spatial.Rmd

---
title: "Spatial Data & Analyses in R"
author: "Alex Slavenko & Maria Novosolov"
date: "19 May 2019"
output: ioslides_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
```

## Types of spatial data

Maria's stuff

## Importing Spatial Data

Now that we've learned about different types of spatial data, let's load some. We'll use the rgdal package for this:

```{r rgdal, echo = TRUE}
library(rgdal)

pkmng_countries <- readOGR(dsn = "Data",
                           layer = "pkmng_polygons")
head(pkmng_countries)
```

##

As usual, there is a tidyverse alternative to view spatial objects and edit the associated data
```{r spdplyr, echo = TRUE}
library(spdplyr)
pkmng_countries
```

##

With this package, you can use tidyverse functions to manipulate your spatial data:
```{r spdplyr 2, echo = TRUE}
#filter out all countries without any Pokemon sightings, and let's rename the column while we're at it:
pkmng_countries %>%
  filter(pkmn_rc > 0) %>%
  rename(Country = NAME)
```

##

Now let's load our point data:
```{r points, echo = TRUE}
pkmng_points <- read_csv("Data/pkmng_points.csv")
pkmng_points
```

We are going to create a shapefile from this table.

```{r points 2, echo = TRUE}
library(sp)
xy <- tibble(longitude = pkmng_points$longitude,
             latitude = pkmng_points$latitude)
pkmng_points <- SpatialPointsDataFrame(coords = xy,
                                      data = pkmng_points)
```

##

```{r geojson, echo = TRUE}
library(geojsonio)
library(rmapshaper)
pkmng_countries_json <- geojson_json(pkmng_countries) #this can be VERY time-consuming
```