Past Code Club Sessions


Note: We also have an overview of all past sessions in table format with links!



R4DS Chapter 10: ggplot layers, part 2
Today, we’ll continue to learn about data visualization with ggplot2, focusing on aesthetic mappings and geoms.
Mar 30, 2023
Chapter 10 - Visualizing Data, 1
In this session of Code Club, we’ll look at how to visualize data in R using ggplot2.
Mar 23, 2023
Chapter 10 - Visualizing Data, 1
S05E07: R4DS (2e) - Ch. 9 - Workflow: scripts and projects
Today we will go over tools for organizing code: scripts and projects, and ways to write code to facilitate getting help.
Mar 9, 2023
S05E05: R for Data Science (2e) - Ch. 8 - Data Import, Part II
Today, we’ll continue with the R4DS on data import (and export)
Mar 2, 2023
S05E05: R for Data Science (2e) - Ch. 8 - Data Import
Today, we’ll cover an essential component of working with R: how to import your data into R! We’ll do so with functions from one of the core tidyverse packages: readr.
Feb 23, 2023
S05E03: R4DS (2e) - Ch. 6 - Tidy Data 2
Today, we will revisit tidy data, review pivot_longer() and learn pivot_wider().
Feb 10, 2023
S05E03: R4DS (2e) - Ch. 6 - Tidy Data 2
S05E02: R4DS (2e) - Ch. 6 - Tidy Data
Today, we’ll start with the R4DS chapter on “tidy” data, learning what it means for a dataframe to be in a tidy format, and how to reshape untidy into tidy data.
Feb 2, 2023
S05E01: R for Data Science (2e) - Ch. 5 - Pipes
We’ll continue with the R for Data Science book in the 5th season of Code Club, now switching to the brand-new second edition! We’ll talk about pipes in R, which is covered in the short Chapter 5 of the book.
Jan 26, 2023
S04E16: R for Data Science - Exploratory Data Analysis II
We continue with this chapter on Exploratory Data Analysis (EDA), now covering missing values (and the ifelse function to turn outliers into missing values) and covariation among variables, with a couple of new plot types: boxplots and heatmaps.
Dec 1, 2022
S04E15: R for Data Science - Exploratory Data Analysis
This chapter covers so-called Exploratory Data Analysis (EDA): computing summary stats and especially making quick plots to explore the variation in and distributions of single variables (this session), and looking at covariation among variables (next session).
Dec 1, 2022