This document provides guidelines and best practices for data analysis in R, including: - Proper spelling and punctuation when writing code - Naming conventions for objects - Using comments and the assignment operator <- - Saving R scripts for reproducibility - Key differences between matrices and dataframes - Performing arithmetic operations on objects like vectors and controlling order of operations

1. DataAnalysis inWF ED
Class Meeting 2, 30 August 2016

8.  Spelling/punctuation
• Dave ≠ dave
• comma quotation – ,” –≠
quotation comma – “,
 Object names are
letters, numbers and the
dot or underline
characters and start with
a letter or the dot not
followed by a number
• Dave
• Dave.one
• Dave_one

9.  Spelling/punctuation
• Dave ≠ dave
• comma quotation – ,” – ≠
quotation comma – “,
 Object names are
letters, numbers and the
dot or underline
characters and start with
a letter or the dot not
followed by a number
• Dave
• Dave.one
• Dave_one
 Comments begin with
hashtag, are never
executed, but are
important
• #This is a comment
 Use “assignment”
operator <- (left arrow
and dash) not =
• As in a <- b, which means
“b replaces a”
 RScripts help
reproduction, so save
them within your project

12. dataframe = matrix
A matrix can hold only numeric data.
A dataframe can hold any kind of data.
(e.g., character data)

14.  Use arithmetic operators (add, subtract,
multiply, divide, square root, raise a number
to a power) – make sure you can control the
order of operation.
 Create objects (scalars, vectors, matrices)
and perform arithmetic operations with
objects (including calculating the mean
value).

15. DataAnalysis inWF ED
Class Meeting 2, 30 August 2016