Lesson Title: Glossary

RStudio allows for a visual way to interact with R

data frames are the most common data type used in R

R has built in functions for many common calculations and operations

functions allow us to reuse code and make it more readable

documenting functions using best practices helps us and others in the future

we can compare stored values

we can automate data production by setting up our choices in our R programs

data can be manipulated by column or individual entry

there are different ways to access parts of a dataframe depending on the user’s needs

there are common issues a user can look for when working with new data

making sure your data is clean before you start analysing will help make it easier

making your code readable allows for others to collaborate

R and markdown allow users to create all-in-one documents with code, text, and outputs

factors are what allow us to work with categorical data

while tibbles are important, users may come accross data that needs to be stored in different data types

loops consume computer resources and should be used sparingly

R has vectorized operations to make classical looping operations faster

loops and vectorized operations help us perform operations on multiple datasets

R can be written and run in RStudio as well as the command line

scope tells us where variables are accessible from

order of processes in the call stack can affect how our code is run

packages are an easy way to share reuseable code