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committedMay 18, 2018
Update README
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‎README.Rmd

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---
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title: "Intro to GIS for R"
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author: "Angela Li"
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date: "`r format(Sys.Date())`"
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output: github_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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```
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This talk is a modified version of my [previous talk](https://github.com/angela-li/rladies-spatial-data) for R-Ladies Chicago, with the goal of inspiring UChicago students to go out and try using R for GIS.
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I rapidly sailed through a lot of content when I gave the talk, so I'd recommend going back and digging through these slides in depth. You can find the data for the talk in the `data` folder.
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Below are the slides from the presentation, which I updated a bit based on feedback I got from y'all. If you have additional comments, please let me know! The shortlink to the slides is [bit.ly/harris-gis](bit.ly/harris-gis) for easy reference.
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```{r echo = FALSE}
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knitr::include_url('https://angela-li.github.io/slides/2018-05-18/gis-r#1')
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```
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**Advanced material: If you know how to fork and clone a Github repository, I'd recommend doing that with this repository to get the data quickly. I've extracted all the code out into a R script (using `knitr::purl(gis-R.Rmd)`) that you can simply run once you've forked this repo and cloned it into RStudio on your local device.

‎README.md

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# gis-r
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Materials for talk introducing GIS for R for Harris Public Policy students
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Intro to GIS for R
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================
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Angela Li
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2018-05-18
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This talk is a modified version of my [previous talk](https://github.com/angela-li/rladies-spatial-data) for R-Ladies Chicago, with the goal of inspiring UChicago students to go out and try using R for GIS.
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I rapidly sailed through a lot of content when I gave the talk, so I'd recommend going back and digging through these slides in depth. You can find the data for the talk in the `data` folder.
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Below are the slides from the presentation, which I updated a bit based on feedback I got from y'all. If you have additional comments, please let me know! The shortlink to the slides is [bit.ly/harris-gis](bit.ly/harris-gis) for easy reference.
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[![](README_files/figure-markdown_github/unnamed-chunk-1-1.png)](https://angela-li.github.io/slides/2018-05-18/gis-r#1)
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\*\*Advanced material: If you know how to fork and clone a Github repository, I'd recommend doing that with this repository to get the data quickly. I've extracted all the code out into a R script (using `knitr::purl(gis-R.Rmd)`) that you can simply run once you've forked this repo and cloned it into RStudio on your local device.

‎README_files/figure-markdown_github/unnamed-chunk-1-1.png

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‎gis-r.R

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## ----setup, include=FALSE------------------------------------------------
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options(htmltools.dir.version = FALSE)
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## ----eval=FALSE, tidy=FALSE----------------------------------------------
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## library(tidyverse)
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## library(sf)
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## library(tmap)
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##
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## sales <- read_csv("output/sales-tidy.csv")
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## tracts <- st_read("data/orig/shapefiles/detroit_tracts.shp")
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## tracts <- rename(tracts, tract = GEOID)
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##
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## sales <- sales %>%
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## right_join(tracts, ., by = "tract")
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##
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## med_sales_map <- tm_shape(sales, unit = "mi") +
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## tm_fill("med_price", palette = "Blues", breaks = quantile(a$med_price), title = "Median Sales Price") +
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## tm_facets("after_hhf") +
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## tm_shape(tracts) +
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## tm_borders() +
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## tm_compass(fontsize = 0.6, color.dark = "dark grey") +
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## tm_scale_bar(color.dark = "dark grey")
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##
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## save_tmap(med_sales_map, "doc/figs/med_sales_map.png")
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## ----eval=FALSE, tidy=FALSE----------------------------------------------
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## install.packages("sf")
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## install.packages("tmap")
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## ----warning=FALSE-------------------------------------------------------
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# Load package
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library(sf)
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# Read in shapefile
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chi <- st_read("data/Neighborhoods_2012b.shp")
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## ------------------------------------------------------------------------
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head(chi)
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## ------------------------------------------------------------------------
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class(chi)
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## ------------------------------------------------------------------------
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# Map it using base R: just shape outlines
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plot(st_geometry(chi))
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## ------------------------------------------------------------------------
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# This maps all the attributes
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plot(chi)
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## ------------------------------------------------------------------------
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chi2 <- st_read("data/ComArea_ACS14_f.shp")
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## ------------------------------------------------------------------------
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# Check what variables we have
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names(chi2)
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# Calculate population density
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library(dplyr)
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chi2 <- mutate(chi2, Pop2014 = Pop2014/shape_area)
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## ------------------------------------------------------------------------
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# Map population density by neighborhood
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plot(chi2["Pop2014"])
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## ----echo=FALSE----------------------------------------------------------
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library(tmap)
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tm_shape(chi2) +
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tm_fill("Pop2014", palette = "Purples",
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title = "Population by Neighborhood, 2014")
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## ----warning=FALSE-------------------------------------------------------
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groceries <- st_read("data/groceries.shp")
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## ----warning=FALSE-------------------------------------------------------
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# Get the CRS (coordinate reference system) of the groceries point data
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groceries_crs <- st_crs(groceries)
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# Project the neighborhood boundaries
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chi2 <- st_transform(chi2, groceries_crs)
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## ----echo=FALSE----------------------------------------------------------
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# Plot both
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tm_shape(chi2) +
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tm_borders() +
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tm_fill("Pop2014", palette = "Purples",
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title = "Population by Neighborhood, 2014") +
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tm_shape(groceries) +
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tm_dots(title = "Groceries", size = 0.1, col = "black")
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## ----message=FALSE-------------------------------------------------------
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chi2 %>%
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st_join(groceries, .) %>%
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group_by(community) %>%
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tally() %>%
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arrange(desc(n))
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## ----eval=FALSE----------------------------------------------------------
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## get_point_counts_in_buffer <- function(points_to_buffer,
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## points_to_intersect,
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## buffer_size = 500) {
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## number_points_within_buffer <- points_to_buffer %>%
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## st_buffer(buffer_size) %>%
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## st_contains(points_to_intersect) %>%
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## map_dbl(length) %>%
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## tibble(pts_in_buffer = .)
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##
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## return(number_points_within_buffer)
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## }
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