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fawda123 / gist:5ecb73e1304e7faee83eb05b922937e7
Created August 31, 2017 20:09
save git log to csv with header
echo sha, contributor, date, message > log.csv
git log --date=local --pretty=format:'%h, %an, %ad, "%s"' >> log.csv
@fawda123
fawda123 / nnet_plot_update.r
Last active May 11, 2022 00:20
nnet_plot_update
plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,
circle.cex=5,node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,
line.stag=NULL,struct=NULL,cex.val=1,alpha.val=1,
circle.col='lightblue',pos.col='black',neg.col='grey',
bord.col='lightblue', max.sp = F,...){
require(scales)
#sanity checks
if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')
@fawda123
fawda123 / gar_fun.r
Last active September 3, 2021 16:32
gar_fun
gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
y.lab=NULL, wts.only = F){
require(ggplot2)
require(plyr)
# function works with neural networks from neuralnet, nnet, and RSNNS package
# manual input vector of weights also okay
#sanity checks
@fawda123
fawda123 / asci_csci_diff.R
Created March 22, 2019 15:11
plot asci/csci diff by nuts and hab
library(tidyverse)
library(patchwork)
toplo1 <- sqidat %>%
select(MasterID, yr, asci_mean, csci_mean, TN, TP, indexscore_cram) %>%
gather('nuts', 'val', TN, TP) %>%
na.omit %>%
mutate(
inddif = asci_mean - csci_mean,
cramcat = case_when(
vif_func<-function(in_frame,thresh=10,trace=T,...){
library(fmsb)
if(any(!'data.frame' %in% class(in_frame))) in_frame<-data.frame(in_frame)
#get initial vif value for all comparisons of variables
vif_init<-NULL
var_names <- names(in_frame)
for(val in var_names){
@fawda123
fawda123 / evaldat
Last active June 23, 2018 18:25
limits of imputePSF, compared with na.mean on simulated time series
library(tidyverse)
library(gridExtra)
library(grid)
library(imputeTestbench)
library(PSF)
library(imputePSF)
library(imputeTS)
library(scales)
# total obs in each simulated time series
@fawda123
fawda123 / chloroex
Last active May 20, 2018 03:42
chloroex
# load libraries
library(sf)
library(maps)
library(ggplot2)
# get states as sf
states <- st_as_sf(map('state', plot = F, fill = T))
# calculate area, add to states
area <- st_area(states)
---
title: "Basic mapping"
author: "Turbo Todd"
output: html_document
---
This lessons covers base graphics, ggplot, and other R packags for mapping spatial data.
```{r}
library(maps)
@fawda123
fawda123 / tmapex
Created May 12, 2018 20:11
tmapex
# load libraries
library(sf)
library(maps)
library(tmap)
# get states as sf
states <- st_as_sf(map('state', plot = F, fill = T))
# calculate area, add to states
area <- st_area(states)
# load packages
library(sf)
library(plotly)
library(mapview)
# download data and save in memory
wsa <- read.csv("https://www.epa.gov/sites/production/files/2014-10/wsa_siteinfo_ts_final.csv")
# make an sf object
wsa <- st_as_sf(wsa, coords = c("LON_DD", "LAT_DD"), crs = 4269,agr = "constant")