mfherman

library(tidyverse)
library(tidycensus)
library(jsonlite)

# get all available census api endpoints
census_api_discovery <- fromJSON("https://api.census.gov/data.json")

# parse responses into df with vintage and dataset name
api_name_vintage <- tibble(
  vintage = census_api_discovery$dataset$c_vintage,

mfherman

library(tidyverse)
library(lubridate)
library(janitor)

# get urls of csv incident files to download for year 2015 - 2022
urls <- map_chr(2015:2022, ~ paste0("https://police-static.cityofomaha.org/crime-data/",
                                    .x, "/Incidents_", .x, ".csv"))

# download all years, combine into one df and clean up variable names
incidents <- read_csv(urls) |>

mfherman

library(tidyverse)

df <- tribble(
  ~agency,   ~offense,    ~type,         ~date,
  "Agency A", "Drug",     "sentence_dt", as.Date("2016-01-04"),
  "Agency A", "Person",   "sentence_dt", as.Date("2017-07-01"),
  "Agency A", "Drug",     "start_dt",    as.Date("2016-04-04"),
  "Agency A", "Drug",     "end_dt",      as.Date("2018-12-09"),
  "Agency B", "Person",   "sentence_dt", as.Date("2017-01-30"),
  "Agency B", "Person",   "start_dt",    as.Date("2017-01-30"),

mfherman

library(tidyverse)

df <- tribble(
  ~agency,   ~offense,    ~type,         ~date,
  "Agency A", "Drug",     "sentence_dt", as.Date("2016-01-04"),
  "Agency A", "Person",   "sentence_dt", as.Date("2017-07-01"),
  "Agency A", "Drug",     "start_dt",    as.Date("2016-04-04"),
  "Agency A", "Drug",     "end_dt",      as.Date("2018-12-09"),
  "Agency B", "Person",   "sentence_dt", as.Date("2017-01-30"),
  "Agency B", "Person",   "start_dt",    as.Date("2017-01-30"),

mfherman

library(tidycensus)
library(tidyverse)

state <- get_decennial(
  geography = "state",
  variables = "P001001",
  state = "CA"
)

tract <- get_decennial(

mfherman

## Estimate race and ethnicity of health care workers by PUMA
# NOTE: this requires the github version of tidycensus
# remotes::install_github("walkerke/tidycensus")

library(tidycensus)
library(tidyverse)

# get industry and race vars from census api
ind_pums <- get_pums(
  variables = c("PUMA", "INDP", "RAC1P", "HISP"),

mfherman

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

options(tigris_use_cache = TRUE)

sro <- read_csv("https://github.com/mfherman/r-study-group/raw/master/nyc_sro.csv")

sro_sf <- sro %>%