benmarwick

library(tidyverse) # you may need to install.packages("tidyverse") in the console
library(googlesheets4) # ditto

# change to your own UW email
gs4_auth("[email protected]")

# Get our google sheet of information about the location of each point
ms_data <- 
  read_sheet("https://docs.google.com/spreadsheets/d/1rDyfJQ6OnWJktIWMZfnLe8YRtCiW1PVrP9nKW1kERN4/edit?pli=1#gid=0") 

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

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

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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 %>% 

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# 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

library(rvest)
library(tidyverse)
library(lubridate)

# https://bringatrailer.com/electric-vehicles/?search=rivian
# from 11 Jan 2023
url <- "~/Downloads/bat.html"

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---
title: "Untitled"
format: html
editor: visual
---

## Introduction

The aim of this report ...

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# This script will take a folder fill of qmd files and try to render each qmd file.
# If one qmd fails to render, the script will continue on to the next one. The
# results of all the attempts to render are collected in a data frame so we can 
# easily inspect and find the files that failed to render We don't have to 
# manually render individual files.

# How to use

# 1. Download all submissions from Canvas (go to assignment page, look for the 
# download button near the speedgrader button), when it arrives on your

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# How many articles on the list in Sept 2021?

# First, run some lines from archaepaperswithcode.R to create repo,
# then: 

## Coerce commits to a data.frame
df <- as.data.frame(repo) 

# filter rows of commits from Sept 2021