Review from Part I

Warm-Up Quiz

Today’s Outline

First half

Second half

R Packages

Packages are what R calls extensions or add-ons.

Installing and Using Packages

Three simple steps to use the functions in a package:

1. Figure out which package you need

2. Install (i.e., download) it (just once)

3. Load it into memory (must do this every time you start RStudio)

library(dplyr)

Functions

The keys to R’s superpowers are functions! There are four things you need to know to use a function:

What Do Functions Return?

Which function should I use?

Finding the right R function, half the battle is. - Jedi MasteR Yoda

Ask your friends

Ask Google

Cheatsheets!

Function Help

Every function has a help page. To get to it enter ? followed by the name of the function (without parentheses):

?rnorm

Function Arguments

Most functions take arguments. Arguments can be required or optional (i.e. have a default value).

See the function’s help page to determine which arguments are expected. Example:

To name or not to name your arguments? That is the question.

All arguments have names. You can explicitly name arguments when calling a function as follows:

rnorm(n = 100, mean = 10, sd = 0.3)

Benefits of naming your arguments:

But you can omit argument names if you pass them in the order expected and don’t skip any.

rnorm(500, 50, 3)  ## generate 500 normally distributed random
                   ## numbers with a mean of 50 and stand dev = 3.

Piping

Piping syntax is an alternative way of writing arguments into functions.

With piping, you use the pipe operator %>% or |> to ‘feed’ the result of one function into the next function.

Piping allows the results of one function to be passed as the first argument of the next function. Hence a series of commands to be written like a sentence.

Example

Consider the expression:

Piping Tips and Tricks

The traditional pipe character requires the magrittr or dplyr package.

library(magrittr)

Keyboard shortcut for inserting the pipe operator: ctrl + shift + m

To split a chain of functions across multiple lines, end each line with a pipe operator:

seq(1, 10, 0.1) %>% 
  log() %>% 
  sum() 

## [1] 141.4023

If the receiving function requires additional arguments, just add them starting with the 2nd argument (or use named arguments):

runif(5) %>% round(2)

## [1] 0.07 0.71 0.06 0.08 0.48

Need to pipe to an argument that isn’t the first? Indicate where pipe values should go with a dot:

## gsub() does find-replace on character values:
c("apple", "banana", "pear") %>% gsub("a", "-", .)

## [1] "-pple"  "b-n-n-" "pe-r"

Exercise 3: Packages and Piping

Break!

Data Frames

R has two data classes that organize data in rows and columns:

Rows and Columns by any other name….

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Key concepts

Pulling out a Single Column

To get an individual column, use $

head(trees)

##   Girth Height Volume
## 1   8.3     70   10.3
## 2   8.6     65   10.3
## 3   8.8     63   10.2
## 4  10.5     72   16.4
## 5  10.7     81   18.8
## 6  10.8     83   19.7

trees$Height

##  [1] 70 65 63 72 81 83 66 75 80 75 79 76 76 69 75 74 85 86 71 64 78 80 74 72 77
## [26] 81 82 80 80 80 87

trees$Height %>% summary()

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      63      72      76      76      80      87

Subsetting rows and columns

You can use square bracket notation or tidyverse methods (dplyr).

Come back for Part 3. Data Wrangling

Importing Data: RStudio’s Import Dataset Wizard

File Paths

When you import (or export) data with code, you have to provide a path to a file.

Paths can be absolute (full), or relative working directory.

Complete path:

my_data = read.csv("C:/Users/Andy/Documents/LREC/n2_study/plots/plot34a/data/soil_moist.csv")

Using Paths Relative to the Working Directory

Working Directory

Even though you don’t see it, there is always working directory.

You can view the current working directory with:

getwd()

Changing the working directory is easy!

…or with code:

setwd("D:/Data/DroneData/black_rascal_creek")

Relative Paths

When using relative paths, the first '.' represents the working directory.

my_data = read.csv("./data/soil_moist.csv")

The main advantage of using relative paths is portability of your project.

Organizing Data and Scripts

Exercise 4: Importing Data and Working with Data Frames

Review: Data Frames

The most common class for storing tabular data in R is the data frame

View the first few rows of a data frame with head()

View an entire data frame with View()

Grab an individual column with $

END!