jebyrnes

#sublinear
library(deSolve)
# adapted from 
# https://hankstevens.github.io/Primer-of-Ecology/DDgrowth.html

sublinear <- function(t, y, p){
  bi <- y[1]
  
  dN.dt <- with(as.list(p),
    r*b0^(1-k)*bi^k - z*bi

jebyrnes

---
title: "test"
format: revealjs
---

# DAG Before You Model

[Which of these is your model?]{.center}

::: {.columns}

jebyrnes

library(dplyr)
library(piecewiseSEM)

make_tests <- function(){
dat <- tibble(
  x <- runif(100),
  x1 = rnorm(100, x, sd = 0.001),
  x2 = rnorm(100, x, sd = 0.001),
  x3 = runif(100),
  y1 = rnorm(100, x1 + x3, sd = 0.1),

jebyrnes

---
title: "1. How to work with satellite data in R"
output:
  html_document:
    df_print: paged
---


This tutorial will show the steps to grab data in ERDDAP from R, how to work with NetCDF files in R and how to make some maps and time-series of sea surface temperature (SST) around the main Hawaiian islands.

jebyrnes

library(shiny)
library(rnoaa)
library(leaflet)

ui <- fluidPage(
  leafletOutput("map"),
  plotOutput("tempPlot")
)

server <- function(input, output) {

jebyrnes

library(palmerpenguins)
library(ggplot2)
library(geomtextpath)


ggplot(penguins,
       aes(x = body_mass_g, y = flipper_length_mm,
           color = species, label = species)) +
  geom_point(alpha = 0.2) +
  geom_labelsmooth(method = "lm", boxlinewidth = 0) +

jebyrnes

#' ----------------------------------
#' Get simulated posterior fits from a gllvm
#' based on code from Francis KC Hui
#' with some light modifications by Jarrett Byrnes
#' ----------------------------------

require(mvtnorm)
require(tidyr)
require(dplyr)

jebyrnes

#gllvm reprex on posthocs
library(palmerpenguins)
penguins <- na.omit(penguins)

Y <- with(penguins, data.frame(bill_length_mm, bill_depth_mm, 
                          flipper_length_mm, body_mass_g))

X <- with(penguins, data.frame(species, sex))

jebyrnes

#'--------------------------------------------------
#' Script for gmail analysis between two people
#' using gmailr
#'--------------------------------------------------

library(gmailr)
library(tidyverse)
library(rties)
library(lubridate)
library(gganimate)

jebyrnes

library(dplyr)

g  <- tibble(x = 1:10,
             y = rnorm(10, x^(0.2), 0.05))


library(ggplot2)

mod <- lm(log(y) ~ log(x), data = g)