Woo and Broman

Woo and Broman, Part II

• Make Backups
• Use Validation
• Save Plain Text Files

tidy data

We will think and talk about data organized in a tidy way where rows represent cases/units/observations and columns represent variables. Many standard forms of enterprise data are not stored in this way though they could be. Accounting data come to mind where there are data in the column names. There are tools that we will later encounter for pivoting from long to wide forms where the tidy and long forms are synonymous.

R’s Variable Types

• Factor: Qualitative labels with attached numbers. Think key-value.
• Character: Strings of letters and numbers demarcated by quotation marks.

There is 'something' or there is "something"
There is 'Hello World!' or there is "Hello World!"

• Numeric [integer, double]
• Complex [if you don’t know what this means, worry not]
• Logical
• Date

The global environment in RStudio helps us out. There is a special combined data structure in R – the data.frame – that combines data of different types organized with units defining the rows and variables defining the columns; it is implicitly tidy.

Two Examples with Excel Data [Bonds]

When the Environment tab is active in the top right of the RStudio. You will see a tab called Import Dataset. The first thing to note is that R reads a number of data types [and link to databases and things].

To import Excel data, From Excel
- We have to give it a file name. NB Paths.

library(readxl)
library(DT)
Bonds <- read_excel("~/Downloads/Week-2.xlsx", 
    sheet = "Bonds")

Two Examples with Excel Data Fast Food

FastFood <- read_excel("~/Downloads/Week-2.xlsx", 
    sheet = "FastFood")
FastFood |> knitr::kable() |> kableExtra::scroll_box()