Week 8 Maps

library(tidyverse)

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## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.1     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.2
## ✔ purrr     1.2.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(sf)

## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

##install.packages("tmap")
library(tmap)
library(crimedata)

data(package = "crimedata")
list_crime_data()

##                city        years
## 1        All cities 2007 to 2022
## 2            Austin 2007 to 2022
## 3            Boston 2016 to 2022
## 4         Charlotte 2017 to 2022
## 5           Chicago 2007 to 2022
## 6  Colorado Springs 2016 to 2022
## 7           Detroit 2009 to 2022
## 8        Fort Worth 2007 to 2019
## 9           Houston 2019 to 2022
## 10      Kansas City 2009 to 2022
## 11      Los Angeles 2010 to 2022
## 12       Louisville 2007 to 2022
## 13          Memphis 2007 to 2022
## 14             Mesa 2016 to 2022
## 15      Minneapolis 2019 to 2022
## 16        Nashville 2013 to 2022
## 17         New York 2007 to 2022
## 18    San Francisco 2007 to 2022
## 19          Seattle 2008 to 2022
## 20         St Louis 2008 to 2020
## 21           Tucson 2009 to 2020
## 22   Virginia Beach 2013 to 2021

df <- get_crime_data(
  years = 2020,
  cities = "San Francisco",
  type = "sample",
  cache = TRUE,
  quiet = !interactive(),
  output = "tbl"
)

?tmap

head(df)

## # A tibble: 6 × 10
##        uid city_name     offense_code offense_type offense_group offense_against
##      <int> <fct>         <fct>        <fct>        <fct>         <fct>          
## 1 26160368 San Francisco 90Z          all other o… all other of… other          
## 2 26160379 San Francisco 280          stolen prop… stolen prope… property       
## 3 26160440 San Francisco 23H          all other l… larceny/thef… property       
## 4 26160468 San Francisco 23C          shoplifting  larceny/thef… property       
## 5 26160519 San Francisco 23D          theft from … larceny/thef… property       
## 6 26160915 San Francisco 290          destruction… destruction/… property       
## # ℹ 4 more variables: date_single <dttm>, longitude <dbl>, latitude <dbl>,
## #   census_block <chr>

Note: my crime_data only includes San Francisco data from 2020.

sf_df <- st_as_sf(df,
  coords = c("longitude", "latitude"),   
  crs = 4326)

tmap_mode("view")

## ℹ tmap modes "plot" - "view"
## ℹ toggle with `tmap::ttm()`

tm_shape(sf_df) +
  tm_dots(size = 0.2) +
  tm_basemap("CartoDB.Positron")

tmap_mode("view")

## ℹ tmap modes "plot" - "view"

tm_shape(sf_df) +
  tm_dots(size = 0.2,
          scale = FALSE,
          col = "offense_against",
          palette = "Dark2") +
  tm_basemap("CartoDB.Positron")

## 
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_tm_dots()`: migrate the argument(s) related to the scale of the
## visual variable `fill` namely 'palette' (rename to 'values') to fill.scale =
## tm_scale(<HERE>).[v3->v4] `tm_dots()`: use 'fill' for the fill color of polygons/symbols
## (instead of 'col'), and 'col' for the outlines (instead of 'border.col').[v3->v4] `tm_tm_dots()`: migrate the argument(s) related to the scale of the
## visual variable `size` namely 'scale' (rename to 'values.scale') to size.scale
## = tm_scale_continuous(<HERE>).
## ℹ For small multiples, specify a 'tm_scale_' for each multiple, and put them in
##   a list: 'size.scale = list(<scale1>, <scale2>, ...)'[cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "Dark2" is named
## "brewer.dark2"Multiple palettes called "dark2" found: "brewer.dark2", "hcl.dark2". The first one, "brewer.dark2", is returned.