micahwoods

## OM246 on a volume basis
## starting thought: what is 5% OM by mass on a volume basis?

library(ggplot2)
library(cowplot)

## assign bulk densities; these don't vary, nor does compressibility
bd_sand <- 1.56
bd_om <- 0.22

micahwoods

# load packages, then read in data from Github

library(VGAM)
library(ggplot2)
library(cowplot)
library(dplyr)
library(RColorBrewer)

# generates an mlsn value for a vector using log logistic distribution
mlsn <- function(x) {

micahwoods

# to look at The Wisley location data 2018 and 2019 based on 
# this Q from Tom Coulson https://twitter.com/TomCoulson85/status/1135905779242995712

library(nasapower)
library(ggplot2)
library(cowplot)
library(RColorBrewer)
library(zoo)

# get the 2018 data

micahwoods

# do a chart animated for 100+ years of Tokyo temperatures
# and check the GP C3 and GP C4

# tokyo monthly mean temperatures
tokyo <- 'http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_s3.php?prec_no=44&block_no=47662&year=&month=&day=&view='

library(zoo)
library(cowplot)
library(ggplot2)
library(gganimate)

micahwoods

# analyse my first 10000 tweets
# based on https://juliasilge.com/blog/ten-thousand-tweets/

library(ggplot2)
library(lubridate)
library(dplyr)
library(cowplot)

# read my tweet archive that I downloaded from Twitter
d <- read.csv("data/tweets.csv",

micahwoods

# make a chart of tweets by followers and with log2 axes
# as shown in https://blog.codecentric.de/en/2017/07/combining-social-network-analysis-topic-modeling-characterize-codecentrics-twitter-friends-followers/
# and adapted from code at https://github.com/ShirinG/blog_posts_prep/blob/master/twitter/twitter_codecentric.Rmd

library(twitteR)
library(dplyr)
library(ggrepel)
library(cowplot)

api_key <- "your API_KEY here"

micahwoods

# get hourly Holly Springs and plot it

library("ggplot2")
library("lubridate")
library("dplyr")
library("cowplot")
library("reshape2")

hollyHourly2015 <- read.table("http://www1.ncdc.noaa.gov/pub/data/uscrn/products/hourly02/2015/CRNH0203-2015-MS_Holly_Springs_4_N.txt",
                            header = FALSE)

micahwoods

# compare C3 growth potential (GP) and growing degree days (GDD)
# at a few locations, start with Sydney, Minneapolis, Tokyo, London
# these data and charts are shown at http://www.blog.asianturfgrass.com/2016/01/g.html

# load libraries
library("dplyr")
library("lubridate")
library("ggplot2")
library("cowplot")
library("RColorBrewer")

micahwoods

# this code will download twitter follower information 

# load packages

library("twitteR")
library("dplyr")

api_key <- "your api key"

api_secret <- "your api secret"

micahwoods

# load packages

library("twitteR")
library("dplyr")
library("lubridate")

api_key <- "your key"

api_secret <- "your secret"