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WEEK 1 (PREWORK DUE BEFORE FIRST CLASS)
Welcome to the Masterclass2 Topics2 Quizzes -
Using ChatGPT1 Quiz
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R basics revision3 Quizzes
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Learn to Type Fast! (optional)
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Workshop 1 | Tidyverse and R markdown revision | RMC Q3 2024
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WEEK 2 (PREWORK DUE WED, 17 JULY)Cohort Notes on the Git Lesson
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Git & GitHub for R Users1 Topic
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Workshop 2 | GitHub Practice | RMC Q3 2024
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WEEK 3 (PREWORK DUE WED, 24 JULY)HTML Documents & Quarto1 Topic
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Publishing HTMLs with GitHub Pages1 Topic
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Creating a Personal Website (optional)
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Workshop 3 | Interactive Plots and HTML with GitHub Pages | RMC Q3 2024
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WEEK 4 (PREWORK DUE WED, 31 JULY)Joining 1: Intro to Joining Datasets1 Topic
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Joining 2: Mismatched Values, One-to-Many & Multi-Key Joins1 Topic
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Workshop 4 | Joining Datasets | RMC Q3 2024
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WEEK 5 (PREWORK DUE WED, 7 AUG)For Loops1 Topic
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Functions and Conditionals1 Topic
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Workshop 5 | For Loops & Functions | RMC Q3 2024
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WEEK 6 (PREWORK DUE WED, 14 AUG)Dates 1: Recognizing and Formatting Dates1 Topic
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String Manipulation1 Topic
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Workshop 6 | Dates & Strings | RMC Q3 2024
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WEEK 7 (PREWORK DUE WED, 21 AUG)Snippet | The dplyr::across function
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Data Cleaning 1: Data Diagnostics1 Topic
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Data Cleaning 2: Fixing Inconsistencies1 Topic
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Workshop 7 | Data Cleaning | RMC Q3 2024
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WEEK 8 (PREWORK DUE WED, 28 AUG)Snippet | Intro to Simple Features Data
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Thematic Maps1 Topic
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Physical Features1 Topic
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Density Maps1 Topic
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Workshop 8 | Geospatial Visualization | RMC Q3 2024
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WEEK 9 (PREWORK DUE WED, SEP 4)Snippet | Sources of Map Data
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Shapefile Data1 Topic
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Boundary Data1 Topic
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Layers1 Topic
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Workshop 9 | Advanced Geospatial Visualization | RMC Q3 2024
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WEEK 10 (PREWORK DUE WED, SEP 11)Dashboards with Quarto1 Topic
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Workshop 10 | Dashboards with Quarto | RMC Q3 2024
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FINAL PROJECT (DUE SUN, SEP 29)Final Project | RMC Q3 2024
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Workshop 11 | Progress Check | RMC Q3 2024
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Lesson code
Please download the zip folder below to code along with the instructor!
After downloading, unzip the folder, open the RStudio Project file within, then open the “VIDEO_CODE_ALONG” script and type along with the instructor in the video above!
Lesson notes
The written guide below is an accompaniment to the lesson video. You may read through it if you prefer written tutorials to video content.
Lesson Content
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In this code,
what role does selecting by name have? I am asking because I get similar results even when I don’t select by name,
Hello Rachel! In this case, it is indeed true that
by = "name"does not need to be specified, since “name” is the only column that appears in both the datasets that you are joining. In such cases, dplyr will automatically guess that it should join by the variables that appear in both datasets. If you don’t add the by argument, you will get a message saying “Joining withby = join_by(name)“, which is R’s way of telling you that it is automatically choosing a “key” column to join by. This is explain is section 5 of the lesson: https://the-graph-courses.github.io/fdar/FDAR_EN_joining_1/FDAR_EN_joining_1.html#5_Joining_syntax. The by argument becomes important when you have NO common names between two datasets, or if you have MULTIPLE columns in common but you only want to join by specific columns.