A&C_Map

I used data from the A&C LA: World Cafe RSVP List for Day of Check-In datasheet.Added zipcode data to practice. My goals of this file is to create a location map showing the different orgs in LA. I would also like to create a word map with data from the ‘Post it: Exercise’.

Load in packages

library(readr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ purrr     1.0.1
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ── 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(DT) #scrollable table
library(leaflet)#Creates interactive web maps using JavaScript 'Leaflet' library
library(tidygeocoder)#Geocoding made easy

## Warning: package 'tidygeocoder' was built under R version 4.3.3

Load in data

## Rows: 69 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Organization / Affiliation
## dbl (1): Zip Code
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data <- read_csv("Data/data.csv")

## Rows: 69 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Organization / Affiliation
## dbl (1): Zip Code
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data_clean <- data %>%
  filter(!is.na(`Zip Code`)) %>%
  mutate(`Zip Code` = as.character(`Zip Code`)) %>%
  # keep only CA zip ranges 90000–96999 if you truly want just CA
  filter(substr(`Zip Code`, 1, 2) %in% c("90","91","92","93","94","95","96")) %>%
  mutate(country = "US")   # new column

data_geo <- data_clean %>%
  geocode(
    postalcode = `Zip Code`,  # tell tidygeocoder this is a postal code
    country = country,        # use the "US" column
    method = "osm",
    lat = latitude,
    long = longitude
  )

## Passing 31 addresses to the Nominatim single address geocoder

## Query completed in: 43.4 seconds

These are all of the orgs/zips included in map.

datatable(
data_geo,
options = list(
pageLength = 10, # or 25, etc.
scrollY = "400px", # vertical scroll height
scrollX = TRUE # horizontal scroll if needed
),
rownames = FALSE
)

This creates the interactive map

leaflet(data_geo) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  setView(lng = -118.25, lat = 34.05, zoom = 9) %>%  # focus on LA region
  addCircleMarkers(
    lng = ~longitude,
    lat = ~latitude,
    radius = 6,
    color = "#004c6d",        # border color
    fillColor = "#1d91c0",    # fill color
    stroke = TRUE,
    weight = 1,
    fillOpacity = 0.85,
    popup = ~paste0(
      "<strong>", `Organization / Affiliation`, "</strong><br>",
      "Zip Code: ", `Zip Code`, "<br>",
      "Country: ", country
    ),
    label = ~`Organization / Affiliation`,  # shows on hover
    clusterOptions = markerClusterOptions( # cluster markers when zoomed out
      spiderfyOnEveryZoom = FALSE,
      showCoverageOnHover = FALSE
    )
  ) %>%
  addScaleBar(
    position = "bottomleft",
    options = scaleBarOptions(maxWidth = 100)
  ) %>%
  addControl(
    html = "<h4 style='margin:0; padding:4px;'>Climate & Arts Network – Los Angeles</h4>",
    position = "topleft"
  )