data_exploration.knit

Writing data

Vector data - use st_write(m_poly, “path/to/file.ext”)

Raster data - writeRaster(my_raster, “path/to/raster.ext”)

# load sf package
library(sf)

## Linking to GEOS 3.11.0, GDAL 3.5.1, PROJ 9.0.1; sf_use_s2() is TRUE

# read in the trees shapefile
trees <- st_read("data/trees/trees.shp")

## Reading layer `trees' from data source 
##   `/Users/duke/learning/RSpatial/spatialanalysis_sf_raster/data/trees/trees.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 65217 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -74.2546 ymin: 40.49894 xmax: -73.70078 ymax: 40.91165
## Geodetic CRS:  WGS 84

# read in neighborhoods
neighborhoods <- st_read("data/neighborhoods/neighborhoods.shp")

## Reading layer `neighborhoods' from data source 
##   `/Users/duke/learning/RSpatial/spatialanalysis_sf_raster/data/neighborhoods/neighborhoods.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 195 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49612 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84

# read in parks
parks <- st_read("data/parks/parks.shp")

## Reading layer `parks' from data source 
##   `/Users/duke/learning/RSpatial/spatialanalysis_sf_raster/data/parks/parks.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 2008 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25609 ymin: 40.49449 xmax: -73.70905 ymax: 40.91133
## Geodetic CRS:  WGS 84

# view the first few trees
head(trees)

## Simple feature collection with 6 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -74.13116 ymin: 40.62351 xmax: -73.80057 ymax: 40.77393
## Geodetic CRS:  WGS 84
##   tree_id  nta longitude latitude stump_diam      species      boroname
## 1  558423 QN76 -73.80057 40.67035          0  honeylocust        Queens
## 2  286191 MN32 -73.95422 40.77393          0 Callery pear     Manhattan
## 3  257044 QN70 -73.92309 40.76196          0  Chinese elm        Queens
## 4  603262 BK09 -73.99866 40.69312          0       cherry      Brooklyn
## 5   41769 SI22 -74.11773 40.63166          0       cherry Staten Island
## 6   24024 SI07 -74.13116 40.62351          0    red maple Staten Island
##                     geometry
## 1 POINT (-73.80057 40.67035)
## 2 POINT (-73.95422 40.77393)
## 3 POINT (-73.92309 40.76196)
## 4 POINT (-73.99866 40.69312)
## 5 POINT (-74.11773 40.63166)
## 6 POINT (-74.13116 40.62351)

Reading Raster data

# load library raster
library(raster)

## Loading required package: sp

# read in tree camopy single-band raster
canopy <- raster("data/canopy.tif")

# Read in manhattan Landsat image multi-band raster
manhattan <- brick("data/manhattan.tif")

# ge the class for the new objects
class(canopy)

## [1] "RasterLayer"
## attr(,"package")
## [1] "raster"

class(manhattan)

## [1] "RasterBrick"
## attr(,"package")
## [1] "raster"

sf objects and data frames

sf are just data frames with some special properties. this means that packages like `dplyr` can be used to manipulate sf objects

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:raster':
## 
##     intersect, select, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# use filter to limit honey locust trees
honeylocust <- trees %>% filter(species == "honeylocust")

# count the number of rows
nrow(honeylocust)

## [1] 6418

# limit to tree_id and boroname variables

honeylocust_lim <- honeylocust %>% select(tree_id, boroname)
head(honeylocust_lim)

## Simple feature collection with 6 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -73.97524 ymin: 40.67035 xmax: -73.80057 ymax: 40.83136
## Geodetic CRS:  WGS 84
##   tree_id boroname                   geometry
## 1  558423   Queens POINT (-73.80057 40.67035)
## 2  548625 Brooklyn POINT (-73.97524 40.68575)
## 3  549439 Brooklyn POINT (-73.94137 40.67557)
## 4  181757 Brooklyn POINT (-73.89377 40.67169)
## 5  379387   Queens  POINT (-73.8221 40.69365)
## 6  383562    Bronx POINT (-73.90041 40.83136)

Geometry as list-columns in sf

A list-column behaves to a certain extent like any other R column. the main difference is that instead of a standard value such as a single number, character or boolean value, each observation value in that column is a piece of an R list and that list can be as complex as needed. The list-column allows you to store farmore information in a single variable. so sf takes this advantage to store all geographic information for each feature in the list.

Reading data

Writing data

Reading Raster data

sf objects and data frames

Geometry as list-columns in sf