dlebauer

sum_out <- broom.mixed::tidyMCMC(coda_out, conf.int = TRUE, conf.level = 0.95) %>% 
  rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>% 
  mutate(sig = if_else(pc2.5 * pc97.5 > 0, TRUE, FALSE)) # test if UCL and LCL both have the same sign

years <- data.frame(year_index = 1:length(unique(x$year)), year = unique(x$year))
states <- data.frame(state_index = 1:length(unique(x$state)), state = unique(x$state))

sum_rep <- broom.mixed::tidyMCMC(coda_rep, conf.int = TRUE, conf.level = 0.95) %>% 
  rename(mean = estimate, sd = std.error, pc2.5 = conf.low, pc97.5 = conf.high) %>% 
  separate(term, sep = '\\[|\\]|,', into = c('term', 'i', 'j', 'other'), convert = TRUE) %>% 

dlebauer

#CSV downloaded from https://quickstats.nass.usda.gov/results/12827E57-2B20-368C-A91F-1B97FF8F7B35#E2E0BF24-9F88-35DB-8526-8E47D941FDBF
library(dplyr)
library(tidyr)
nass_data <- readr::read_csv("raw_data/E2E0BF24-9F88-35DB-8526-8E47D941FDBF.csv",na = '(D)',
                             skip = 1,
                              
                             col_names = c(
                               "program", "period", "week_ending", "geo_level", "state", "state_ansi", 
                               "ag_district", "ag_district_code", "county", "county_ansi", "zip_code", 
                               "region", "watershed_code", "watershed", "commodity", "data_item", 

dlebauer

library(ggplot2)
library(dplyr)

download.file(url = 'https://datadryad.org/stash/downloads/file_stream/624637', destfile = 'trait_data.zip', mode = 'wb')

unzip(zipfile = 'trait_data.zip', unzip = 'unzip')
s6heights <- readr::read_csv('traits/season_6_traits/season_6_canopy_height_sensor.csv')
s4heights <- readr::read_csv('traits/season_4_traits/season_4_canopy_height_sensor.csv')

heights <- bind_rows(s4heights, s6heights)

dlebauer

select
  t.treatment_id :: text as treatmentDbId,
	t.site_id :: text as observationUnitDbId, 
  t.variable_id :: text as observationVariableDbId, 
  v.name as observationVariableName, 
  t.id :: text as observationDbId, 
  t.mean :: text as value, 
  t.date as observationTimeStamp, 
  s.sitename as observationUnitName, 
  t.cultivar_id :: text as germplasmDbId, 

dlebauer

n_sim <- 10000

mu <- -10
sd <- 2
n  <- 30

z <- array(NA, c(3,n_sim))
for(i in 1:n_sim){
  x <- rnorm(n, mu, sd)
  z[1, i] <- mean(x)

dlebauer

Environment comparisons

This folder contains code used to compare setaria growth in different environments.

Contents:

• inputs/

• scripts/ folder:

dlebauer

<?xml version="1.0"?><pecan>
  <info>
    <notes/>
    <userid>-1</userid>
    <username/>
    <date>2021/01/28 19:56:08 +0000</date>
  </info>
  <outdir>/data/workflows/PEcAn_99000000009</outdir>
  <database>
    <bety>

dlebauer

import pgzrun
import time
import random

WIDTH = 400
HEIGHT = 400

guy = Actor("purple_guy")
guy.pos = (200, 200)
guy.angle = -25

dlebauer

library(tidyverse)
library(wesanderson)
library(cowplot)
library(lubridate)
allfields <- read_csv('~/Downloads/canopycover_ratiomask.csv') %>% 
  mutate(method = 'French')
field3 <- read_csv('~/Downloads/canopycover_ratiomask_field3.csv') %>% 
  mutate(method = 'French')
all_tr <- read_csv('~/Downloads/allcanopycover (1).csv') %>% select(-X1) %>% 
  mutate(method = 'Li')

dlebauer

---
title: "Meta analysis plot organization"
output: html_document
---

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

## Lets look at what MA outputs look like