sdtaylor

prior_version = 3.3

library_path = .libPaths()[1]
library_path = substr(library_path, 1, nchar(library_path)-3)

old_library_path=paste0(library_path, prior_version)

old_packages = list.files(old_library_path)

install.packages(old_packages)

sdtaylor

#Create a new file
sudo fallocate -l 4G /swapfile

#RW for root only
sudo chmod 600 /swapfile

sudo mkswap /swapfile
sudo swapon /swapfile

#check swap

sdtaylor

#Use dplyr inside a function with arbitray column names
#The dots stuff is from the internal workings of dplyr
#Don't forget the _ at the end of the group by.
#Other function also have the _ variation with the .dots argument available, 
#but when I first made this in early 2016 not all of them did. 
my_function=function(original_df, variables){
  dots=lapply(variables, as.symbol)
  summarized_data = original_data %>%
    group_by_(.dots = dots ) %>%
    summarize(freq=sum(freq)) %>%

sdtaylor

library(dplyr)
library(tidyr)

shrub_data<-read.csv("~/dplyrLesson/shrub_volume_experiment.csv")

########################
#some basics
#######################
#ordering rows!
#no more using shrub_data=shrub_data[order(shrub_data$site),]

sdtaylor

Sunrise

Oh sunrise, you must be so lonely.
Hardly a soul spends time with you
before you coalesce into the day.

Your sister, sunset, is the Hollywood star
everyone knows. Yet sunrise, you are
just a hometown hero for early risers.

sdtaylor

> path.expand('~')
[1] "C:/Users/shawntaylor/Documents"
> normalizePath(path.expand('~'))
[1] "C:\\Users\\shawntaylor\\Documents"
> wd = getwd()
> wd
[1] "C:/Users/shawntaylor/Documents/portalPredictions"
> path.expand(wd)
[1] "C:/Users/shawntaylor/Documents/portalPredictions"
> normalizePath(path.expand(wd))

sdtaylor

Paneer Sak

• 1 pound (450 grams) fresh spinach, washed and steamed
• 2 pinches asafetida
• 1 tablespoons ghee or vegetable oil
• 3 tablespoons water
• 2 teaspoons ground coriander
• 2/3 cup (150 milliliter) sour cream (optional
• 1/2 teaspoon turmeric
• 8 ounce (225 grams) paneer, cubed
• 1/4 teaspoon cayenne pepper

sdtaylor

Here is the projection for these shapefiles in a proj4string format, as defined in https://www.fs.fed.us/nrs/atlas/littlefia/albers_prj.txt

'+proj=aea +lat_1=38 +lat_2=42 +lat_0=40 +lon_0=-82 +x_0=0 +y_0=0 +ellps=clrk66 +units=m +no_defs'

sdtaylor

library(lubridate)
library(dplyr)

####################################################################################
#' Download downscaled climate forecast data
#' 
#' Individual climate models available are are c('CFSv2','CMC1','CMC2','GFDL-FLOR','GFDL','NASA','NCAR'),
#' 'ENSMEAN' is the mean of all models.
#' lead_time is the months into the future to obtain forecasts. Max of 7
#' Default lat and lon are for Portal, AZ

sdtaylor

library(tidyverse)
library(cowplot)

#########################
# Get data
zipped_files = c("1997_daily.csv.zip", "1998_daily.csv.zip", "1999_daily.csv.zip", "2000_daily.csv.zip", "2001_daily.csv.zip", "2002_daily.csv.zip",
                 "2003_daily.csv.zip", "2004_daily.csv.zip", "2005_daily.csv.zip", "2006_daily.csv.zip", "2007_daily.csv.zip", "2008_daily.csv.zip",
                 "2009_daily.csv.zip", "2010_daily.csv.zip", "2011_daily.csv.zip", "2012_daily.csv.zip", "2013_daily.zip", "2014_daily.zip",
                 "2015_daily.zip", "2016_daily.zip", "2017_daily.zip", "2018_daily.zip","2019_daily.zip","2020_daily.zip")
data_folder = 'fawn_data/'