benmarwick

# another method
# remotes::install_github("ropenscilabs/reviewer")
browseURL(reviewer::diff_rmd("analysis/paper/paper.qmd", 
                             # this gets the sha of the previous commit
                             git2r::commits(n=2)[[2]]$sha)$raw)

benmarwick

---
title: "AY24-25 ARCHY course time overlaps"
format: html
execute: 
  echo: false
  warning: false
self-contained: true
---

benmarwick

library(tidyverse)

base_url <- "http://academicjobs.wikia.com/wiki/Archaeology_Jobs_"

# starts at 2010-2011

years <- map_chr(2010:2019, ~str_glue('{.x}-{.x +1}'))
# though it seems to start at 2007-8: https://academicjobs.fandom.com/wiki/Archaeology_07-08

urls_for_each_year <- str_glue('{base_url}{years}')

benmarwick

library(httr2)
library(tidyverse)
library(sf)
library(lutz)
library(hms)

req <- request("https://api2.sota.org.uk/api/")

# get associations so we can get their time zones via lat-long
resp <- req %>% 

benmarwick

# This script was prepared for ARCHY 486 AU23. It will draw a plot of particle size distribution 
# of multiple sediment samples on a log scale with major size categories indicated for easy 
# comparison. The data should be formatted as in this sheet:
# https://docs.google.com/spreadsheets/d/11RfkGzjpeAT1MAt1w-L5HFgdqzX_kNcXeX3EymvIBpo/edit#gid=390081773
# with column names exactly as found there. 

# get raw data on mass of sediment in the sieves
sieve_measurements <- 
  lab_data_my_group %>% 
  select(sieve_starting_mass_g, 

benmarwick

# following example from https://tensorflow.rstudio.com/tutorials/keras/classification

# do these steps first : https://tensorflow.rstudio.com/install/
library(keras)
library(tidyverse)
library(png)
library(magick)

# get images into R
imgs <- 

benmarwick

# get canvas gradebook as CSV...

library(tidyverse)
students <-
  readr::read_csv("2023-10-01T2053_Grades-ARCHY_482_A.csv") %>%
  # exclude test student
  filter(!is.na(`SIS User ID`)) %>%
  select(Student) %>%
  as.data.frame()

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(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

library(rvest)

url <- "https://www.maestronet.com/history/makers_list.cfm?ID=1"


links_on_page <- 
url %>%
  read_html() %>% 
  html_nodes(".text a") %>%