szimmer

library(tidycensus)
library(tidyverse)

d21_in <- get_acs(
  geography = "place",
  variables = c("DP03_0024P"),
  summary_var ="DP03_0018",
  year = 2021,
  survey="acs1")

szimmer

data ds1;
length id dob age 8.;
format dob mmddyy10.;
informat dob ANYDTDTE.;
input id dob age;
cards;
1 03292007 .
2 06262006 .
3 . 15
4 . .

szimmer

library(tidycensus)
library(tidyverse)

library(geofacet)

lv19 <- load_variables(2019, "acs1")

d20 <- get_decennial(
   "state",
   variables=c("HU_Tot"="H1_001N",

szimmer

---
title: "Untitled"
author: "Stephanie"
date: "5/25/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

szimmer

library(tidyverse)

x <- list(1, 2, 3)
y <- list(3, 3, 3)
z <- list(100, 200, 300)

j <- function(first, second, third) (first + third) * second

szimmer

library(tidyverse)
library(tidycensus)

nyc_counties <- c("005", "047", "061", "081", "085")

acsvars <- load_variables(2015, "acs5", cache=TRUE)

options(tigris_use_cache = TRUE)
nyc_tract <- get_acs(geography="tract",
                     variables=c("B16001_001", "B16001_066", "B16001_067", "B16001_068"),

szimmer

library(tidyverse)
library(survey)
library(srvyr)


data(api)

apisrs_mut <- apisrs %>%
  as_tibble() %>%
  mutate(stype_factor=parse_factor(as.character(stype), levels=c("E", "M", "H")),

szimmer

# https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-03-31/readme.md
library(tidyverse)

# Get the Data

brewing_materials <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewing_materials.csv')
beer_taxed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_taxed.csv')
brewer_size <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewer_size.csv')
beer_states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_states.csv')

szimmer

library(tidyverse)
set.seed(406)
N <- 2000


library(rbenchmark)

benchmark(
  "rowwise"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))

szimmer

library(tmaptools)
library(sf)
library(dplyr)
library(stringr)
library(tidycensus)


addresses <- c("3040 E Cornwallis Rd, Durham, NC") # This is RTI HQ

# Google geocodes lat/lon as 35.906156, -78.864160