Working with Geospatial Data

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# Working with Geospatial Data
## MacLeish
### Ben Baumer
### SDS 192</br>April 6th, 2020</br>(<a href="http://beanumber.github.io/sds192/lectures/mdsr_geo_02-macleish.html" class="uri">http://beanumber.github.io/sds192/lectures/mdsr_geo_02-macleish.html</a>)

---

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# `macleish`
---

## The `macleish` package

.pull-left[

- Weather data
    - Whately (Jan 2012 -now)
    - Orchard (June 2014 - now)
    - temperature, wind speed, humidity, pressure, rainfall, etc.

]

.pull-right[

- Spatial data
    - Buildings
    - Streams
    - Forests
    - Slopes
    - etc.

]

- Get the CRAN version

```r
install.packages("macleish")
```

- Get the latest version

```r
devtools::install_github("beaumber/macleish")
```

---

## Layers

```r
library(macleish)
names(macleish_layers)
```

```
##  [1] "landmarks"         "forests"           "streams"          
##  [4] "challenge_courses" "buildings"         "wetlands"         
##  [7] "boundary"          "research"          "soil"             
## [10] "trails"            "contours_30ft"     "contours_3m"
```

```r
macleish_layers[["trails"]]
```

```
## Simple feature collection with 15 features and 2 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: -72.68513 ymin: 42.44177 xmax: -72.67731 ymax: 42.4618
## CRS:            EPSG:4326
## First 10 features:
##                name  color                       geometry
## 1   Porcupine Trail  White LINESTRING (-72.68291 42.45...
## 2      Western Loop    Red LINESTRING (-72.68111 42.45...
## 3  Poplar Hill Road   Road LINESTRING (-72.68155 42.45...
## 4  Vernal Pool Loop Yellow LINESTRING (-72.68265 42.44...
## 5      Eastern Loop   Blue LINESTRING (-72.68113 42.45...
## 6      Western Loop    Red LINESTRING (-72.68401 42.45...
## 7      Western Loop    Red LINESTRING (-72.68088 42.44...
## 8       entry trail      - LINESTRING (-72.68088 42.44...
## 9      Eastern Loop   Blue LINESTRING (-72.68063 42.45...
## 10         Easy Out    Red LINESTRING (-72.67962 42.45...
```

---
## Accessing items in a `data.frame`

```r
names(mtcars)
```

```
##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"
```

```r
# vector within a data.frame
mtcars$mpg
```

```
##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
```

```r
# same, tidyverse way
mtcars %>% 
* pull(mpg)
```

```
##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
```

---
## Accessing items in a `data.frame`

```r
class(mtcars["mpg"])
```

```
## [1] "data.frame"
```

```r
class(mtcars[["mpg"]])
```

```
## [1] "numeric"
```

```r
class(mtcars$mpg)
```

```
## [1] "numeric"
```

```r
class(pull(mtcars, mpg))
```

```
## [1] "numeric"
```

---
## Accessing items in a `list`

```r
library(macleish)
class(macleish_layers)
```

```
## [1] "list"
```

```r
class(macleish_layers["buildings"]) # returns list
```

```
## [1] "list"
```

```r
class(macleish_layers[["buildings"]]) # returns sf
```

```
## [1] "sf"         "data.frame"
```

```r
# tidyverse way
macleish_layers %>%
  pluck("buildings") %>%    
  class()
```

```
## [1] "sf"         "data.frame"
```

---
## Selectors

- give me the same kind of thing
    - `[`

- extract the thing and give it to me
    - base R: `$`, `[[`
    - `tidyverse`: 
        - from a `data.frame`: `pull()`
        - from a `list`: `pluck()`

---
## Tidy pipelines

```r
macleish_layers %>%
  pluck("trails")
```