Introducing Packages

• Duration: ~15 Minutes
• 5 Minute Break

Learning Objectives

• What is a package?
• How to install?
• How to load?
• Introducing dplyr

What is a package?

How to install?

## - Always plural
## - Package name in quotes
## - Capitalization matters
install.packages("dplyr")
    

How to load?

## - Always singular
## - Quotes not needed
## - Capitalization matters
library(dplyr)
    

Introducing: dplyr

Your Turn!

## Init ========================================================================
library(dplyr)
data(cars)

## Are these two lines of code equivalent? =====================================
cars[cars$speed > 20,]
filter(cars, speed > 20)
    

Answer on next slide

Yes!

Because otherwise I was going to look silly.

  speed dist
1    22   66
2    23   54
3    24   70
4    24   92
5    24   93
6    24  120
7    25   85
    

Summary Statistics Made EASY

Not enough room for Captain Smith, but run this code too.

## Easy, but limited
colMeans(cars)
        

speed  dist 
15.40 42.98 
        

## More complex, more powerful
cars %>%
summarize(n_rows = n(),
          speed = mean(speed),
          dist = mean(dist) 
)
        

+ + + +  
 n_rows       speed      dist
1     50       15.4     42.98
        

And therein lies the rub

• There are often multiple ways to do the same thing.
• R is ludicrously powerful.
• But it can also be very complex.
• Today's goal - help you find a path forward.

5 Minute Break!

Titanic in Cobh Harbour, County Cork Ireland