View om_mass_volume.R
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## 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 |
View p_high_ph.R
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# 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) { |
View nasa_power_wisley.R
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# 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 |
View tokyo_time_series_animated_gp.R
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# 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) |
View ten_thousand_analysis.R
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# 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", |
View log2_twitter_followers_tweet_rate.R
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# 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" |
View 20160625_holly_springs_hourly.R
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# 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) |
View gp_vs_gdd.R
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# 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") |
View turf_twitter.R
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# this code will download twitter follower information | |
# load packages | |
library("twitteR") | |
library("dplyr") | |
api_key <- "your api key" | |
api_secret <- "your api secret" |
View before_and_after_interactions.R
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# load packages | |
library("twitteR") | |
library("dplyr") | |
library("lubridate") | |
api_key <- "your key" | |
api_secret <- "your secret" |
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