benmarwick

library(ggplot2)

# coords for vertices of polygons that make the zones
polydata <- rbind(
  data.frame(x = c(0, 0.01, 0.99, 1), 
             y = c(0, 0.25, 0.25, 0), 
             group = "zone 1", 
             fill = "grey80", xc = .5, yc = 0.125),
  data.frame(x = c(0, 0.025, 0.495, 0.495), 
             y = c(-0.01, -0.25, -0.25, -0.01), 

benmarwick

library("googlesheets")
suppressPackageStartupMessages(library("dplyr"))


third_party_gap <- "https://docs.google.com/spreadsheets/d/1xbMp7UZ7Nusjb2u1b1RihZrNzPMINZ8BHy9x2urGTzo/edit#gid=0" %>%
  gs_url()

wp <- 
third_party_gap %>% 
  gs_read(ws = "Writing progress")

benmarwick

# related to my question here: https://github.com/tidyverse/googledrive/issues/218

# How to get access to specific revisions of a google drive document? Yes, we can do that. 

# How to get the username for each revision, and the size of the document at each revision (bytes or words, assuming we are specifically talking about docs and not sheets or other types of files) Yes, we can do that.

# Goal is to measure the contributions of each author to a collaboratively-authored google doc with as little work as possible
# How to get access to specific revisions of a google drive document?

# Getting the revisions for a Google Doc

benmarwick

# to make traces...
# Import an EPS file in Inkscape
# Trace over it to show half of vessel profile, from top of rim to centre of base, about 20-30 segements
# detelete EPS from from Inkscape
# export PNG of trace from Inkscape



library(magick)
lf <- list.files("../traces", full.names = TRUE)

benmarwick

# read in the images...
library(magick)
img_paths <- list.files("../outline_P/", full.names = TRUE )

n <- 100 # just get a sample
imgs <- image_read(img_paths[1:n])

# reduce size
imgs_cropped <- image_crop(imgs, "100x100")

benmarwick

# Change point analysis 

library(tidyverse)

# get the data of population per state and fatalities per state
source(here::here("/Analysis/Tables/permillcalculation.R"))

# compute fatalities per 1000 peple per year per state, using
# the custom function in permillcalculation.R
ratio_data <- permillcalc()

benmarwick

# data.frame to be filled
wf_df <- data.frame()

# effective population sizes
sizes <- c(5, 10, 100, 500)

# starting allele frequencies
starting_p <- c(.01, .1, .5, .8)

# number of generations

benmarwick

library(sf) 
library(tidyverse)

# read in the shapefile first, that gives us the CRS for the analysis
polygons <- st_read("polygons.shp")

# read in the points
points <- read_csv('points.csv')

benmarwick

require(maps)
require(sp)
require(maptools)
require(tmap)
require(cartogram)

# get a SpatialPolygonsDataFrame of US states
usa <- map("state", fill = TRUE)
IDs <- sapply(strsplit(usa$names, ":"), function(x) x[1])
usa <- map2SpatialPolygons(usa, IDs=IDs, proj4string=CRS("+proj=longlat +datum=WGS84"))

benmarwick

# read in data
soil_colour <- 
  readxl::read_excel("KWL_chronology.xlsx", 
                     sheet = "soil_color")

layer_depths <- 
  readxl::read_excel("KWL_chronology.xlsx", 
                     sheet = "depth") # some of the Pit IDs don't match the Pit IDs in the soil_colour sheet...

library(tidyverse)