codeclub

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

Today we will cover the mutate() function to create new columns in dataframes. While this function itself is simple enough, we will get to see some interesting data manipulation techniques and operators such as those for integer division and remainder.

In these two sessions of Code Club, we look at sorting dataframes with arrange() and filtering rows of a dataframe based on certain conditions with filter().

This chapter covers the manipulation of rectangular data (data frames, think data from spreadsheets) with the dplyr package that is part of the tidyverse. We'll learn about data frames & tibbles, R variable types, comparison and logical operators, and missing values (NAs) in addition to the first of the core dplyr functions: filter()

In this first session of Code Club for Fall '22, we will continue working our way through the book R for Data Science. Today, we'll look at three very short chapters on some R and RStudio basics.

We will introduce a new season of Code Club, in which we'll do things a little differently than before: we are going to work our way through a book: R for Data Science. Today, we'll look at the first, introductory chapter of the book.

In this second session on strategies for repeating operations without copy-pasting your code, we will focus on for loops.

In this first session on strategies for repeating operations without copy-pasting your code, we will focus on vectorization.

In this session of Code Club, we'll learn about Markdown syntax and some of the great functionality of R Markdown.

This will be the first of several sessions broadly about iterating in order to avoid copying-and-pasting of code, and today we will talk about vectorization and for loops.