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library(tidyverse)
library(scales)
madison_vrh <- get_ntd(agency = "City of Madison", ntd_variable = "VRH")
extrafont::loadfonts()
madison_vrh |> 
  filter(modes == "MB") |> 
  mutate(year = year(month), mo = month(month)) |>
  filter(mo == 3) |> 
  summarize(mean_vrh = mean(value), .by = year) |> 
  ggplot(aes(year, mean_vrh)) +

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download_bps_monthly <- function(bps_year, bps_month) {
  bps_url <- paste0("https://www.census.gov/construction/bps/xls/cbsamonthly_",
                    bps_year,
                    bps_month,
                    ".xls")
  bps_dest <- paste0("data/bps/bps_",
                     bps_year,
                     bps_month,
                     ".xls")

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# Load packages ----
library(sf)
library(terra)
library(dplyr)
library(ggplot2)
library(rayshader)
library(rgl)
library(tidyverse)

# Data sources

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library(tidycensus)
library(tidyverse)
library(sf)
library(tmap)

birth <- get_acs(geography = "county", year = 2023, table = "B13002", geometry = TRUE,
                 summary_var = "B13002_001")
marriage <- get_acs(geography = "county", year = 2023, table = "B12001",
                    summary_var = "B12001_001")

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library(tidyverse)
clim <- read_csv("c:/Users/user1/Downloads/StnData.csv", col_names = c("date",
                                                                                 "avg",
                                                                                 "hi",
                                                                                 "lo"))
clim |> group_by(yr = year(date), mo = month(date, label = T, abbr = TRUE)) |> 
  summarize(avg_temp = mean(avg)) |> 
  filter(mo %in% c("Jan", "Feb")) |> 
  pivot_wider(names_from = mo, values_from = avg_temp) |> 
  mutate(feb_warmer = if_else(Feb > Jan, TRUE, FALSE)) |> 

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#    input: ["B04", "B03", "B02"],

DN <- 10000 # unit scaling. See page 40 in https://sentinel.esa.int/documents/247904/685211/Sentinel-2-MSI-L2A-Product-Format-Specifications.pdf

# make list of bands. any order, we'll use names
# adjust aggregation factor for quick testing
smp <- list(B02 = rast("B02.tif")  |> aggregate(2) / DN,
           B03 = rast("B03.tif")  |> aggregate(2) /DN,
           B04 = rast("B04.tif") |> aggregate(2) /DN
)

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library(pins)
library(sf)
library(tidyverse)
library(lubridate)
library(jsonlite)


board <- board_s3("vzpins", 
                  region = "us-east-1", 
                  access_key = "", 

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harambee_tracts <- c(44, 45, 67, 69, 70, 81, 106, 1856, 1857, 1860)

milwaukee_tracts_2022 <- get_acs(geography = "tract", 
                                 state = 55, 
                                 county = "Milwaukee", 
                                 table = "B02001", 
                                 # summary_var = "B02001_001", 
                                 year = 2022, 
                                 output = "wide")

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madison <- x |> filter(agency == "City of Madison" & modes == "MB" & tos == "DO" & year(month) >2018)

max_month <-  madison |>  filter(year(month) == max(year(month))) |> 
                                   summarize(max(month(month, label = T, abbr = F))) |> pull()
madison |> 
  mutate(month_label = month(month, label = T, abbr = T)) |> 
  ggplot(aes(month, value)) +
  geom_col(aes(fill = month_label)) +
  geom_line() +
  theme_minimal() +

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library(hexSticker)
library(ggplot2)
library(ntdr)
library(cowplot)
library(tidyverse)

bus <- magick::image_read_svg("c:/Users/user1/Downloads/train bus ferry.svg")
madison_bus <- get_ntd(agency = "City of Madison", modes = "MB")

p <- madison_bus |>