Intro to Spatial Data Analysis with R
SCGIS 2023 Annual Conference

Packages, Functions, Data Frames,
and Importing Data




R Packages

Packages are what R calls extensions or add-ons.

What’s in a package?

  • additional functions!
  • function help pages
  • tutorials (Vignettes)
  • datasets

Where do you find them?

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)

  1. 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:

x and size have no default value → they are required

replace and prob have default values → they are optional


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:

  1. Helps you remember what each argument is
  2. You can enter then in any order
  3. You can skip arguments if you’re happy with the default value


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:

zoo(moo(boo(foo(99)),n=4))

Piping Tips and Tricks

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

You can tell RStudio which pipe to insert under Global Options >> Code

|> (‘native’ pipe introduced R4.0)

%>% (from magrittr package)


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] 0.43 0.18 1.00 0.49 0.32


Exercise 3: Packages, Calling Functions, and Piping

Exercise 3 Topics

  1. Install a packages
  2. Loading packages into memory with library()
  3. Calling functions with arguments
  4. Piping syntax

Break!


Same Function Name, Different Packages

Occasionally two or more packages will have different functions that use the same name.

When this happens, R will use whichever one was loaded first.

Best practice: use the package name and the :: reference to specify which package a function is from.

x <- sp::over()
x <- grDevices::over()

y <- raster::select()
y <- dplyr::select()

When you use the package_name::function_name syntax, you don’t actually have to first load the package with library().


Resolving Name Conflicts with the conflicted Package

When you call a function that exists in multiple packages, R uses whichever package was loaded first.

The conflicted package helps you avoid problems with duplicate function names, by specifying which one to prioritize no matter what order they were loaded.

library(conflicted)

# Set conflict preference
conflict_prefer("filter", "dplyr")
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")

# From here on out, anytime we call select() or filter(), R will
# always use the dplyr version.


Data Frames

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

Rows and Columns by any other name….





rows

aka:
- record
- case
- feature (spatial)

columns

aka: variable, field

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


Windows users: Beware the slashes!

The following will not work beause of the slashes:

C:\noworm\trials\data

Back slashes must be converted to one of the following:

C:/noworm/trials/data

C:\\noworm\\trials\\data


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.


Exercise 4: Importing CSV Files and Working with Data Frames

Exercise 4 Topics

  1. import a csv
  2. create data frames from scratch
  3. explore R’s built-in data frames
  4. compute summary stats on individual columns
  5. subset rows
  6. identify NA values