data analysis problems that involve both programming and writing. They are peer-graded by the students. Students will have at least one week to complete each homework and two weeks to complete each project. The submission deadlines for homeworks and projects will be Thursdays at 11pm.\nThere will be seven homeworks and three projects. Both homeworks and projects need to be submitted electronically on Canvas. Homeworks are worth 20 points and projects are worth 100 points. The lowest-scoring homework will be dropped, so that a maximum of 120 points can be obtained from the homeworks.\nProjects are peer-graded, which involves evaluating three projects by other students according to a detailed grading rubric that will be provided. The final grade for each project is the mean of the peer-graded projects. The peer-grading itself will be graded by the TA, who will also oversee and spot-check the assigned peer grades. Experience has shown that peer-grading is often the most instructive component of this class, so don’t take this lightly.\nParticipation is assessed in two ways. First, students will receive 2 points for every lecture they attend. This is tracked via simple quizzes on Canvas. Second, each week students can receive up to 4 points for making substantive contributions to the Canvas online discussion (2 points per contribution). Total participation points are capped at 52 (13 weeks of class times 4 points), so students can compensate for lack of in-person attendance by participating in discussions and vice versa. You do not have to get full points in both in-person attendance and online discussions. No participation is assessed in the first week of class.\n\nAssignment type\nNumber\nPoints per assignment\nTotal points\nHomework\n6 (+1)\n20\n120\nProject\n3\n100\n300\nPeer grading\n3\n16\n48\nParticipation\n26 (+26)\n2\n52\n\nThus, in summary, each project (+ peer grading) contributes 22% to the final grade, the totality of all homeworks contributes another 23% to the final grade, and participation contributes 10%. There are no traditional exams in this class and there is no final.\nThe class will use +/- grading, and the exact grade boundaries will be determined at the end of the semester. However, the following minimum grades will be guaranteed:\n\nPoints achieved\nMinimum guaranteed grade\n468 (90%)\nA-\n416 (80%)\nB-\n364 (70%)\nC-\n260 (50%)\nD-\n\nLate assignment policy\nHomeworks that are submitted past the posted deadline will not be graded and will receive 0 points.\nProject submissions will have a 1-day grace period. Projects submitted during the grace period will have 25 points deducted from the obtained grade. After the grace period, students who have not submitted their project will receive 0 points.\nPeer grades need to be submitted by the posted deadline. Late submissions will result in 0 points for the peer-grading effort.\nIn case of illness or other unforeseen circumstances out of your control, please reach out to Claus Wilke as soon as possible. We will consider your request on a case-by-case basis. If you need a deadline extension for valid reasons, please reach out before the official submission deadline and state how much of an extension you would need. Whether deadline extensions are possible depends on the severity of your situation as well as whether the solutions to the assignment have already been published.\nOffice hours\nBoth the graduate TA and myself will be available at posted times or by appointment. Office hours will be over Zoom. The most effective way to request an appointment for office hours outside of posted times is to suggest several times that work for you. I would suggest to write an email such as the following:\nDear Dr. Wilke,\n\nI would like to request a meeting with you outside of \nregular office hours this week. I am available Thurs.\nbetween 1pm and 2:30pm or Fri. before 11am or after 4pm.\n\nThanks a lot,\n John Doe\nNote that we will not usually make appointments before 9am or after 5pm.\nEmail policy\nWhen emailing about this course, please put “SDS375” into the subject line. Emails to the instructor or TA should be restricted to organizational issues, such as requests for appointments, questions about course organization, etc. For all other issues, post in the discussions on Canvas, ask a question during open Zoom, or make an appointment for a one-on-one session.\nSpecifically, we will not discuss technical issues related to assignments over email. Technical issues are questions concerning how to approach a particular problem, whether a particular solution is correct, or how to use the statistical software R. These questions should be posted as issues on GitHub. Also, we will not discuss grading-related matters over email. If you have a concern about grading, schedule a one-on-one Zoom meeting.\nSpecial accommodations\nStudents with disabilities. Students with disabilities may request appropriate accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 512-471-6259, https://diversity.utexas.edu/disability/\nReligious holy days. Students who must miss a class or an assignment to observe a religious holy day will be given an opportunity to complete the missed work within a reasonable time after the absence. According to UT Austin policy, such students must notify me of the pending absence at least fourteen days prior to the date of observance of a religious holy day.\nAcademic dishonesty\nThis course is built upon the idea that student interaction is important and a powerful way to learn. We encourage you to communicate with other students, in particular through the discussion forums on Canvas. However, there are times when you need to demonstrate your own ability to work and solve problems. In particular, your homeworks and projects are independent work, unless explicitly stated otherwise. You are allowed to confer with fellow students about general approaches to solve the problems in the assignments, but you have to do the assignments on your own and describe your work in your own words. Students who violate these expectations can expect to receive a failing grade on the assignment and will be reported to Student Judicial Services. These types of violations are reported to professional schools, should you ever decide to apply one day. Don’t do it—it’s not worth the consequences.\nSharing of Course Materials is Prohibited\nAny materials in this class that are not posted publicly may not be shared online or with anyone outside of the class unless you have my explicit, written permission. This includes but is not limited to lecture hand-outs, videos, assessments (quizzes, exams, papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets. Unauthorized sharing of materials promotes cheating. It is a violation of the University’s Student Honor Code and an act of academic dishonesty. We are well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.\nAny materials posted on the public class website (https://wilkelab.org/SDS375/) are considered public and can be shared under the Creative Commons Attribution CC BY 4.0 license.\nClass Recordings\nIf any class recordings are provided they are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.\nReuse\nText and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT. Note that figures in slides may be pulled in from external sources and may be licensed under different terms. For such images, image credits are available in the slide notes, accessible via pressing the letter ‘p’.\n\n\n\n", - "last_modified": "2024-02-19T16:39:55-06:00" + "last_modified": "2024-02-19T16:43:20-06:00" } ], "collections": [] diff --git a/docs/syllabus.html b/docs/syllabus.html index 2e356fc..2a25335 100644 --- a/docs/syllabus.html +++ b/docs/syllabus.html @@ -2466,7 +2466,8 @@

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Class 8: Data wrangling 1 Class 9: Data wrangling 2 Class 10: Visualizing proportions -Class 11: Getting to know your data +Class 11: Getting to know your data 1 +Class 11: Getting to know your data 2 Class 12: Getting things into the right order Class 13: Figure design Class 14: Color spaces and color-vision deficiency