The sf package

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# The sf package
### Kamal Abdelrahman
### 05-15-2019

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# Table of Contents
1. Getting to Know GIS

2. Introducing the sf package

3. Example

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# Getting To Know GIS
Geographic Information Systems - Framework of capturing, storing, manipulating, analyzing, managing, and presenting spatial data
  + Features - The geometry of a shape, bits of points that comprise its structure on given place on Earth
  
  + Spatial Data - Data identifies geoprahical location
  
  + Attribute Data - Data that identifies characteristics of locations
  
  + Shapefiles - Geometric shape of spatial data
      + **NOTE** - MUST BE KEPT IN SAME FOLDER of supporting files
  
  + Choropleth - Thematic map with areas of study site shaded in proportion to values of an area

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# Introducing the sf package

The sf, short for **simple features**, package allows analysts to process and analyze geospatial data in R, noted by tibbles/data.frames with the **geometry column**

* Importing/Exporting shapefiles
  
  * Creating choropleths
  
  * Works well with ggplot2 syntax

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#Example

You're analyzing disparities of income levels across New York City and want to use a choropleth to visualize that

##What will we need?

* New York City Shapefile (Community District Level)
  * New York City Median Incomes (Community District Level)

##How will we actually make one?
  1. Read in our shapefile, and dataset
  2. Merge them together 
  3. Visualize the map using ggplot syntax
  
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# Step 1 - Reading In Shapefile/Data

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

```r
library(sf)

#Reading From File
NycCommunityDistricts <- read_sf("/Users/kamal/Documents/GitHub/FinalPresentation/Community Districts/geo_export_56ffd4c2-465e-4b9e-ab06-8ab9ebaa596f.shp")

#Reading From Website
NycCommunityDistricts <- read_sf("https://data.cityofnewyork.us/api/geospatial/yfnk-k7r4?method=export&format=GeoJSON")

NYCIncome <- fread("https://raw.githubusercontent.com/kamalabdel97/FinalPresentation/master/Median%20Incomes.csv")
```

`read_sf` - Reads in simple features
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# New York Stand Up!

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# Step 2 - Joining the data to the shapefile

```r
NycCommunityDistrictsIncome <- merge(NycCommunityDistricts, NYCIncome, by.x = "boro_cd", by.y = "Fips", all.x = T)
```

`NycCommunityDistricts` - The shapefile of the New York City community districts

`NYCIncome` - The attribute data contain the income levels

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#Step 3 - Making the choropleth

```r
ggplot() + 
  geom_sf(data = NycCommunityDistrictsIncome,
  aes(fill = Income)) 
```

`ggplot` - Invokes the ggplot syntax for data visualization

`geom_sf` - Visualizes the shapefile feature
* `NycCommunityDistrictsIncome` - The shapefile dataframe containing BOTH the shapefile (spatial) and income levels (attribute)

* `Income` - The statistical value in the shapefile dataframe for each community district

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#Final Map!

```
## Warning: Ignoring unknown aesthetics: na.value
```

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#Let's Connect!

Github: [kamalabdel97](http://www.github.com/kamalabdel97)

Linkedin: [Kamal Abdelrahman](http://www.linkedin.com/in/KamalAbdel)