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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(

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library(tidyverse)
library(gridExtra)
library(grid)
library(imputeTestbench)
library(PSF)
library(imputePSF)
library(imputeTS)
library(scales)

# total obs in each simulated time series

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# 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)

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# 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")

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# 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)

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# load libraries
library(sp) # for meuse
library(sf) # for sf objects
library(ggmap) # for base maps
library(ggplot2) # for mapping

# import dataset, check structure
data(meuse)
str(meuse)

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---
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)

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echo sha, contributor, date, message > log.csv
git log --date=local --pretty=format:'%h, %an, %ad, "%s"' >> log.csv

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#' Format a text file for mothur
#'
#' @param fl chr string for input file
#' @param rev_cmp logical if file needs reverse complemeNT
#' @param savefl logical if output file is saved, otherwise the output is returned in the console
moth_form <- function(fl, rev_cmp = FALSE, savefl = TRUE, flnm = 'myfile.txt'){

  # import file
  dat <- read.table(fl, sep = '\t')
  

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plotFlowConc <- function(eList, month = c(1:12), years = NULL, col_vec = c('red', 'green', 'blue'), ylabel = NULL, xlabel = NULL, alpha = 1, size = 1,  allflo = FALSE, ncol = NULL, grids = TRUE, scales = NULL, interp = 4, pretty = TRUE, use_bw = TRUE, fac_nms = NULL, ymin = 0){
  
  localDaily <- getDaily(eList)
  localINFO <- getInfo(eList)
  localsurfaces <- getSurfaces(eList)
  
  # plot title
  toplab <- with(eList$INFO, paste(shortName, paramShortName, sep = ', '))

  # flow, date info for interpolation surface