mbjoseph

---
title: "Untitled"
author: "Max Joseph"
date: "2/6/2019"
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}

mbjoseph

---
title: "Using earthpy via R Markdown"
author: "Max Joseph"
date: "December 18, 2018"
output: html_document
---

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

mbjoseph

library(tidyverse)
library(raster)
library(sf)
library(rmapshaper)
library(elevatr)
library(USAboundaries)

dir.create('out')

# RGB satellite imagery over Rocky Mountain National Park -------------

mbjoseph

---
title: 'Mapping the 2018 East Africa floods from space with smapr'
author: "Max Joseph"
date: "2018-09-14"
output:
  html_document:
    keep_md: yes
---

```{r setup, include=FALSE}

mbjoseph

Keybase proof

I hereby claim:

• I am mbjoseph on github.
• I am mbjoseph (https://keybase.io/mbjoseph) on keybase.
• I have a public key ASABiVndWu_-6CJIwyKMRAlsmF3sEsWB33XMDPoZ2GAbvAo

To claim this, I am signing this object:

mbjoseph

library(tidyverse)
library(rstan)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)

# Simulate true occupancy states ------------------------------------------
# define a design matrix for site-level occupancy
n_site <- 200
m_psi <- 3    # m_psi is the number of columns in the site level design matrix
X_psi <- matrix(c(rep(1, n_site), rnorm((m_psi - 1) * n_site)), 

mbjoseph

## DATA GENERATION
# reproducibility
library(bayesplot)
set.seed(1839)

# matches the stan function
inv_logit <- function(x){
  exp(x) / (1 + exp(x))
}

mbjoseph

print("Hello, world!")

mbjoseph

library(raster)
library(tidyverse)
library(lubridate)
library(mgcv)

# Boulder temperature prediction ------------------------------------------
# Goal: use historical temperature data to predict the weather a week from today

# Fetch raw data from NOAA
data_url <- "https://www.esrl.noaa.gov/psd/boulder/data/boulderdaily.complete"

mbjoseph

# Importing data from USDA into large data file ---------------------------
library(stringr)
library(tidyverse)

convert_to_df <- function(file) {
  # takes a text file as input and parses the file to generate a data.frame
  lines <- readLines(file)

  # identify blank lines which denote separate files