chriscarrollsmith

Projects

Runner-Up: Doctor of Credit

• Creator: Evan Mullen
• Description: Extract structured data about bank deposit bonuses from the Doctor of Credit website.

Prerequisites

Google Chrome CLI entrypoint

pletcher

#html-to-freq.py

# original lesson: https://programminghistorian.org/en/lessons/counting-frequencies

# We've added json to the list of imports — you don't need to install anything,
# json is part of the Python standard library.
import urllib.request, urllib.error, urllib.parse, json, obo

# Notice that instead of requesting the HTML directly, we're now
# making a request to the backend API — meaning a server that returns

andrewheiss

# ------------------------------------------------------------------------------------------
# Basically all translated from the Python example at https://atproto.com/blog/create-post
# ------------------------------------------------------------------------------------------

library(httr2)

# Create a logged-in API session object
session <- request("https://bsky.social/xrpc/com.atproto.server.createSession") |> 
  req_method("POST") |> 
  req_body_json(list(

andrewheiss

Original xkcd

See Georgios Karamanis's example of doing this by converting the map into a raster object

library(tidyverse)
library(sf)
library(rnaturalearth)

# Map data

andrewheiss

library(tidyverse)
library(tidytext)
library(gutenbergr)
library(cleanNLP)

little_women_raw <- gutenberg_download(514, meta_fields = "title")

little_women <- little_women_raw %>% 
  slice(70:n()) %>% 
  mutate(chapter_start = str_detect(text, "^CHAPTER"),

nuest

Switching from XML to xml2

Rationale

The R package XML for parsing and manipulation of XML documents in R is not actively maintained anymore, but used by many:

The R package xml2 is an actively maintained, more recent alternative.

This file documents useful resources and steps for moving from XML to xml2.

ltrgoddard

# Takes an ordered vector of numeric values and returns a small bar chart made
# out of Unicode block elements. Works well inside dplyr mutate() or summarise()
# calls on grouped data frames.

sparkbar <- function(values) {
  span <- max(values) - min(values)
  if(span > 0 & !is.na(span)) {
    steps <- round(values / (span /  7))
    blocks <- c('▁', '▂', '▃', '▄', '▅', '▆', '▇', '█')
    paste(sapply(steps - (min(steps) - 1), function(i) blocks[i]), collapse = '')

mhbeals

import requests

number = 1

# Note, this will take a long time!
while number < 1318493:
        
    # Make sure to change your API key at the end of the URL
    urltext = "http://api.digitalnz.org/v3/records.xml?api_key=################&and[collection][]=Papers+Past&sort=date&text=+&and[category][]=Newspapers&direction=asc&page=" + str(number)
    

colbenkharrl

Click to view more!

ramhiser

# Uses a subset of the Iris data set with different proportions of the Species factor
set.seed(42)
iris_subset <- iris[c(1:50, 51:80, 101:120), ]

stratified_sample <- iris_subset %>%
  group_by(Species) %>%
  mutate(num_rows=n()) %>%
  sample_frac(0.4, weight=num_rows) %>%
  ungroup