R4DS Chapter 10: ggplot layers, part 2

Today, we'll continue to learn about data visualization with ggplot2, focusing on aesthetic mappings and geoms.

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.

S05E05: R for Data Science (2e) - Ch. 8 - Data Import, Part II

Today, we'll continue with the R4DS on data import (and export)

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.

S05E03: R4DS (2e) - Ch. 6 - Tidy Data 2

Today, we will revisit tidy data, review pivot_longer() and learn pivot_wider().

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.

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.

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.

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).

S04E14: R for Data Science - Chapter 5.6: summarize, some more

Today we will continue to investigate the summarize() function. Together with group_by(), this function is extremely useful to produce summary statistics of your data by group.