R Notebook

library(ggplot2)
library(tidyverse)

## ── Attaching packages ───────────────────────── tidyverse 1.2.1 ──

## ✔ tibble  2.1.3     ✔ purrr   0.3.3
## ✔ tidyr   1.0.0     ✔ dplyr   0.8.3
## ✔ readr   1.3.1     ✔ stringr 1.4.0
## ✔ tibble  2.1.3     ✔ forcats 0.4.0

## ── Conflicts ──────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

library(tmap)
arrests <- read_csv('data/aug6_12_arrest_data.csv')

## Parsed with column specification:
## cols(
##   latitude = col_double(),
##   longitude = col_double(),
##   zipcode = col_double(),
##   arr_date2 = col_date(format = ""),
##   arrest_time = col_time(format = ""),
##   age = col_double(),
##   sex = col_character(),
##   race_cat = col_character(),
##   arrest_type = col_character(),
##   charge = col_character()
## )

arrests_sf <- st_as_sf(arrests, coords = c("longitude", "latitude"), crs = 4326)

#geometry type:  MULTIPOLYGON
la_zips <- st_read(dsn = "data/Los_Angeles_City_Zip_Codes/Los_Angeles_City_Zip_Codes.shp")

## Reading layer `Los_Angeles_City_Zip_Codes' from data source `/Users/timdennis/instruction/afam188/2019-11-18-afam188/data/Los_Angeles_City_Zip_Codes/Los_Angeles_City_Zip_Codes.shp' using driver `ESRI Shapefile'
## Simple feature collection with 157 features and 7 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -118.6682 ymin: 33.70491 xmax: -118.1554 ymax: 34.33731
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs

tmap_mode("view")

## tmap mode set to interactive viewing

tm <- tm_shape(la_zips) +
  tm_polygons() +
tm_shape(arrests_sf) +
  tm_dots() 

tm

## Warning: The shape la_zips is invalid. See sf::st_is_valid