Welcome to Space!!

# Welcome to Space!!

**Session 1**

.light[HES597: Introduction to Spatial Data in R<br>
Boise State University Human-Environment Systems<br>
Fall 2021]

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# Plan for today

Introductions

Why (not) R?

Orientation to the course

Parting thoughts

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# Introductions
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# Introductions

A bit about me

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# Introductions

.pull-left.center[
<figure>
  <img src="img/01/Williamson_SpaSES_Fig1.jpg" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>from Williamson et al. 2018</figcaption>
</figure>
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- Crooked paths

- My research

- Why I teach this class
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# Introductions

A bit about you

- Where are you from?

- What is your research?

- What do you like most about Boise?

- What do you miss most about "home"?

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# Why (not) R?
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# Why R?
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- Open Source

- Integrated analysis pipelines

- Reproducible workflows
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# Why R?
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```r
library(maps)
library(socviz)
library(tidyverse)
party_colors <- c("#2E74C0", "#CB454A") 
us_states <- map_data("state")
election$region <- tolower(election$state)
us_states_elec <- left_join(us_states, election)
p0 <- ggplot(data = us_states_elec,
             mapping = aes(x = long, y = lat,
                           group = group, fill = party))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 
p2 <- p1 + scale_fill_manual(values = party_colors) +
    labs(title = "Election Results 2016", fill = NULL)
```
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```r
p0 <- ggplot(data = subset(election, st %nin% "DC"),
             mapping = aes(x = r_points,
                           y = reorder(state, r_points),
                           color = party))
p1 <- p0 + geom_vline(xintercept = 0, color = "gray30") +
    geom_point(size = 2)
p2 <- p1 + scale_color_manual(values = party_colors)
p3 <- p2 + scale_x_continuous(breaks = c(-30, -20, -10, 0, 10, 20, 30, 40),
                              labels = c("30\n (Clinton)", "20", "10", "0",
                                         "10", "20", "30", "40\n(Trump)"))
```
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# Why Not R?

Coding can be hard...

Memory challenges

Speed

Decision fatigue
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# Class Details
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# Logistics

- Tuesdays - generally a lecture with some live coding demonstrations
- Thursdays - "lab" to work through the example, work on the assignments, get help
- [Course Webpage](http://isdrfall21.classes.spaseslab.com/)
- Class during COVID

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# The Syllabus

```
## 
## ----------------------------------------
## Assignment             Points   Percent 
## --------------------- -------- ---------
## Exercises (14 × 15)     210       51%   
## 
## Mini project 1           50       12%   
## 
## Mini project 2           50       12%   
## 
## Final project           100       24%   
## 
## Total                   410        —    
## ----------------------------------------
```
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# R, RStudio, and Git

- We'll use RStudio server to minimize setup troubleshooting (you should still have R on your machine)

- Data, code, and assignments managed via GitHub classroom (we'll go through all of this on Thurs)

- This is going to be a team effort!! (check out the [Resources](/resource/) for info on getting started)
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# Data types and structures in R

- R has 6 basic data types

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    - character, numeric, integer, logical, and complex
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  - R has __A LOT__ of different data structures

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    - vectors, data frames, tibbles, lists, and matrices
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  - R even has special object types

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    - stanfit, grobs, lm
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  - Knowing the kind of data and structure it's in helps guide coding
  
  - Spatial data often in lists, matrices, tibbles, and special objects
  
  - We'll explore the implications of different data structures for accessing, modifying, and plotting in our first lab
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# Parting Thoughts
- Am I making a map or running an analysis
- What is the goal of my analysis or figure?
- Is the spatial arrangement important?

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# Before next time
- Sign up for github (and send me your username)
- Enter your seat number in my.boisestate.edu
- Verify that you are able to login to the RStudio server with the VPN